the three research methods

News alert: UC Berkeley has announced its next university librarian

Secondary menu

Log in to your Library account
Hours and Maps
Connect from Off Campus
UC Berkeley Home

Search form

Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism, run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

Bipolar Disorder
Therapy Center
When To See a Therapist
Types of Therapy
Best Online Therapy
Best Couples Therapy
Best Family Therapy
Managing Stress
Sleep and Dreaming
Understanding Emotions
Self-Improvement
Healthy Relationships
Student Resources
Personality Types
Guided Meditations
Verywell Mind Insights
2023 Verywell Mind 25
Mental Health in the Classroom
Editorial Process
Meet Our Review Board
Crisis Support

Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

stevecoleimages/Getty Images

Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

Case studies
Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In: Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Methodology: Overview of Research Methodology

Overview of Research Methodology
General Encyclopedias on Research Methodology
General Handbooks on Research Methodology
Focus Groups
Case Studies
Cost Benefit Analysis
Participatory Action Research
Archival Research
Data Analysis

Research Methods Overview

If you are planning to do research - whether you are doing a student research project, IQP, MQP, GPS project, thesis, or dissertation, you need to use valid approaches and tools to set up your study, gather your data, and make sense of your findings. This research methods guide will help you choose a methodology and launch into your research project.

Data collection and data analysis are research methods that can be applied to many disciplines. There is Qualitative research and Quantitative Research. The focus of this guide, includes most popular methods including:

focus groups

case studies

We are happy to answer questions about research methods and assist with choosing a method that is right for your research in person or online. below is a video on how to book a research consultation

"How-To": Booking a Research Consultation

" Research Data Management " by Peter Neish is marked with CC0 1.0 .

Research Design vs Research Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question. A research method is a strategy used to implement that plan. Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal. It depends on what subjects (and who) you want to study. Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus. To answer these questions, you need to make a decision about how to collect your data. Most frequently used methods include:

Observation / Participant Observation
Experiments
Secondary Data Analysis / Archival Study
Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity. For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider. An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time. Using a survey helps you collect more data quickly, yet it may lack details. So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

Research Data Management

Research Data Management (RDM) refers to how you are going to keep and share your data over longer time frame - like after you graduate. It is defined as the organization, documentation, storage, and preservation of the data resulting from the research process, where data can be broadly defined as the outcome of experiments or observations that validate research findings, and can take a variety of forms including numerical output ( quantitative data ), qualitative data , documentation, images, audio, and video.

"Research Design" by George C Gordon Library is licensed under CC BY 4.0 / A derivative from the original work

Next: General Encyclopedias on Research Methodology >>
Last Updated: Jul 31, 2023 3:07 PM
URL: https://libguides.wpi.edu/researchmethod

How can we help?

[email protected]
Consultation
508-831-5410

Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation, or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Sage research methods.

SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

Next: Finding qualitative research >>
Last Updated: Apr 18, 2024 11:16 AM
URL: https://libguides.newcastle.edu.au/researchmethods

Introduction to Research Methodology

First Online: 01 March 2024

Cite this chapter

Animesh Hazari 2

74 Accesses

The term “research methodology” most often echoes among students, research scholars, and faculty members. Though the application of research methodology is diverse, we shall focus on the content specific to academia and industry. This book would be most helpful to health science students and allow them to learn the process of research in a simple and step-by-step process. In my personal experience, I have found that students are very apprehensive when it comes to learning research methodology as a subject. They often encounter problems in understanding the research methodology as the process starts and throughout the course. At times, they may have completed their research but failed to understand the whole process of how scientifically it was conducted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Durable hardcover edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and affiliations.

College of Health Sciences, Gulf Medical University, Ajman, Ajman, United Arab Emirates

Animesh Hazari

You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Hazari, A. (2023). Introduction to Research Methodology. In: Research Methodology for Allied Health Professionals. Springer, Singapore. https://doi.org/10.1007/978-981-99-8925-6_1

Download citation

DOI : https://doi.org/10.1007/978-981-99-8925-6_1

Published : 01 March 2024

Publisher Name : Springer, Singapore

Print ISBN : 978-981-99-8924-9

Online ISBN : 978-981-99-8925-6

eBook Packages : Medicine Medicine (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
Explain the goals of descriptive research and the statistical techniques used to interpret it.
Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

Descriptive, correlational, and experimental research designs are used to collect and analyze data.
Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
Independent, r=0.00. The plots on the graph are spread out around the centre.
Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

school Campus Bookshelves
menu_book Bookshelves
perm_media Learning Objects
login Login
how_to_reg Request Instructor Account
hub Instructor Commons
Download Page (PDF)
Download Full Book (PDF)
Periodic Table
Physics Constants
Scientific Calculator
Reference & Cite
Tools expand_more
Readability

selected template will load here

This action is not available.

2.3: Research Methods

Last updated
Save as PDF
Page ID 164438

Learning Objectives

By the end of this section, you should be able to:

Recall the 6 Steps of the Scientific Method
Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

A person holds a tablet in their lap. The screen shows a survey.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

An audience member voting for a contestant using an electronic response system that uses numbers as answers

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

A person is shown taking notes outside a tent in the mountains

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Two pictures depict Lady Gaga and Beyoncé performing.

Sociologist have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and ﬁngers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

A person stands next to people seated in a restaurant.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Four people sit around a large table on which are several office supplies and mailing tubes. Three are using computers; the fourth is using a phone. One person is using a wheelchair.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Early 20th century black and white photo showing female students at their desks.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous incite. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

An Experiment in Action

The image shows a state police car that has pulled over another car near a highway exit.

A real-life example will help illustrate the experiment process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted an experiment. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The experiment was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

Introduction to Research Methods

3 types of research.

In the last chapter we talked about the ways that research is all around us. You do it yourself almost every day in small and big ways, but we’re not really here to help you become more rigorous in your search for the best tacos in town. Looking at yelp is research, it’s just not really the type of research we’re going to talk about today. In the first section of this chapter we’ll talk about different types of research. Then we’ll describe different fields of research within social sciences, and finally we’ll discuss the steps of doing research.

I’m going to break types of research into three categories, which probably don’t match the way they’re described in other textbooks.

A lot of the research you do in your daily life could probably be called secondary research . You have a question (“where are the best tacos?”, “when did the Civil War start?”, “is coffee bad for my heart?”) and so you seek an answer. That’s still research, it just doesn’t involve the collection of new data or a lot of detailed steps. Google and other search engines are incredible tools that will direct you towards an answer to your questions. What you’re doing there is secondary research, using the research of others to answer your question. Your collecting, reviewing, or synthesizing existing research, not creating new data to answer a question.

You can be better at secondary research by identifying reputable sources, accessing multiple opinions, and understanding how they produced their findings. That’s part of the research we’re talking about in this class, but only a small part. We’ll return to secondary research in a later chapter, because in order to be really good at it you have to understand how to do the original research yourself. Secondary research thus involves reviewing the research of others and is motivated by getting an answer to a question.

You can only do secondary research if someone has already researched the question you have. Another type of research people do is what could be called applied research, or research that is intended for immediate public dissemination. The idea of applied research is that there is a very clear connection between the research question and the importance of the research. Imagine you’re in a sorority and you’re planning dinner for the new pledges, so you poll everyone to ask which of three options they’d pick. You gather the data and you get an answer – the most common answer is tacos. Why do you care about the answer? Because you needed to know where to go for dinner.

Would people be open to changing the colors on the United States flag? I don’t know, and based on a quick Google search no one has answered the question. No one has polled Americans to find out whether they think red white and blue is a little dated (or maybe just too similar to France). I don’t know why we’d want to change the colors, maybe so everyone has to go buy a new flag. I can’t get an answer to that question based on secondary research though. I have to collect original data if I want an answer.

Polls are a great example of applied research. Who is currently winning the race for president? How do people feel about policies designed to slow climate change? How much trust do citizens have in their government? Those are all questions you can find written about in wonky news sources like the [NY Times], Vox, 538, or others. Why do we care? Because we want to know who is winning the race, or peoples views towards certain policies.

Take another example. A radio station wants to know the demographics of its listeners so that it can make sure the commercials they run are matched to who listens. There isn’t an esoteric question to answer, but they need to collect data to improve their business operations.

The research question and the importance are very directly linked. Thus, applied research involves original research, not just reviewing what others have done, but like secondary research it is motivated to get an answer.

The third type is the least common, but is also generally the focus of a textbook like this. Academic research is the type of research that your professors do most of the time. What differentiates public research from academic research? Public research is concerned with providing new facts, academic research is concerned with testing theories and seeking explanations.

I could spend thousands of dollars to run a new poll with a very rigorous research design to understand exactly what percentage of Americans would support new taxes. If I did that research I might be able to get it published in popular sources like the New York Times, but I could never get it published in an academic journal – and those are the papers that get professors tenure.

Why? Good polls tell us something about the world at this moment, but sciences goal, both the social and hard sciences, is to tell us something about the world beyond this moment. More accurately, it’s concerned with explaining the causes of the phenomena we see. Scientists weren’t just concerned with tracking that rocks fell from buildings, but wanted to identify the force that explained why that occurred (gravity). Similarly, social scientists aren’t just concerned with knowing what percentage of people are in poverty (although that is important) they want to identify the cause of poverty so that those conditions can be changed.

My poll might find that 46% of Americans plan to vote. What academic research is concerned with is the ‘why?’ Why did 46% say that, why did one person say yes and the other no, what does that help us to understand about the society? What we want to understand is the causes of the phenomena we see every day so that we can better understand the world of tomorrow.

Let’s say we did a study and found that 32% of elementary age children are significantly overweight. That’s good to know, it gives everyone an idea of the status of the health of children at this moment. What would be more important to know is why. If we know why 32% of children are significantly overweight and the other 68% aren’t, we can make changes that affect the future. Is it a lack of recess in schools, do children not have enough access to fruits and vegetable, are the foods they’re eating changing – understanding why is just as important as knowing the what, so that we know where to make changes.

Change is the only constant. I do most of my research on urban policy, which sort of means I study cities and the changes they undergo. If I looked at data on all of the neighborhoods in a city a decade ago and look again a decade later some would have gotten richer and others would have gotten poorer.

Change in Income in Nashville 2000 to 2018

That map might look interesting, and it might be important for people living in those communities to know. But unless we can provide an explanation for the change, we haven’t really learned anything. Neighborhoods change. People change. Demographics change. Everything changes. Are the neighborhoods shaded darker going to keep getting richer, or will there be a return to the mean and they’ll get poorer in the next decade? Did something happen that changed the fate of those neighborhoods, or was it just random decisions by a lot of different actors that lead to a new geography of the city? Those questions might seem unnecessary. Some neighborhoods are getting poorer, they need more support! Who cares about the why!? But if we’re going to try and figure out what neighborhoods will get poorer in the future, or want to change the future, we have to understand the underlying causes of those changes. That’s what academic research is trying to untangle. Not just what’s going on, but why, so that we can try to get more control of the future. Thus, academic research involves original research, like applied research, but is focused on developing theories as much as it is getting an answer to a question.

3.2 What’s theory got to do with it?

Let’s first define theory , because the way it’s used in science and the way it’s used in everyday conversation are slightly different. In everyday conversation you might hear the word theory used as the equivalent of “hunch” or “idea”: “oh, that’s just your theory”. In the sciences it means a bit more. A theory in the sciences is a well-substantiated explanation for a set of observations. A law is accepted as true by scientists, it is confirmed fact. A theory is on its way to becoming a law, it just needs more observations to be fully accepted.

The social sciences have plenty of theories, and fewer explanations that can be accepted as laws because as we discussed in the previous chapter humans just make it hard to get consistent findings.

For instance, researchers in in political science and public administration often use:

elite theory which posits that a small minority of elites be they the wealthy or those that drive the creation of policy, holds most of the power in society even within democratic systems.
democratic peace theory argues that democracies generally do not go to war or have armed conflicts with other democracies
representative bureaucracy argues that governing bodies throughout society should be representative of the community they serve or govern.

Those theories and others get applied in different studies to continue testing them and refining them. For instance, I might want to study whether counties with growing Latinx/Latino/Latina populations see changes in who is elected for county wide offices. If I just do that research and report the results it could just be applied research. The public has an interest in knowing who their elected representatives are. If I use the research to test representative bureaucracy as a way of explaining my results, my research is now venturing into academic territory. The theoretical argument is all about explaining whatever I find, whether representation changes in those counties or not.

3.3 Inputs to research

The most difficult thing to accept and internalize in developing a research project is that it is iterative, not linear. We like linear processes like following a recipe. You can follow those steps and you get the end and then you have cookies. Following the research recipe isn’t that clean. It will be a lot of one step forward two steps back, which is progress, but can be frustrating.

Research values novelty. One should not spend a lot of time gathering original data in order to answer a question that has already been answered. So in order to develop a research question worth researching, it is really important to understand what has already been studied on that topic. I’ve learned this from experience, both in my own research and teaching, but you really can’t develop a good research question without doing a lot of reading.

If you’re starting to develop a research project, start with the things you care about. You’re going to spend a lot of time studying it and reading about the subject – it should be something you enjoy. Think about the things you observe in the world, the odd processes or changes you see around you. And think about the things you know a lot about. Whatever your interests are, whether they’re video games or hiking or reading, try to embed that interest in the research.

But again, you have to do a lot of reading. If I was walking down the street and someone stopped me and forced me to come up with a research question in sociology I would surely stumble and I might eventually stammer something out like “why do people leave online communities?” I don’t know much about that, it sounds kind of interesting. And online communities are a somewhat recent development (in comparison to say churches) so maybe the research will be interesting. Almost certainly not. I can guarantee a lot of research has been done on that question. That doesn’t mean I have to abandon the idea, it just means I need to start by reading all the research that has been done, and continue to refine my question. As I read thorough the literature on online group membership I’ll probably find answers to questions that never occurred to me, and as I read I might find questions that haven’t been answered yet.

A similar pattern occurred as I began my PhD. I wanted to study big important questions, and when I got to my program I was given a lot of freedom to decide what I would do research on. I decided I’d start by answering a question I constantly heard debated by policymakers: do sports stadiums create economic activity. I was going to be the researcher that answered the question. But pretty immediately I discovered that, actually, about 100 other researchers had already answered that question (it’s a definitive no, stadiums don’t create any economic activity, they’re a really bad deal for cities). The fact that the public didn’t regard it as a settled question doesn’t mean that researchers hadn’t already answered it. Did I give up? No, I kept reading the literature and I started to uncover related questions that hadn’t been answered yet. I ended up doing my dissertation on minor league baseball stadiums and their impact on the neighborhoods where they are located, which wasn’t the most important question ever but it hadn’t been answered before.

One recommendation I would make as you start reading the literature in a given area is to keep an annotated bibliography . As you read new articles to down a few sentences summarizing them - those few sentences can often be gleaned just from the abstract of the article. That way you’ll have a record of what you read, and as the project changes you can go back and wont have to search through the literature over and over. As your project begins to gain focus, you can pull the relevant articles from your annotated bibliography and begin to build out your paper. I would also recommend using a computer program like Zotero where you can save the details of articles and generate the bibliography of papers later. I don’t know the difference between MLA or APA or any system, because I make the computer do it for me. In the video below I walk through these things with a brief demonstration for anyone starting out a new research project.

One problem you will face in reading about your topic is accessing the articles that are relevant to your topic. If you go to a journals webpage, you’ll see that you can buy the article for probably $30. $30! That’s as much as a book, and you probably won’t even be sure if the article is good before buying it. There have been a lot of arguments made against these paywalls particularly given that taxpayers fund most of the research that is then sent to these journals. Researchers aren’t paid directly for the researcher they publish, which we give the copyright over to journals because it helps us to get tenure; journals then charge for people to read the research, and universities pay subscription fees so their researchers have access. It’s a circular economy, with me working for free, and journals making out like bandits. When you find an article you want to read see if you can access it through your library, but you can also check a few different websites where people post articles in order to “free” them. You can also just reach out to the author of the article and request a copy. Authors generally have the right to share the article even though they’ve given the journal the copyright over its contents, and most researchers are just happy to see someone engage with their work.

Once you’ve got a research question that’s worth studying and hasn’t been answered before, it’s time to answer it yourself. That’ll mean collecting data though, to answer that question. I should probably start by trying to figure out if data already exists that was collected by someone else first. You can do a lot of research in political science based on surveys that are posted online by Gallup or Pew. It’ll be easier to do the research if I can find the data from the start. If I can’t find the data, I’ll be faced with the choice of changing my research question to match the data that’s available or collecting my own data. Collecting your own data can be expensive and difficult, but if you’re interested in breaking new ground in your research it might be necessary.

The two steps are thus iterative. Knowing the literature on a topic will help you to develop new questions and lead you towards data necessary to answer them. But looking at data may help you to generate new questions and lead you back to the literature to understand how it can be used.

As you settle on a research question, and begin to look around for data to answer it, it is good to be explicit about your unit of analysis . The unit of analysis is whatever entity of body you wish to be able to answer your question about at the end of your study. Related, there is also the unit of observation , which is whatever unit you are measuring phenomena at. The unit of analysis and observation can be the same (they often are), but they can also be different.

Your unit of analysis (and observation) can be nations, cities, neighborhoods, individuals, or any other such grouping. Let’s use a few examples. If our research question is ‘why are some nations rich?’ we can answer that by collecting data for different nations, or we could use survey data about the individuals within different countries to make a comparison. Our unit of analysis is the same (countries), but our unit of observation (countries in the first, individuals in the second) can change.

Often we can study similar subjects using different units of observation or analysis. If I want to broadly study volunteering, I could collect data to understand volunteering rates for individuals, cities, states, or countries. The unit of observation will all depend upon what data I either collect or is available. And the unit of analysis can change as well, because I might want to study predictors of why some individuals volunteer and others don’t, or I may want to understand why different countries have different rates of volunteering. Which is all to say that it’ll depend on what you’re actually studying, but you should be explicit from the start about who you are studying.

3.5 Writing research

This is sort of where I get frustrated with myself as a teacher, or more specifically frustrated at myself for you. I just laid out three types of research. One you definitely do, which is secondary research. The second, applied research, is something you’ll see in the world all around you, and there’s some chance you might end up doing in your professional life. And the third, academic research, is generally inaccessible, uncommon, and probably not something you’ll ever do in your life. And yet, here we are, in a class on research, about to start talking about how to write a highly structure research paper using a format you’ll never use again.

Why? Why am I going to do that? In part it’s so that you can better understand the field you’re studying. It’s important to understand what it means not just to be curious about politics or sociology, but to understand what it means to study that field.

Beyond that, the best way to learn something new is to break it apart. This will be a bit like learning to drive by first building a car from parts. You could just move straight to secondary research or driving the car, but for you to really understand why things are working or where a breakdown might be, you have to understand the underlying parts. Each part of your car is important for getting you from point A to B, and each step that goes into research is important to getting you from your question to the right answer.

So when you see a headline in the future like

You’ll be able to better understand how that headline was made. The people that wrote that headline were using some academic research that is being translated to the public to use as secondary research. Where did it come from though, what did the researchers do to know that was the right answer? If it’s good research, they probably followed a process like what we’re about to lay out.

3.5.1 Introduction

If you look at published research in an academic journal it will typically follow a basic structure with 5 sections. The introduction explains the subject of your research and clearly identifies your research question. It provides a bit of background about the subject the current relevancy of it or maybe recent events that heightened its importance.

A good introduction thus has two purposes. First, it should explain to the reader what the paper is about. The thing you learn as you continue to write is the value of being clear in the introduction. Tell the reader what you do in the paper, the order of information you’re going to present, and what they takeaway is going to be. There shouldn’t be any surprise endings or twists. Just give them everything up front.

You can see that is done in the article excerpted and annotated below. This is from an article I published in 2020 Evaluating the impact of short-term rental regulations on Airbnb in New Orleans . The title should generally give you an idea of what the paper is about. It’s not the best paper I’ve ever written, but it’s short and so it’s easy to identify the structure of what I’m describing there. Read the complete introduction below to see how I describe the purpose of the paper in a direct manner with some background to prepare the reader.

3.5.2 Literature Review

There are two themes that should be described in any literature review . The two aren’t separate sections, they’re both intertwined.

What has been done in the area of your research before
What I need to know as a reader to understand what you’re going to do.

You want to prove to the reader that you’re aware of what’s been done in the area of your research before so they’ll believe your research is informed and new. The worst feedback you can get from a reviewer is that someone has already done the same study you’re attempting to publish. With any research question you identify you’ll find that a lot has been done before, and that’s fine. But describe what has been done so that I can better understand what makes yours different or unique.

And you’re also trying to make sure I can understand the background of your topic. What are the key words you’re going to be using? How have other people studied the issue? It’s all the background I need to understand your new contribution. Imagine you’re explaining a movie to someone so that they can see the sequel with you. Who are the main characters, what was the story, where did it leave off? Get them excited to see the sequel because you’re going to finally answer the question that was left lingering by all those past researchers!

You’ll want to use the literature you review to build hypotheses for your article. A hypothesis is a statement of what you expect to find. Hypothesis: toddlers that drink milk will be taller as adults.

That statement about toddlers and milk might be right or wrong, and that’s okay! That’s what the paper is building towards, proving whether the hypothesis is correct or not. Because right or wrong, if that hypothesis hasn’t been tested before we’re learning something new. But the hypothesis will be a lot better and more reasonable if it’s based on existing literature. Why would I think milk would help toddlers grow? I’d want to base that prediction on studies about milks effect on the bodies and what non-milk drinkers might consume and anything else that would be relevant.

The literature review for the paper below is 10 paragraphs in all, but I want to just pull out two. What the literature review is trying to do is just get the reader ready. Again, it’s the “previously on…” intro to your favorite show.

3.5.3 Methodology

The introduction and literature review are all used to set up your new study. Now you can explain how you’ll do whatever new and impressive thing you’re about to do in the methodology section. Describe the data you collected and how you’ll analyze it. Essentially you want to draw the reader a road map so they can understand exactly how to redo your study. It’s similar to the chain of custody in evidence for criminal cases. How did you find this information, where did it come from, why should we trust that this data is good? You don’t just wake up and find data on the side of the road, it had be collected somehow and the way it’s collected could impact whether we trust it or not.

One of the big concerns in science is replicability. We’ll talk about that later, but the study design section is a nod towards it. If I wanted to redesign your study, recreate the experiment with similar subjects in a similar setting, how would I do that? In science, we don’t just trust your word for how you generated your results. Tell us how you generated them, so that we can consider whether there were any potential problems present.

You can start writing the methodology section as you begin the research. As you start you should have a design in mind at the beginning of any project, including what data you are going to collect, how will you collect it, and how you will analyze it. Answers to those questions might evolve as you conduct the research, but you can begin by setting it out as a research design , describing your reserach plan, and then revise it as you write the paper. Regardless, your collection and analysis should be guided by a research design, whether formally written or just a mental plan.

Where did you get the data, and what are you gonna do with it now that you have it? What’s written below may or may not make sense at this stage, but I’m including it just to illustrate the way that researchers attempt to write clearly and directly in describing their studies.

3.6 Results

Once you’ve explained how you conducted your study, you can go ahead and tell the reader what you found in the results section. Exactly what you’ll say here will differ based on what you studied, but there isn’t a lot more to say at this stage.

3.6.1 Discussion

The paper then concludes with a discussion of the significance of the results and their implications. You found something, why do we care? How does that change the field? Should policymakers react? Should scientists react? You’ll often start with an overview of what the paper found, before launching into some of the more specific takeaways you want readers to get.

