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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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.

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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What Is the Purpose of Research Design?

Research design, like any framework of research, has its methods and purpose. The purpose of research design is to provide a clear plan of the research, based on independent and dependent variables, and to consider the cause and effect evoked by these variables.

Methods that constitute research design can include:

• Observations
• Experiments
• Working with archives
• Other methods

As we’ve explained here , the methods of research always back up the purpose and the goals of the research.

Main Goals of Research Design

• The main goal of research design is for a researcher to make sure that the conclusions they’ve come to are justified. It means that the research has to confirm or deny the hypothesis.
• Another purpose of research design is to broaden the researcher’s understanding of the topic, and to make them more conscious about various places, groups, and settings.
• Finally, research design allows the researcher to achieve an accurate understanding of the topic they are working on, and be able to explain the topic to others.

Usually, you cannot accomplish all these three goals at the same time, but you can try to do that by using multi-stage design in your study, which is also known as multi-stage sampling .

Examples of Research Design

One should choose the type of research design corresponding to the particular purpose of their research. Here are some examples to choose from when you’re doing research.

General Structure and Writing Style

The chosen type should help the researcher work on the hypothesis accurately; while collecting data, you have to choose specific evidence, which will show you the differences between the variables. Before conducting research, think about what information you will need and how you're going to use it. Otherwise, you can get lost.

Action Research

When you’ve developed an understanding of the problem, and you’ve begun planning an interventional strategy, action research design helps to bring the deviation to the surface. You make an observation and bookmark the deviation. Then you commit an action, and compare the outcome of having committed the action with the outcome of not having committed the action.

We would use a case study when studying a particular problem and when we take some broad samples and try to narrow them down to subjects that are researchable. This one is also cool when we need to check if a certain theory applies to a certain real-world problem.

Causal Research

If A is X, then B is Y — that's how we can simplify causality. By comparing two or more variables, a researcher can understand the effect they have on each other. This effect can then be measured in terms of causality: i.e., what constitutes the cause and the effect.

Cohort Study

Cohort studies are more often used in medical studies, but are now gaining popularity in social science. They are based on a quantitative framework, where a researcher makes notes on a statistical occurrence within a subgroup, with members of the group having similar features that are related to the study topic, rather than analyzing mathematical occurrences within a custom group. Observation is the main method of data gathering in cohort studies.

How to Study Research Design

You can learn research design:

• At universities
• Through online tutorials
• In online classes
• By reading scientific papers

Research with FlowMapp’s Tools

FlowMapp specializes in strategic website planning and user experience analysis and develops special tools for such analysis. Although all of these products focus on web design, FlowMapp's analysis and planning tools can be used by a wide range of other professionals:

• developers;
• copywriters;
• UX strategists;
• researchers;
• sales managers;
• product managers;
• project managers;

User Flow Tool

User flow is a visual representation of the sequence of actions that users perform to achieve their goal. In fact, User flow tool allow you to look at the interaction between the user and the web product through the eyes of the user.

You can see how this looks like in the following example :

Wireframe tool

The Wireframe tool by FlowMapp is a powerful online tool that allows for the rapid creation of website prototypes. With its extensive library of templates for every website block, this tool enables designers to quickly build hi-fi prototypes. These wireframes can be easily shared via a link as a real webpage.

This tool is useful for quickly analyzing the labor required for website development, visualize future project ideas, and effectively present them to stakeholders.

The Notes tool by FlowMapp is a content gathering system that allows you to store all types of data, files, and ideas in one place, and collaborate on them with others. This tool is particularly useful for creating briefs, reports, documentation, and for storing all project-related data.

During the research phase, the Notes tool is a valuable asset for gathering and organizing ideas, especially when working on UX/UI design, user behavior and user flow analysis, or website development projects.

Remember that the step-by-step approach is the one that will help you most. Also, you can try to implement other research formats from multiple spheres and try some more mixed methods of research. Don’t be afraid to experiment – register at our website and get access to all tools we mentioned above!

Peak-end Rule in UX Design

What Is System Usability Scale (SUS)?

• Chapter 1: Home
• Narrowing Your Topic
• Problem Statement

Purpose Statement Overview

Best practices for writing your purpose statement, writing your purpose statement, sample purpose statements.

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Jump to DSE Guide

The purpose statement succinctly explains (on no more than 1 page) the objectives of the research study. These objectives must directly address the problem and help close the stated gap. Expressed as a formula:

Good purpose statements:

• Flow from the problem statement and actually address the proposed problem
• Are concise and clear
• Answer the question ‘Why are you doing this research?’
• Match the methodology (similar to research questions)
• Have a ‘hook’ to get the reader’s attention
• Set the stage by clearly stating, “The purpose of this (qualitative or quantitative) study is to ...

In PhD studies, the purpose usually involves applying a theory to solve the problem. In other words, the purpose tells the reader what the goal of the study is, and what your study will accomplish, through which theoretical lens. The purpose statement also includes brief information about direction, scope, and where the data will come from.

A problem and gap in combination can lead to different research objectives, and hence, different purpose statements. In the example from above where the problem was severe underrepresentation of female CEOs in Fortune 500 companies and the identified gap related to lack of research of male-dominated boards; one purpose might be to explore implicit biases in male-dominated boards through the lens of feminist theory. Another purpose may be to determine how board members rated female and male candidates on scales of competency, professionalism, and experience to predict which candidate will be selected for the CEO position. The first purpose may involve a qualitative ethnographic study in which the researcher observes board meetings and hiring interviews; the second may involve a quantitative regression analysis. The outcomes will be very different, so it’s important that you find out exactly how you want to address a problem and help close a gap!

The purpose of the study must not only align with the problem and address a gap; it must also align with the chosen research method. In fact, the DP/DM template requires you to name the  research method at the very beginning of the purpose statement. The research verb must match the chosen method. In general, quantitative studies involve “closed-ended” research verbs such as determine , measure , correlate , explain , compare , validate , identify , or examine ; whereas qualitative studies involve “open-ended” research verbs such as explore , understand , narrate , articulate [meanings], discover , or develop .

A qualitative purpose statement following the color-coded problem statement (assumed here to be low well-being among financial sector employees) + gap (lack of research on followers of mid-level managers), might start like this:

In response to declining levels of employee well-being, the purpose of the qualitative phenomenology was to explore and understand the lived experiences related to the well-being of the followers of novice mid-level managers in the financial services industry. The levels of follower well-being have been shown to correlate to employee morale, turnover intention, and customer orientation (Eren et al., 2013). A combined framework of Leader-Member Exchange (LMX) Theory and the employee well-being concept informed the research questions and supported the inquiry, analysis, and interpretation of the experiences of followers of novice managers in the financial services industry.

A quantitative purpose statement for the same problem and gap might start like this:

In response to declining levels of employee well-being, the purpose of the quantitative correlational study was to determine which leadership factors predict employee well-being of the followers of novice mid-level managers in the financial services industry. Leadership factors were measured by the Leader-Member Exchange (LMX) assessment framework  by Mantlekow (2015), and employee well-being was conceptualized as a compound variable consisting of self-reported turnover-intent and psychological test scores from the Mental Health Survey (MHS) developed by Johns Hopkins University researchers.

Both of these purpose statements reflect viable research strategies and both align with the problem and gap so it’s up to the researcher to design a study in a manner that reflects personal preferences and desired study outcomes. Note that the quantitative research purpose incorporates operationalized concepts  or variables ; that reflect the way the researcher intends to measure the key concepts under study; whereas the qualitative purpose statement isn’t about translating the concepts under study as variables but instead aim to explore and understand the core research phenomenon.  

Always keep in mind that the dissertation process is iterative, and your writing, over time, will be refined as clarity is gradually achieved. Most of the time, greater clarity for the purpose statement and other components of the Dissertation is the result of a growing understanding of the literature in the field. As you increasingly master the literature you will also increasingly clarify the purpose of your study.

The purpose statement should flow directly from the problem statement. There should be clear and obvious alignment between the two and that alignment will get tighter and more pronounced as your work progresses.

The purpose statement should specifically address the reason for conducting the study, with emphasis on the word specifically. There should not be any doubt in your readers’ minds as to the purpose of your study. To achieve this level of clarity you will need to also insure there is no doubt in your mind as to the purpose of your study.

Many researchers benefit from stopping your work during the research process when insight strikes you and write about it while it is still fresh in your mind. This can help you clarify all aspects of a dissertation, including clarifying its purpose.

Your Chair and your committee members can help you to clarify your study’s purpose so carefully attend to any feedback they offer.

The purpose statement should reflect the research questions and vice versa. The chain of alignment that began with the research problem description and continues on to the research purpose, research questions, and methodology must be respected at all times during dissertation development. You are to succinctly describe the overarching goal of the study that reflects the research questions. Each research question narrows and focuses the purpose statement. Conversely, the purpose statement encompasses all of the research questions.

Identify in the purpose statement the research method as quantitative, qualitative or mixed (i.e., “The purpose of this [qualitative/quantitative/mixed] study is to ...)

Avoid the use of the phrase “research study” since the two words together are redundant.

Follow the initial declaration of purpose with a brief overview of how, with what instruments/data, with whom and where (as applicable) the study will be conducted. Identify variables/constructs and/or phenomenon/concept/idea. Since this section is to be a concise paragraph, emphasis must be placed on the word brief. However, adding these details will give your readers a very clear picture of the purpose of your research.

Developing the purpose section of your dissertation is usually not achieved in a single flash of insight. The process involves a great deal of reading to find out what other scholars have done to address the research topic and problem you have identified. The purpose section of your dissertation could well be the most important paragraph you write during your academic career, and every word should be carefully selected. Think of it as the DNA of your dissertation. Everything else you write should emerge directly and clearly from your purpose statement. In turn, your purpose statement should emerge directly and clearly from your research problem description. It is good practice to print out your problem statement and purpose statement and keep them in front of you as you work on each part of your dissertation in order to insure alignment.

It is helpful to collect several dissertations similar to the one you envision creating. Extract the problem descriptions and purpose statements of other dissertation authors and compare them in order to sharpen your thinking about your own work.  Comparing how other dissertation authors have handled the many challenges you are facing can be an invaluable exercise. Keep in mind that individual universities use their own tailored protocols for presenting key components of the dissertation so your review of these purpose statements should focus on content rather than form.

Once your purpose statement is set it must be consistently presented throughout the dissertation. This may require some recursive editing because the way you articulate your purpose may evolve as you work on various aspects of your dissertation. Whenever you make an adjustment to your purpose statement you should carefully follow up on the editing and conceptual ramifications throughout the entire document.

In establishing your purpose you should NOT advocate for a particular outcome. Research should be done to answer questions not prove a point. As a researcher, you are to inquire with an open mind, and even when you come to the work with clear assumptions, your job is to prove the validity of the conclusions reached. For example, you would not say the purpose of your research project is to demonstrate that there is a relationship between two variables. Such a statement presupposes you know the answer before your research is conducted and promotes or supports (advocates on behalf of) a particular outcome. A more appropriate purpose statement would be to examine or explore the relationship between two variables.

Your purpose statement should not imply that you are going to prove something. You may be surprised to learn that we cannot prove anything in scholarly research for two reasons. First, in quantitative analyses, statistical tests calculate the probability that something is true rather than establishing it as true. Second, in qualitative research, the study can only purport to describe what is occurring from the perspective of the participants. Whether or not the phenomenon they are describing is true in a larger context is not knowable. We cannot observe the phenomenon in all settings and in all circumstances.

It is important to distinguish in your mind the differences between the Problem Statement and Purpose Statement.

The Problem Statement is why I am doing the research

The Purpose Statement is what type of research I am doing to fit or address the problem

The Purpose Statement includes:

• Method of Study
• Specific Population

Remember, as you are contemplating what to include in your purpose statement and then when you are writing it, the purpose statement is a concise paragraph that describes the intent of the study, and it should flow directly from the problem statement.  It should specifically address the reason for conducting the study, and reflect the research questions.  Further, it should identify the research method as qualitative, quantitative, or mixed.  Then provide a brief overview of how the study will be conducted, with what instruments/data collection methods, and with whom (subjects) and where (as applicable). Finally, you should identify variables/constructs and/or phenomenon/concept/idea.

Qualitative Purpose Statement

Creswell (2002) suggested for writing purpose statements in qualitative research include using deliberate phrasing to alert the reader to the purpose statement. Verbs that indicate what will take place in the research and the use of non-directional language that do not suggest an outcome are key. A purpose statement should focus on a single idea or concept, with a broad definition of the idea or concept. How the concept was investigated should also be included, as well as participants in the study and locations for the research to give the reader a sense of with whom and where the study took place. 

Creswell (2003) advised the following script for purpose statements in qualitative research:

“The purpose of this qualitative_________________ (strategy of inquiry, such as ethnography, case study, or other type) study is (was? will be?) to ________________ (understand? describe? develop? discover?) the _________________(central phenomenon being studied) for ______________ (the participants, such as the individual, groups, organization) at __________(research site). At this stage in the research, the __________ (central phenomenon being studied) will be generally defined as ___________________ (provide a general definition)” (pg. 90).

Quantitative Purpose Statement

Creswell (2003) offers vast differences between the purpose statements written for qualitative research and those written for quantitative research, particularly with respect to language and the inclusion of variables. The comparison of variables is often a focus of quantitative research, with the variables distinguishable by either the temporal order or how they are measured. As with qualitative research purpose statements, Creswell (2003) recommends the use of deliberate language to alert the reader to the purpose of the study, but quantitative purpose statements also include the theory or conceptual framework guiding the study and the variables that are being studied and how they are related. 

Creswell (2003) suggests the following script for drafting purpose statements in quantitative research:

“The purpose of this _____________________ (experiment? survey?) study is (was? will be?) to test the theory of _________________that _________________ (compares? relates?) the ___________(independent variable) to _________________________(dependent variable), controlling for _______________________ (control variables) for ___________________ (participants) at _________________________ (the research site). The independent variable(s) _____________________ will be generally defined as _______________________ (provide a general definition). The dependent variable(s) will be generally defined as _____________________ (provide a general definition), and the control and intervening variables(s), _________________ (identify the control and intervening variables) will be statistically controlled in this study” (pg. 97).