3.7 Summary

This chapter has covered some of the different ways we do research, and one way (a formal paper) that we report our research. It might seem a little overwhelming to think about how to write up your research results before you even know how to do research. And that’s fine, this is something of getting a fly over of the forest before we start to look more closely at the trees. It’s good to have an idea of what your final paper might look like, before we get started. Now we can begin to get a little more detailed about how we fill in all those words between the title and the final period on a research paper.

Home » Research Approach – Types Methods and Examples

Research Approach – Types Methods and Examples

Table of Contents

Research Approach

Definition:

Research approaches refer to the systematic and structured ways that researchers use to conduct research, and they differ in terms of their underlying logic and methods of inquiry.

Types of Research Approach

The Three main research approaches are deductive, inductive, and abductive.

Deductive Approach

The deductive approach starts with a theory or a hypothesis, and the researcher tests the hypothesis through the collection and analysis of data. The researcher develops a research design and data collection methods based on the theory or hypothesis. The goal of this approach is to confirm or reject the hypothesis.

Inductive Approach

The inductive approach starts with the collection and analysis of data. The researcher develops a theory or an explanation based on the patterns and themes that emerge from the data. The goal of this approach is to generate a new theory or to refine an existing one.

Abductive Approach

The abductive approach is a combination of deductive and inductive approaches. It starts with a problem or a phenomenon that is not fully understood, and the researcher develops a theory or an explanation that can account for the data. The researcher then tests the theory through the collection and analysis of more data. The goal of this approach is to generate a plausible explanation or theory that can be further refined or tested.

Research Approach Methods

Research approach methods are the specific techniques or tools that are used to conduct research within a particular research approach. Below are some examples of methods that are commonly used in each research approach:

Deductive approach methods:

Surveys and questionnaires: to collect data from a large sample of participants
Experiments: to manipulate variables and test hypotheses under controlled conditions
Statistical analysis: to test the significance of relationships between variables
Content analysis: to analyze and interpret text-based data

Inductive approach methods:

Interviews: to collect in-depth data and explore individual experiences and perspectives
Focus groups: to collect data from a group of participants who share common characteristics or experiences
Observations: to gather data on naturalistic settings and behaviors
Grounded theory: to develop theories or concepts from data through iterative cycles of analysis and interpretation

Abductive approach methods:

Case studies: to examine a phenomenon in its real-life context and generate new insights or explanations
Triangulation: to combine multiple data sources or methods to enhance the validity and reliability of findings
Exploratory research: to gather preliminary data and generate new research questions
Concept mapping: to visually represent relationships and patterns in data and develop new theoretical frameworks.

Applications of Research Approach

Here are some common applications of research approach:

Academic Research : Researchers in various academic fields, such as sociology, psychology, economics, and education, use research approaches to study a wide range of topics.
Business Research : Organizations use research approaches to gather information on customer preferences, market trends, and competitor behavior to make informed business decisions.
Medical Research : Researchers use research approaches to study various diseases and medical conditions, develop new treatments and drugs, and improve public health.
Social Research: Researchers use research approaches to study social issues, such as poverty, crime, discrimination, and inequality, and to develop policies and programs to address these issues.
Environmental Research: Researchers use research approaches to study environmental problems, such as climate change, pollution, and biodiversity loss, and to develop strategies to mitigate these problems.
Marketing Research : Companies use research approaches to study consumer behavior, preferences, and needs in order to develop effective marketing strategies.
Educational Research: Researchers use research approaches to study teaching and learning processes, develop new teaching methods and materials, and improve educational outcomes.
Legal Research : Lawyers and legal scholars use research approaches to study legal precedents, statutes, and regulations in order to make legal arguments and develop new laws and policies.

Examples of Research Approach

Examples Deductive approach:

A researcher starts with a theory or hypothesis and then develops a research design to test it. For example, a researcher might hypothesize that students who receive positive feedback from their teachers are more likely to perform well academically. The researcher would then design a study to test this hypothesis, such as surveying students to assess their feedback from teachers and comparing their academic performance.
Another example of a deductive approach is a clinical trial to test the effectiveness of a new medication. The researchers start with a theory that the medication will be effective and then design the study to test this theory by comparing the outcomes of patients who receive the medication with those who receive a placebo.

Examples Inductive approach:

A researcher begins with data and then develops a theory or explanation to account for it. For example, a researcher might collect data on the experiences of immigrants in a particular city and then use that data to develop a theory about the factors that contribute to their success or challenges.
Another example of an inductive approach is ethnographic research, where the researcher immerses themselves in a cultural context to observe and document the practices, beliefs, and values of the community. The researcher might then develop a theory or explanation for these practices based on the observed patterns and themes.

Examples Abductive approach:

A researcher starts with a puzzle or a phenomenon that is not easily explained by existing theories and uses a combination of deductive and inductive reasoning to generate a new explanation or theory. For example, a researcher might notice a pattern of behavior in a particular group of people that is not easily explained by existing theories and then use both deductive and inductive reasoning to develop a new theory to explain the behavior.
Another example of an abductive approach is diagnosis in medicine. A physician starts with a set of symptoms and uses deductive reasoning to generate a list of possible diagnoses. The physician then uses inductive reasoning to gather more information about the patient and the symptoms to narrow down the list of possible diagnoses and arrive at a final diagnosis.

Purpose of Research Approach

The purpose of a research approach is to provide a systematic and logical way of conducting research to achieve the research goals and objectives. It helps the researcher to plan, design, and conduct research effectively and efficiently, ensuring that the research is reliable, valid, and useful. Different research approaches have different purposes and are suited for different types of research questions and contexts.

Here are some specific purposes of different research approaches:

Deductive approach:

To test hypotheses or theories
To confirm or refute existing knowledge
To generalize findings to broader populations or contexts

Inductive approach:

To generate new theories or hypotheses
To identify patterns, themes, or relationships in data
To develop an understanding of social or natural phenomena

Abductive approach:

To develop new explanations or theories when existing ones are inadequate
To identify new patterns or phenomena that may be overlooked by existing theories
To propose new research questions or directions

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Institutional Review Board – Application Sample...

Evaluating Research – Process, Examples and...

What Are The 3Rs? About Beyond3Rs

The 3rs of animal research are reduction, refinement, and replacement..

The 3Rs were conceptualized by William Russell and Rex Burch in The Principles of Humane Experimental Technique , first published in 1959. Since then, the principles of Reduction , Refinement , and Replacement (the 3Rs )* have guided thinking, practice, and regulation of humane research animal use.

While they have been a helpful framework for the past 70 years, the 3Rs represent the beginning, not the end, of humane laboratory animal science. Though we firmly believe that the 3Rs continue to serve an essential role, we aim to expand upon these principles. Modern research presents innovative opportunities to go Beyond3Rs , which can improve the welfare and validity of animals as research subjects.

Below, we take a closer look at each "R" and how we can go Beyond.

"Reduction means reduction in the numbers of animals used to obtain information of a given amount and precision."

— Russell and Burch, The Principles of Humane Experimental Technique (1959)

What is Reduction?

In other words, Reduction means using the least amount of animals needed to conduct an experiment that is robust, reproducible, and truly adds to the knowledge base.

How can we go Beyond?

Going Beyond Reduction involves viewing animals as patients and prioritizing scientific rigor in conduct and reporting of animal studies. We can go Beyond Reduction by:

Applying principles of human clinical experimental design and analysis (e.g., use of randomization, heterogenization, and blinding).
Using appropriate modern statistical analyses to reduce the number of animals needed to find meaningful results.
Including and accounting for individual differences, so that we can see a wider range of phenotypes and responses to treatment.

This has far-reaching implications for human health — one major reason why animal experiments fail to translate to humans is because they do not account for natural variation within the population. Improving generalizability of results can increase the likelihood that animal use will yield benefits which translate to humans.

"Refinement means any decrease in the incidence or severity of inhumane procedures applied to those animals which still have to be used."

What is refinement.

In other words, Refinement refers to any methods which minimize the pain, suffering, distress or lasting harm that research animals might experience.

Many refinement techniques focus on pain management and minimizing negative physical experiences during experiments. Going Beyond Refinement acknowledges that animal welfare science is an evolving discipline, and we now have more evidence-based strategies to make positive impacts on animal health and psychological well-being. These strategies can be applied throughout an animal's entire life in research, rather than only within an experiment. We can go Beyond Refinement by:

Devoting resources to implementation of Refinement strategies (e.g., having dedicated staff for animal welfare and behavior who stay informed on current research).
Providing species-appropriate environmental enrichment that meets an animal's needs and provides opportunities for them to make choices and have positive experiences.
Promoting a culture where we always strive to improve (e.g., performing ongoing assessments of programs, and changing practices when a better way to do something is discovered).

Recognizing that in addition to improving animal experiences, implementing Refinements to housing and husbandry can improve research quality.

"Replacement means the substitution for conscious living higher animals of insentient material."

What is replacement.

In other words, Replacement means avoiding the use of animals in an experiment where possible and using a non-animal method instead. Many non-animal methods, such as cell cultures and computer modeling, are under development around the world.

Going Beyond Replacement involves reconsidering two key concepts in Russell & Burch’s original discussion: relative vs absolute Replacement, and higher vs lower organisms.

Russell and Burch discussed “relative” and “absolute” Replacement as two distinct categories. Going Beyond3Rs involves a more nuanced view which considers Replacement as a spectrum.

Thinking of Replacement as a spectrum from "soft" to "hard" emphasizes that any relative movement towards absolute Replacement is beneficial, even if absolute Replacement is not possible (e.g., in behavioral studies). In fields of research that continue to require animal models, hard replacement methods can be useful to identify research targets before soft replacement methods are employed to test the most promising candidates.

Additionally, the original 3Rs concept of replacing “higher” with “lower” organisms relies on the assumption that there is a “ladder of life” from “lower” to “higher” organisms, and that this is somehow meaningful for well-being (i.e., "lower" organisms are often assumed to have lower propensity to feel pain or distress). Modern biology now views life as a tree, not a ladder. A perceived lower level of sentience is not a valid justification for model choice. Going Beyond Replacement requires matching the best model species to the research question, and adopting the same level of rigor and care for all species.

*We present the 3Rs in the order of Reduction, Refinement, and Replacement, rather than the original order proposed by Russell and Burch (Replacement, Reduction, and Refinement). This is not to minimize the importance of implementing Replacement; rather, moving Beyond3Rs acknowledges that developing innovative Replacement techniques or implementing Soft Replacement requires an understanding of modern Reduction and Refinement methods, so we introduce these concepts first as a foundation.

Additional 3Rs Resources

National Centre for the 3Rs (NC3Rs)

→ Experimental Design Assistant

→ How to Handle Mice and Rats

→ Mouse Grimace Scale

Animal Welfare Institute Refinement Database

North American 3Rs Collaborative (NA3RsC)

NIH Office of Laboratory Animal Welfare (OLAW)

Institute for Laboratory Animal Research (ILAR)

Board on Animal Health Sciences, Conservation, and Research (BAHSCR)

Universities Federation for Animal Welfare (UFAW)

How to Go Beyond3Rs

Embracing Variability

Harmonizing animal and human research, spontaneous disease models, and biomarkers

Housing and Husbandry

Improving enclosures, enrichment, handling, nutrition, and other husbandry practices

Reproducibility and Translation

Experimental design, animal-to-human translation, and "the science of doing science"

Open access
Published: 25 April 2024

A scoping review of academic and grey literature on migrant health research conducted in Scotland

G. Petrie 1 ,
K. Angus 2 &
R. O’Donnell 2

BMC Public Health volume 24 , Article number: 1156 ( 2024 ) Cite this article

73 Accesses

1 Altmetric

Metrics details

Migration to Scotland has increased since 2002 with an increase in European residents and participation in the Asylum dispersal scheme. Scotland has become more ethnically diverse, and 10% of the current population were born abroad. Migration and ethnicity are determinants of health, and information on the health status of migrants to Scotland and their access to and barriers to care facilitates the planning and delivery of equitable health services. This study aimed to scope existing peer-reviewed research and grey literature to identify gaps in evidence regarding the health of migrants in Scotland.

A scoping review on the health of migrants in Scotland was carried out for dates January 2002 to March 2023, inclusive of peer-reviewed journals and grey literature. CINAHL/ Web of Science/SocIndex and Medline databases were systematically searched along with government and third-sector websites. The searches identified 2166 journal articles and 170 grey literature documents for screening. Included articles were categorised according to the World Health Organisation’s 2016 Strategy and Action Plan for Refugee and Migrant Health in the European region. This approach builds on a previously published literature review on Migrant Health in the Republic of Ireland.

Seventy-one peer reviewed journal articles and 29 grey literature documents were included in the review. 66% were carried out from 2013 onwards and the majority focused on asylum seekers or unspecified migrant groups. Most research identified was on the World Health Organisation’s strategic areas of right to health of refugees, social determinants of health and public health planning and strengthening health systems. There were fewer studies on the strategic areas of frameworks for collaborative action, preventing communicable disease, preventing non-communicable disease, health screening and assessment and improving health information and communication.

While research on migrant health in Scotland has increased in recent years significant gaps remain. Future priorities should include studies of undocumented migrants, migrant workers, and additional research is required on the issue of improving health information and communication.

Peer Review reports

The term migrant is defined by the International Organisation for Migration as “ a person who moves away from his or her place of usual residence, whether within a country or across an international border, temporarily or permanently, and for a variety of reasons. The term includes several well-defined legal categories of people, including migrant workers; persons whose particular types of movements are legally-defined, such as smuggled migrants; as well as those whose status are not specifically defined under international law, such as international students.” [ 1 ] Internationally there are an estimated 281 million migrants – 3.6% of the world population, including 26.4 million refugees and 4.1 million asylum seekers – the highest number ever recorded [ 2 ]. The UN Refugee Society defines the term refugee as “ someone who has been forced to flee his or her country because of persecution, war or violence…most likely, they cannot return home or are afraid to do so .” The term asylum-seeker is defined as “someone whose request for sanctuary has yet to be processed.” [ 3 ].

Net-migration to Europe was negative in the 19th century due to higher levels of emigration, however in the mid-20th century immigration began to rise, because of an increase in migrant workers and following conflicts in the Middle East and North Africa [ 4 ]. Current migration drivers include conflicts alongside world-wide economic instability, exacerbated by the Covid-19 pandemic [ 5 ]. Environmental damage due to climate change is expected to inflate the number of asylum seekers entering Europe in future [ 6 ]. The increase in migration to Europe is not a short-term influx but a long-term phenomenon, and European nations must adapt and find solutions to resulting financial, safeguarding and health challenges [ 7 ].

Data on healthcare use by migrants in Europe is variable, which means cross-country comparisons are inadequate [ 8 ]. Many countries do not record migration information within health records and all use disparate criteria to classify migrant status. The lack of comparative data hinders public health surveillance and effective interventions [ 9 ]. Even where information is available, results can be contradictory due to the multifarious migrant population. Migrants have a wide range of origin countries, socio-economic position, age and journeys undertaken which can affect health status [ 10 ].

Migrants initially may have better health than the general population, known as the ‘Healthy Migrant effect’ [ 11 ]. However, health declines with increasing length of residence [ 12 ] and over time to levels comparable with the general population [ 13 ]. Second generation immigrants may have higher mortality than average [ 14 ]. The process of acculturation to the host country, with adoption of unhealthy lifestyle and behaviours, increases the risk for chronic disease [ 15 ]. In addition, inequalities in health of migrants compared to host populations has been confirmed by wide-ranging research [ 16 ].

Host countries may limit healthcare access, with undocumented migrants sometimes only entitled to emergency care [ 17 ]. Even when access is granted, inequitable services can affect quality of care due to language barriers and cultural factors [ 18 ]. Poor working/living conditions and discrimination can exacerbate health inequalities [ 12 ]. Processing facilities for asylum seekers are frequently overpopulated, stressful environments [ 19 ] and threat of deportation, lack of citizenship rights and integration can negatively affect health and access to care [ 20 ]. Undocumented workers are unprotected by health and safety legislation leading to dangerous working conditions and injuries [ 15 ].

A systematic review of migrant health in the European Union (EU) found migrants have worse self-perceived health than the general population [ 21 ]. Research evidence indicates increased prevalence of cardiovascular disease, diabetes, mental health disorders and adverse pregnancy outcomes. Exposure to conflict, harsh travel conditions and suboptimal vaccine programmes can mean higher risk of communicable disease [ 22 ]. Scoping reviews have also been conducted to describe trends within migration health research in the United Kingdom (UK) [ 23 ] and identify gaps for future research agendas in the UK [ 23 ] and in the Republic of Ireland [ 24 ].

Almost three-quarters (73%) of published migration health research in the UK has been conducted in England, focusing primarily on infectious diseases and mental health. There is limited evidence on the social determinants of health, access to and use of healthcare and structural and behavioural factors behaviours that influence migrant health in the UK [ 23 ]. By contrast, a large amount of the migration research conducted in the Republic of Ireland has focused on the social determinants of health, and on health system adaptations, with a paucity of research focusing on improving health information systems [ 24 ].

Migration and Health in Scotland

Immigration to Scotland began to rise in 2003 with the expansion of the EU [ 25 ]. The population in Scotland increased from 5.11 million to 5.47 million between 2005 and 2020 and is predicted to continue rising until 2028 [ 26 ] despite low birth rates, with the increased population resulting from inward migration [ 27 ]. Scotland’s population is becoming more ethnically diverse [ 28 ] and susceptibility to different health conditions varies by ethnic group, which has implications for the planning and provision of health services [ 29 ]. 7% of the current Scottish population are non-UK nationals and 10% were born outside Britain. The commonest countries of origin were Poland, Ireland, Italy, Nigeria and India [ 30 ].

Within Scotland, linking health data to ethnicity is standard in order to monitor and improve health of minority groups [ 31 ]. Ethnic background can differ from country of birth which means migration status cannot be assumed [ 32 ], although health inequalities experienced by migrants often extend to affect all ethnic minority groups [ 33 ]. The Scottish Health and Ethnicity Linkage Study (SHELS) linked census data to health records of 91% of the population which has provided information on mortality and morbidity by ethnic group and country of birth [ 34 ]. SHELS research indicates that the white-Scottish population have a higher mortality rate than other ethnic groups. This may be consequent to the comparatively poor health of the Scottish population relative to other European nations: high mortality rates in the general population may cause a perception that the health of minorities is more advantageous than in reality [ 35 ].

Cezard et al’s [ 13 ] analysis of self-perceived health among people in Scotland found that being born abroad had a positive impact on health status. Health declined with increased length of residence, which may be explained by cultural convergence with the majority population. Allik et al. [ 36 ] compared health inequalities by ethnic background and found that with increasing age, health differences reduced thus people aged over 75 of all ethnicities had similar or worse health status than White-Scottish people. While working-age migrants appear to be healthier than the White Scottish population, it cannot be assumed that in future this would extend to older age groups.

Research has shown deprivation as a cause of heath inequalities among ethnic minority and migrant groups [ 37 ]. The socio-economic status of minority ethnic groups in Scotland is unusual, as most are of similar or higher status than the white-Scottish population [ 38 ]. Therefore, public health interventions targeting deprivation may not address risk-factors for ethnic minorities and migrants [ 36 ]. Further research on determinants of health in migrants can help with planning and design of inclusive policies.

The 2011 census indicated that 50% of immigrants lived in the cities of Edinburgh, Glasgow, and Aberdeen. Glasgow had a greater percentage of non-European immigrants due to participation in the Asylum dispersal programme [ 39 ]. 10% of UK asylum seekers are placed in Glasgow, but records are not kept following approval of asylum claims, therefore the size of the refugee population is unknown [ 40 ]. While immigration is controlled by the British government, in policy areas devolved to the Scottish government, refugees and asylum seekers have more rights than elsewhere in UK, including access to primary healthcare for undocumented migrants [ 40 ]. Despite the mitigating effect of Scottish policies, asylum seekers’ health is worsened by the asylum process and associated poverty, marginalisation, and discrimination [ 40 ]. Health deteriorates with increasing length of time in the asylum system [ 40 ] and asylum seekers and refugees have additional health needs and require enhanced support [ 41 ]. Research on the health needs of asylum seekers in Scotland is required to ensure adequate healthcare.

Aim and objectives

While scoping reviews on migrant health have been carried out in Europe [ 12 ], Ireland [ 24 ] and the UK [ 23 ] none are currently specific to the Scottish context. Given the devolved government of Scotland and demographics described above, a targeted review would help to clarify research priorities, with the aim of improving health and health care within the migrant community in Scotland. This work therefore builds on the published scoping review of migrant health in the Republic of Ireland [ 24 ]. The authors recommend replication of the study in other countries to facilitate cross-country comparison. Our aim was to scope peer-reviewed research and grey literature on migrant health conducted in Scotland and identify any gaps in the evidence. Our objectives were to: [1] understand the extent of the available research by topic area [2] summarise the types of research already conducted, populations studied, topics covered and approaches taken [3], map the existing research conducted in Scotland and [4] identify areas for future research based on any gaps in the evidence identified.

A scoping review was conducted as they can aid detection of evidence gaps [ 42 ] and allow incorporation of grey literature in topics with insufficient published research [ 43 ]. Arksey and O’Malley’s [ 44 ] five stage scoping review framework was used.

Stage 1: identifying the research question

Arskey and O’Malley [ 44 ] suggest maintaining a broad approach to identifying the research question, in order to generate breadth of coverage. On this basis, and in line with the research question identified in the Villarroel et al. [ 24 ] scoping review, our research question was framed as follows: What is the scope, main topics and gaps in evidence in the existing literature on health of international migrants living in Scotland? Arksey and O’Malley [ 44 ] highlight the importance of defining terminology at the outset of scoping reviews. For consistency, we used the broad definition of ‘migrant’ as per Villaroel et al. [ 24 ], from the International Organisation for Migration (IOM) [ 1 ]. References to refugees or asylum seekers followed the United Nations Refugee Agency definitions [ 3 ].

Stage 2: identifying relevant studies

Electronic database searches identified reports alongside a grey literature search, in line with Arskey and O’Malley’s [ 44 ] guidance to search for evidence via different sources. CINAHL, Web of Science, SocIndex and Medline academic databases were selected with input from co-authors. Search terms for the review were based upon those used by Villaroel et al. [ 24 ] with additional relevant terms from Hannigan et al. [ 9 ] The strategy combined three sets of terms for: Migrants (e.g., refugee, migrant, immigrant or newcomer), Scotland and Health. Both free text terms and index terms were used and adapted to the 4 academic databases and searches were run on 10th March 2023 (see Additional File 1 for database search strategies). Thirteen Government, University, and third-sector websites in Scotland were scoped for selection then hand-searched for grey literature (listed in Additional File 1 ).

Stage 3: study selection

Net-migration to Scotland increased in the 2000s [ 27 ] hence a date range of January 2002-March 2023 was used to identify evidence. The search was limited to English only. Inclusion/exclusion criteria for the studies were based on those used by Villaroel et al. [ 24 ] and expanded upon following discussion with co-authors (see Table 1 ). Reports were included if based on primary or secondary research on the health of international migrants in Scotland and used qualitative, quantitative or mixed methods research design. International or UK based reports were only included if Scottish results were documented separately. Reports on the health of ethnic minority groups in Scotland was included if place of birth was recorded. Research on internal (non-international) migrants within Scotland, either moving from one Scottish area to another or from another part of the United Kingdom to Scotland, were excluded.

Stage 4: data charting

All records were saved to RefWorks for screening. Records were first screened at title/abstract stage with 10% independently checked by the co-authors. The remaining reports were single screened using full text by the first author. Data from the included records was extracted and organised in tabular form under the following headings, which were agreed by team members: article type (peer-reviewed article or grey literature), publication date, geographical setting, study/intervention’s target population, funding, primary research focus on migrant health (y/n), study objective, data collection method, study design (qualitative/quantitative/mixed) and main finding. Reports were not critically appraised in this scoping review.