• The purpose of this qualitative study was to determine how participation in service-learning in an alternative school impacted students academically, civically, and personally.  There is ample evidence demonstrating the failure of schools for students at-risk; however, there is still a need to demonstrate why these students are successful in non-traditional educational programs like the service-learning model used at TDS.  This study was unique in that it examined one alternative school’s approach to service-learning in a setting where students not only serve, but faculty serve as volunteer teachers.  The use of a constructivist approach in service-learning in an alternative school setting was examined in an effort to determine whether service-learning participation contributes positively to academic, personal, and civic gain for students, and to examine student and teacher views regarding the overall outcomes of service-learning.  This study was completed using an ethnographic approach that included observations, content analysis, and interviews with teachers at The David School.
• The purpose of this quantitative non-experimental cross-sectional linear multiple regression design was to investigate the relationship among early childhood teachers’ self-reported assessment of multicultural awareness as measured by responses from the Teacher Multicultural Attitude Survey (TMAS) and supervisors’ observed assessment of teachers’ multicultural competency skills as measured by the Multicultural Teaching Competency Scale (MTCS) survey. Demographic data such as number of multicultural training hours, years teaching in Dubai, curriculum program at current school, and age were also examined and their relationship to multicultural teaching competency. The study took place in the emirate of Dubai where there were 14,333 expatriate teachers employed in private schools (KHDA, 2013b).
• The purpose of this quantitative, non-experimental study is to examine the degree to which stages of change, gender, acculturation level and trauma types predicts the reluctance of Arab refugees, aged 18 and over, in the Dearborn, MI area, to seek professional help for their mental health needs. This study will utilize four instruments to measure these variables: University of Rhode Island Change Assessment (URICA: DiClemente & Hughes, 1990); Cumulative Trauma Scale (Kira, 2012); Acculturation Rating Scale for Arabic Americans-II Arabic and English (ARSAA-IIA, ARSAA-IIE: Jadalla & Lee, 2013), and a demographic survey. This study will examine 1) the relationship between stages of change, gender, acculturation levels, and trauma types and Arab refugees’ help-seeking behavior, 2) the degree to which any of these variables can predict Arab refugee help-seeking behavior.  Additionally, the outcome of this study could provide researchers and clinicians with a stage-based model, TTM, for measuring Arab refugees’ help-seeking behavior and lay a foundation for how TTM can help target the clinical needs of Arab refugees. Lastly, this attempt to apply the TTM model to Arab refugees’ condition could lay the foundation for future research to investigate the application of TTM to clinical work among refugee populations.
• The purpose of this qualitative, phenomenological study is to describe the lived experiences of LLM for 10 EFL learners in rural Guatemala and to utilize that data to determine how it conforms to, or possibly challenges, current theoretical conceptions of LLM. In accordance with Morse’s (1994) suggestion that a phenomenological study should utilize at least six participants, this study utilized semi-structured interviews with 10 EFL learners to explore why and how they have experienced the motivation to learn English throughout their lives. The methodology of horizontalization was used to break the interview protocols into individual units of meaning before analyzing these units to extract the overarching themes (Moustakas, 1994). These themes were then interpreted into a detailed description of LLM as experienced by EFL students in this context. Finally, the resulting description was analyzed to discover how these learners’ lived experiences with LLM conformed with and/or diverged from current theories of LLM.
• The purpose of this qualitative, embedded, multiple case study was to examine how both parent-child attachment relationships are impacted by the quality of the paternal and maternal caregiver-child interactions that occur throughout a maternal deployment, within the context of dual-military couples. In order to examine this phenomenon, an embedded, multiple case study was conducted, utilizing an attachment systems metatheory perspective. The study included four dual-military couples who experienced a maternal deployment to Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF) when they had at least one child between 8 weeks-old to 5 years-old.  Each member of the couple participated in an individual, semi-structured interview with the researcher and completed the Parenting Relationship Questionnaire (PRQ). “The PRQ is designed to capture a parent’s perspective on the parent-child relationship” (Pearson, 2012, para. 1) and was used within the proposed study for this purpose. The PRQ was utilized to triangulate the data (Bekhet & Zauszniewski, 2012) as well as to provide some additional information on the parents’ perspective of the quality of the parent-child attachment relationship in regards to communication, discipline, parenting confidence, relationship satisfaction, and time spent together (Pearson, 2012). The researcher utilized the semi-structured interview to collect information regarding the parents' perspectives of the quality of their parental caregiver behaviors during the deployment cycle, the mother's parent-child interactions while deployed, the behavior of the child or children at time of reunification, and the strategies or behaviors the parents believe may have contributed to their child's behavior at the time of reunification. The results of this study may be utilized by the military, and by civilian providers, to develop proactive and preventive measures that both providers and parents can implement, to address any potential adverse effects on the parent-child attachment relationship, identified through the proposed study. The results of this study may also be utilized to further refine and understand the integration of attachment theory and systems theory, in both clinical and research settings, within the field of marriage and family therapy.

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Research Design: Definition, Types, Characteristics & Study Examples

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A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

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Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

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FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

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For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

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Descriptive Research Design – Types, Methods and Examples

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Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 14 May 2024

Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study

• Jocelyn Schroeder 1 ,
• Barbara Pesut 1 , 2 ,
• Lise Olsen 2 ,
• Nelly D. Oelke 2 &
• Helen Sharp 2  

BMC Nursing volume  23 , Article number:  326 ( 2024 ) Cite this article

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Medical Assistance in Dying (MAiD) was legalized in Canada in 2016. Canada’s legislation is the first to permit Nurse Practitioners (NP) to serve as independent MAiD assessors and providers. Registered Nurses’ (RN) also have important roles in MAiD that include MAiD care coordination; client and family teaching and support, MAiD procedural quality; healthcare provider and public education; and bereavement care for family. Nurses have a right under the law to conscientious objection to participating in MAiD. Therefore, it is essential to prepare nurses in their entry-level education for the practice implications and moral complexities inherent in this practice. Knowing what nursing students think about MAiD is a critical first step. Therefore, the purpose of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context.

The design was a mixed-method, modified e-Delphi method that entailed item generation from the literature, item refinement through a 2 round survey of an expert faculty panel, and item validation through a cognitive focus group interview with nursing students. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

During phase 1, a 56-item survey was developed from existing literature that included demographic items and items designed to measure experience with death and dying (including MAiD), education and preparation, attitudes and beliefs, influences on those beliefs, and anticipated future involvement. During phase 2, an expert faculty panel reviewed, modified, and prioritized the items yielding 51 items. During phase 3, a sample of nursing students further evaluated and modified the language in the survey to aid readability and comprehension. The final survey consists of 45 items including 4 case studies.

Systematic evaluation of knowledge-to-date coupled with stakeholder perspectives supports robust survey design. This study yielded a survey to assess nursing students’ attitudes toward MAiD in a Canadian context.

The survey is appropriate for use in education and research to measure knowledge and attitudes about MAiD among nurse trainees and can be a helpful step in preparing nursing students for entry-level practice.

Peer Review reports

Medical Assistance in Dying (MAiD) is permitted under an amendment to Canada’s Criminal Code which was passed in 2016 [ 1 ]. MAiD is defined in the legislation as both self-administered and clinician-administered medication for the purpose of causing death. In the 2016 Bill C-14 legislation one of the eligibility criteria was that an applicant for MAiD must have a reasonably foreseeable natural death although this term was not defined. It was left to the clinical judgement of MAiD assessors and providers to determine the time frame that constitutes reasonably foreseeable [ 2 ]. However, in 2021 under Bill C-7, the eligibility criteria for MAiD were changed to allow individuals with irreversible medical conditions, declining health, and suffering, but whose natural death was not reasonably foreseeable, to receive MAiD [ 3 ]. This population of MAiD applicants are referred to as Track 2 MAiD (those whose natural death is foreseeable are referred to as Track 1). Track 2 applicants are subject to additional safeguards under the 2021 C-7 legislation.

Three additional proposed changes to the legislation have been extensively studied by Canadian Expert Panels (Council of Canadian Academics [CCA]) [ 4 , 5 , 6 ] First, under the legislation that defines Track 2, individuals with mental disease as their sole underlying medical condition may apply for MAiD, but implementation of this practice is embargoed until March 2027 [ 4 ]. Second, there is consideration of allowing MAiD to be implemented through advanced consent. This would make it possible for persons living with dementia to receive MAID after they have lost the capacity to consent to the procedure [ 5 ]. Third, there is consideration of extending MAiD to mature minors. A mature minor is defined as “a person under the age of majority…and who has the capacity to understand and appreciate the nature and consequences of a decision” ([ 6 ] p. 5). In summary, since the legalization of MAiD in 2016 the eligibility criteria and safeguards have evolved significantly with consequent implications for nurses and nursing care. Further, the number of Canadians who access MAiD shows steady increases since 2016 [ 7 ] and it is expected that these increases will continue in the foreseeable future.

Nurses have been integral to MAiD care in the Canadian context. While other countries such as Belgium and the Netherlands also permit euthanasia, Canada is the first country to allow Nurse Practitioners (Registered Nurses with additional preparation typically achieved at the graduate level) to act independently as assessors and providers of MAiD [ 1 ]. Although the role of Registered Nurses (RNs) in MAiD is not defined in federal legislation, it has been addressed at the provincial/territorial-level with variability in scope of practice by region [ 8 , 9 ]. For example, there are differences with respect to the obligation of the nurse to provide information to patients about MAiD, and to the degree that nurses are expected to ensure that patient eligibility criteria and safeguards are met prior to their participation [ 10 ]. Studies conducted in the Canadian context indicate that RNs perform essential roles in MAiD care coordination; client and family teaching and support; MAiD procedural quality; healthcare provider and public education; and bereavement care for family [ 9 , 11 ]. Nurse practitioners and RNs are integral to a robust MAiD care system in Canada and hence need to be well-prepared for their role [ 12 ].

Previous studies have found that end of life care, and MAiD specifically, raise complex moral and ethical issues for nurses [ 13 , 14 , 15 , 16 ]. The knowledge, attitudes, and beliefs of nurses are important across practice settings because nurses have consistent, ongoing, and direct contact with patients who experience chronic or life-limiting health conditions. Canadian studies exploring nurses’ moral and ethical decision-making in relation to MAiD reveal that although some nurses are clear in their support for, or opposition to, MAiD, others are unclear on what they believe to be good and right [ 14 ]. Empirical findings suggest that nurses go through a period of moral sense-making that is often informed by their family, peers, and initial experiences with MAID [ 17 , 18 ]. Canadian legislation and policy specifies that nurses are not required to participate in MAiD and may recuse themselves as conscientious objectors with appropriate steps to ensure ongoing and safe care of patients [ 1 , 19 ]. However, with so many nurses having to reflect on and make sense of their moral position, it is essential that they are given adequate time and preparation to make an informed and thoughtful decision before they participate in a MAID death [ 20 , 21 ].

It is well established that nursing students receive inconsistent exposure to end of life care issues [ 22 ] and little or no training related to MAiD [ 23 ]. Without such education and reflection time in pre-entry nursing preparation, nurses are at significant risk for moral harm. An important first step in providing this preparation is to be able to assess the knowledge, values, and beliefs of nursing students regarding MAID and end of life care. As demand for MAiD increases along with the complexities of MAiD, it is critical to understand the knowledge, attitudes, and likelihood of engagement with MAiD among nursing students as a baseline upon which to build curriculum and as a means to track these variables over time.

Aim, design, and setting

The aim of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context. We sought to explore both their willingness to be involved in the registered nursing role and in the nurse practitioner role should they chose to prepare themselves to that level of education. The design was a mixed-method, modified e-Delphi method that entailed item generation, item refinement through an expert faculty panel [ 24 , 25 , 26 ], and initial item validation through a cognitive focus group interview with nursing students [ 27 ]. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

Participants

A panel of 10 faculty from the two nursing education programs were recruited for Phase 2 of the e-Delphi. To be included, faculty were required to have a minimum of three years of experience in nurse education, be employed as nursing faculty, and self-identify as having experience with MAiD. A convenience sample of 5 fourth-year nursing students were recruited to participate in Phase 3. Students had to be in good standing in the nursing program and be willing to share their experiences of the survey in an online group interview format.

The modified e-Delphi was conducted in 3 phases: Phase 1 entailed item generation through literature and existing survey review. Phase 2 entailed item refinement through a faculty expert panel review with focus on content validity, prioritization, and revision of item wording [ 25 ]. Phase 3 entailed an assessment of face validity through focus group-based cognitive interview with nursing students.

Phase I. Item generation through literature review

The goal of phase 1 was to develop a bank of survey items that would represent the variables of interest and which could be provided to expert faculty in Phase 2. Initial survey items were generated through a literature review of similar surveys designed to assess knowledge and attitudes toward MAiD/euthanasia in healthcare providers; Canadian empirical studies on nurses’ roles and/or experiences with MAiD; and legislative and expert panel documents that outlined proposed changes to the legislative eligibility criteria and safeguards. The literature review was conducted in three online databases: CINAHL, PsycINFO, and Medline. Key words for the search included nurses , nursing students , medical students , NPs, MAiD , euthanasia , assisted death , and end-of-life care . Only articles written in English were reviewed. The legalization and legislation of MAiD is new in many countries; therefore, studies that were greater than twenty years old were excluded, no further exclusion criteria set for country.

Items from surveys designed to measure similar variables in other health care providers and geographic contexts were placed in a table and similar items were collated and revised into a single item. Then key variables were identified from the empirical literature on nurses and MAiD in Canada and checked against the items derived from the surveys to ensure that each of the key variables were represented. For example, conscientious objection has figured prominently in the Canadian literature, but there were few items that assessed knowledge of conscientious objection in other surveys and so items were added [ 15 , 21 , 28 , 29 ]. Finally, four case studies were added to the survey to address the anticipated changes to the Canadian legislation. The case studies were based upon the inclusion of mature minors, advanced consent, and mental disorder as the sole underlying medical condition. The intention was to assess nurses’ beliefs and comfort with these potential legislative changes.

Phase 2. Item refinement through expert panel review

The goal of phase 2 was to refine and prioritize the proposed survey items identified in phase 1 using a modified e-Delphi approach to achieve consensus among an expert panel [ 26 ]. Items from phase 1 were presented to an expert faculty panel using a Qualtrics (Provo, UT) online survey. Panel members were asked to review each item to determine if it should be: included, excluded or adapted for the survey. When adapted was selected faculty experts were asked to provide rationale and suggestions for adaptation through the use of an open text box. Items that reached a level of 75% consensus for either inclusion or adaptation were retained [ 25 , 26 ]. New items were categorized and added, and a revised survey was presented to the panel of experts in round 2. Panel members were again asked to review items, including new items, to determine if it should be: included, excluded, or adapted for the survey. Round 2 of the modified e-Delphi approach also included an item prioritization activity, where participants were then asked to rate the importance of each item, based on a 5-point Likert scale (low to high importance), which De Vaus [ 30 ] states is helpful for increasing the reliability of responses. Items that reached a 75% consensus on inclusion were then considered in relation to the importance it was given by the expert panel. Quantitative data were managed using SPSS (IBM Corp).

Phase 3. Face validity through cognitive interviews with nursing students

The goal of phase 3 was to obtain initial face validity of the proposed survey using a sample of nursing student informants. More specifically, student participants were asked to discuss how items were interpreted, to identify confusing wording or other problematic construction of items, and to provide feedback about the survey as a whole including readability and organization [ 31 , 32 , 33 ]. The focus group was held online and audio recorded. A semi-structured interview guide was developed for this study that focused on clarity, meaning, order and wording of questions; emotions evoked by the questions; and overall survey cohesion and length was used to obtain data (see Supplementary Material 2  for the interview guide). A prompt to “think aloud” was used to limit interviewer-imposed bias and encourage participants to describe their thoughts and response to a given item as they reviewed survey items [ 27 ]. Where needed, verbal probes such as “could you expand on that” were used to encourage participants to expand on their responses [ 27 ]. Student participants’ feedback was collated verbatim and presented to the research team where potential survey modifications were negotiated and finalized among team members. Conventional content analysis [ 34 ] of focus group data was conducted to identify key themes that emerged through discussion with students. Themes were derived from the data by grouping common responses and then using those common responses to modify survey items.

Ten nursing faculty participated in the expert panel. Eight of the 10 faculty self-identified as female. No faculty panel members reported conscientious objector status and ninety percent reported general agreement with MAiD with one respondent who indicated their view as “unsure.” Six of the 10 faculty experts had 16 years of experience or more working as a nurse educator.

Five nursing students participated in the cognitive interview focus group. The duration of the focus group was 2.5 h. All participants identified that they were born in Canada, self-identified as female (one preferred not to say) and reported having received some instruction about MAiD as part of their nursing curriculum. See Tables  1 and 2 for the demographic descriptors of the study sample. Study results will be reported in accordance with the study phases. See Fig.  1 for an overview of the results from each phase.

Fig. 1  Overview of survey development findings

Phase 1: survey item generation

Review of the literature identified that no existing survey was available for use with nursing students in the Canadian context. However, an analysis of themes across qualitative and quantitative studies of physicians, medical students, nurses, and nursing students provided sufficient data to develop a preliminary set of items suitable for adaptation to a population of nursing students.

Four major themes and factors that influence knowledge, attitudes, and beliefs about MAiD were evident from the literature: (i) endogenous or individual factors such as age, gender, personally held values, religion, religiosity, and/or spirituality [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], (ii) experience with death and dying in personal and/or professional life [ 35 , 40 , 41 , 43 , 44 , 45 ], (iii) training including curricular instruction about clinical role, scope of practice, or the law [ 23 , 36 , 39 ], and (iv) exogenous or social factors such as the influence of key leaders, colleagues, friends and/or family, professional and licensure organizations, support within professional settings, and/or engagement in MAiD in an interdisciplinary team context [ 9 , 35 , 46 ].