Stage 5: collating, summarising and reporting results

A report (either a peer-reviewed journal article or grey literature report) is used as our unit of analysis. In order to present the range of research identified, reports were grouped by the different headings in our data charting table and the outcomes considered for relevance to our scoping review’s aim. Our Results summarise the recency, focus, study designs and funding sources of the identified research, followed by the geographical settings and whether Scotland was included in international research reports. Reports were grouped by their study population and further sub-divided by publication type and geographical area for summarising. Finally, the WHO’s European strategy and action plan (SAAP) for refugee and migrant health [ 7 ] is a policy framework designed to help governments and other stakeholders monitor and improve migrant health in Europe. There are nine strategic areas in the WHO’s SAAP, which prioritise the most salient issues. In line with Villaroel et al’s [ 24 ] approach and in order to compare scoping review outcomes, these areas were used to categorise the findings of this review. Each report was matched to the most appropriate SAAP:

Establishing a Framework for Collaborative Action.

Advocating for the right to health of refugees.

Addressing the social determinants of health.

Achieving public health preparedness and ensuring an effective response.

Strengthening health systems and their resilience.

Preventing communicable disease.

Preventing and reducing the risks caused by non-communicable disease.

Ensuring ethical and effective health screening and assessment.

Improving health information and communication.

The primary focus (aims and objectives) of each report was used to identify the relevant SAAP area/areas. To improve reliability, results were compared using coding criteria used in Villaroel et al’s study (MacFarlane 2023, personal communication, 31st May). 10% of the reports were checked by one co-author to ensure consistent coding to SAAP categories. Any instances of uncertainty in mapping reports to the relevant SAAP area/areas were discussed and resolved by team members.

This scoping review of the literature on migrant health in Scotland identified 2166 records from academic literature databases, following duplicate removal, and 170 records from website searches (see Fig. 1 ). Following screening, a total of 71 peer-reviewed journal articles and 29 grey literature studies (totalling 100 reports) were included for analysis (Results table and reference list are presented in Additional File 2 ).

Flow chart illustrating the identification of sources of evidence included in the scoping review

Overall findings

The majority of reports were published between 2013 and 2022. Fifty-eight reports (58%) focused exclusively on migrant health [ 18 , 39 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 ]. 23 centred on health but included other populations in addition to migrants – for example research on ethnic minorities or other vulnerable groups [ 13 , 31 , 35 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 ]. Seventeen reports were included where the sample population were migrants, but the primary topic was not health – for example destitution, integration, and service needs [ 27 , 73 , 74 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 ]. Health data was reported as part of the wider subject matter. One report [ 136 ] looked at the social determinants of breastfeeding including migrant status and one [ 137 ] compared attitudes to aging and family support between countries.

Funding sources were not declared for 35 (35%) of reports. The Scottish Government funded 20 reports (20%) [ 13 , 27 , 32 , 39 , 45 , 46 , 47 , 66 , 77 , 88 , 99 , 100 , 101 , 102 , 113 , 116 , 119 , 121 , 129 , 134 ]. Other common sources of funding included Government funded public bodies ( n = 13) [ 45 , 48 , 49 , 50 , 51 , 52 , 53 , 104 , 107 , 113 , 116 , 131 , 136 ], the Scottish Health Service ( n = 18) (either the National Health Service (NHS) [ 13 , 54 , 56 , 57 , 58 , 59 , 102 , 113 , 116 ], local NHS trusts [ 45 , 60 , 61 , 77 , 102 , 103 , 112 ] or by Public Health Scotland [ 13 , 113 ]) Eleven reports (11%) were funded by Universities. The charity sector financed 15 (15%) reports [ 53 , 63 , 66 , 69 , 70 , 71 , 72 , 73 , 74 , 103 , 111 , 123 , 125 , 132 , 138 ] and the EU and Scottish local authorities funded four reports each [ 45 , 62 , 75 , 76 , 77 , 102 , 125 , 135 ]. Professional bodies financed one report [ 126 ] as did the Japanese government [ 64 ]. No reports received funding from the business sector. The biggest sources of funding for grey literature were Refugee charities (40%) and the Scottish government (30%) (see Fig. 2 ).

Sources of funding for migrant health research in Scotland

Research methods and data collection

52% of reports used qualitative research methods. Forty-five reports (86%) collected data using 1–1 interviews and 24 (46%) used focus groups. Other methods of data collection included questionnaires (six studies (11%)), workshops (two studies (3.85%)) and observation (two studies (3.85%)). Oral/written evidence, guided play sessions, family case studies and participatory activity sessions were used in one report each.

28% of reports used quantitative research methods, most commonly cross section design (ten studies (36%)) and cohort design (18 studies (64%)). Information was obtained from databases including medical records, Census data and national records in 21 reports (75%). Questionnaires were used in six reports (21%). Other methods including body measurements, food diaries, blood samples, interviews and case reviews were used in 1 report each.

20% of reports used mixed methods. The most common method of data collection was questionnaires in 14 reports (70%), interviews in ten reports (50%), focus groups in seven reports (35%), workshops in three reports (13.6%), and databases in three reports (13.6%). Other methods included literature review in two reports (10%), case note reviews in two reports (10%) and one reports each used mapping and school records.

Geographical areas of study

Ninety-one reports were situated in Scotland, of which 35 (38.5%) covered the whole country and 56 (61.5%) specified a city or area where research was undertaken. Some UK and international reports also specified the area of Scotland. The largest share of research within Scotland overall was in Glasgow with 36 reports, followed by Edinburgh with 16 reports, Lothian with six reports, Aberdeen with five reports and Grampian with three reports. The Northeast, Stirling, Highlands, Inverness, Lanarkshire, Motherwell and Selkirk had one report in each area.

There were seven international reports, three on mortality by country of birth [ 75 , 76 , 78 ], one on cross cultural communication [ 79 ], one on maternity care in Poland and Scotland [ 99 ], one comparing attitudes to aging in China and Scotland [ 137 ] and one on the link between birthweights and integration of migrants [ 64 ]. The remaining two reports were UK based, one on immunisation of Roma and traveller communities [ 117 ] and one on the link between ethnic diversity and mortality [ 104 ]. All the included international and UK reports documented the Scottish data separately within results.

Migrant population

Thirty-one reports included all migrants in the study population. The remaining reports included 30 studies on asylum seekers/refugees, 11 on Polish migrants, ten on Africans, six each on South Asians/Chinese/European, three on Arabs, and two on Roma populations (see Fig. 3 ). Most reports did not specify the country of origin for Asylum seekers and refugees - where country of birth was specified, reports were also included in the appropriate category.

Migrant populations studied in health research in Scotland

Grey literature and peer-reviewed reports differed in population focus. The most common populations of interest in grey literature were asylum seekers/refugees consisting of 18 reports (62%) [ 27 , 47 , 54 , 55 , 59 , 63 , 70 , 71 , 72 , 73 , 74 , 123 , 125 , 127 , 128 , 132 , 134 , 138 ] while for peer-reviewed journals 24 reports (34%) focused on all migrants [ 13 , 35 , 45 , 48 , 64 , 76 , 78 , 79 , 80 , 81 , 104 , 105 , 108 , 109 , 113 , 114 , 115 , 116 , 118 , 120 , 121 , 122 , 136 ].

Migrant study population also differed by local area; Glasgow city, where the majority of research occurred, had 18 reports of 36 (50%) on Asylum seekers/refugees [ 47 , 48 , 52 , 53 , 54 , 55 , 58 , 63 , 70 , 71 , 72 , 82 , 83 , 127 , 128 , 130 , 138 , 139 ] eight reports (22%) on Africans [ 52 , 53 , 84 , 85 , 86 , 87 , 106 , 107 ], seven reports (19%) on all migrants [ 45 , 48 , 80 , 102 , 104 , 105 , 121 ] and two reports (5.5%) on Roma migrants [ 103 , 117 ]. Other populations had one reports each. In Edinburgh five reports of 16 (31%) were on the Polish population [ 56 , 67 , 68 , 89 , 90 ], and two reports (12.5%) on Asylum seekers/refugees [ 60 , 133 ], Chinese [ 62 , 137 ], South Asian [ 46 , 119 ], all migrants [ 105 , 121 ] and Africans [ 87 , 107 ]. The remaining migrant groups had one report each. Other areas of Scotland show no clear pattern with studies in disparate migrant population groups.

Number of reports per Strategic and Action Plan (SAAP) Area

SAAP Area mapping

1. establishing a framework for collaborative action.

Nine reports had a primary focus on collaborative action and were categorised under SAAP area 1 (see Fig. 4 ) [ 66 , 70 , 72 , 73 , 103 , 125 , 129 , 132 , 134 ]. Four reports (33%) used a mixed methods study design, the remaining five reports (67%) used a qualitative design. One report [ 66 ] focused on the epidemiology of female genital mutilation and a proposed intervention strategy. One report [ 66 ] focused on the epidemiology of female genital mutilation and a proposed intervention strategy. One report [ 103 ] evaluated service provision to the Roma community in Glasgow. The remaining reports focused on refugees and asylum seekers: four [ 73 , 125 , 132 , 134 ] evaluations of refugee integration projects, one [ 70 ] on services available to pregnant women, and one [ 72 ] an assessment of a peer-education service. One report [ 129 ] was a review of service provisions for migrants during the Covid-19 pandemic. All reports in SAAP area 1 were grey literature and three (37.5%) had a primary focus on migrant health while four (50%) focused on integration, one (11%) included data on ethnic minorities and one (11%) on services during the covid-19 pandemic. The majority (seven reports (78%)) were also categorised to another SAAP area most commonly area 2 (five studies (55%)) or area 5 (four studies (44%)).

2. Advocating for the right to health of refugees

Nineteen reports focused on SAAP area 2, advocating for the right to health of refugees (see Fig. 4 ) [ 47 , 52 , 53 , 54 , 55 , 63 , 70 , 71 , 83 , 103 , 123 , 124 , 125 , 127 , 128 , 129 , 134 , 138 , 140 ]. Sixteen reports (84%) had a qualitative study design and the remaining three (16%) reports used mixed methods. Nine reports (47%) focused on the health impact of the asylum system [ 52 , 55 , 71 , 74 , 123 , 127 , 128 , 129 , 138 ], five (26%) on health and access to care [ 47 , 54 , 83 , 103 , 124 ], two (10.5%) on maternity care [ 63 , 70 ], two (10.5%) on integration services [ 125 , 134 ] and one report on mental health in HIV positive migrants [ 53 ]. Nine reports (47%) had a primary focus on migrant health while the remaining 10 (53%) also involved wider social issues. The majority (15 (79%)) of reports were grey literature. All the articles in this group overlapped with another SAAP area. Area 3 is the most common joint category with ten reports (53%) followed by area 5 with seven reports (37%), area 1 shares five reports (26%), while areas 4 and 8 share one report each (5%).

3. Addressing the social determinants of health

Twenty-nine reports were categorised to SAAP area 3 – addressing the social determinants of health (see Fig. 4 ) [ 13 , 27 , 45 , 50 , 52 , 55 , 60 , 62 , 63 , 65 , 68 , 71 , 74 , 80 , 81 , 82 , 91 , 92 , 93 , 102 , 112 , 123 , 124 , 127 , 128 , 136 , 137 , 138 ]. The majority (14 (48%)) used a qualitative study method, eight (28%) used quantitative methodology and the remaining seven reports (24%) used mixed methods. Nineteen reports (65.5%) were peer-reviewed journals [ 13 , 45 , 50 , 52 , 60 , 62 , 63 , 65 , 68 , 80 , 81 , 82 , 91 , 92 , 93 , 104 , 112 , 124 , 136 , 137 ] and ten (34.5%) were grey literature [ 27 , 55 , 63 , 71 , 74 , 102 , 123 , 127 , 128 , 138 ]. Ten reports (34.5%) discussed the effects of the asylum system on health [ 27 , 52 , 63 , 71 , 74 , 123 , 124 , 127 , 128 , 137 ] and one (3.5%) migration and health [ 50 ]. Six reports (21%) focused on culture and ethnicity [ 82 , 92 , 102 , 104 , 112 , 137 ], five reports (17%) discussed economic and environmental determinants of health [ 13 , 45 , 67 , 81 , 93 ] and five reports (17%) the health impact of social activities [ 55 , 60 , 62 , 80 , 91 ]. Of the remaining reports, one [ 65 ] discussed Brexit and mental health of European migrants and one discussed the effect of coping strategies on wellbeing in Polish migrants [ 68 ]. Most reports, 18 (62%) had a primary focus on migrant health [ 45 , 50 , 52 , 55 , 60 , 62 , 63 , 65 , 67 , 68 , 71 , 80 , 81 , 82 , 91 , 92 , 93 , 102 ], six reports (21%) discussed wider social factors in addition to health [ 74 , 123 , 124 , 127 , 128 , 138 ]. Of the remaining reports three (10%) looked at ethnic background and country of birth [ 13 , 112 , 136 ], one [ 27 ] included other vulnerable groups and one [ 137 ] included people living in China and Chinese migrants to Scotland. Thirteen reports were also categorised to one or more additional SAAP area - ten (34%) were also applicable to area 2 [ 52 , 55 , 63 , 71 , 74 , 123 , 124 , 127 , 128 , 138 ], three (10%) to area 5 [ 63 , 82 , 92 ] and one (7%) to area 4 [ 27 ].

4. Achieving public health preparedness and ensuring an effective response

Twenty-one reports were assigned to SAAP area 4 (see Fig. 4 ) [ 27 , 31 , 35 , 39 , 47 , 57 , 64 , 75 , 76 , 77 , 78 , 94 , 104 , 108 , 109 , 111 , 113 , 114 , 116 , 120 , 135 ] of which fourteen (67%) used quantitative research methods, four (19%) mixed methods and three (14%) qualitative methods. Thirteen (62%) reports were peer-reviewed journals [ 35 , 59 , 64 , 75 , 78 , 104 , 108 , 109 , 111 , 113 , 114 , 116 , 120 ] and eight (38%) grey literature [ 27 , 31 , 39 , 47 , 57 , 77 , 94 , 135 ]. Most reports (12 (57%)) focused on morbidity and mortality in migrant populations [ 31 , 35 , 64 , 75 , 76 , 78 , 104 , 108 , 109 , 113 , 114 , 116 ]. Six (29%) investigated health status and healthcare needs in migrant groups in Scotland [ 39 , 47 , 57 , 77 , 94 , 135 ]. Two reports (9.5%) analysed the epidemiology of HIV infections [ 111 , 120 ] and the remaining report focused on the health needs of young people during the covid-19 pandemic [ 27 ]. Nine reports (43%) had a primary focus on migrant health [ 39 , 47 , 55 , 64 , 75 , 76 , 77 , 78 , 94 ] while eight (38%) also analysed data by ethnicity [ 31 , 35 , 104 , 108 , 109 , 113 , 114 , 116 ]. Of the remaining reports, three (14%) included other populations within Scotland [ 27 , 111 , 120 ] and one (5%) included other characteristics in addition to health information [ 135 ]. Ten reports (48%) were also categorised to another SAAP area; one to area 2 [ 47 ], one to area 3 [ 27 ], four to area 5 [ 47 , 57 , 77 , 135 ], two to area 6 [ 111 , 120 ] and two to area 9 [ 31 , 108 ].

5. Strengthening health systems and their resilience

Twenty-nine reports were assigned to SAAP area 5 (see Fig. 4 ) [ 18 , 47 , 48 , 49 , 54 , 57 , 63 , 69 , 70 , 72 , 77 , 79 , 82 , 83 , 92 , 95 , 96 , 97 , 99 , 101 , 103 , 118 , 119 , 126 , 129 , 131 , 133 , 135 , 141 ] of which 23 (79%) used qualitative research methods. Three reports used quantitative methods (10.3%) and the remaining three used mixed methods (10.3%). Twelve reports (41%) examined migrants needs and experiences of health care [ 47 , 49 , 54 , 57 , 58 , 77 , 83 , 95 , 103 , 119 , 129 , 135 ], eight (24%) focused on pregnancy and childcare [ 63 , 70 , 92 , 96 , 97 , 99 , 101 , 118 ] and two (7%) on barriers to healthcare access [ 48 , 131 ]. Two reports (7%) evaluated healthcare programmes [ 72 , 133 ] and two focused on communication in primary care [ 79 ] and maternity services [ 69 ]. The remaining three reports (10%) covered sexual health [ 82 ], health information needs of Syrian refugees [ 126 ] and general practitioner training [ 18 ]. Nineteen (65.5%) were peer reviewed journals [ 18 , 48 , 49 , 58 , 69 , 79 , 82 , 83 , 92 , 95 , 96 , 97 , 99 , 101 , 118 , 119 , 125 , 131 , 133 ] and ten (34.5%) were grey literature [ 47 , 54 , 57 , 63 , 70 , 72 , 77 , 103 , 129 , 135 ]. Twenty-one (72%) had a primary focus on migrant health [ 18 , 47 , 48 , 49 , 54 , 57 , 58 , 63 , 69 , 70 , 72 , 77 , 79 , 82 , 83 , 92 , 95 , 96 , 97 , 99 , 101 ]. Six reports (21%) included research on other characteristics or services [ 103 , 126 , 129 , 131 , 133 , 135 ]. The remaining two reports (7%) included ethnic groups as well as migrants in the data [ 118 , 119 ]. Nineteen reports (65.5%) were also assigned to one or more other category areas: five reports (17%) to area 1 [ 47 , 70 , 72 , 103 , 129 ], five reports (17%) to area 2 [ 54 , 63 , 83 , 103 , 129 ], three reports (10%) to area 3 [ 63 , 82 , 92 ], four reports (14%) to area 4 [ 47 , 57 , 77 , 135 ], one (3.5%) to area 7 [ 119 ] and one (3.5%) to area 9 [ 48 ].

6. Preventing communicable diseases

Fourteen reports were assigned to SAAP area 6 (see Fig. 4 ) [ 56 , 61 , 87 , 88 , 89 , 90 , 105 , 106 , 107 , 111 , 115 , 117 , 120 , 122 ] of which four (31%) used quantitative methods, five (38%) used qualitative methods and five (38%) used mixed methods. Five reports (38.5%) examined immunisation behaviour [ 56 , 61 , 89 , 90 , 117 ], five (38%) on epidemiology and treatment of HIV [ 106 , 107 , 111 , 120 , 122 ]. The remaining four reports (31%) focused on tuberculosis in healthcare workers [ 115 ], malaria [ 105 ] and sexual health services [ 87 , 88 ]. Only one reports was grey literature [ 88 ], the remainder were peer-reviewed journals. Six reports (46%) had a primary focus on migrant health [ 56 , 61 , 87 , 88 , 89 , 90 ] while seven reports (54%) also included other at-risk groups in the analysis. Four reports (31%) were also assigned to another SAAP category, two (15%) to area 4 [ 111 , 120 ] and two (15%) to area 8 [ 88 , 115 ].

7. Preventing and reducing the risks posed by non-communicable diseases

Eight reports were categorised to SAAP area 7 (see Fig. 4 ) [ 46 , 51 , 59 , 84 , 85 , 86 , 98 , 119 ] of which six (75%) used qualitative research methods, one (12.5%) used quantitative methods and one (12.5%) used mixed methods. Only one report (12.5%) was grey literature [ 59 ] the remaining seven reports (87.5%) were peer-reviewed journals [ 48 , 87 , 92 , 126 , 127 , 128 , 140 ]. Three reports (37.5%) focused on health behaviours [ 51 , 85 , 98 ], two (25%) on mental health, two (25%) on diabetes and one (12.5%) on chronic disease. Seven reports(87.5%) had a primary focus on migrant health [ 46 , 51 , 59 , 84 , 85 , 86 , 98 ], with the remaining report (12.5%) including ethnic minority groups [ 119 ]. One report (12.5%) was also assigned to SAAP area number 5 [ 119 ].

8. Ensuring ethical and effective health screening and assessment

There were six reports assigned to category 8 (see Fig. 4 ) [ 53 , 88 , 100 , 110 , 115 , 121 ] of which two (33%) used a quantitative research method, three (50%) used a qualitative method and one used mixed methods. One report (14%) was grey literature [ 88 ] the remaining five reports (83%) were peer reviewed journals [ 53 , 100 , 110 , 115 , 121 ]. Three reports (50%) focused on cancer screening in migrant women [ 21 , 100 , 110 ], one (17%) analysed access to HIV testing among African migrants [ 53 ], one (17%) on T.B in healthcare workers [ 72 ] and one (17%) on sexual health [ 36 ]. Three reports (50%) had a primary focus on migrant health [ 53 , 88 , 100 ] while the remaining three reports (50%) included other at-risk groups in the analysis [ 110 , 115 , 121 ]. There were three reports which overlapped with other SAAP areas: one [ 53 ] (17%) was categorised to area 2 while two [ 88 , 115 ] (33%) were categorised to area 6.

9. Improving health information and communication

Three reports were assigned to SAAP area 9 (see Fig. 4 ) [ 31 , 108 , 130 ]. One of these (33%) used a qualitative approach, one (33%) used a quantitative approach and one (33%) used mixed methods. Two [ 108 , 130 ] (66%) were peer-reviewed journal articles and one [ 31 ] (33%) was grey literature. Two reports (66%) focused on improving migrant demographics and health information using databases [ 31 , 108 ] while one (33%) described an information-needs matrix for refugees and asylum seekers [ 130 ]. Two [ 31 , 108 ] included ethnicities in the data while one [ 130 ] had a primary focus on migrant health. Two reports [ 31 , 108 ] (66%) also applied to SAAP area 4 while one report [ 130 ] (33%) was in SAAP area 9 only.

To our knowledge this is the first scoping review conducted on migrant health in Scotland. A previous rapid literature review [ 94 ] found most research focused on health behaviours, mental health, communicable disease and use of and access to healthcare; however, the review limited migrant definition to those who had immigrated within five years and asylum seekers were not included.

In our review, the majority of reports were published from 2013 onwards, aligning with the expansion in migrant research internationally [ 142 ]. 52% used qualitative research methods, 28% used quantitative methods and 20% used mixed methods. 58% focused on migrant health: the remaining papers included other populations or health as part of a wider remit. Research funding was mostly provided by the Scottish Government, NHS, refugee charities and Universities. No studies received funding from the private sector, although this sector has the potential resource and capacity to play a key role in funding future research to improve migrant health in Scotland. Geographically, most studies took place in Glasgow (36%), nationwide (38.5%) or Edinburgh (16%) – other areas were under-represented including Aberdeen (5%), despite being the city with the largest migrant population [ 30 ]. There was a lack of studies in rural localities. These findings concur with a UK migrant health review by Burns et al. [ 23 ] where research was concentrated in larger cities and data was sparse in rural areas relative to the migrant population.

Half of the research identified that was conducted in Glasgow focused on asylum seekers/refugees. Glasgow was previously the only Scottish city to host asylum seekers [ 143 ] and currently supports the most asylum seekers of any local authority in the UK [ 29 ]. In April 2022, the UK government widened the Asylum dispersal scheme to all local authorities [ 144 ]. Around 70% of Scotland’s refugee support services are based in Glasgow and the South-west [ 145 ]. As reduced access to services may impact the health of asylum seekers, research in Glasgow may not be generalizable to other regions of Scotland.