Studies of nursing students also suggest overlap across these categories. For example, value for patient autonomy [ 23 ] and the moral complexity of decision-making [ 37 ] are important factors that contribute to attitudes about MAiD and may stem from a blend of personally held values coupled with curricular content, professional training and norms, and clinical exposure. For example, students report that participation in end of life care allows for personal growth, shifts in perception, and opportunities to build therapeutic relationships with their clients [ 44 , 47 , 48 ].

Preliminary items generated from the literature resulted in 56 questions from 11 published sources (See Table  3 ). These items were constructed across four main categories: (i) socio-demographic questions; (ii) end of life care questions; (iii) knowledge about MAiD; or (iv) comfort and willingness to participate in MAiD. Knowledge questions were refined to reflect current MAiD legislation, policies, and regulatory frameworks. Falconer [ 39 ] and Freeman [ 45 ] studies were foundational sources for item selection. Additionally, four case studies were written to reflect the most recent anticipated changes to MAiD legislation and all used the same open-ended core questions to address respondents’ perspectives about the patient’s right to make the decision, comfort in assisting a physician or NP to administer MAiD in that scenario, and hypothesized comfort about serving as a primary provider if qualified as an NP in future. Response options for the survey were also constructed during this stage and included: open text, categorical, yes/no , and Likert scales.

Phase 2: faculty expert panel review

Of the 56 items presented to the faculty panel, 54 questions reached 75% consensus. However, based upon the qualitative responses 9 items were removed largely because they were felt to be repetitive. Items that generated the most controversy were related to measuring religion and spirituality in the Canadian context, defining end of life care when there is no agreed upon time frames (e.g., last days, months, or years), and predicting willingness to be involved in a future events – thus predicting their future selves. Phase 2, round 1 resulted in an initial set of 47 items which were then presented back to the faculty panel in round 2.

Of the 47 initial questions presented to the panel in round 2, 45 reached a level of consensus of 75% or greater, and 34 of these questions reached a level of 100% consensus [ 27 ] of which all participants chose to include without any adaptations) For each question, level of importance was determined based on a 5-point Likert scale (1 = very unimportant, 2 = somewhat unimportant, 3 = neutral, 4 = somewhat important, and 5 = very important). Figure  2 provides an overview of the level of importance assigned to each item.

Ranking level of importance for survey items

After round 2, a careful analysis of participant comments and level of importance was completed by the research team. While the main method of survey item development came from participants’ response to the first round of Delphi consensus ratings, level of importance was used to assist in the decision of whether to keep or modify questions that created controversy, or that rated lower in the include/exclude/adapt portion of the Delphi. Survey items that rated low in level of importance included questions about future roles, sex and gender, and religion/spirituality. After deliberation by the research committee, these questions were retained in the survey based upon the importance of these variables in the scientific literature.

Of the 47 questions remaining from Phase 2, round 2, four were revised. In addition, the two questions that did not meet the 75% cut off level for consensus were reviewed by the research team. The first question reviewed was What is your comfort level with providing a MAiD death in the future if you were a qualified NP ? Based on a review of participant comments, it was decided to retain this question for the cognitive interviews with students in the final phase of testing. The second question asked about impacts on respondents’ views of MAiD and was changed from one item with 4 subcategories into 4 separate items, resulting in a final total of 51 items for phase 3. The revised survey was then brought forward to the cognitive interviews with student participants in Phase 3. (see Supplementary Material 1 for a complete description of item modification during round 2).

Phase 3. Outcomes of cognitive interview focus group

Of the 51 items reviewed by student participants, 29 were identified as clear with little or no discussion. Participant comments for the remaining 22 questions were noted and verified against the audio recording. Following content analysis of the comments, four key themes emerged through the student discussion: unclear or ambiguous wording; difficult to answer questions; need for additional response options; and emotional response evoked by questions. An example of unclear or ambiguous wording was a request for clarity in the use of the word “sufficient” in the context of assessing an item that read “My nursing education has provided sufficient content about the nursing role in MAiD.” “Sufficient” was viewed as subjective and “laden with…complexity that distracted me from the question.” The group recommended rewording the item to read “My nursing education has provided enough content for me to care for a patient considering or requesting MAiD.”

An example of having difficulty answering questions related to limited knowledge related to terms used in the legislation such as such as safeguards , mature minor , eligibility criteria , and conscientious objection. Students were unclear about what these words meant relative to the legislation and indicated that this lack of clarity would hamper appropriate responses to the survey. To ensure that respondents are able to answer relevant questions, student participants recommended that the final survey include explanation of key terms such as mature minor and conscientious objection and an overview of current legislation.

Response options were also a point of discussion. Participants noted a lack of distinction between response options of unsure and unable to say . Additionally, scaling of attitudes was noted as important since perspectives about MAiD are dynamic and not dichotomous “agree or disagree” responses. Although the faculty expert panel recommended the integration of the demographic variables of religious and/or spiritual remain as a single item, the student group stated a preference to have religion and spirituality appear as separate items. The student focus group also took issue with separate items for the variables of sex and gender, specifically that non-binary respondents might feel othered or “outed” particularly when asked to identify their sex. These variables had been created based upon best practices in health research but students did not feel they were appropriate in this context [ 49 ]. Finally, students agreed with the faculty expert panel in terms of the complexity of projecting their future involvement as a Nurse Practitioner. One participant stated: “I certainly had to like, whoa, whoa, whoa. Now let me finish this degree first, please.” Another stated, “I'm still imagining myself, my future career as an RN.”

Finally, student participants acknowledged the array of emotions that some of the items produced for them. For example, one student described positive feelings when interacting with the survey. “Brought me a little bit of feeling of joy. Like it reminded me that this is the last piece of independence that people grab on to.” Another participant, described the freedom that the idea of an advance request gave her. “The advance request gives the most comfort for me, just with early onset Alzheimer’s and knowing what it can do.” But other participants described less positive feelings. For example, the mature minor case study yielded a comment: “This whole scenario just made my heart hurt with the idea of a child requesting that.”

Based on the data gathered from the cognitive interview focus group of nursing students, revisions were made to 11 closed-ended questions (see Table  4 ) and 3 items were excluded. In the four case studies, the open-ended question related to a respondents’ hypothesized actions in a future role as NP were removed. The final survey consists of 45 items including 4 case studies (see Supplementary Material 3 ).

The aim of this study was to develop and validate a survey that can be used to track the growth of knowledge about MAiD among nursing students over time, inform training programs about curricular needs, and evaluate attitudes and willingness to participate in MAiD at time-points during training or across nursing programs over time.

The faculty expert panel and student participants in the cognitive interview focus group identified a need to establish core knowledge of the terminology and legislative rules related to MAiD. For example, within the cognitive interview group of student participants, several acknowledged lack of clear understanding of specific terms such as “conscientious objector” and “safeguards.” Participants acknowledged discomfort with the uncertainty of not knowing and their inclination to look up these terms to assist with answering the questions. This survey can be administered to nursing or pre-nursing students at any phase of their training within a program or across training programs. However, in doing so it is important to acknowledge that their baseline knowledge of MAiD will vary. A response option of “not sure” is important and provides a means for respondents to convey uncertainty. If this survey is used to inform curricular needs, respondents should be given explicit instructions not to conduct online searches to inform their responses, but rather to provide an honest appraisal of their current knowledge and these instructions are included in the survey (see Supplementary Material 3 ).

Some provincial regulatory bodies have established core competencies for entry-level nurses that include MAiD. For example, the BC College of Nurses and Midwives (BCCNM) requires “knowledge about ethical, legal, and regulatory implications of medical assistance in dying (MAiD) when providing nursing care.” (10 p. 6) However, across Canada curricular content and coverage related to end of life care and MAiD is variable [ 23 ]. Given the dynamic nature of the legislation that includes portions of the law that are embargoed until 2024, it is important to ensure that respondents are guided by current and accurate information. As the law changes, nursing curricula, and public attitudes continue to evolve, inclusion of core knowledge and content is essential and relevant for investigators to be able to interpret the portions of the survey focused on attitudes and beliefs about MAiD. Content knowledge portions of the survey may need to be modified over time as legislation and training change and to meet the specific purposes of the investigator.