Almost one-third (30%) of all reports focused on asylum seekers and refugees – an overrepresentation given that only 18% of migrants to the UK are asylum seekers [ 146 ] and as low as 2% of all migrants in Scotland [ 147 ]. Asylum seekers and refugees are at risk of poor health due to trauma, difficult journeys, overcrowded camps, poor nutrition and lack of access to healthcare [ 148 ]. They have worse maternity outcomes and increased rates of mental illness [ 149 ]. Increased research on health of asylum seekers and refugees is necessary due to their additional vulnerabilities [ 142 ]. However, asylum seeker’s country of origin was generally not specified. Asylum seekers have heterogenic backgrounds [ 150 ] and nationality and trauma experience affect health status [ 151 ]. Further research focused on specific nationalities of asylum seekers would enhance understanding of the health needs in this population.

Almost one-third (31%) of studies did not specify a migrant group. This concurs with a Norwegian migrant health study by Laue et al. [ 152 ] where 36% of research did not identify country of birth. Where nationality was identified, Polish, African and South Asian were most prevalent. Poles are the largest migrant group in Scotland, however for the other most common immigrant groups of Irish, Italian and Nigerian [ 30 ] there was an absence of research. No studies took place on Nigerian migrants – nine studies indicated African populations, but country of birth was not specified. Since March 2022, 23,000 Ukrainians have migrated to Scotland [ 153 ], however no studies on Ukrainians were identified currently. Research may be underway which is yet to be published.

Only one study explored the impact of Brexit on European migrants’ health despite 56% of migrants to Scotland being EU nationals [ 30 ]. Again, research may be taking place currently, which is yet to be published. No studies involved undocumented migrants despite this populations’ high rates of poor physical/mental health exacerbated by poor housing and working conditions [ 154 ]. An estimated 7.2–9.5% of the workforce in the UK are migrant workers who have higher risks of poor working conditions and injury [ 155 ]. Scotland depends on a migrant workforce for some industries such as agriculture [ 156 ] but only two research papers specified migrant workers.

Most research papers related to the right to health of refugees (SAAP 2), social determinants of health (SAAP 3), public health planning (SAAP 4) and strengthening health systems (SAAP 5). Areas with less research were frameworks for collaborative action (SAAP 1), preventing communicable disease (SAAP 6), preventing non-communicable disease (SAAP 7) and health screening and assessment (SAAP 8). Only three studies related to improving health information and communication (SAAP 9). Lebano et al. [ 12 ] conducted a literature review of migrant health in Europe and found data collection unreliable and disorganised. There is a lack of data on the numbers and types of migrants entering Scotland and research tends not to differentiate between ethnic minorities and migrants [ 94 ]. As poor-quality information hinders surveillance and planning of services SAAP area 9 is an important consideration for increased research.

Villarroel et al. [ 24 ] also found more research in SAAP areas 3 to 5 and less in areas 6 to 9. However, their study returned no results in category 1, collaborative action, or 2, the right to health of refugees, while this study assigned 9% of articles to category 1 and 19% to category 2. Most articles in our study relating to categories 1 and 2 were grey literature, which was excluded from the original Irish scoping review. This highlights a potential difference in the focus of peer-reviewed articles compared to government/refugee charity commissioned reports. Collaborative action and the right to health of refugees and asylum seekers are entwined in Scotland due to the complex policy environment; the social determinants of health such as housing, education, welfare rights and social integration are influenced by a variety of UK and Scottish statutory bodies as well as third sector organisations [ 157 ]. Despite this complexity, organisations work well together [ 158 ]. Further academic research in this area would enhance joint working practices and networks.

A scoping review in the UK [ 23 ] found similar quantities of research corresponding to SAAP areas 3, 2 and 9. However in Scotland areas 1, 5 and 8 were a combined 44% of included papers compared with 27.8% of results on health systems and structures in Burns et al’s [ 23 ] study. Almost half of the articles in SAAP areas 1,5 and 8 were grey literature, which was not included in Burns et al’s [ 23 ] review. Conversely, Burns et al. [ 23 ] found 81.9% of research in the UK related to epidemiology, equivalent to SAAP categories 4,6 and 7. In a Norwegian scoping review of migrant health [ 152 ] 65% of research was related to epidemiological data on health and disease. Only 42% of the research in this current study related to epidemiological data; the quantity of evidence was reduced by excluding combined research from the UK. As Scotland has higher mortality and morbidity than elsewhere in the UK [ 29 ] it is important to undertake further epidemiological research limited to Scotland.

Strengths and weaknesses

Strengths of this review include the use of the WHO’s SAAP categories [ 7 ] to classify data, in accordance with the Villarroel et al’s [ 24 ] study: this means results are linked to policy on migrant health and facilitates comparability to the Irish study results. Additionally results include data on migrant groups, locality, and funding of included papers; these highlight potential omissions for future research consideration. Results include diverse research methods and published and grey literature giving a wide overview of available evidence, reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) checklist (see Additional File 3 ) [ 159 ].

Limitations included the lack of an open-access protocol and search limitations of English language and selected databases. This means some relevant reports may be omitted. Due to time and resource limitations no quality appraisal was planned for included reports. Whilst we did not synthesise the findings for each topic area and migrant group, future systematic reviews could be undertaken to address this limitation and build on this work.

Conclusions

Immigration and ethnic diversity in Scotland have increased since 2002 which is reflected in the expansion of migrant health research. This review highlights evidence gaps including a lack of research in rural areas, undocumented migrants and migrant workers. There is a tendency to cluster asylum seekers together rather than differentiate between national groups. Within the SAAP areas there is less evidence relating to collaborative action, preventing communicable disease, preventing non-communicable disease and health screening and assessment. Further research is required on improving health information and communication for migrant populations in Scotland – a significant omission given the importance of accurate information for health service planning.

Availability of data and materials

All data analysed during this review comes from the papers listed in Additional file 2 .

Abbreviations

European Union

Human Immunodeficiency Virus

National Health Service

Strategy and Action Plan

The Scottish Health and Ethnicity Linkage Study

United Kingdom

World Health Organisation

International Organisation for Migration (IOM). IOM Definition of Migrant. 2024. Available from: https://www.iom.int/about-migration .Cited 2024 Feb 8.

International Organisation for Migration United Nations. World Migration Report. 2022. Available from: available: https://worldmigrationreport.iom.int/wmr-2022-interactive/ .

The United Nations Refugee Angency. Refugee facts: What is a refugee? 2024. Available from: https://www.unrefugees.org/refugee-facts/what-is-a-refugee/ . Cited 2024 Feb 8.

Migration Data Portal. Migration data in Europe. 2023. Available from: https://www.migrationdataportal.org/regional-data-overview/europe#past-and-present-trends . Cited 2023 Aug 22.

International Centre for Migration Policy Development. Migration Outlook 2022 Twelve migration issues to look out for in 2022 Origins, key events and priorities for Europe. 2022. Available from: https://www.icmpd.org/file/download/56783/file/ICMPD%2520Migration%2520Outlook%25202022.pdf .

European Parliament. Exploring migration causes: why people migrate. 2023. Available from: https://www.europarl.europa.eu/news/en/headlines/world/20200624STO81906/exploring-migration-causes-why-people-migrate .

World Health Organisation. Strategic plan: Strategy and Action Plan for Refugee and Migrant Health in the WHO European Region 2016–2022. 2016. Available from: https://www.who.int/publications/i/item/strategic-plan-strategy-and-action-plan-for-refugee-and-migrant-health-in-the-who-european-region-2016-2022 .

Graetz V, Rechel B, Groot W, Norredam M, Pavlova M. Utilization of health care services by migrants in Europe—a systematic literature review. Br Med Bull. 2017;121(1):5–18. Available from: https://www.academic.oup.com/bmb/article-lookup/doi/10.1093/bmb/ldw057 .

Article CAS PubMed Google Scholar

Hannigan A, O'Donnell P, O'Keeffe M, MacFarlane A. How do Variations in Definitions of “Migrant” and their Application Influence the Access of Migrants to Health Care Services? Copenhagen: WHO Regional Office for Europe; 2016. (Health Evidence Network Synthesis Report, No. 46.) Available from: https://www.ncbi.nlm.nih.gov/books/NBK391032/ .

Rechel B, Mladovsky P, Ingleby D, Mackenbach JP, McKee M. Migration and health in an increasingly diverse Europe. Lancet. 2013;381(9873):1235–45. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673612620868 .

Article PubMed Google Scholar

Giannoni M, Franzini L, Masiero G. Migrant integration policies and health inequalities in Europe. BMC Public Health. 2016;16(1):463. Available from: http://www.bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-016-3095-9 .

Article PubMed PubMed Central Google Scholar

Lebano A, Hamed S, Bradby H, Gil-Salmerón A, Durá-Ferrandis E, Garcés-Ferrer J, et al. Migrants’ and refugees’ health status and healthcare in Europe: a scoping literature review. BMC Public Health. 2020;20(1):1039. Available from: https://www.bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-08749-8 .

Cézard G, Finney N, Kulu H, Marshall A. Ethnic differences in self-assessed health in Scotland: The role of socio-economic status and migrant generation. Popul Space Place. 2022;28(3):e2403. Available from: https://onlinelibrary.wiley.com/doi/10.1002/psp.2403 .

Article Google Scholar

Anson J. The migrant mortality advantage: a 70 month follow-up of the brussels population. Eur J Popul. 2004;20(3):191–218.

World Health Organisation. Health of refugees and migrants. WHO European Region. 2018. Available from: https://www.who.int/publications/i/item/health-of-refugees-and-migrants---who-european-region-(2018) .

Mladovsky P. A framework for analysing migrant health policies in Europe. Health Policy (New York). 2009;93(1):55–63. Available from: https://www.linkinghub.elsevier.com/retrieve/pii/S0168851009001444 .

De Vito E, de Waure C, Specchia ML, Parente P, Azzolini E, Frisicale EM, et al. Are undocumented migrants’ entitlements and barriers to healthcare a public health challenge for the European Union? Public Health Rev. 2016;37(1):13. Available from: http://publichealthreviews.biomedcentral.com/articles/10.1186/s40985-016-0026-3 .

Katikireddi SV, Bhopal R, Quickfall JA. GPs need training and funding in caring for refugees and asylum seekers. BMJ. 2004;328(7442):770.1. Available from: https://www.bmj.com/lookup/doi/10.1136/bmj.328.7442.770 .

Carballo M, Hargreaves S, Gudumac I, Maclean EC. Evolving migrant crisis in Europe: implications for health systems. Lancet Glob Heal. 2017;5(3):e252-253. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2214109X17300402 .

Juárez SP, Honkaniemi H, Dunlavy AC, Aldridge RW, Barreto ML, Katikireddi SV et al. Effects of non-health-targeted policies on migrant health: a systematic review and meta-analysis. Lancet Glob Heal. 2019;7(4):e420–35. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2214109X18305606 .

Nielsen SS, Krasnik A. Poorer self-perceived health among migrants and ethnic minorities versus the majority population in Europe: a systematic review. Int J Public Health. 2010;55(5):357–71. Available from: ( http://link.springer.com/10.1007/s00038-010-0145-4 ).

World Health Organsation. World report on the health of refugees and migrants. 2022. Available from: https://www.who.int/publications/i/item/9789240054462 .

Burns R, Zhang CX, Patel P, Eley I, Campos-Matos I, Aldridge RW. Migration health research in the United Kingdom: a scoping review. J Migr Heal. 2021;4:100061. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2666623521000283 .

Villarroel N, Hannigan A, Severoni S, Puthoopparambil S, MacFarlane A. Migrant health research in the Republic of Ireland: a scoping review. BMC Public Health. 2019;19(1):324. Available from: ( https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6651-2 ).

Scottish Government. Demographic Change in Scotland. 2010. Available from: https://www.gov.scot/binaries/content/documents/govscot/publications/research-and-analysis/2010/11/demographic-change-scotland/documents/0108163-pdf/0108163-pdf/govscot%3Adocument/0108163.pdf .

National Records of Scotland. Projected Population of Scotland (Interim) 2020-based. 2022. Available from: https://www.nrscotland.gov.uk/files/statistics/population-projections/2020-based/pop-proj-2020-scot-nat-pub.pdf .

Scottish Government. Coronavirus (COVID-19) - experiences of vulnerable children, young people, and parents: research. 2021. Available from: https://www.gov.scot/publications/experiences-vulnerable-children-young-people-parents-during-covid-19-pandemic/ .

Scotland’s Census. Scotland’s Census: Ethnicity. 2011. Available from: https://www.scotlandscensus.gov.uk/census-results/at-a-glance/ethnicity/#:~:text=Scotland’spopulationwas96.0%25 .

Walsh D. The changing ethnic profiles of Glasgow and Scotland, and the implications for population health. 2017. Available from: https://www.gcph.co.uk/assets/0000/6255/The_changing_ethnic_profiles_of_Glasgow_and_Scotland.pdf .

National Records of Scotland. Migration Statistics Quarterly Summary for Scotland. 2021. Available from: https://www.nrscotland.gov.uk/files/statistics/migration/quarterly-summary/miration-statistics-quarterly-summary-february-2021.pdf .

The Scottish Ethnicity and Health Research Strategy Working Group. Health in our Multi-ethnic Scotland Future Research Priorities. 2009. Available from: https://www.healthscotland.scot/media/1842/health-in-our-multi-ethnic-scotland-full-report.pdf .

The Scottish Public Health Observatory. Ethnic minorities: defining ethnicity and race. 2023. Available from: https://www.scotpho.org.uk/population-groups/ethnic-minorities/defining-ethnicity-and-race/ . Cited 2023 Aug 22.

Krasnik A, Bhopal RS, Gruer L, Kumanyika SK. Advancing a unified, global effort to address health disadvantages associated with migration, ethnicity and race. Eur J Public Health. 2018;28(suppl_1). Available from: https://academic.oup.com/eurpub/article/doi/10.1093/eurpub/cky046/4956664 .

Bhopal R, Fischbacher C, Povey C, Chalmers J, Mueller G, Steiner M, et al. Cohort profile: scottish health and ethnicity linkage study of 4.65 million people exploring ethnic variations in disease in Scotland. Int J Epidemiol. 2011;40(5):1168–75. Available from: https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dyq118 .

Bhopal RS, Gruer L, Cezard G, Douglas A, Steiner MFC, Millard A, et al. Mortality, ethnicity, and country of birth on a national scale, 2001–2013: a retrospective cohort (Scottish Health and Ethnicity Linkage Study). Basu S, editor. Plos Med. 2018;15(3):e1002515. https://doi.org/10.1371/journal.pmed.1002515 . Basu S, editor.

Allik M, Brown D, Dundas R, Leyland AH. Differences in ill health and in socioeconomic inequalities in health by ethnic groups: a cross-sectional study using 2011 Scottish census. Ethn Health. 2022;27(1):190–208. https://doi.org/10.1080/13557858.2019.1643009 ( https://www.tandfonline.com/doi/full/ ).

Watkinson RE, Sutton M, Turner AJ. Ethnic inequalities in health-related quality of life among older adults in England: secondary analysis of a national cross-sectional survey. Lancet Public Hea. 2021;6(3):e145-154.

Fischbacher CM, Cezard G, Bhopal RS, Pearce J, Bansal N. Measures of socioeconomic position are not consistently associated with ethnic differences in cardiovascular disease in Scotland: methods from the Scottish Health and Ethnicity Linkage Study (SHELS). Int J Epidemiol. 2014;43(1):129–39. Available from: https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dyt237 .

Scottish Government. Characteristics of Recent and Established EEA and non-EEA migrants in Scotland: Analysis of the 2011 Census. 2015. Available from: https://www.gov.scot/publications/characteristics-recent-established-eea-non-eea-migrants-scotland-analysis-2011-census/ .

House of Lords Library. Refugees and asylum-seekers: UK policy. 2022. https://lordslibrary.parliament.uk/refugees-and-asylum-seekers-uk-policy/ .

British Medical Association. Refugee and asylum seeker patient health toolkit. Unique health challenges for refugees and asylum seekers. 2022. Available from: https://www.bma.org.uk/advice-and-support/ethics/refugees-overseas-visitors-and-vulnerable-migrants/refugee-and-asylum-seeker-patient-health-toolkit/unique-health-challenges-for-refugees-and-asylum-seekers .

Khalil H, Peters M, Godfrey CM, McInerney P, Soares CB, Parker D. An evidence-based approach to scoping reviews. Worldviews Evidence-Based Nurs. 2016;13(2):118–23. Available from: https://doi.org/10.1111/wvn.12144 .

Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. Available from: ( http://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-5-69 ).

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.Available from: http://www.tandfonline.com/doi/abs/10.1080/1364557032000119616 ).

Kearns A, Whitley E, Egan M, Tabbner C, Tannahill C. Healthy migrants in an unhealthy city? The Effects of time on the health of migrants living in deprived areas of glasgow. J Int Migr Integr. 2017;18(3):675–98. Available from: http://link.springer.com/10.1007/s12134-016-0497-6 .

PubMed Google Scholar

Porqueddu T. Herbal medicines for diabetes control among Indian and Pakistani migrants with diabetes. Anthropol Med. 2017;24(1):17–31. Available from: https://www.tandfonline.com/doi/full/10.1080/13648470.2016.1249338 .

Roshan N. Supporting new communities: a qualitative study of health needs among asylum seekers and refugee communities in North Glasgow final report. 2005. Available from: https://www.stor.scot.nhs.uk/handle/11289/579930 .

Piacentini T, O’Donnell C, Phipps A, Jackson I, Stack N. Moving beyond the ‘language problem’: developing an understanding of the intersections of health, language and immigration status in interpreter-mediated health encounters. Lang Intercult Commun. 2019;19(3):256–71. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1353829214001233 .

Sime D. ‘I think that Polish doctors are better’: Newly arrived migrant children and their parents׳ experiences and views of health services in Scotland. Health Place. 2014;30:86–93. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1353829214001233 .

Steven K, Munoz S, Migrants, Matter. Report of a Peer Researched Project on EU Migrant Health in the Highlands of Scotland. University of the Highlands and Islands. 2016. Available from: https://www.spiritadvocacy.org.uk/assets/Birchwood-Highland-HUG-Migrants-Matter-study-2015-2016.pdf .

Anderson AS, Bush H, Lean M, Bradby H, Williams R, Lea E. Evolution of atherogenic diets in South Asian and Italian women after migration to a higher risk region. J Hum Nutr Diet. 2005;18(1):33–43. Available from: ( https://onlinelibrary.wiley.com/doi/10.1111/j.1365-277X.2004.00584.x ).

Isaacs A, Burns N, Macdonald S, O’Donnell CA. ‘I don’t think there’s anything I can do which can keep me healthy’: how the UK immigration and asylum system shapes the health and wellbeing of refugees and asylum seekers in Scotland. Crit Public Health. 2022;32(3):422–32. Available from: https://www.tandfonline.com/doi/full/10.1080/09581596.2020.1853058 .

Palattiyil G, Sidhva D. Caught in a web of multiple jeopardy: post-traumatic stress disorder and HIV-positive asylum seekers in Scotland. Clin Soc Work J. 2015;43(4):362–74. Available from: http://link.springer.com/10.1007/s10615-015-0542-5 ).

Abdulkadir J, Azzudin A, Buick A, Curtice L, Dzingisai M, Easton D, et al. What do you mean, I have a right to health? Participatory action research on health and human rights. 2016. Available from: https://strathprints.strath.ac.uk/58209/1/Abdulkadir_etal_IPPI_2016_What_do_you_mean_I_have_a_right_to_health.pdf .

Strang A, Quinn N. Integration or isolation? Mapping social connections and well-being amongst refugees in Glasgow. 2014. Available from: https://eresearch.qmu.ac.uk/bitstream/handle/20.500.12289/4139/eResearch%25204139.pdf?sequence=1&isAllowed=y .

Gorman DR, Bielecki K, Larson HJ, Willocks LJ, Craig J, Pollock KG. Comparing vaccination hesitancy in polish migrant parents who accept or refuse nasal flu vaccination for their children. Vaccine. 2020;38(13):2795–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0264410X20302255 .

Love J, Vertigans S, Domaszk E, Zdeb K, Love A, Sutton P. Health & ethnicity in Aberdeenshire: a study of Polish in-migrants; a report for the Scottish Health Council. 2007. Available from: https://rgu-repository.worktribe.com/output/247667 .

O’Donnell CA, Higgins M, Chauhan R, Mullen K. Asylum seekers’ expectations of and trust in general practice: a qualitative study. Br J Gen Pract. 2008;58(557):e1-11. Available from: https://bjgp.org/lookup/doi/10.3399/bjgp08X376104 .

Quinn N, Shirjeel S, Siebelt L, Donnelly R, Pietka E. An evaluation of the sanctuary community conversation programme to address mental health stigma with asylum seekers and refugees in Glasgow. 2011. Available from: https://www.healthscotland.com/uploads/documents/5584-SanctuaryCommunityConversationEvaluation.pdf .

Ager A. Community contact and mental health amongst socially isolated refugees in Edinburgh. J Refug Stud. 2002;15(1):71–80. Available from: https://academic.oup.com/jrs/article-lookup/doi/10.1093/jrs/15.1.71 .

Sim JA, Ulanika AA, Katikireddi SV, Gorman D. Out of two bad choices, I took the slightly better one’: Vaccination dilemmas for Scottish and Polish migrant women during the H1N1 influenza pandemic. Public Health. 2011;125(8):505–11. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350611001697 .

Zhao S, Patuano A. International Chinese Students in the UK: association between use of green spaces and lower stress levels. Sustainability. 2021;4(1):89. Available from: https://www.mdpi.com/2071-1050/14/1/89 .

Da Lomba S, Murray N. Women and Children First? Refused asylum seekers’ access to and experiences of maternity care in Glasgow. 2014. Available from: https://strathprints.strath.ac.uk/58655/1/Lomba_Murray_SRC_2014_Women_and_Children_First_Refused_Asylum_Seekers_Access_to_and_Experiences.pdf .

Sørbye IK, Vangen S, Juarez SP, Bolumar F, Morisaki N, Gissler M, et al. Birthweight of babies born to migrant mothers - What role do integration policies play? SSM - Popul Heal. 2019;9:100503. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352827319301971 .

Teodorowski P, Woods R, Czarnecka M, Kennedy C. Brexit, acculturative stress and mental health among EU citizens in Scotland. Popul Space Place. 2021;27(6). Available from: https://onlinelibrary.wiley.com/doi/10.1002/psp.2436 .

Baillot H, Murray N, Connelly E, Howard N. Tackling Female Genital Mutilation in Scotland: A Scottish model of intervention. 2014. Available from: https://www.celcis.org/application/files/8116/2185/5421/Tackling_Female_Genital_Mutilation_-_A_Scottish_Model_of_Intervention.pdf .

Weishaar HB. Consequences of international migration: a qualitative study on stress among Polish migrant workers in Scotland. Public Health. 2008;122(11):1250–6. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350608000942 .

Weishaar HB. You have to be flexible—coping among polish migrant workers in Scotland. Health Place. 2010;16(5):820–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1353829210000432 .

Crowther S, Lau A. Migrant polish women overcoming communication challenges in scottish maternity services: a qualitative descriptive study. Midwifery. 2019;72:30–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0266613819300361 .

Fassetta G, Da Lomba S, Quinn N. A healthy start? Experiences of pregnant refugee and asylum seeking women in Scotland. 2016. Available from: https://www.redcross.org.uk/-/media/documents/about-us/research-publications/refugee-support/a-healthy-start-report.pdf .

Positive Action in Housing. 12 months since the Park Inn Tragedy in Glasgow, one in three hotel asylum seekers say their mental health has deteriorated. 2021. Available from: https://www.paih.org/one-in-three-glasgow-asylum-seekers-suffering-depression-and-anxiety .