Given the sensitive nature of the topic, it is strongly recommended that surveys be conducted anonymously and that students be provided with an opportunity to discuss their responses to the survey. A majority of feedback from both the expert panel of faculty and from student participants related to the wording and inclusion of demographic variables, in particular religion, religiosity, gender identity, and sex assigned at birth. These and other demographic variables have the potential to be highly identifying in small samples. In any instance in which the survey could be expected to yield demographic group sizes less than 5, users should eliminate the demographic variables from the survey. For example, the profession of nursing is highly dominated by females with over 90% of nurses who identify as female [ 50 ]. Thus, a survey within a single class of students or even across classes in a single institution is likely to yield a small number of male respondents and/or respondents who report a difference between sex assigned at birth and gender identity. When variables that serve to identify respondents are included, respondents are less likely to complete or submit the survey, to obscure their responses so as not to be identifiable, or to be influenced by social desirability bias in their responses rather than to convey their attitudes accurately [ 51 ]. Further, small samples do not allow for conclusive analyses or interpretation of apparent group differences. Although these variables are often included in surveys, such demographics should be included only when anonymity can be sustained. In small and/or known samples, highly identifying variables should be omitted.

There are several limitations associated with the development of this survey. The expert panel was comprised of faculty who teach nursing students and are knowledgeable about MAiD and curricular content, however none identified as a conscientious objector to MAiD. Ideally, our expert panel would have included one or more conscientious objectors to MAiD to provide a broader perspective. Review by practitioners who participate in MAiD, those who are neutral or undecided, and practitioners who are conscientious objectors would ensure broad applicability of the survey. This study included one student cognitive interview focus group with 5 self-selected participants. All student participants had held discussions about end of life care with at least one patient, 4 of 5 participants had worked with a patient who requested MAiD, and one had been present for a MAiD death. It is not clear that these participants are representative of nursing students demographically or by experience with end of life care. It is possible that the students who elected to participate hold perspectives and reflections on patient care and MAiD that differ from students with little or no exposure to end of life care and/or MAiD. However, previous studies find that most nursing students have been involved with end of life care including meaningful discussions about patients’ preferences and care needs during their education [ 40 , 44 , 47 , 48 , 52 ]. Data collection with additional student focus groups with students early in their training and drawn from other training contexts would contribute to further validation of survey items.

Future studies should incorporate pilot testing with small sample of nursing students followed by a larger cross-program sample to allow evaluation of the psychometric properties of specific items and further refinement of the survey tool. Consistent with literature about the importance of leadership in the context of MAiD [ 12 , 53 , 54 ], a study of faculty knowledge, beliefs, and attitudes toward MAiD would provide context for understanding student perspectives within and across programs. Additional research is also needed to understand the timing and content coverage of MAiD across Canadian nurse training programs’ curricula.

The implementation of MAiD is complex and requires understanding of the perspectives of multiple stakeholders. Within the field of nursing this includes clinical providers, educators, and students who will deliver clinical care. A survey to assess nursing students’ attitudes toward and willingness to participate in MAiD in the Canadian context is timely, due to the legislation enacted in 2016 and subsequent modifications to the law in 2021 with portions of the law to be enacted in 2027. Further development of this survey could be undertaken to allow for use in settings with practicing nurses or to allow longitudinal follow up with students as they enter practice. As the Canadian landscape changes, ongoing assessment of the perspectives and needs of health professionals and students in the health professions is needed to inform policy makers, leaders in practice, curricular needs, and to monitor changes in attitudes and practice patterns over time.

Availability of data and materials

The datasets used and/or analysed during the current study are not publicly available due to small sample sizes, but are available from the corresponding author on reasonable request.

Abbreviations

British Columbia College of Nurses and Midwives

Medical assistance in dying

Nurse practitioner

Registered nurse

University of British Columbia Okanagan

Nicol J, Tiedemann M. Legislative Summary: Bill C-14: An Act to amend the Criminal Code and to make related amendments to other Acts (medical assistance in dying). Available from: https://lop.parl.ca/staticfiles/PublicWebsite/Home/ResearchPublications/LegislativeSummaries/PDF/42-1/c14-e.pdf .

Downie J, Scallion K. Foreseeably unclear. The meaning of the “reasonably foreseeable” criterion for access to medical assistance in dying in Canada. Dalhousie Law J. 2018;41(1):23–57.

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We would like to acknowledge the faculty and students who generously contributed their time to this work.

JS received a student traineeship through the Principal Research Chairs program at the University of British Columbia Okanagan.

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JS made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. JS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. BP made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. BP has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. LO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. LO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. NDO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. NDO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. HS made substantial contributions to drafting and substantively revising the work. HS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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Schroeder, J., Pesut, B., Olsen, L. et al. Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study. BMC Nurs 23 , 326 (2024). https://doi.org/10.1186/s12912-024-01984-z

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• Apple cider vinegar for weight management in Lebanese adolescents and young adults with overweight and obesity: a randomised, double-blind, placebo-controlled study
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• http://orcid.org/0000-0002-0214-242X Rony Abou-Khalil 1 ,
• Jeanne Andary 2 and
• Elissar El-Hayek 1
• 1 Department of Biology , Holy Spirit University of Kaslik , Jounieh , Lebanon
• 2 Nutrition and Food Science Department , American University of Science and Technology , Beirut , Lebanon
• Correspondence to Dr Rony Abou-Khalil, Department of Biology, Holy Spirit University of Kaslik, Jounieh, Lebanon; ronyaboukhalil{at}usek.edu.lb

Background and aims Obesity and overweight have become significant health concerns worldwide, leading to an increased interest in finding natural remedies for weight reduction. One such remedy that has gained popularity is apple cider vinegar (ACV).

Objective To investigate the effects of ACV consumption on weight, blood glucose, triglyceride and cholesterol levels in a sample of the Lebanese population.

Materials and methods 120 overweight and obese individuals were recruited. Participants were randomly assigned to either an intervention group receiving 5, 10 or 15 mL of ACV or a control group receiving a placebo (group 4) over a 12-week period. Measurements of anthropometric parameters, fasting blood glucose, triglyceride and cholesterol levels were taken at weeks 0, 4, 8 and 12.

Results Our findings showed that daily consumption of the three doses of ACV for a duration of between 4 and 12 weeks is associated with significant reductions in anthropometric variables (weight, body mass index, waist/hip circumferences and body fat ratio), blood glucose, triglyceride and cholesterol levels. No significant risk factors were observed during the 12 weeks of ACV intake.

Conclusion Consumption of ACV in people with overweight and obesity led to an improvement in the anthropometric and metabolic parameters. ACV could be a promising antiobesity supplement that does not produce any side effects.

• Weight management
• Lipid lowering

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjnph-2023-000823

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Recently, there has been increasing interest in alternative remedies to support weight management, and one such remedy that has gained popularity is apple cider vinegar (ACV).

A few small-scale studies conducted on humans have shown promising results, with ACV consumption leading to weight loss, reduced body fat and decreased waist circumference.

WHAT THIS STUDY ADDS

No study has been conducted to investigate the potential antiobesity effect of ACV in the Lebanese population. By conducting research in this demographic, the study provides region-specific data and offers a more comprehensive understanding of the impact of ACV on weight loss and metabolic health.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

The results might contribute to evidence-based recommendations for the use of ACV as a dietary intervention in the management of obesity.

The study could stimulate further research in the field, prompting scientists to explore the underlying mechanisms and conduct similar studies in other populations.