Strang A. Refugee Peer Education for Health and Well-being. Evaluation Report. 2015. Available from: https://www.scottishrefugeecouncil.org.uk/wp-content/uploads/2019/10/Peer-Education-Evaluation-Report.pdf .

Strang A, Marsden R, Mignard E. The Holistic Integration Service: Learning and Evaluation Year 1. 2014. Available from: https://www.scottishrefugeecouncil.org.uk/wp-content/uploads/2019/10/Holistic-Integration-Service-year-1-evaluation-report.pdf .

British Red Cross. How will we survive? Steps to preventing destitution in the asylum system. 2021. Available from: https://www.redcross.org.uk/-/media/documents/about-us/how-will-we-survive-preventing-destitution-in-the-asylum-system.pdf .

Bhopal RS, Rafnsson SB, Agyemang C, Fagot-Campagna A, Giampaoli S, Hammar N, et al. Mortality from circulatory diseases by specific country of birth across six European countries: test of concept. Eur J Public Health. 2012;22(3):353–9. Available from: https://academic.oup.com/eurpub/article-lookup/doi/10.1093/eurpub/ckr062 .

Rafnsson SB, Bhopal RS, Agyemang C, Fagot-Campagna A, Harding S, Hammar N, et al. Sizable variations in circulatory disease mortality by region and country of birth in six European countries. Eur J Public Health. 2013;23(4):594–605. Available from: https://academic.oup.com/eurpub/article-lookup/doi/10.1093/eurpub/ckt023 ).

de Lima P, Masud Chaudhry M, Whelton R, Arshad R. A study of migrant workers in Grampian. 2007. Available from: . http://www.communitiesscotland.gov.uk/stellent/groups/public/%0Adocuments/webpages/pubcs_019731.pdff .

Ikram UZ, Mackenbach JP, Harding S, Rey G, Bhopal RS, Regidor E, et al. All-cause and cause-specific mortality of different migrant populations in Europe. Eur J Epidemiol. 2016;31(7):655–65. Available from: http://link.springer.com/10.1007/s10654-015-0083-9 .

de Brún T, De-Brún MO, van Weel-Baumgarten E, van Weel C, Dowrick C, Lionis C, et al. Guidelines and training initiatives that support communication in cross-cultural primary-care settings: appraising their implementability using Normalization Process Theory. Fam Pract. 2015;cmv022. Available from: https://academic.oup.com/fampra/article-lookup/doi/10.1093/fampra/cmv022 .

García-Medrano S, Panhofer H. Improving migrant well-being: spontaneous movement as a way to increase the creativity, spontaneity and welfare of migrants in Glasgow. Body Mov Danc Psychother. 2020;15(3):189–203. Available from: https://www.tandfonline.com/doi/full/10.1080/17432979.2020.1767208 .

Jamil NA, Gray SR, Fraser WD, Fielding S, Macdonald HM. The relationship between vitamin D status and muscle strength in young healthy adults from sunny climate countries currently living in the northeast of Scotland. Osteoporos Int. 2017;28(4):1433–43. Available from: http://link.springer.com/10.1007/s00198-016-3901-3 .

Kaneoka M, Spence W. The cultural context of sexual and reproductive health support: an exploration of sexual and reproductive health literacy among female Asylum Seekers and Refugees in Glasgow. Int J Migr Heal Soc Care. 2019;16(1):46–64. Available from: https://www.emerald.com/insight/content/doi/10.1108/IJMHSC-01-2019-0002/full/html .

O’Donnell CA, Higgins M, Chauhan R, Mullen K. They think we’re OK and we know we’re not. A qualitative study of asylum seekers’ access, knowledge and views to health care in the UK. BMC Health Serv Res. 2007;7(1):75. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-7-75 .

Cooper M, Harding S, Mullen K, O’Donnell C. ‘A chronic disease is a disease which keeps coming back … it is like the flu’: chronic disease risk perception and explanatory models among French- and Swahili-speaking African migrants. Ethn Health. 2012;17(6):597–613. Available from: http://www.tandfonline.com/doi/abs/10.1080/13557858.2012.740003 .

Ezika EA. An exploration of smoking behavior of african male immigrants living in glasgow. Tob Use Insights. 2014;7:TUI .S13262. Available from: http://journals.sagepub.com/doi/10.4137/TUI.S13262 .

Karadzhov D, White R. Between the whispers of the devil and the revelation of the word : christian clergy’s mental health literacy and pastoral support for BME congregants. J Spiritual Ment Heal. 2020;22(2):147–72. Available from: https://www.tandfonline.com/doi/full/10.1080/19349637.2018.1537755 ).

Yakubu BD, Simkhada P, van Teijlingen E, Eboh W. Sexual health information and uptake of sexual health services by African women in Scotland: a pilot study. Int J Heal Promot Educ. 2010;48(3):79–84. Available from: http://www.tandfonline.com/doi/abs/10.1080/14635240.2010.10708186 .

Goff J, Kay K, Lima M, Shallangwa S, We All Have A. Different Consciousness About It: Exploring the Sexual Health Needs of People From African Communities in Scotland. 2021. Available from: https://www.waverleycare.org/wp-content/uploads/2023/05/We_All_Have_Different_Consciousness_About_It_Report.pdf .

Bielecki K, Craig J, Willocks LJ, Pollock KG, Gorman DR. Impact of an influenza information pamphlet on vaccination uptake among Polish pupils in Edinburgh, Scotland and the role of social media in parental decision making. BMC Public Health. 2020;20(1):1381. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-09481-z .

Article CAS PubMed PubMed Central Google Scholar

Gorman DR, Bielecki K, Willocks LJ, Pollock KG. A qualitative study of vaccination behaviour amongst female Polish migrants in Edinburgh, Scotland. Vaccine. 2019;37(20):2741–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0264410X19304220 .

Bak-Klimek A, Karatzias T, Elliott L, MacLean R. The determinants of well-being among polish economic immigrants. Testing the sustainable happiness model in migrant population. J Happiness Stud. 2018;19(6):1565–88. Available from: http://link.springer.com/10.1007/s10902-017-9877-7 .

Cheung NF. The cultural and social meanings of childbearing for Chinese and Scottish women in Scotland. Midwifery. 2002;18(4):279–95. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0266613802903281 .

Papadaki A, Scott J. The impact on eating habits of temporary translocation from a Mediterranean to a Northern European environment. Eur J Clin Nutr. 2002;56(5):455–61. Available from: https://www.nature.com/articles/1601337 .

McCann A, Mackie P. Improving the Health of Migrants to Scotland: An update for Scottish Directors of Public Health. 2016. Available from: https://www.scotphn.net/wp-content/uploads/2016/04/2016_03_23-Migrant-Health-Report-FINAL-1.pdf .

Ahmed A, Cameron S, Dickson C, Mountain K. Arabic-speaking students’ primary care experiences in Scotland. Community Pract J Community Pract Heal Visit Assoc. 2010;83(2):23–6.

Google Scholar

Bray J, Gorman D, Dundas K, Sim J. Obstetric care of New European migrants in Scotland: an audit of antenatal care, obstetric outcomes and communication. Scott Med J. 2010;55(3):26–31. Available from: ( http://journals.sagepub.com/doi/10.1258/rsmsmj.55.3.26 .

Cheung NF. Choice and control as experienced by Chinese and Scottish childbearing women in Scotland. Midwifery. 2002;18(3):200–13. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0266613802903153 .

Spence W, Zhu L. Perceptions of smoking cessation among Glasgow’s Chinese community. Tob Prev Cessat. 2017;3(October). Available from: http://www.journalssystem.com/tpc/Perceptions-of-smoking-cessation-among-Glasgow-s-Chinese-community,77942,0,2.html .

Gorman DR, Katikireddi SV, Morris C, Chalmers JWT, Sim J, Szamotulska K, et al. Ethnic variation in maternity care: a comparison of Polish and Scottish women delivering in Scotland 2004–2009. Public Health. 2014;128(3):262–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350613003910 .

Gorman DR, Porteous LA. Influences on Polish migrants’ breast screening uptake in Lothian, Scotland. Public Health. 2018;158:86–92. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350617304018 .

Hogg R, de Kok B, Netto G, Hanley J, Haycock-Stuart E. Supporting Pakistani and Chinese families with young children: perspectives of mothers and health visitors. Child Care Health Dev. 2015;41(3):416–23. Available from: https://onlinelibrary.wiley.com/doi/10.1111/cch.12154 .

Kearns A, Whitley E. Health, Wellbeing and Social Inclusion of Migrants in North Glasgow. 2010. Available from: https://www.gowellonline.com/assets/0000/0521/Health_Wellbeing_and_Social_Inclusion_of_Migrants_in_North_Glasgow.pdf .

Poole L, Adamson K. Report on the Situation of the Roma Community in Govanhill, Glasgow. 2008. Available from: https://www.bemis.org.uk/resources/gt/scotland/reportonthesituationoftheromacommunityingovanhill,Glasgow.pdf .

Schofield L, Walsh D, Feng Z, Buchanan D, Dibben C, Fischbacher C, et al. Does ethnic diversity explain intra-UK variation in mortality? A longitudinal cohort study. BMJ Open. 2019;9(3):e024563. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2018-024563 .

Unger HW, McCallum AD, Ukachukwu V, McGoldrick C, Perrow K, Latin G, et al. Imported malaria in Scotland – an overview of surveillance, reporting and trends. Travel Med Infect Dis. 2011;9(6):289–97. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1477893911001074 .

Young I, Flowers P, McDaid LM. Barriers to uptake and use of pre-exposure prophylaxis (PrEP) among communities most affected by HIV in the UK: findings from a qualitative study in Scotland. BMJ Open. 2014;4(11):e005717. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2014-005717 .

Young I, Flowers P, McDaid LM. Key factors in the acceptability of treatment as prevention (TasP) in Scotland: a qualitative study with communities affected by HIV. Sex Transm Infect. 2015;91(4):269–74. Available from: https://sti.bmj.com/lookup/doi/10.1136/sextrans-2014-051711 .

Bhopal R, Cm FI, Teiner SM, Halmers CJ, Ovey PC, Amieson J. Ethnicity and health in Scotland: Can we fill the information gap ? A demonstration project focusing on coronary heart disease and linkage of census and health records. Ethics. 2005. Available from: http://www.cphs.mvm.ed.ac.uk/docs/Retrocodingfinalreport.pdf .

Cezard GI, Bhopal RS, Ward HJT, Bansal N, Bhala N. Ethnic variations in upper gastrointestinal hospitalizations and deaths: the Scottish Health and Ethnicity Linkage Study. Eur J Public Health. 2016;26(2):254–60. Available from: https://academic.oup.com/eurpub/article-lookup/doi/10.1093/eurpub/ckv182 .

Christie-de Jong F, Kotzur M, Amiri R, Ling J, Mooney JD, Robb KA. Qualitative evaluation of a codesigned faith-based intervention for muslim women in Scotland to encourage uptake of breast, colorectal and cervical cancer screening. BMJ Open. 2022;12(5):e058739. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2021-058739 .

Cree VE, Sidhva D. Children and HIV in Scotland: findings from a cross-sector needs assessment of children and young people infected and affected by HIV in Scotland. Br J Soc Work. 2011;41(8):1586–603. Available from: https://academic.oup.com/bjsw/article-lookup/doi/10.1093/bjsw/bcr036 .

Gallimore A, Irshad T, Cooper M, Cameron S. Influence of culture, religion and experience on the decision of Pakistani women in Lothian, Scotland to use postnatal contraception: a qualitative study. BMJ Sex Reprod Heal. 2021;47(1):43–8. Available from: https://jfprhc.bmj.com/lookup/doi/10.1136/bmjsrh-2019-200497 .

Gruer LD, Cézard GI, Wallace LA, Hutchinson SJ, Douglas AF, Buchanan D, et al. Complex differences in infection rates between ethnic groups in Scotland: a retrospective, national census-linked cohort study of 1.65 million cases. J Public Health (Bangkok). 2022;44(1):60–9. Available from: https://academic.oup.com/jpubhealth/article/44/1/60/6106111 .

Article CAS Google Scholar

Bhala N, Cézard G, Ward HJT, Bansal N, Bhopal R. Ethnic variations in liver- and alcohol-related disease hospitalisations and mortality: the Scottish health and ethnicity linkage study. Alcohol Alcohol. 2016;51(5):593–601. Available from: https://academic.oup.com/alcalc/article-lookup/doi/10.1093/alcalc/agw018 .

Pollock KG, McDonald E, Smith-Palmer A, Johnston F, Ahmed S. Tuberculosis in healthcare workers, Scotland. Scott Med J. 2017;62(3):101–3. Available from: http://journals.sagepub.com/doi/10.1177/0036933017727963 .

Gruer LD, Millard AD, Williams LJ, Bhopal RS, Katikireddi SV, Cézard GI, et al. Differences in all-cause hospitalisation by ethnic group: a data linkage cohort study of 4.62 million people in Scotland, 2001–2013. Public Health. 2018;161:5–11. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350618301501 .

Jackson C, Bedford H, Cheater FM, Condon L, Emslie C, Ireland L, et al. Needles, Jabs and Jags: a qualitative exploration of barriers and facilitators to child and adult immunisation uptake among Gypsies, Travellers and Roma. BMC Public Health. 2017;17(1):254. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-017-4178-y .

John JR, Curry G, Cunningham-Burley S. Exploring ethnic minority women’s experiences of maternity care during the SARS-CoV-2 pandemic: a qualitative study. BMJ Open. 2021;11(9):e050666. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2021-050666 .

Lawton J, Ahmad N, Hanna L, Douglas M, Hallowell N. Diabetes service provision: a qualitative study of the experiences and views of Pakistani and Indian patients with Type 2 diabetes. Diabet Med. 2006;23(9):1003–7. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1464-5491.2006.01922.x .

Livingston MR, Shaw LE, Codere G, Goldberg DJ. Human immunodeficiency virus acquired heterosexually abroad: expert panel assessment of the indigenous/nonindigenous to the united kingdom status of cases. J Travel Med. 2006;12(1):19–25. Available from: https://academic.oup.com/jtm/article-lookup/doi/10.2310/7060.2005.00005 .

Nelson M, Patton A, Robb K, Weller D, Sheikh A, Ragupathy K, et al. Experiences of cervical screening participation and non-participation in women from minority ethnic populations in Scotland. Heal Expect. 2021;24(4):1459–72. Available from: https://onlinelibrary.wiley.com/doi/10.1111/hex.13287 .

Noble G, Okpo E, Tonna I, Fielding S. Factors associated with late HIV diagnosis in North-East Scotland: a six-year retrospective study. Public Health. 2016;139:36–43. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0033350616301020 .

Gillespie M. Trapped: Destitution and Asylum in Scotland. 2012. Available from: http://www.rst.org.uk/wp-content/uploads/2012/11/Trapped-destitution-and-asylum-summary-final-compressed-pictures.pdf .

Hopkins P, Hill M. The needs and strengths of unaccompanied asylum-seeking children and young people in Scotland. Child Fam Soc Work. 2010;15(4):399–408. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2206.2010.00687.x .

Marsden R, Harris C. “We started life again”: Integration experiences of refugee families reuniting in Glasgow. 2015. Available from: https://www.refworld.org/docid/560cde294.html .

Martzoukou K, Burnett S. Exploring the everyday life information needs and the socio-cultural adaptation barriers of Syrian refugees in Scotland. J Doc. 2018;74(5):1104–32. Available from: https://www.emerald.com/insight/content/doi/10.1108/JD-10-2017-0142/full/html .

McKenna R. From pillar to post: Destitution among people refused asylum in Scotland. 2019; Available from: https://www.rst.org.uk/wp-content/uploads/2019/03/From-Pillar-to-Post-Feb-2019.pdf .

Independent Commission of Inquiry. Failings in the provision of care to New Scots during the Covid pandemic: Part 2. 2022. Available from: https://static1.squarespace.com/static/62af1289a666c80e00b17253/t/636b9190408f81778746eaa7/1667994032702/AIS+Phase+2+Report+Full.pdf .

Trevena P, Gawlewicz A, Wright S. Addressing the needs of Scotland’s migrant and minority ethnic populations under Covid-19: lessons for the future. 2022. Available from: https://migrantessentialworkers.com/wp-content/uploads/2022/11/SC-Migrant-C19-Innovations.pdf .

Oduntan O, Ruthven I. The information needs matrix: a navigational guide for refugee integration. Inf Process Manag. 2019;56(3):791–808. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0306457318306939 .

Sime D, Fox R, Migrant C. Social capital and access to services post-migration: transitions, negotiations and complex agencies. Child Soc. 2015;29(6):524–34. Available from: https://onlinelibrary.wiley.com/doi/10.1111/chso.12092 .

Strang A, Baillot H, Mignard E. Insights into integration pathways. New Scots and the Holistic Integration Service. A report drawing on year two of the Holistic Integration Service. 2015. Available from: https://scottishrefugeecouncil.org.uk/wp-content/uploads/2019/10/Holistic-Integration-Service-Year-2-report.pdf .

Weir KEA, Wilson SJ, Gorman DR. The Syrian vulnerable person resettlement programme: evaluation of Edinburgh’s reception arrangements. J Public Health (Bangkok). 2018;40(3):451–60. Available from: https://academic.oup.com/jpubhealth/article/40/3/451/4600209 .

Hammond CN. Scots 2- Engagement analysis of the New Scot Refugee Integration Strategy 2018–2022. 2018. Available from: https://www.gov.scot/binaries/content/documents/govscot/publications/research-and-analysis/2018/06/news-scots-2-engagement-analysis-new-scots-refugee-integration-strategy/documents/00537019-pdf/00537019-pdf/govscot%3Adocument/00537019.pdf .

Blake Stevenson. A8 Nationals in Glasgow. 2007. Available from: http://crosshillandgovanhill.org.uk/grindocs/A8NationalsinGlasgow.pdf .

Ajetunmobi O, Whyte B, Chalmers J, Fleming M, Stockton D, Wood R. Informing the ‘early years’ agenda in Scotland: understanding infant feeding patterns using linked datasets. J Epidemiol Community Health. 2014;68(1):83–92. Available from: https://jech.bmj.com/lookup/doi/10.1136/jech-2013-202718 .

Laidlaw K, Wang D, Coelho C, Power M. Attitudes to ageing and expectations for filial piety across Chinese and British cultures: a pilot exploratory evaluation. Aging Ment Health. 2010;14(3):283–92. Available from: http://www.tandfonline.com/doi/abs/10.1080/13607860903483060 .

Marsden R, Aldegheri E, Khan A, Lowe M, Strang A, Salinas E, et al. “What’s going on?” A study into destitution and poverty faced by asylum seekers and refugees in Scotland. 2005. Available from: http://www.rst.org.uk/wp-content/uploads/2012/11/Whats_going_on_A_study.pdf .

Quinn N. Participatory action research with asylum seekers and refugees experiencing stigma and discrimination: the experience from Scotland. Disabil Soc. 2014;29(1):58–70. Available from: http://www.tandfonline.com/doi/abs/10.1080/09687599.2013.769863 .

British Red Cross, Refugee Survival Trust. How will we survive? Steps to preventing destitution in the asylum system. 2021. Available from: https://mcusercontent.com/c17c136fc126588cb51e5471d/files/a35dd0e1-d785-f962-6a41-01e928493775/DASS_Research_Report_2021.pdf .

O’Donnell R, Angus K, McCulloch P, Amos A, Greaves L, Semple S. Fathers’ views and experiences of creating a smoke-free home: a scoping review. Int J Environ Res Public Health. 2019;16(24):5164. Available from: https://www.mdpi.com/1660-4601/16/24/5164 .

Sweileh WM, Wickramage K, Pottie K, Hui C, Roberts B, Sawalha AF, et al. Bibliometric analysis of global migration health research in peer-reviewed literature (2000–2016). BMC Public Health. 2018;18(1):777. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5689-x .

Wren K. Supporting asylum seekers and refugees in glasgow: the role of multi-agency networks. J Refug Stud. 2007;20(3):391–413. Available from: https://academic.oup.com/jrs/article-lookup/doi/10.1093/jrs/fem006 .

UK Government Home Office. A Fairer Asylum Accommodation System. 2022. Available from: https://www.emcouncils.gov.uk/write/Migration/Asylum_Dispersal_Factsheet_PDF.pdf .

Scottish Refugee Council. Scotland’s Welcome: an analysis of community support for refugee integration. 2020. Available from https://scottishrefugeecouncil.org.uk/wp-content/uploads/2021/01/Community-support-analysis-2020.pdf .

Sturge G, UK Parliament House of Commons Library Asylum statistics Research Briefing. 2023. Available from: https://commonslibrary.parliament.uk/research-briefings/sn01403/#:~:text=IntheyearendingJune,ofimmigrantstotheUK .

The Migration Observatory. Where do migrants live in the UK? The Migration Observatory at the University of Oxford. 2022. Available from: https://migrationobservatory.ox.ac.uk/resources/briefings/where-do-migrants-live-in-the-uk .

Pavli A, Maltezou H. Health problems of newly arrived migrants and refugees in Europe. J Travel Med. 2017;24(4). Available from: http://academic.oup.com/jtm/article/doi/10.1093/jtm/tax016/3095987/Health-problems-of-newly-arrived-migrants-and .

Humphris R, Bradby H. Health Status of Refugees and Asylum Seekers in Europe. In: Oxford Research Encyclopedia of Global Public Health. Oxford University Press; 2017. Available from: https://oxfordre.com/publichealth/view/10.1093/acrefore/9780190632366.001.0001/acrefore-9780190632366-e-8 .

Bradby H, Humphris R, Newall D, Phillimore J. Public Health Aspects of Migrant Health: A Review of the Evidence on Health Status for Refugees and Asylum Seekers in the European Region. (Health Evidence Network Synthesis Report, No. 44.) ANNEX 2, DEFINITIONS OF REFUGEES, ASYLUM SEEKERS AND MIGRANTS IN THE LITERATURE. Copenhagen: Eerat; 2015. Available from: https://www.ncbi.nlm.nih.gov/books/NBK379415/ .

Gerritsen AAM, Bramsen I, Devillé W, van Willigen LHM, Hovens JE, van der Ploeg HM. Physical and mental health of Afghan, Iranian and Somali asylum seekers and refugees living in the Netherlands. Soc Psychiatry Psychiatr Epidemiol. 2006;41(1):18–26. Available from: http://link.springer.com/10.1007/s00127-005-0003-5 .

Laue J, Diaz E, Eriksen L, Risør T. Migration health research in Norway: a scoping review. Scand J Public Health. 2023;51(3):381–90. Available from: http://journals.sagepub.com/doi/10.1177/14034948211032494 .

Scottish Refugee Council. Ukraine response one year on. 2023. Available from: https://scottishrefugeecouncil.org.uk/ukraine-response-one-year-on/ . Cited 2023 Aug 26.

Woodward A, Howard N, Wolffers I. Health and access to care for undocumented migrants living in the European Union: a scoping review. Health Policy Plan. 2014;29(7):818–30. Available from: https://academic.oup.com/heapol/article-lookup/doi/10.1093/heapol/czt061 .

Simon J, Kiss N, Laszewska A, Mayer S. Public health aspects of migrant health: a review of the evidence on health status for labour migrants in the European Region. Health Evidence Network Synthesis Report 43. 2015. Available from: http://www.epgencms.europarl.europa.eu/cmsdata/upload/114f16b6-1667-44ab-802b-a5a83dd50af0/WHO-HEN-Report-A5-1-Labour-FINAL_EN.pdf .

Scottish Government. Seasonal migrant workers in Scottish agriculture: research report. 2023. Available from: https://www.gov.scot/publications/seasonal-migrant-workers-scottish-agriculture/pages/10/ .

Scottish Government. New Scots: refugee integration strategy 2018–2022. 2018. Available from: https://www.gov.scot/publications/new-scots-refugee-integration-strategy-2018-2022/pages/11/ .

Oliva A, Palavra V, Caloun J. Refugees in Scotland: understanding the policy domain. 2016. Available from: https://www.academia.edu/34097718/REFUGEES_IN_SCOTLAND_UNDERSTANDING_THE_POLICY_DOMAIN .

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73. Available from: https://www.acpjournals.org/doi/10.7326/M18-0850 .

Download references

Acknowledgements

Thank-you to Professor Anne MacFarlane and PHD student Anne Cronin, of the University of Limerick, Ireland for sharing the coding guidelines currently used in an update to Villarroel et. al’s 2019 study on Migrant Health in the Republic of Ireland.

No funding was received for this work, which was undertaken as G. Petrie’s Master of Public Health dissertation module at the University of Stirling.

Author information

Authors and affiliations.

Caledonia House, NHS Greater Glasgow and Clyde, Glasgow, Scotland, UK

Institute for Social Marketing and Health, Faculty of Health Sciences and Sport, University of Stirling, Stirling, FK9 4LA, Scotland, UK

K. Angus & R. O’Donnell

You can also search for this author in PubMed Google Scholar

Contributions

KA, RO and GP finalised the study design collectively. GP conducted the searches, analysis and write up, with support from KA and RO. All three authors read and approved the manuscript prior to submission.

Corresponding author

Correspondence to R. O’Donnell .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., supplementary material 3., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Petrie, G., Angus, K. & O’Donnell, R. A scoping review of academic and grey literature on migrant health research conducted in Scotland. BMC Public Health 24 , 1156 (2024). https://doi.org/10.1186/s12889-024-18628-1

Download citation

Received : 04 September 2023

Accepted : 16 April 2024

Published : 25 April 2024

DOI : https://doi.org/10.1186/s12889-024-18628-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Asylum seekers
Scoping review
Research funding
Immigration

BMC Public Health

ISSN: 1471-2458

Submission enquiries: [email protected]
General enquiries: [email protected]

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

Publications
Our Methods
Short Reads
Tools & Resources

Read Our Research On:

U.S. Surveys

Pew Research Center has deep roots in U.S. public opinion research. Launched initially as a project focused primarily on U.S. policy and politics in the early 1990s, the Center has grown over time to study a wide range of topics vital to explaining America to itself and to the world. Our hallmarks: a rigorous approach to methodological quality, complete transparency as to our methods, and a commitment to exploring and evaluating ongoing developments in data collection. Learn more about how we conduct our domestic surveys here .

The American Trends Panel

Try our email course on polling

Want to know more about polling? Take your knowledge to the next level with a short email mini-course from Pew Research Center. Sign up now .

From the 1980s until relatively recently, most national polling organizations conducted surveys by telephone, relying on live interviewers to call randomly selected Americans across the country. Then came the internet. While it took survey researchers some time to adapt to the idea of online surveys, a quick look at the public polls on an issue like presidential approval reveals a landscape now dominated by online polls rather than phone polls.

Most of our U.S. surveys are conducted on the American Trends Panel (ATP), Pew Research Center’s national survey panel of over 10,000 randomly selected U.S. adults. ATP participants are recruited offline using random sampling from the U.S. Postal Service’s residential address file. Survey length is capped at 15 minutes, and respondents are reimbursed for their time. Respondents complete the surveys online using smartphones, tablets or desktop devices. We provide tablets and data plans to adults without home internet. Learn more about how people in the U.S. take Pew Research Center surveys.

Methods 101

Our video series helps explain the fundamental concepts of survey research including random sampling , question wording , mode effects , non probability surveys and how polling is done around. the world.

The Center also conducts custom surveys of special populations (e.g., Muslim Americans , Jewish Americans , Black Americans , Hispanic Americans , teenagers ) that are not readily studied using national, general population sampling. The Center’s survey research is sometimes paired with demographic or organic data to provide new insights. In addition to our U.S. survey research, you can also read more details on our international survey research , our demographic research and our data science methods.

Our survey researchers are committed to contributing to the larger community of survey research professionals, and are active in AAPOR and is a charter member of the American Association of Public Opinion Research (AAPOR) Transparency Initiative .

Frequently asked questions about surveys

Why am I never asked to take a poll?
Can I volunteer to be polled?
Why should I participate in surveys?
What good are polls?
Do pollsters have a code of ethics? If so, what is in the code?
How are your surveys different from market research?
Do you survey Asian Americans?
How are people selected for your polls?
Do people lie to pollsters?
Do people really have opinions on all of those questions?
How can I tell a high-quality poll from a lower-quality one?

Reports on the state of polling

Key Things to Know about Election Polling in the United States
A Field Guide to Polling: 2020 Edition
Confronting 2016 and 2020 Polling Limitations
What 2020’s Election Poll Errors Tell Us About the Accuracy of Issue Polling
Q&A: After misses in 2016 and 2020, does polling need to be fixed again? What our survey experts say
Understanding how 2020 election polls performed and what it might mean for other kinds of survey work
Can We Still Trust Polls?
Political Polls and the 2016 Election
Flashpoints in Polling: 2016

Sign up for our Methods newsletter

The latest on survey methods, data science and more, delivered quarterly.

OTHER RESEARCH METHODS

Fresh data delivered Saturday mornings

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries

Research Topics

Age & Generations
Coronavirus (COVID-19)
Economy & Work
Family & Relationships
Gender & LGBTQ
Immigration & Migration
International Affairs
Internet & Technology
Methodological Research
News Habits & Media
Non-U.S. Governments
Other Topics
Politics & Policy
Race & Ethnicity
Email Newsletters

ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Terms & Conditions

Cookie Settings

Reprints, Permissions & Use Policy

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
My Account Login
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 25 April 2024

An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion

Wang Tiantian 1 ,
Zhihua Hu 2 &
Yurong Guan 2

Scientific Reports volume 14 , Article number: 9554 ( 2024 ) Cite this article

1 Altmetric

Metrics details

Computer science
Information technology

While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15–50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.

An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

Interactive residual coordinate attention and contrastive learning for infrared and visible image fusion in triple frequency bands

A blind image super-resolution network guided by kernel estimation and structural prior knowledge

Introduction.

Image denoising involves the removal of unwanted noise from images, a crucial process in applications ranging from surveillance and transportation to medical care. The introduction of noise during image acquisition is inevitable, resulting from limitations in the imaging environment and the equipment used. Given these constraints, noise removal becomes an imperative step, whether the aim is to achieve visually appealing images or to prepare images for subsequent computer vision tasks such as image segmentation, recognition, and target detection 1 .

Historically, image denoising has been a classic inverse problem within the realm of computer vision 2 . Over the years, a plethora of effective methods have been proposed to address this challenge. These methods can be broadly categorized into two types: model-based and learning-based approaches 3 . Model-based methods involve modeling the distribution of natural images or the noise itself. Once modeled, this distribution is used as the prior, and optimization algorithms are then employed to generate clearer images. Frequently used prior features in this domain include local smoothness, sparsity, non-local self-similarity, and external statistical priors. In particular, non-local self-similarity and sparsity have been instrumental in enhancing the performance of image denoising methods. For instance, the Non-Local Means (NLM) technique 4 identifies and averages similar regions within an image to effectively mitigate Gaussian noise. Similarly, the Block-Matching and 3D Filtering (BM3D) approach 5 , 6 identifies similar two-dimensional image blocks and then processes these blocks in three-dimensional groups to produce a denoised image. The Weighted Nuclear Norm Minimization (WNNM) method 7 stands out for its ability to preserve intricate texture details while significantly reducing noise. However, while these methods have shown promise, they come with their own set of challenges. These include the necessity for manual parameter tuning and the reliance on computationally expensive optimization algorithms.

Deep learning emerges as a pivotal solution in this context. Owing to their intrinsic flexibility and powerful learning capabilities, deep neural network architectures stand out as optimal solutions for the challenges previously highlighted in image denoising. The advent and progressive development of deep neural networks have catalyzed substantial advancements in learning-based denoising methods 8 , 9 , 10 , 11 , marking a significant evolution in this field. For instance, the Denoising Convolutional Neural Network (DnCNN) 12 incorporates residual learning (RL) and batch normalization, enabling faster convergence and superior performance. However, increasing the depth of such networks can sometimes lead to diminishing returns in terms of performance. To counter this, techniques like the Deep Recursive Network(DRN) 13 and the Fast and Flexible Denoising Network (FFDNet) 14 have been introduced. Another notable method, the Convolutional Blind Denoising (CBDNet) 15 , offers a holistic approach, factoring in both synthetic and real noise during network training, thereby elevating the denoising efficacy and generalizability of the network.

Despite these advancements, challenges in image denoising persist:

Achieving a balance between preserving spatial details and maintaining high-level context remains elusive. Many denoising networks rely on single-scale local convolutions, leading to a limited receptive field and potentially inconsistent semantic outputs.

Edge preservation is a concern, with many techniques resulting in the unintended smoothing of edges and loss of critical edge information.

There is a missed opportunity in leveraging the rich feature information from shallow models within deeper networks, leading to suboptimal denoising outcomes.

To address these challenges, this study tackles the prevalent challenge of high computational complexity caused by the excessive depth of networks in current deep learning-based denoising approaches. By integrating the strengths of DB and RL 16 , along with a progressive fusion tactic, we introduced a residual fusion dense network specifically designed for the elimination of Gaussian and real-world noise. Diverging from the well known DnCNN 12 approach that primarily relies on a straightforward concatenation of convolutional layers for noise reduction, our method strategically implements densely interconnected DB within the network. Each layer of this network is engineered to process the feature maps from preceding layers, utilizing a progressive methodology to systematically link shallow convolutional features with deeper features extracted from each DB, thereby creating residual blocks. Within the CAFFM, a tripartite attention mechanism generates relative attention weights that capture the interrelations among three dimensions. These weights are subsequently applied and distributed to the pair of feature planes designated for fusion. This non-linear approach to feature fusion discerns the interplay among various feature planes, thereby significantly enhancing the efficacy of the fusion process. This design significantly enhances the network’s ability to accurately predict noise distribution. Moreover, the densely connected structure substantially lowers the computational complexity, reduces the overall number of network parameters, and effectively shortens the algorithm’s computation time.

In short, the key contributions of the proposed model are as follows:

By combining DBs with RL,Our approach utilizes dense connectivity for enhanced feature extraction and residual connections to maintain information flow, allowing the network to learn more effective denoising functions at greater depths without the usual performance decline.

Our model introduces a unique progressive residual fusion strategy that combines surface-level and deeply-extracted features, ensuring thorough use of information and enhancing its ability to robustly denoise a broad spectrum of noise types and intensities.

The integration of CAFFM precisely captures and merges features across dimensions, overcoming existing attention mechanisms’ limitations by analyzing the relationships between channel, height, and width dimensions. This allows for a refined adaptation to the dynamic aspects of image features and noise.

Through the strategic deployment of bottleneck structures and weighted averaging within CAFFM, our model not only reduces computational load but also significantly improves the quality of feature fusion. This leads to a more efficient network that does not compromise on denoising performance.

Related research

In recent years, significant advancements have been made in the field of image denoising, leading to the emergence of a plethora of sophisticated algorithms. Broadly, these algorithms can be categorized into two primary groups: the conventional methods reliant on artificial features, and those anchored in deep learning techniques 17 . Moreover, within the realm of traditional image denoising, methods have been largely rooted in artificial features and can be categorized into two distinct approaches: the spatial domain and the transform domain.

1. Spatial Domain Denoising: One technique that has gained prominence in this category is the neighborhood mean method. At its heart, this method revolves around the principle of leveraging the average values within a neighborhood to execute approximate calculations. This approach is tailored to combat and eliminate noise that manifests itself through local similarities in images. While the method is proficient in neutralizing certain types of localized and random similarities in noise, it occasionally falters by overlooking intricate, localized details that may be inherent in the image.

2. Transform Domain Denoising: Transitioning to the transform domain, the methodology is predicated on the ability to represent genuine image signals with a minimal set of linear elements. By invoking specific transformations, notably the discrete cosine transform and wavelet transform 18 , this technique transposes genuine image signals into the transform domain. An insightful advancement in this domain was presented by Luo et al. 19 . They brought to the fore a hybrid adaptive image denoising technique. Central to this approach is the proactive learning from prior images. This strategy not only offers the potential for reduced computational complexity through algorithmic simplification but also poses challenges. Specifically, the identification of suitable priors becomes cumbersome in scenarios populated by multiple images.

An overarching evaluation of these traditional methods illuminates certain challenges that cannot be overlooked. Notably, there’s a pronounced disparity between the encoded feature information they generate and the genuine characteristics of images. This divergence, compounded with their inherent rigidity, makes their adaptability in practical scenarios quite limited. Furthermore, the traditional extraction processes for image features are marred by their intricate nature and demand for excessive time and computational resources. With the multifaceted and intricate noise distributions observed in practical applications, these traditional techniques often find themselves ill-equipped to handle such challenges effectively 20 .

(2) Deep Learning-Based Image Denoising Methods: In recent advancements, a significant emphasis has been placed on image denoising techniques rooted in deep learning. These techniques are characterized by their formidable learning capabilities. They can adeptly accommodate noise of a more intricate distribution, offering the dual advantage of enhanced accuracy and reduced computational time, often surpassing the performance of traditional methods 21 .

One notable example is the model introduced by Kim et al. 22 . It leverages residual networks, which build upon earlier layers to progressively refine the image, and incorporates a convolutional attention module 23 . This module focuses on important image features, enhancing the network’s ability to distinguish noise from true details. While deep learning training improves denoising with this method, it can face challenges. Overdependence on residual connections can lead to overfitting, where the model memorizes training data instead of generalizing to unseen images

Pushing the boundaries further, Chen et al. 24 presented a GAN-based model called GCBD. Its unique feature is the generator’s ability to create artificial noise blocks. These blocks are then combined with real images, significantly expanding the training dataset. This cleverly addresses the common issue of limited paired data (noisy and clean image pairs) available in real-world scenarios. Another GAN-based approach, ADGAN 25 employed a feature loss function. This ensures that the denoised image retains the essence of the original, especially delicate details, by comparing specific features between the two. These examples showcase the diverse strategies employed in modern image denoising, each with its own strengths and limitations. Continued research in this area promises even more effective and robust methods for restoring pristine images from noisy data.

However, despite their demonstrated efficacy, a common characteristic shared by these denoising methods is their dependence on paired training datasets. This reliance presents a substantial obstacle, as obtaining such datasets proves challenging in practical applications. In response to this challenge, Li et al. 26 utilized cycle consistent adversarial networks (CycleGAN) 27 to denoise low-dose CT images without the requirement for paired training datasets. This innovative approach involved leveraging previously acquired full-dose CT images and aligning them with subsequent low-dose CT images from diverse patients, thereby enhancing denoising efficacy. However, there remains a need for further refinement of this methodology, particularly in preserving image details such as edges, textures, and ensuring overall image fidelity.

Within the domain of CycleGAN, CycleWGAN 28 has emerged as a supervised learning variant. By substituting JS divergence and period loss in the original CycleGAN network with Wasserstein distance and introducing a supervised loss, it effectively addresses the issue of mode collapse. Nevertheless, this method does require prolonged training durations. Another derivative, CaGAN 29 , employed dual attention modules to reinforce feature correlation in spatial and channel dimensions, concurrently refining the loss function. Simultaneously, in their quest to enhance training stability, Li et al. 30 replaced the adversarial loss in the original CycleGAN with a least squares loss function. Their approach decentralizes image discrimination by evaluating individual patches before amalgamating their outcomes for a comprehensive result. This not only streamlines the discriminator structure but also facilitates superior learning of image details.

Finally, Tan et al. 31 proposed an unsupervised denoising paradigm based on CycleGAN, enriched with a bilateral network in selective kernel networks (SK-NET) to selectively choose features. By incorporating a patchGAN discriminator and perceptual loss, this model ensures that the processed images closely mirror the intricate details of the original images.

In the subsequent subsections, we delve into the intricacies of previous CNN modules that hold significant potential for integration into our proposed model. Understanding these modules is essential to appreciate the novelty and robustness of our approach. We encourage readers to pay close attention to these foundational elements.

The evolution of CNNs has significantly propelled the field of image denoising, offering advanced solutions that surpass traditional methods. Traditional approaches often struggle to extract intricate image features or adapt to the diverse nature of noise, leading to suboptimal denoising results. Recognizing these challenges, the motivation for our proposed method emerges from the desire to enhance feature extraction capabilities and computational efficiency in the denoising process. DB and RL principles have shown promise in addressing these issues, yet their full potential remains untapped in the context of image denoising. The introduction of DB offers a way to leverage feature richness through layer interconnectivity, while RL promises to counteract the degradation of network performance with depth. However, the integration of these modules lacks a cohesive framework that can efficiently and effectively utilize both local and global features for superior denoising performance. This gap underscores the need for a novel approach that can harness the strengths of both DB and RL, along with advanced attention mechanisms, to set a new benchmark in image denoising.

Dense blocks

CNNs consist of three components: input layer, hidden layer, and output layer. The main structure of the hidden layer alternates between linear convolution and non-linear activation functions, primarily serving to map features from the input. In the domain of image denoising, the advantage of CNNs over other traditional methods is that the hidden layer can better extract image features. The shared weights significantly reduce the computational burden of the network model, effectively reducing the number of network parameters, resulting in a more efficient model. Taking into consideration how to extract more image features while also significantly reducing the computation parameters of the model, a dense network was designed. The core module in this network is the DB 32 . Its structure is shown in Fig. 1 , and the structure of the Bottleneck module is illustrated in Fig. 2 . Within the DB structure, each layer is connected via short connections. The input for each layer comes from the output of all previous layers, and this connection can be represented by:

where $X_l$ denotes the output feature map of the $l^{th}$ layer, and $[X_0,X_1,...,X_{l-1}]$ represents the channel-wise concatenation of the output feature maps from layer 0 to $(l-1)$ , without any further operation on the channels. $(H_l)$ is a function that inputs the concatenated feature map into the BN. As the input of each layer accumulates outputs from all preceding layers, integrating all previously extracted feature maps, the input channels for subsequent layers will be relatively large. To reduce the number of input feature maps, a 1 $\times $ 1 convolutional layer is designed within the BN. This not only minimizes parameters, reducing network computational cost, but also effectively merges features across channels, ensuring more efficient gradient propagation and comprehensive learning of noise distribution.

Graphical representation of dense block.

Overall structure of bottleneck.

Residual learning

The primary motivation behind RL is to address the problem of performance degradation in CNN as their depth increased. By learning residual mappings, residual networks ensure that training accuracy does not degrade with increased network depth, addressing the problem of network degradation. Considering the image denoising domain, the residual network is designed to learn noise images with relatively low information content through a skip-connection architecture. The noisy image is then subtracted from the predicted noise image by the network to obtain the underlying clean image, expressed by the equation:

where y denotes the noisy image, x is the original clean image, and n represents additive noise. In the early stages of convolutional feature extraction, the design of the DB extracts rich image features, laying a foundation for subsequent learning of noise distribution.

Proposed methodology

Inspired by the DenseNet network structure proposed by Huang et al. 33 , this paper uses dense networks to enrich the extracted image features and utilizes the dense short connection structure to reduce the computational complexity and the number of network parameters. After studying and replicating the denoising network based on residual DB proposed by Zhang et al. 34 , it was observed that the network did not fully utilize shallow convolutional features. Therefore, by adopting a progressive approach, three residual blocks are designed to merge shallow convolutional features with deep dense network-extracted features, ensuring that the deep dense network fully utilizes shallow features to learn noise distribution. Moreover, research into dense networks found that its structure did not integrate global features for learning. Inspired by the RDN network structure proposed by Zhang et al. 35 , a concatenation layer is designed before the reconstruction output layer. In this layer, the features that have been extracted by preceding dense networks are consolidated and then inputted into an attention mechanism. This mechanism guarantees that the network comprehensively assimilates both local and global features, which results in enhanced denoising outcomes. Further elaboration on the modules utilized in the proposed approach is provided in the subsequent sections.

Network structure

This study provides insight into the progressive residual fusion dense network’s comprehensive architecture. The network comprises three DB modules that mirror each other structurally. Within each module, convolutional layers are followed by rectified linear unit (ReLU) activations, configured to address issues like vanishing gradients while reducing parameter interdependencies and instilling sparsity. An illustrative depiction of the network, shown in Fig. 3 , details its design. Commencing with an initial convolutional layer employing a 3 $\times $ 3 kernel with 64 filters paired with ReLU activation, this stratum extracts shallow image features. The subsequent layer maintains the kernel size but reduces filters to 24, regulating feature map channels and preempting excessive proliferation that could escalate computational demands. The network’s core comprises three DB modules, residual blocks, and interleaved Transition and ReLU+Conv layers across layers three through twelve. This multilayered structure facilitates intricate feature learning. Within DB, BN layers utilize 1 $\times $ 1 and 3 $\times $ 3 kernels with 48 and 12 filters respectively, allowing each layer to adaptively discern the noise distribution through fused features. The consistent 1 $\times $ 1 kernel size in Transition Layers, equipped with 24 filters, refines parameter efficiency by consolidating channel features. The thirteenth layer, a Concatenation stratum, synergizes output maps from the first two DB with the final module to fortify global feature assimilation. Culminating in the fourteenth reconstruction output layer with a solitary 3 $\times $ 3 kernel, this terminal stratum amalgamates forged global features while maintaining input dimensional congruence. This consistency underpins RL, empowering precise noise extraction and production of the denoised output image. The network’s meticulous design achieves a balance between feature extraction precision and computational efficiency, representing an advance in the domain.

Overall structure of the proposed model.

Attention mechanism

In recent years, the attention mechanism has become one of the hottest research directions in computer vision. The most representative is the SENet 36 , which captures the relationships between channels through the channel attention mechanism but neglects the important role of spatial attention information in feature representation. Subsequently, researchers have improved SENet by integrating attention information of different scales. Woo et al. 23 proposed the Convolutional Block Attention Module (CBAM), which merges channel attention information with spatial attention information to create more robust feature attention representations. Dai et al. 37 introduced Attentional Feature Fusion (AFF), which combines global and local channel attention information to adapt to features of different scales in images. However, these methods still did not consider the relationship between channel attention information and spatial attention information. For this reason, Hou et al. 38 proposed the Stripe Pooling Network, which obtains the relationships between channels and width, and channels and height through stripe pooling layers. Misra et al. 39 proposed the Convolutional Triplet Attention Module, learning the relationship between the three dimensions through a three-branch attention mechanism. Moreover, the attention mechanism also shows great potential in feature fusion. Liu et al. 40 introduced Feature Pyramid Encoding Network that fuses deep channel attention information with shallow spatial attention information, merging semantic features and spatial details. AFF 37 is used for feature fusion in both short and long skip connections. This module learns the relative attention weights between feature planes of different scales through the attention mechanism and merges them through nonlinear weighted fusion, significantly improving the network’s segmentation accuracy.

Graphical representation of convolutional attention feature fusion module.