Introduction

Obesity is a growing global health concern characterised by excessive body fat accumulation, often resulting from a combination of genetic, environmental and lifestyle factors. 1 It is associated with an increased risk of numerous chronic illnesses such as type 2 diabetes, cardiovascular diseases, several common cancers and osteoarthritis. 1–3

According to the WHO, more than 1.9 billion adults were overweight worldwide in 2016, of whom more than 650 million were obese. 4 Worldwide obesity has nearly tripled since 1975. 4 The World Obesity Federation’s 2023 Atlas predicts that by 2035 more than half of the world’s population will be overweight or obese. 5

According to the 2022 Global Nutrition Report, Lebanon has made limited progress towards meeting its diet-related non-communicable diseases target. A total of 39.9% of adult (aged ≥18 years) women and 30.5% of adult men are living with obesity. Lebanon’s obesity prevalence is higher than the regional average of 10.3% for women and 7.5% for men. 6 In Lebanon, obesity was considered as the most important health problem by 27.6% and ranked fifth after cancer, cardiovascular, smoking and HIV/AIDS. 7

In recent years, there has been increasing interest in alternative remedies to support weight management, and one such remedy that has gained popularity is apple cider vinegar (ACV), which is a type of vinegar made by fermenting apple juice. ACV contains vitamins, minerals, amino acids and polyphenols such as flavonoids, which are believed to contribute to its potential health benefits. 8 9

It has been used for centuries as a traditional remedy for various ailments and has recently gained attention for its potential role in weight management.

In hypercaloric-fed rats, the daily consumption of ACV showed a lower rise in blood sugar and lipid profile. 10 In addition, ACV seems to decrease oxidative stress and reduces the risk of obesity in male rats with high-fat consumption. 11

A few small-scale studies conducted on humans have shown promising results, with ACV consumption leading to weight loss, reduced body fat and decreased waist circumference. 12 13 In fact, It has been suggested that ACV by slowing down gastric emptying, might promote satiety and reduce appetite. 14–16 Furthermore, ACV intake seems to ameliorate the glycaemic and lipid profile in healthy adults 17 and might have a positive impact on insulin sensitivity, potentially reducing the risk of type 2 diabetes. 8 10 18

Unfortunately, the sample sizes and durations of these studies were limited, necessitating larger and longer-term studies for more robust conclusions.

This work aims to study the efficacy and safety of ACV in reducing weight and ameliorating the lipid and glycaemic profiles in a sample of overweight and obese adolescents and young adults of the Lebanese population. To the best of our knowledge, no study has been conducted to investigate the potential antiobesity effect of ACV in the Lebanese population.

Materials and methods

Participants.

A total of 120 overweight and obese adolescents and young adults (46 men and 74 women) were enrolled in the study and assigned to either placebo group or experimental groups (receiving increasing doses of ACV).

The subjects were evaluated for eligibility according to the following inclusion criteria: age between 12 and 25 years, BMIs between 27 and 34 kg/m 2 , no chronic diseases, no intake of medications, no intake of ACV over the past 8 weeks prior to the beginning of the study. The subjects who met the inclusion criteria were selected by convenient sampling technique. Those who experienced heartburn due to vinegar were excluded.

Demographic, clinical data and eating habits were collected from all participants by self-administered questionnaire.

Study design

This study was a double-blind, randomised clinical trial conducted for 12 weeks.

Subjects were divided randomly into four groups: three treatment groups and a placebo group. A simple randomisation method was employed using the randomisation allocation software. Groups 1, 2 and 3 consumed 5, 10 and 15 mL, respectively, of ACV (containing 5% of acetic acid) diluted in 250 mL of water daily, in the morning on an empty stomach, for 12 weeks. The control group received a placebo consisting of water with similar taste and appearance. In order to mimic the taste of vinegar, the placebo group’s beverage (250 mL of water) contained lactic acid (250 mg/100 mL). Identical-looking ACV and placebo bottles were used and participants were instructed to consume their assigned solution without knowing its identity. The subject’s group assignment was withheld from the researchers performing the experiment.

Subjects consumed their normal diets throughout the study. The contents of daily meals and snacks were recorded in a diet diary. The physical activity of the subjects was also recorded. Daily individual phone messages were sent to all participants to remind them to take the ACV or the placebo. A mailing group was also created. Confidentiality was maintained throughout the procedure.

At weeks 0, 4, 8 and 12, anthropometric measurements were taken for all participants, and the level of glucose, triglycerides and total cholesterol was assessed by collecting 5 mL of fasting blood from each subject.

Anthropometric measurements

Body weight was measured in kg, to the nearest 0.01 kg, by standardised and calibrated digital scale. Height was measured in cm, to the nearest 0.1 cm, by a stadiometer. Anthropometric measurements were taken for all participants, by a team of trained field researchers, after 10–12 hours fast and while wearing only undergarments.

Body mass indices (BMIs) were calculated using the following equation:

The waist circumference measurement was taken between the lowest rib margin and the iliac crest while the subject was in a standing position (to the nearest 0.1 cm). Hip circumference was measured at the widest point of the hip (to the nearest 0.1 cm).

The body fat ratio (BFR) was measured by the bioelectrical impedance analysis method (OMRON Fat Loss Monitor, Model No HBF-306C; Japan). Anthropometric variables are shown in table 1 .

• View inline

Baseline demographic, anthropometric and biochemical variables of the three apple cider vinegar groups (group 1, 2 and 3) and the placebo group (group 4)

Blood biochemical analysis

Serum glucose was measured by the glucose oxidase method. 19 Triglyceride levels were determined using a serum triglyceride determination kit (TR0100, Sigma-Aldrich). Cholesterol levels were determined using a cholesterol quantitation kit (MAK043, Sigma-Aldrich). Biochemical variables are shown in table 1 .

Statistical methods and data analysis

Data are presented as mean±SD. Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) software (version 23.0). Significant differences between groups were determined by using an independent t-test. Statistical significance was set at p<0.05.

Ethical approval

The study protocol was reviewed and approved by the research ethics committee (REC) of the Higher Centre for Research (HCR) at The Holy Spirit University of Kaslik (USEK), Lebanon. The number/ID of the approval is HCR/EC 2023–005. The participants were informed of the study objectives and signed a written informed consent before enrolment. The study was conducted in accordance to the International Conference and Harmonisation E6 Guideline for Good Clinical Practice and the Ethical principles of the Declaration of Helsinki.

Sociodemographic, nutritional and other baseline characteristics of the participants

A total of 120 individuals (46 men and 74 women) with BMIs between 27 and 34 kg/m 2 , were enrolled in the study. The mean age of the subjects was 17.8±5.7 years and 17.6±5.4 years in the placebo and experimental groups respectively.

The majority of participants, approximately 98.3%, were non-vegetarian and 89% of them reported having a high eating frequency, with more than four meals per day. Eighty-seven per cent had no family history of obesity and 98% had no history of childhood obesity. The majority reported not having a regular exercise routine and experiencing negative emotions or anxiety. All participants were non-smokers and non-drinkers. A small percentage (6.7%) were following a therapeutic diet.

Effects of ACV intake on anthropometric variables

The addition of 5 mL, 10 mL or 15 mL of ACV to the diet resulted in significant decreases in body weight and BMI at weeks 4, 8 and 12 of ACV intake, when compared with baseline (week 0) (p<0.05). The decrease in body weight and BMI seemed to be dose-dependent, with the group receiving 15 mL of ACV showing the most important reduction ( table 2 ).

Anthropometric variables of the participants at weeks 0, 4, 8 and 12

The impact of ACV on body weight and BMI seems to be time-dependent as well. Reductions were more pronounced as the study progressed, with the most significant changes occurring at week 12.

The circumferences of the waist and hip, along with the Body Fat Ratio (BFR), decreased significantly in the three treatment groups at weeks 8 and 12 compared with week 0 (p<0.05). No significant effect was observed at week 4, compared with baseline (p>0.05). The effect of ACV on these parameters seems to be time-dependent with the most prominent effect observed at week 12 compared with week 4 and 8. However it does not seem to be dose dependent, as the three doses of ACV showed a similar level of efficacy in reducing the circumferences of the waist/hip circumferences and the BFR at week 8 and 12, compared with baseline ( table 2 ).

The placebo group did not experience any significant changes in the anthropometric variables throughout the study (p>0.05). This highlights that the observed improvements in body weight, BMI, waist and hip circumferences and Body Fat Ratio were likely attributed to the consumption of ACV.

Effects of ACV on blood biochemical parameters

The consumption of ACV also led to a time and dose dependent decrease in serum glucose, serum triglyceride and serum cholesterol levels. ( table 3 ).