Inspired by the prior model 39 , this paper proposed CAFFM, as shown in Fig. 4 . While ensuring a small computational load, it improves the quality of feature fusion in lightweight convolutional neural networks. CAFFM captures the pairwise relationships between the three dimensions of channel, height, and width through a three-branch structure, generating three two-dimensional attention sub-maps, and merges them into one complete three-dimensional attention map to adapt to changes in feature information of different dimensions; finally, it merges feature planes of different scales through weighted averaging. In addition, it uses two 1 $\times $ 1 convolutional layers to form a bottleneck structure, reducing the number of channels in the feature planes, further decreasing the computational load of CAFFM.

In CAFFM, given two feature planes $X, Y \in {\mathbb {R}}^{C \times H \times W} $ , by default, assume X is the output feature plane from the shallow stage of the encoder and Y is from the deep stage. By element-wise addition, X and Y are first merged into an input tensor $I \in {\mathbb {R}}^{C \times H \times W}$ , then I is processed by a $1 \times 1$ convolutional layer $P_{1}$ to obtain $T \in {\mathbb {R}}^{C/r \times H \times W}$ , where r is the reduction ratio, the formula is:

where $\beta $ represents BN; r is the reduction ratio mapping the tensor to a lower channel dimension space. T is fed into three separate branches, each capturing the pairwise relationships between channels, height, and width, and finally merging into a three-dimensional tensor.

The first branch learns the relationship between the height and width dimensions. By encoding T in the channel dimension through channel average pooling, we obtain $T_{h,w} \in {\mathbb {R}}^{1 \times H \times W}$ , specifically,

where $T_{h,w}$ is then processed by a 7 $\times $ 7 standard convolutional layer $S_1$ , obtaining a two-dimensional tensor $O_{h,w} \in {\mathbb {R}}^{1 \times H \times W}$ that contains the relationship between the two dimensions of height and width, i.e.,

The second branch learns the relationship between the channel and height dimensions. Similar to the first branch, but encoding T in the width dimension and reshaping it to ${\mathbb {R}}^{W \times H \times C/r}$ , then through channel average pooling, we obtain $T_{c,w}$ , and after passing it through a 7x7 standard convolutional layer, we get $O_{c,w} \in {\mathbb {R}}^{1 \times C/r \times W}$ . After reshaping, we obtain $O_{c,w} \in {\mathbb {R}}^{C/r \times H \times 1}$ , and finally, we extend it to $O_2 \in {\mathbb {R}}^{C/r \times H \times W}$ . The third branch learns the relationship between the channel and width dimensions. By reshaping T into ${\mathbb {R}}^{H \times C/r \times W}$ and encoding it through channel average pooling on the height dimension, we obtain $T_{c,w}$ . After processing through a 7 $\times $ 7 convolutional layer, we get $O_{c,w}$ , and after reshaping and extending, we obtain $O_3 \in {\mathbb {R}}^{C/r \times H \times W}$ . By element-wise addition and arithmetic averaging, we merge the output tensors of the three branches, which have the same shape, into a three-dimensional tensor $O \in {\mathbb {R}}^{C/r \times H \times W}$ , containing the complete relationships between the dimensions, i.e.,

where $\oplus $ represents element-wise addition. To integrate the global context information of the three output tensors and restore the channel number of O to the same as X and Y , we introduce a 1 $\times $ 1 convolutional layer $P_2$ ; then through a Sigmoid activation function $\sigma $ , we obtain a three-dimensional attention map $\alpha \in {\mathbb {R}}^{C \times H \times W}$ , i.e.,

The attention map is then weighted and assigned to X and Y , obtaining the output tensor $Z \in {\mathbb {R}}^{C \times H \times W}$ ,

where $\odot $ represents element-wise multiplication; the weights in $\alpha $ and $1 - \alpha $ range from 0 to 1, and after being assigned to X and Y , the sum of each position is 1, which can be considered as a weighted average between X and Y .

Image denoising algorithm based on residual fusion

The specific flow of the algorithm is shown in Fig. 5 . During the training process, the original image is cropped into image blocks of the same size. These original image blocks, added with noise, are input into the designed network. Through the loss function, parameters are adjusted via backpropagation until the network converges. In the testing phase, noisy images are input into the already converged network to directly output the corresponding predicted denoised images. The loss function expression used in this paper’s algorithm is:

where $R(y_i,\Theta )$ is the estimated residual image of the noise input, $ y_i$ is the input noisy image, and $x_i$ is the clean image. $(y_i-x_i)$ gives the standard residual image. N represents the number of input samples in a batch. The training process continuously iterates to reduce the loss function, i.e., reduce the error between the estimated residual and the standard residual. In this way, the predicted denoised image will be closer to the original clean image, achieving better denoising effects.

Structure of the image denoising technique utilizing residual fusion.

Different modules and their combinations

Our proposed methodology innovatively integrates DB, RL, and the CAFFM to set a new benchmark in image denoising. This section delves into the rationale behind this specific combination, highlighting how their synergistic interaction leads to unparalleled denoising performance.

Complementarity of components

The core strength of our model lies in the complementary nature of its components. DB ensure that our network can extract a rich set of features from the input image by leveraging the outputs of all preceding layers, thus creating a comprehensive feature set. This is critical for understanding the complex nature of image noise and for extracting the underlying clean image information. RL, on the other hand, addresses the challenge of training deeper networks without succumbing to performance degradation. By learning the residual noise instead of the clean image directly, our model efficiently identifies and filters out noise, even in cases where it is subtly intertwined with image content. This approach not only simplifies the learning objective but also enhances the model’s ability to generalize across different noise patterns. The inclusion of CAFFM brings a focused efficiency to the model. By implementing an attention mechanism, CAFFM directs the model’s computational resources toward features most relevant for denoising, ensuring that the network does not get overwhelmed by irrelevant data. This results in a more precise denoising process, as the model learns to prioritize and refine features that significantly contribute to noise reduction.

Synergistic effects

The synergy between DB, RL, and CAFFM is not merely additive but multiplicative in terms of enhancing model performance. DB provide a rich feature base that is essential for any denoising task. RL optimizes the network’s depth, ensuring that even the subtlest noise can be identified and removed. Similarly, CAFFM, through its attention mechanism, acts as a good filter for the better focus of the model to find important features contributing most importantly towards the process of denoising. This combination ensures that our model is not just deep but also smart in processing information. By efficiently managing computational resources and focusing on the most relevant features, our model achieves a high degree of precision in noise identification and removal, outperforming existing models that might rely on depth or feature richness alone.

Empirical validation

Empirical evidence underscores the success of combining DB, RL, and CAFFM within our model. This combination’s efficacy is further scrutinized by examining alternative attention mechanisms such as Spatial Attention, Channel Attention, Self-Attention, Multi-Head Attention, Hybrid Attention, Local Attention, and Layer Attention-paired with DB and RL. Through this comprehensive validation as shown in Fig. 6 , it became clear that our selected integration of technologies outperformed others across various datasets. This deeper analysis not only confirmed our initial findings but also highlighted the unique effectiveness of our chosen blend in enhancing model performance.

In short, the integration of DB, RL, and CAFFM into our model represents a holistic approach to the challenge of image denoising. By leveraging the unique strengths and synergistic potential of these components, our model attained superior performance, setting a new standard in the field.

Quantitative analysis of PSNR and SSIM on Set12, BSD68, and Kodat24 datasets.

Experimental results

Implementation details.

In this study, a total of 33,725 images are meticulously chosen for the training of the neural network. These images include contributions from both the ImageNet 41 and BSD400 42 datasets. Additionally, grayscale images affected by noise, as well as their colored counterparts, are included in the training set, each sized at 180 $\times $ 180 pixels. These images cover a range of subjects like natural scenery, animals, people, and buildings. To improve the training efficiency and speed up convergence, we used a cropping technique. This involved setting the crop size to 40 and a stride of 10. This approach resulted in 380 images being cropped into 215,552 smaller images, each 40 $\times $ 40 pixels, which form the main part of the training dataset. We also set aside 20 images for validation to test the network’s performance and utility with different test sets. The test set for the experiment is randomly selected from the Set12, Set68 12 , DND 43 datasets.

Experiment parameters: batch size is 64, training 33,725 sample data per epoch, epoch is 150, learning rate is fixed at 0.001. The computer’s CPU is Intel Core i7, GPU is GTX1080Ti, RAM is 11 GB, OS is Windows 10, and the network is trained, verified, and tested on the PyTorch deep learning framework. This framework can use GPU acceleration to save training time. The software used for training and testing is PyCharm, and the Python version is 3.9.

Measurement standards

The evaluation standards used in the experiment include subjective and objective evaluations. Subjective evaluation refers to visual inspection of images, evaluating the denoising effect of the model’s output image. Objective evaluation uses peak signal to noise ratio (PSNR) and structural similarity (SSIM). PSNR, based on mean square error (MSE), is an image quality evaluation index 2 . The higher the PSNR value, the better the image quality. In the experiment, the higher the PSNR value indicates a higher similarity between the denoised image and the original image. PSNR is calculated as:

where M and N are the predicted and true values, respectively. j and k are all pixels in the image. H and W represent the height and width of the image, and n is set to 8. Structural Similarity (SSIM) is an evaluation metric to measure the similarity between two images. It estimates similarity based on image brightness, contrast, and structure. The mean is used as the brightness estimate, the standard deviation as the contrast estimate, and the covariance as the measure of structural similarity. SSIM is calculated as:

where $\mu _m $ is the mean of image M, and $\mu _n$ is the mean of image N. $\sigma _m^2$ and $\sigma _n^2$ represent the variances of images M and N, respectively. $\sigma _{mn}$ represents the covariance between images M and N . c 1 and c 2 are constants for stability. The SSIM value ranges between 0 and 1, with a higher SSIM indicating more similarity. When SSIM is 1, the two images being compared are identical.

Furthermore, we also utilized Feature Similarity Index for Color images (FSIMc), is quantitatively evaluate the score of image quality. In simple words, it can be used to measure the similarity between two color images. The FSIMc is an extension of the FSIM, which is originally designed for grayscale images. The equation for FSIMc typically looks something like this:

where X and Y are the two color images being compared, $PC_m$ is the phase congruency at a given pixel, $GM_m$ is the gradient magnitude at the pixel, and $S_l$ and $S_r$ are the similarity measures for the left and right images respectively. The sums are taken over all pixels ( x , y ) in the images. The FSIMc score is a value between 0 and 1, where a higher value indicates greater similarity between the two images. This makes it suitable for assessing the effectiveness of algorithms in tasks like image compression, watermarking, and denoising, especially where color fidelity is crucial.

Visual analysis of grayscale image denoising using the Set12 and BSD68 datasets with noise level $\sigma =25, 50$ .

Qualitative and quantitative results

To compare traditional denoising techniques with deep neural network-based methods, we conducted both quantitative and qualitative evaluations on diverse datasets. The quantitative assessment involved using important metrics such as PSNR, SSIM and FSIMc to numerically evaluate the quality of the denoised images. Additionally, we performed a qualitative evaluation by visually representing the restored images, allowing for an intuitive understanding of their visual quality and accuracy. This comprehensive evaluation approach provides valuable insights into the denoising capabilities of different techniques across various datasets, making a significant contribution to image processing research.

The objective of this study is to systematically assess the denoising performance of the proposed algorithm. This dual-level assessment allows for a thorough understanding of each algorithm’s performance under varying noise intensities. Using both subjective and objective measures, the study aims to provide a holistic view of the denoising capabilities.

Visual outcomes of grayscale images using Set12 dataset with noise level $\sigma =25$ .

Quantitative analysis of PSNR on the Set12 dataset with noise level $\sigma =25$ . ( a ) House. ( b ) Pepper. ( c ) Ship. ( d ) Man. ( e ) Landscape. ( f ) Airplane.

Quantitative analysis of SSIM on the Set12 dataset with noise level $\sigma =25$ . ( a ) House. ( b ) Pepper. ( c ) Ship. ( d ) Man. ( e ) Landscape. ( f ) Airplane.

Grayscale image denoising

Initially, the efficacy of both the proposed and prior approaches is evaluated through experiments conducted on grayscale images taken from the Set12 and BSD68 datasets. The visual results from these models are illustrated in Fig. 7 . We expanded our analysis by comparing our method with established techniques such as DnCNN 12 , FFDNet 14 , BM3D 5 , and FCNN 44 . The denoising efficacy of each mentioned algorithms is evaluated under conditions of Gaussian white noise at levels of $\sigma = 25$ and $\sigma = 50$ . In the analysis of the region of interest (ROI) depicted in Fig. 7 , marked by red and green rectangles, it becomes clear that algorithms such as DnCNN, FFDNet, and BM3D tend to oversmooth the image’s edges, resulting in diminished clarity of the content. In contrast, the FCNN’s visual outputs exhibit enhanced texture and structural definition. Building upon this comparative observation, our proposed method exhibits a notably superior performance. It distinctly excels in preserving sharpness along the edges and capturing intricate details. Simultaneously, it upholds visual fidelity, especially in the smoother areas of the images, thus striking a balance between detail preservation and smoothness.

Moreover, the efficacy of our proposed method is further validated through a comparative analysis with two models, namely PDTDF 45 and FCNN 44 . The images utilized for this experiment are sourced from Set12 and Set14 datasets. A thorough examination of the visual outcomes in Fig. 8 revealed that while PDTDF and FCNN succeed in restoring images to a significant degree, they tend to oversmooth edges and textures. This observation leads to a nuanced understanding of their limitations in preserving fine details. In contrast, our proposed approach achieved a higher caliber of results, characterized by sharp edges and enhanced visual quality, thereby indicating its superiority in maintaining image fidelity while effectively adjusting details.

Besides, we performed quantitative analysis to validate the proposed and prior approaches. The PSNR and SSIM results are shown in Figs. 9 and 10 , respectively. Six images are taken from Set12 dataset and the noise level is set to 25. It is evident from the measured PSNR values that EPLL 46 results have noise and that is the underline reason of low PSNR score of this model. Besides, AVMF 47 and BM3D 5 approaches attained almost same PSNR scores. Likewise, DIBS 48 and FFDNet 14 models’ PSNR scores are close to each others. Yet, on one images the PSNR score of AVMF is higher than proposed model but our approach demonstrates superior denoising performance for all other images.

Comparative visual analysis of prior models versus our model on Kodak24 dataset with noise level $\sigma =50$ .

In addition to qualitative assessments, a rigorous quantitative analysis is conducted to validate the efficacy of the proposed denoising method against established approaches. The results, encapsulated in terms of PSNR and SSIM, are depicted in Figs. 9 and 10 , respectively. For this analysis, a sample of six images from the Set12 dataset is utilized, with the noise level uniformly set at 25. Upon examination of the PSNR values, it becomes apparent that the EPLL 46 method suffers from residual noise artifacts, which is the fundamental cause for its relatively low PSNR scores. Conversely, AVMF 47 and BM3D 5 exhibit comparable PSNR outcomes, indicating a similar level of noise suppression. In parallel, the PSNR scores of the DIBS 48 and FFDNet 14 are also closely matched, suggesting that these models perform comparably under the test conditions. However, it is noteworthy that while the AVMF method achieved a higher PSNR score than the proposed model in one instance, our approach consistently demonstrates superior denoising performance across the remainder of the image set.

Color image denoising of prior models versus our model on SIDD dataset with noise level $\sigma =50$ .

A comparison of the denoising effectiveness of traditional methods and the proposed method with noise level $\sigma =50$ .

A visual comparison of denoising results that demonstrates the performance of proposed methods versus previous approaches in real-world scenarios with noise level $\sigma =15, 30, 50$ .

Color image denoising

In addressing real noise scenarios, our study utilized the SIDD 49 . The SIDD encompasses a comprehensive training set and a separate testing suite, with the former containing subsets of varied scales: small (160 noisy-clean image pairs), medium (320 pairs), and large (24,000 pairs). The model retraining is conducted using the small-scale subset of the SIDD training collection, employing data augmentation techniques such as horizontal and vertical flipping to enhance the robustness of the findings. Given that the SIDD test suite does not include corresponding clean images, it is leveraged solely for the purpose of assessing denoising performance. This suite comprises 40 images, from which we extracted three distinct samples representing varying levels of scene brightness to conduct an in-depth comparative study of single-image denoising metrics. These images are resized prior to testing to ensure a clear distinction from those used during the training phase. The denoising outcomes are depicted in Figs. 11 and 12 , revealed that while NNS 50 can mitigate a portion of the real noise, it inadvertently introduces blurriness to the images. The TDFN 51 approach suppressed a considerable amount of noise but the edges are not very clear. The DDM 52 technique, while effective at removing substantial noise, also results in a degree of image blur. Conversely, techniques like DLSF 53 and AWQD 54 exhibit superior noise elimination proficiency, resulting in images of aesthetically satisfying sharpness. However, the method proposed in this study achieves enhanced restoration of textural and structural details in comparison to the aforementioned models.

A closer inspection of the denoising results within the ROI for each scene in Figs. 11 and 12 unveiled that proposed method consistently retains minimal residual noise. This nuanced analysis underscored the advanced denoising capabilities of the proposed algorithm, which holds promise for setting a new standard in image restoration practices amidst varied lighting conditions.

Evaluation of denoising performance in terms of average PSNR and SSIM across SIDD and DND datasets with noise level $\sigma =50$ .

Moreover, Fig. 13 presents the results of the proposed method compared to traditional denoising methods such as NNS, TDFN, DDM, DLSF, and AWQD when dealing with random impulse noise in a parrot image with a noise density of 50. This image is taken from the Kodak24 dataset. From Fig. 13 , it can be observed that the results obtained by prior denoising methods still contain some noticeable noise, and the details are not well-preserved. In contrast, our model effectively removed noise while preserved the edge and texture details of the image. Hence, our model demonstrated superior performance compared to other algorithms. It generated denoised images that retained a much closer resemblance to the original, particularly under high noise levels. The other prior algorithms included in the comparison exhibited various shortcomings in their denoised outputs. These issues manifested as artifacts, distortions, and even residual noise remaining in the images.

Additionally, the performance of both proposed and existing models is evaluated within a real-world context. This assessment is illustrated through visual results depicted in Fig. 14 , which sources its images from two datasets known for their real-world applicability, namely Kodak24 and Urban100. The experiment is conducted with both sets of images subjected to a noise level of $\sigma =50$ . Models such as DIBS 48 , DenSformer 55 , FFDNet 14 , KBNet 56 , and GRDN 22 demonstrated improved outcomes; however, the resultant smooth effect tended to obscure the textures. Conversely, LIDN 57 and DRANet 58 managed to enhance the image quality satisfactorily, preserving texture to a certain extent while effectively reducing noise. In contrast, our model distinguishes itself by efficiently removing noise while also preserving the sharpness of edges and the textures in the images. Furthermore, it exhibited unparalleled performance metrics, notably in PSNR and SSIM, surpassing other algorithms. It succeeded in producing denoised images that closely mirror the original, especially under conditions of high noise levels. The comparison also highlighted the limitations of other algorithms, which included various issues such as artifacts, an overly smoothed appearance, and the presence of residual noise, underscoring the superior efficacy of our model in handling real-world imaging challenges.

To further solidifying efficacy of the model, our proposed method achieved demonstrably superior performance in two additional experiments employing diverse datasets,i.e., SIDD and DND. Focusing on a noise level of $\sigma = 50$ and utilizing PSNR and SSIM as metrics, our method surpassed all other compared network models, as showcased in Fig. 15 a. MCWNNM 59 model achieved a PSNR of 33.4, while the TWSC 60 showed a notable improvement with a PSNR of 35.33. The DnCNN 12 , although an influential model, lags behind with a PSNR of 23.66, which suggests a potential deficiency in handling the noise levels present within the test datasets. CBDNet 15 offered a moderate performance boost with a PSNR of 30.78. Noteworthy is the RIDNet 61 model, which demonstrates a significant leap in denoising capability, achieving a PSNR of 38.71. It is closely followed by the VDN 62 and VDIR 63 models, with PSNRs of 39.28 and 39.26, respectively, indicating their effectiveness in noise reduction while maintaining high image quality. Most impressively, our proposed model surpassed all compared models with a leading PSNR of 39.94. Likewise, the outcomes in Fig. 15 b shown that the proposed model outperformed the prior models. Besides, our method attained higher SSIM and PSNR values on SIDD and DND datasets as shown in Fig. 15 c,d. This substantiates the efficacy of our model in denoising images, underscoring the advancements our approach contributes to the field. The empirical evidence strongly suggests that our method sets a new benchmark for denoising performance, potentially redefining the state-of-the-art standards.

To further validate the restoration performance of the proposed model on color images, we performed quantitative analysis in terms of PSNR and FSIMc. The measured results are shown in Table 1 that presents a quantitative scores of various image denoising methods evaluated on the Kodak24 dataset. The proposed method achieved the highest PSNR values across all four different noise level, with scores of 40.77, 36.75, 35.07, and 31.94, respectively. These results indicate a notable improvement in image quality and noise reduction over other methods. Similarly, the proposed method outperformed in terms of FSIMc, achieved the highest scores of 0.998, 0.996, 0.995, and 0.986, suggested it maintained color and structural integrity to a degree superior to that of the other evaluated methods.

FFDNet and ADNet also showed robust performance with PSNR values exceeding 31 and FSIMc scores above 0.978 in their worst cases, indicating strong denoising capabilities. These methods and our approach stand out as significant contributors to the field of image denoising. Conversely, older methods like EPLL and BM3D showed lower PSNR and FSIMc scores, which could be indicative of the rapid evolution and improvement in denoising techniques over the last decade. From this data, it is clear that the proposed method represents a substantial advancement in denoising technology, setting a new benchmark for both PSNR and FSIMc metrics on the Kodak24 dataset. This suggests that the method could be highly effective in practical applications where maintaining image quality is critical.

Furthermore, we performed another comprehensive quantitative analysis of various image denoising methods on the PolyU dataset, which is also evaluated in terms of PSNR and FSIMc as shown in Table 2 . The proposed method achieved the highest PSNR values at 52.09, 47.01, 46.48, and 42.12, and dominated in FSIMc scores with 0.999, 0.998, 0.998, and 0.994 across the four noise level. These results indicated that the proposed method not only excels in reducing noise but also maintained image quality effectively. Other noteworthy methods include ADNet and FFDNet. Specifically, ADNet demonstrated a significant PSNR value of 52.03 and the highest FSIMc score of 0.999 in one category, indicating its strong denoising capability. FFDNet also showed remarkable results, especially with the highest PSNR score of 47.17 in one category. Comparatively, earlier techniques like EPLL and AVMF registered lower scores in both PSNR and FSIMc, reflecting the advancements in denoising technology over the past decade.

Overall, the proposed method exhibited superior performance, not only in noise reduction but also in preserving image fidelity, as reflected in the high FSIMc scores.

Computational complexity analysis

In our study, we conducted a thorough comparison of various deep learning denoising algorithms against our proposed model to evaluate computational efficiency. We standardized the network input to an image size of $256\times 256$ across three channels for a fair comparison. The parameters and computational complexities for each considered network are meticulously detailed in Table 3 , which also includes execution times under a uniform testing environment for images of identical size. Our investigation revealed that our model significantly outperforms established methods such as ADNet, S2S-LSD, FFDNet, RIDNet, and DnCNN in terms of denoising efficiency, demonstrating a noteworthy reduction in execution time. This efficiency gain is largely due to our model’s optimized network structure, which achieves a balance between depth and computational demand, unlike some prior algorithms whose deeper structures lead to increased parameters and complexity. Moreover, we introduced additional contemporary methods to the comparison, further highlighting our model’s advanced capabilities and efficiency.

This extended analysis not only confirms the superior denoising performance of our method but also showcases its remarkable efficiency and practicality for real-world applications. By integrating a streamlined architecture, our model demonstrates an exceptional capability to deliver high-quality denoising results.

Limitations

Transforming image denoising models that are initially designed for grayscale images to efficiently process color images is a complex endeavor. Such models, especially those employing advanced techniques like Progressive Residual and Convolutional Attention Feature Fusion, often struggle to accurately capture and maintain the essential connections between different color channels. This accuracy is vital for ensuring that the colors in the denoised images remain true to the original. The intricacy of this challenge increases significantly when these models are faced with unfamiliar or sophisticated noise patterns that deviate from standard training scenarios. Often, the datasets used to train these models do not encompass the full spectrum of noise found in real-world settings, which hampers the model’s ability to effectively apply its denoising capabilities across diverse types of noise. To overcome these hurdles, it is imperative to direct future research efforts towards the creation of more advanced learning mechanisms. These mechanisms should be adept at discerning the subtle dependencies between color channels and enhancing the model’s resilience against a broad array of noise distributions. Improving the model’s architecture and pioneering novel training methodologies are crucial steps in this direction. By incorporating a wider array of noise characteristics into the training process, these advancements will equip the model with the versatility needed to tackle the unpredictable and varied nature of noise in real-world images. This approach not only promises to elevate the performance of image denoising models on color images but also aims to bridge the gap between theoretical models and practical applications, ensuring that denoising technologies can meet the demands of real-world challenges with greater efficacy.

Concluding remarks

In this article, we critically examined the prevalent issues associated with current deep learning approaches to image denoising, i.e., the excessive depth and large parameter sets of networks, which consequentially impede denoising velocity. To navigate these challenges, we integrated the merits of dense block architectures and residual learning frameworks, coupled with a sequential fusion strategy. Consequently, we introduced an innovative sequential residual fusion dense network tailored for mitigating Gaussian noise and real-world noise. Our proposed methodology commenced with the deployment of dense blocks, meticulously engineered to map the distribution of noise within the images. This initial phase significantly streamlines the network’s parameters, simultaneously facilitating an exhaustive extraction of local image attributes. Subsequently, the network employs a methodical approach, progressively amalgamating superficial convolutional features with their more profound counterparts. This step-by-step integration gives rise to a robust residual fusion infrastructure, proficient in harvesting a comprehensive array of global features pertinent to the identified noise. This procedure reaches its zenith with the amalgamation of the resultant feature maps emanating from each dense block. Besides, a tripartite attention mechanism called CAFFM is employed to compute relative attention weights that reflect the interconnectedness of three distinct dimensions. These weights are then validly applied to a duo of feature planes targeted for fusion. This non-linear methodology for merging features is adept at identifying the interactions among multiple feature planes, thereby substantially augmenting the effectiveness of the fusion procedure. These are then adeptly channeled towards the reconstruction layer, which is responsible for synthesizing the final denoised image output. This sophisticated architecture ensures a fusion of both depth and precision, culminating in an efficient and effective denoising process. Empirical studies in environments with Gaussian white noise and natural noise showed a significant performance improvement. This is evidenced by higher mean values in PSNR, SSIM, and FSIMc, outperformed more than 20 existing methods across six different datasets.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Goyal, B., Dogra, A., Agrawal, S., Sohi, B. S. & Sharma, A. Image denoising review: From classical to state-of-the-art approaches. Inf. Fusion 55 , 220–244 (2020).

Article Google Scholar

Elad, M., Kawar, B. & Vaksman, G. Image denoising: The deep learning revolution and beyond-a survey paper. SIAM J. Imag. Sci. 16 , 1594–1654 (2023).

Article MathSciNet Google Scholar

Zhou, L., Zhou, D., Yang, H. & Yang, S. Multi-scale network toward real-world image denoising. Int. J. Mach. Learn. Cybern. 14 , 1205–1216 (2023).

Pierazzo, N., Lebrun, M., Rais, M., Morel, J.-M. & Facciolo, G. Non-local dual image denoising. In 2014 IEEE International Conference on Image Processing (ICIP) , 813–817 (IEEE, 2014).

Ri, G.-I., Kim, S.-J. & Kim, M.-S. Improved bm3d method with modified block-matching and multi-scaled images. Multimed. Tools Appl. 81 , 12661–12679 (2022).

Habib, M. et al. Convolved feature vector based adaptive fuzzy filter for image de-noising. Appl. Sci. 13 , 4861 (2023).

Article CAS Google Scholar

Pham, T. T., Mai, T. T. N. & Lee, C. Unrolling multi-channel weighted nuclear norm minimization for image denoising. In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) , 243–244 (IEEE, 2022).

Liu, Y. et al. Invertible denoising network: A light solution for real noise removal. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 13365–13374 (2021).

A Sharif, S., Naqvi, R. A. & Biswas, M. Beyond joint demosaicking and denoising: An image processing pipeline for a pixel-bin image sensor. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 233–242 (2021).

Zamir, S. W. et al. Multi-stage progressive image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 14821–14831 (2021).

Zhang, K. et al. Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. 44 , 6360–6376 (2021).

Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26 , 3142–3155 (2017).

Article ADS MathSciNet PubMed Google Scholar

Wang, C., Ren, C., He, X. & Qing, L. Deep recursive network for image denoising with global non-linear smoothness constraint prior. Neurocomputing 426 , 147–161 (2021).

Zhang, K., Zuo, W. & Zhang, L. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Trans. Image Process. 27 , 4608–4622 (2018).

Article ADS MathSciNet Google Scholar

Guo, S., Yan, Z., Zhang, K., Zuo, W. & Zhang, L. Toward convolutional blind denoising of real photographs. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 1712–1722 (2019).

Shafiq, M. & Gu, Z. Deep residual learning for image recognition: A survey. Appl. Sci. 12 , 8972 (2022).

Sen, A. P. & Rout, N. K. A comparative analysis of the algorithms for de-noising images contaminated with impulse noise. Sens. Imaging 23 , 11 (2022).

Article ADS Google Scholar

You, N., Han, L., Zhu, D. & Song, W. Research on image denoising in edge detection based on wavelet transform. Appl. Sci. 13 , 1837 (2023).

Luo, E., Chan, S. H. & Nguyen, T. Q. Adaptive image denoising by mixture adaptation. IEEE Trans. Image Process. 25 , 4489–4503 (2016).

Tian, C. et al. Deep learning on image denoising: An overview. Neural Netw. 131 , 251–275 (2020).

Article PubMed Google Scholar

Ilesanmi, A. E. & Ilesanmi, T. O. Methods for image denoising using convolutional neural network: A review. Complex Intell. Syst. 7 , 2179–2198 (2021).

Kim, D.-W., Ryun Chung, J. & Jung, S.-W. Grdn: Grouped residual dense network for real image denoising and gan-based real-world noise modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 0–0 (2019).

Woo, S., Park, J., Lee, J.-Y. & Kweon, I. S. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) , 3–19 (2018).

Chen, J., Chen, J., Chao, H. & Yang, M. Image blind denoising with generative adversarial network based noise modeling. In Proceedings of the IEEE conference on computer vision and pattern recognition , 3155–3164 (2018).

Lin, K., Li, T. H., Liu, S. & Li, G. Real photographs denoising with noise domain adaptation and attentive generative adversarial network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 0–0 (2019).

Li, Z., Huang, J., Yu, L., Chi, Y. & Jin, M. Low-dose ct image denoising using cycle-consistent adversarial networks. In 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 1–3 (IEEE, 2019).

Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision , 2223–2232 (2017).

Zhou, L., Schaefferkoetter, J. D., Tham, I. W., Huang, G. & Yan, J. Supervised learning with cyclegan for low-dose fdg pet image denoising. Med. Image Anal. 65 , 101770 (2020).

Huang, Z. et al. Cagan: A cycle-consistent generative adversarial network with attention for low-dose ct imaging. IEEE Trans. Comput. Imaging 6 , 1203–1218 (2020).

Li, Z., Zhou, S., Huang, J., Yu, L. & Jin, M. Investigation of low-dose ct image denoising using unpaired deep learning methods. IEEE Trans. Rad. Plasma Med. Sci. 5 , 224–234 (2020).

Tan, C., Yang, M., You, Z., Chen, H. & Zhang, Y. A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose ct denoising. Precis. Clin. Med. 5 , pbac011 (2022).

Huang, G., Liu, S., Van der Maaten, L. & Weinberger, K. Q. Condensenet: An efficient densenet using learned group convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition , 2752–2761 (2018).

Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition , 4700–4708 (2017).

Zhang, J., Zhu, Y., Li, W., Fu, W. & Cao, L. Drnet: A deep neural network with multi-layer residual blocks improves image denoising. IEEE Access 9 , 79936–79946 (2021).

Zhang, Y., Tian, Y., Kong, Y., Zhong, B. & Fu, Y. Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43 , 2480–2495 (2020).

Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition , 7132–7141 (2018).

Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y. & Barnard, K. Attentional feature fusion. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , 3560–3569 (2021).

Hou, Q., Zhang, L., Cheng, M.-M. & Feng, J. Strip pooling: Rethinking spatial pooling for scene parsing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 4003–4012 (2020).

Misra, D., Nalamada, T., Arasanipalai, A. U. & Hou, Q. Rotate to attend: Convolutional triplet attention module. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , 3139–3148 (2021).

Liu, M. & Yin, H. Feature pyramid encoding network for real-time semantic segmentation. arXiv preprint arXiv:1909.08599 (2019).

Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115 , 211–252 (2015).

Martin, D., Fowlkes, C., Tal, D. & Malik, J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision . ICCV 2001, vol. 2, 416–423 (IEEE, 2001).

Plotz, T. & Roth, S. Benchmarking denoising algorithms with real photographs. In Proceedings of the IEEE conference on computer vision and pattern recognition , 1586–1595 (2017).

Lu, X. & Li, F. Fine-tuning convolutional neural network based on relaxed Bayesian-optimized support vector machine for random-valued impulse noise removal. J. Electron. Imaging 32 , 013006–013006 (2023).

Teng, L., Li, H. & Yin, S. Modified pyramid dual tree direction filter-based image denoising via curvature scale and nonlocal mean multigrade remnant filter. Int. J. Commun Syst 31 , e3486 (2018).

Zoran, D. & Weiss, Y. From learning models of natural image patches to whole image restoration. In 2011 international conference on computer vision , 479–486 (IEEE, 2011).

Roy, A., Singha, J., Manam, L. & Laskar, R. H. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Proc. 11 , 352–361 (2017).

Satti, P., Shrotriya, V., Garg, B. & Surya Prasath, V. Dibs: distance-and intensity-based separation filter for high-density impulse noise removal. Signal, Image and Video Processing 17 , 4181–4188 (2023).

Abdelhamed, A., Lin, S. & Brown, M. S. A high-quality denoising dataset for smartphone cameras. In Proceedings of the IEEE conference on computer vision and pattern recognition , 1692–1700 (2018).

Orazaev, A., Lyakhov, P., Baboshina, V. & Kalita, D. Neural network system for recognizing images affected by random-valued impulse noise. Appl. Sci. 13 , 1585 (2023).

Qin, N. & Gong, Z. Color image denoising by means of three-dimensional discrete fuzzy numbers. Vis. Comput. 39 , 2051–2063 (2023).

Maliński, Ł. Decomposed dissimilarity measure for evaluation of digital image denoising. Sensors 23 , 5657 (2023).

Article ADS PubMed PubMed Central Google Scholar

Radlak, K., Malinski, L. & Smolka, B. Deep learning based switching filter for impulsive noise removal in color images. Sensors 20 , 2782 (2020).

Jin, L., Zhu, Z., Song, E. & Xu, X. An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism. Signal Process. 155 , 334–345 (2019).

Yao, C., Jin, S., Liu, M. & Ban, X. dense residual transformer for image denoising. Electronics 11 , 418 (2022).

Zhang, Y. et al. kbnet: Kernel basis network for image restoration. arXiv preprint arXiv:2303.02881 (2023).

Wang, J., Lu, Y. & Lu, G. Lightweight image denoising network with four-channel interaction transform. Image Vis. Comput. 104766 (2023).

Wu, W., Liu, S., Xia, Y. & Zhang, Y. dual residual attention network for image denoising. Pattern Recogn. 149 , 110291 (2024).

Xu, J., Zhang, L., Zhang, D. & Feng, X. Multi-channel weighted nuclear norm minimization for real color image denoising. In Proceedings of the IEEE international conference on computer vision , 1096–1104 (2017).

Xu, J., Zhang, L. & Zhang, D. A trilateral weighted sparse coding scheme for real-world image denoising. In Proceedings of the European conference on computer vision (ECCV) , 20–36 (2018).

Chen, C., Xiong, Z., Tian, X., Zha, Z.-J. & Wu, F. Real-world image denoising with deep boosting. IEEE Trans. Pattern Anal. Mach. Intell. 42 , 3071–3087 (2019).

Yue, Z., Yong, H., Zhao, Q., Meng, D. & Zhang, L. Variational denoising network: Toward blind noise modeling and removal. Adv. Neural Inf. Process. Syst. 32 (2019).

Soh, J. W. & Cho, N. I. Variational deep image restoration. IEEE Trans. Image Process. 31 , 4363–4376 (2022).

Article ADS PubMed Google Scholar

Valsesia, D., Fracastoro, G. & Magli, E. Deep graph-convolutional image denoising. IEEE Trans. Image Process. 29 , 8226–8237 (2020).

Tian, C. et al. Attention-guided cnn for image denoising. Neural Netw. 124 , 117–129 (2020).

Quan, Y., Chen, M., Pang, T. & Ji, H. Self2self with dropout: Learning self-supervised denoising from single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 1890–1898 (2020).

Zhuo, S., Jin, Z., Zou, W. & Li, X. Ridnet: Recursive information distillation network for color image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 0–0 (2019).

Download references

Author information

Authors and affiliations.

School of Computer and Software Engineering, Xias University, Zhengzhou, 451150, Henan, China

Wang Tiantian

School of Computer, Huanggang Normal University, Huanggang, 438000, Hubei, China

Zhihua Hu & Yurong Guan

You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, methodology, software, formal analysis, validation, and data processing, W.T.; writing—original draft preparation, W.T. and Y.G; investigation, resources, supervision, and project administration, Z.H and Y.G; writing—review and editing visualization, W.T and Y.G;. all authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Zhihua Hu or Yurong Guan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Tiantian, W., Hu, Z. & Guan, Y. An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion. Sci Rep 14 , 9554 (2024). https://doi.org/10.1038/s41598-024-60139-x

Download citation

Received : 29 December 2023

Accepted : 19 April 2024

Published : 25 April 2024

DOI : https://doi.org/10.1038/s41598-024-60139-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Image denoising
Deep learning
Image processing
Computer vision

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Soc 320 Research Opportunity, Fall 2024 – Spring 2025: Reproductive Justice and Incarceration

Term : Fall 2024 – Spring 2025 (Soc 320, Section 278) Status : OPEN Contact : Please contact Molly Clark-Barol at [email protected] . It is available for 2-3 credits. Description : This mixed-methods action research project is in collaboration with a community partner: FREE, a statewide organization of formerly incarcerated women. We will be looking at factors related to the success or failure of legislation related to reproductive justice (“Dignity Bills”) for incarcerated women. The first phase consists of creating legislative histories of attempts to pass this legislation, including generating provisional hypotheses about mechanisms. Then, we will be conducting qualitative data collection with relevant stakeholders in a subset of state cases. We may also conduct limited quantitative analyses based on the original data set. The research experience will also include 1-2 justice-impacted community research fellows (women impacted by the criminal legal system, who may or may not be undergraduates). Duties : We will have a ‘lab’ meeting once per week, either virtually or in person depending on if the community research fellows are located in Madison, as well as at least some standard co-working hours. Requirements : Undergraduates will be expected to already have research training (eg HDFS 425, CSCS 570, SOC 357). Students will enroll in an independent study or research practicum course, for research credit (e.g., CSCS 601, HDFS 592, or SOC 320 depending on the needs/departmental home of the student) for 2-3 credits (6-9 hours/week). To Apply : Please submit a cover letter and a resume to Molly Clark-Barol at [email protected] . The ideal candidate would also be open to continuing with a second semester in Spring 2025.

Twitter Logo
Linkedin Logo

COMMENTS

Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Research Methods--Quantitative, Qualitative, and More: Overview
About Research Methods. This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. As Patten and Newhart note in the book Understanding Research Methods, "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge.
Research Methods
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Research Methods
You can also take a mixed methods approach, where you use both qualitative and quantitative research methods. Primary vs secondary data. Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys, observations and experiments). Secondary data are information that has already been collected by other researchers (e.g. in ...
(PDF) Understanding research methods: An overview of the essentials
A perennial bestseller since 1997, this updated tenth edition of Understanding Research Methods provides a detailed overview of all the important concepts traditionally covered in a research ...
Research Methods In Psychology
Olivia Guy-Evans, MSc. Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.
Introduction to Research Methods in Psychology
Psychology research can usually be classified as one of three major types. 1. Causal or Experimental Research. When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables.
Overview of Research Methodology
This research methods guide will help you choose a methodology and launch into your research project. Data collection and data analysis are research methods that can be applied to many disciplines. There is Qualitative research and Quantitative Research. The focus of this guide, includes most popular methods including: surveys.
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Understanding Research Methods
There are 4 modules in this course. This MOOC is about demystifying research and research methods. It will outline the fundamentals of doing research, aimed primarily, but not exclusively, at the postgraduate level. It places the student experience at the centre of our endeavours by engaging learners in a range of robust and challenging ...
Research Methods: What are research methods?
What are research methods. Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic. There are different types of research methods which use different tools for data collection.
What Is a Research Methodology?
Step 1: Explain your methodological approach. Step 2: Describe your data collection methods. Step 3: Describe your analysis method. Step 4: Evaluate and justify the methodological choices you made. Tips for writing a strong methodology chapter. Other interesting articles. Frequently asked questions about methodology.
The 3 Descriptive Research Methods of Psychology
Descriptive research methods can be crucial for psychological researchers to establish and describe the natural details of a particular phenomenon. There are three major methods of descriptive ...
Introduction to Research Methodology
1.3 Significance of Research Methodology. The process of research methodology has crucial significance to scientific research with a wide application to academia and industry. The process of sound research methods plays a pivotal role in producing valid and meaningful research outcomes. It establishes the foundation for effective data ...
Research Methodology
Qualitative Research Methodology. This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.
Types of Research Methods (With Best Practices and Examples)
Research methods are processes used to collect data. You can use this data to analyze current methods or procedures and to find additional information on a topic. Professionals use research methods while studying medicine, human behavior and other scholarly topics. There are two main categories of research methods: qualitative research methods ...
3.2 Psychologists Use Descriptive, Correlational, and Experimental
A research design is the specific method a researcher uses to collect, analyze, and interpret data. Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs.
2.3: Research Methods
This page titled 2.3: Research Methods is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by OpenStax via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request. Sociologists use research methods to design a study—perhaps a detailed ...
3 Types of research
Thus, applied research involves original research, not just reviewing what others have done, but like secondary research it is motivated to get an answer. The third type is the least common, but is also generally the focus of a textbook like this. Academic research is the type of research that your professors do most of the time.
Research Approach
The Three main research approaches are deductive, inductive, and abductive. Deductive Approach. The deductive approach starts with a theory or a hypothesis, and the researcher tests the hypothesis through the collection and analysis of data. The researcher develops a research design and data collection methods based on the theory or hypothesis.
What are Different Research Approaches? Comprehensive Review of
a comprehensive review of qualitative, quantitative, and mixed-method research methods. Each method is clearly defined and specifically discussed based on applications, types, advantages, and limitations to help researchers identify select the most relevant type based on each study and navigate accordingly. Keywords: Research methodology
Types of Research Designs Compared
Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.
What Are The 3Rs?
The 3Rs were conceptualized by William Russell and Rex Burch in The Principles of Humane Experimental Technique, first published in 1959.Since then, the principles of Reduction, Refinement, and Replacement (the 3Rs)* have guided thinking, practice, and regulation of humane research animal use.. While they have been a helpful framework for the past 70 years, the 3Rs represent the beginning, not ...
A scoping review of academic and grey literature on migrant health
Research methods and data collection. 52% of reports used qualitative research methods. Forty-five reports (86%) collected data using 1-1 interviews and 24 (46%) used focus groups. Other methods of data collection included questionnaires (six studies (11%)), workshops (two studies (3.85%)) and observation (two studies (3.85%)).
U.S. Surveys
Pew Research Center has deep roots in U.S. public opinion research. Launched initially as a project focused primarily on U.S. policy and politics in the early 1990s, the Center has grown over time to study a wide range of topics vital to explaining America to itself and to the world.Our hallmarks: a rigorous approach to methodological quality, complete transparency as to our methods, and a ...
A FAIR and modular image‐based workflow for knowledge discovery in the
Methods in Ecology and Evolution is an open access journal publishing papers across a wide range of subdisciplines, ... 4.3 Modularity. Machine learning research can result in multiple tools being maintained in a single large and therefore monolithic repository that performs many tasks. Modularizing these tools as workflow components made our ...
Overall Survival with Adjuvant Pembrolizumab in Renal-Cell Carcinoma
The previous analysis of disease-free survival occurred after a median follow-up of 24.1 months (estimated percentage of participants who were alive and free from recurrence at 24 months, 77.3% in ...
An efficient lightweight network for image denoising using ...
These methods can be broadly categorized into two types: model-based and learning-based approaches 3. Model-based methods involve modeling the distribution of natural images or the noise itself.
Soc 320 Research Opportunity, Fall 2024
Term: Fall 2024 - Spring 2025 (Soc 320, Section 278) Status: OPEN Contact: Please contact Molly Clark-Barol at [email protected] is available for 2-3 credits. Description: This mixed-methods action research project is in collaboration with a community partner: FREE, a statewide organization of formerly incarcerated women.We will be looking at factors related to the success or failure of ...
What Is Qualitative Research?
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...

Secondary menu

Search form

About Research Methods

START HERE: SAGE Research Methods

Library Data Services at UC Berkeley

Other Data Services at Berkeley

General Research Methods Resources

Consultants

Related Resourcex

Have a language expert improve your writing

Research Methods | Definition, Types, Examples

Table of contents

Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

Prevent plagiarism, run a free check.

Qualitative analysis methods

Quantitative analysis methods

Is this article helpful?

Research Methods In Psychology

Experimental Design

Experimental Methods

Correlational Studies

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Interview Methods

Observations

Pilot Study

Research Design

Reliability

Meta-Analysis

Peer Review

Types of Data

Features of Science

Statistical Testing

Ethical Issues

Introduction to Research Methods in Psychology

Three Main Types of Research in Psychology

1. Causal or Experimental Research

2. Descriptive Research

3. Relational or Correlational Research

Theory vs. Hypothesis in Psychology Research

The Effect of Time on Research Methods in Psychology

Causal Relationships Between Psychology Research Variables

Correlational Relationships Between Psychology Research Variables

Research Methodology: Overview of Research Methodology

Research Methods Overview

"How-To": Booking a Research Consultation

Research Design vs Research Method

How can we help?

Research Methods: What are research methods?

What are research methods

Types of research

Data collection tools

Introduction to Research Methodology

Cite this chapter

Access this chapter

Author information

Rights and permissions

Copyright information

About this chapter

Download citation

Share this chapter

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Descriptive Research: Assessing the Current State of Affairs

Correlational Research: Seeking Relationships among Variables

Experimental Research: Understanding the Causes of Behaviour

Research Focus: Video Games and Aggression

Key Takeaways

Exercises and Critical Thinking

Image Attributions

Long Descriptions

Share This Book

2.3: Research Methods

Learning Objectives

Field Research

Sociology in the Real World

Participant Observation

Ethnography

Institutional Ethnography

Sociological Research