Biochemical variables of the participants at weeks 0, 4, 8 and 12

Serum glucose levels decreased significantly by three doses of ACV at week 4, 8 and 12 compared with week 0 (p<0.05) ( table 3 ). Triglycerides and total cholesterol levels decreased significantly at weeks 8 and 12, compared with week 0 (p<0.05). A dose of 15 mL of ACV for a duration of 12 weeks seems to be the most effective dose in reducing these three blood biochemical parameters.

There were no changes in glucose, triglyceride and cholesterol levels in the placebo groups at weeks 4, 8 and 12 compared with week 0 ( table 3 ).

These data suggest that continued intake of 15 mL of ACV for more than 8 weeks is effective in reducing blood fasting sugar, triglyceride and total cholesterol levels in overweight/obese people.

Adverse reactions of ACV

No apparent adverse or harmful effects were reported by the participants during the 12 weeks of ACV intake.

During the past two decades of the last century, childhood and adolescent obesity have dramatically increased healthcare costs. 20 21 Diet and exercise are the basic elements of weight loss. Many complementary therapies have been promoted to treat obesity, but few are truly beneficial.

The present study is the first to investigate the antiobesity effectiveness of ACV, the fermented juice from crushed apples, in the Lebanese population.

A total of 120 overweight and obese adolescents and young adults (46 men and 74 women) with BMIs between 27 and 34 kg/m 2 , were enrolled. Participants were randomised to receive either a daily dose of ACV (5, 10 or 15 mL) or a placebo for a duration of 12 weeks.

Some previous studies have suggested that taking ACV before or with meals might help to reduce postprandial blood sugar levels, 22 23 but in our study, participants took ACV in the morning on an empty stomach. The choice of ACV intake timing was motivated by the aim to study the impact of apple cider vinegar without the confounding variables introduced by simultaneous food intake. In addition, taking ACV before meals could better reduce appetite and increase satiety.

Our findings reveal that the consumption of ACV in people with overweight and obesity led to an improvement in the anthropometric and metabolic parameters.

It is important to note that the diet diary and physical activity did not differ among the three treatment groups and the placebo throughout the whole study, suggesting that the decrease in anthropometric and biochemical parameters was caused by ACV intake.

Studies conducted on animal models often attribute these effects to various mechanisms, including increased energy expenditure, improved insulin sensitivity, appetite and satiety regulation.

While vinegar is composed of various ingredients, its primary component is acetic acid (AcOH). It has been shown that after 15 min of oral ingestion of 100 mL vinegar containing 0.75 g acetic acid, the serum acetate levels increases from 120 µmol/L at baseline to 350 µmol/L 24 ; this fast increase in circulatory acetate is due to its fast absorption in the upper digestive tract. 24 25

Biological action of acetate may be mediated by binding to the G-protein coupled receptors (GPRs), including GPR43 and GPR41. 25 These receptors are expressed in various insulin-sensitive tissues, such as adipose tissue, 26 skeletal muscle, liver, 27 and pancreatic beta cells, 28 but also in the small intestine and colon. 29 30

Yamashita and colleagues have revealed that oral administration of AcOH to type 2 diabetic Otsuka Long-Evans Tokushima Fatty rats, improves glucose tolerance and reduces lipid accumulation in the adipose tissue and liver. This improvement in obesity-linked type 2 diabetes is due to the capacity of AcOH to inhibit the activity of carbohydrate-responsive, element-binding protein, a transcription factor involved in regulating the expression of lipogenic genes such as fatty acid synthase and acetyl-CoA carboxylase. 26 31 Sakakibara and colleagues, have reported that AcOH, besides inhibiting lipogenesis, reduces the expression of genes involved in gluconeogenesis, such as glucose-6-phosphatase. 32 The effect of AcOH on lipogenesis and gluconeogenesis is in part mediated by the activation of 5'-AMP-activated protein kinase in the liver. 32 This enzyme seems to be an important pharmacological target for the treatment of metabolic disorders such as obesity, type 2 diabetes and hyperlipidaemia. 32 33

5'-AMP-activated protein kinase is also known to stimulate fatty acid oxidation, thereby increasing energy expenditure. 32 33 These data suggest that the effect of ACV on weight and fat loss may be partly due to the ability of AcOH to inhibit lipogenesis and gluconeogenesis and activate fat oxidation.

Animal studies suggest that besides reducing energy expenditure, acetate may also reduce energy intake, by regulating appetite and satiety. In mice, an intraperitoneal injection of acetate significantly reduced food intake by activating vagal afferent neurons. 32–34 It is important to note that animal studies done on the effect of acetate on vagal activation are contradictory. This might be due to the site of administration of acetate and the use of different animal models.

In addition, in vitro and in vivo animal model studies suggest that acetate increases the secretion of gut-derived satiety hormones by enter endocrine cells (located in the gut) such as GLP-1 and PYY hormones. 25 32–35

Human studies related to the effect of vinegar on body weight are limited.

In accordance with our study, a randomised clinical trial conducted by Khezri and his colleagues has shown that daily consumption of 30 mL of ACV for 12 weeks significantly reduced body weight, BMI, hip circumference, Visceral Adiposity Index and appetite score in obese subjects subjected to a restricted calorie diet, compared with the control group (restricted calorie diet without ACV). Furthermore, Khezri and his colleagues showed that plasma triglyceride and total cholesterol levels significantly decreased, and high density lipoprotein cholesterol concentration significantly increased, in the ACV group in comparison with the control group. 13 32–34

Similarly, Kondo and his colleagues showed that daily consumption of 15 or 30 mL of ACV for 12 weeks reduced body weight, BMI and serum triglyceride in a sample of the Japanese population. 12 13 32–34

In contrast, Park et al reported that daily consumption of 200 mL of pomegranate vinegar for 8 weeks significantly reduced total fat mass in overweight or obese subjects compared with the control group without significantly affecting body weight and BMI. 36 This contradictory result could be explained by the difference in the percentage of acetate and other potentially bioactive compounds (such as flavonoids and other phenolic compounds) in different vinegar types.

In Lebanon, the percentage of the population with a BMI of 30 kg/m 2 or more is approximately 32%. The results of the present study showed that in obese Lebanese subjects who had BMIs ranging from 27 and 34 kg/m 2 , daily oral intake of ACV for 12 weeks reduced the body weight by 6–8 kg and BMIs by 2.7–3.0 points.

It would be interesting to investigate in future studies the effect of neutralised acetic acid on anthropometric and metabolic parameters, knowing that acidic substances, including acetic acid, could contribute to enamel erosion over time. In addition to promoting oral health, neutralising the acidity of ACV could improve its taste, making it more palatable. Furthermore, studying the effects of ACV on weight loss in young Lebanese individuals provides valuable insights, but further research is needed for a comprehensive understanding of how the effect of ACV might vary across different age groups, particularly in older populations and menopausal women.

The findings of this study indicate that ACV consumption for 12 weeks led to significant reduction in anthropometric variables and improvements in blood glucose, triglyceride and cholesterol levels in overweight/obese adolescents/adults. These results suggest that ACV might have potential benefits in improving metabolic parameters related to obesity and metabolic disorders in obese individuals. The results may contribute to evidence-based recommendations for the use of ACV as a dietary intervention in the management of obesity. The study duration of 12 weeks limits the ability to observe long-term effects. Additionally, a larger sample size would enhance the generalisability of the results.

Ethics statements

Patient consent for publication.

Consent obtained from parent(s)/guardian(s)

Ethics approval

This study involves human participants and was approved by the research ethics committee of the Higher Center for Research (HCR) at The Holy Spirit University of Kaslik (USEK), Lebanon. The number/ID of the approval is HCR/EC 2023-005. Participants gave informed consent to participate in the study before taking part.

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Supplementary materials

• Press release

Contributors RA-K: conceptualisation, methodology, data curation, supervision, guarantor, project administration, visualisation, writing–original draft. EE-H: conceptualisation, methodology, data curation, visualisation, writing–review and editing. JA: investigation, validation, writing–review and editing.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests No, there are no competing interests.

Provenance and peer review Not commissioned; externally peer reviewed.

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