type and method of research

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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

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

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

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

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

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

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

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

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

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

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

Library GIS Services

Other Data Services at Berkeley

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

Related Resourcex

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

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

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

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

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

Second, decide how you will analyse the data .

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

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

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

Qualitative vs quantitative data

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

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

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

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

Primary vs secondary data

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

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

Descriptive vs experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

During an experiment.
Using probability sampling methods .

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

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

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

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

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

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

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

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

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

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

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

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

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

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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15 Types of Research Methods

types of research methods, explained below

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Example of Ethnography

Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Example of Phenomenological Research

A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Example of Content Analysis

How is Islam Portrayed in Western Media? by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory during and after data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Grounded Theory Example

Developing a Leadership Identity by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA) and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

What exactly research methodology means
What qualitative , quantitative and mixed methods are
What sampling strategy is
What data collection methods are
What data analysis methods are
How to choose your research methodology
Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

What type of data to collect (e.g., qualitative or quantitative data )
Who to collect it from (i.e., the sampling strategy )
How to collect it (i.e., the data collection method )
How to analyse it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just what methodological choices were made, but also explains why they were made. In other words, the methodology chapter should justify the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions.

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president.

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study. There are many different sampling methods you can choose from, but the two overarching categories are probability sampling and non-probability sampling .

Probability sampling involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable to the entire population.

Non-probability sampling , on the other hand, doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

Interviews (which can be unstructured, semi-structured or structured)
Focus groups and group interviews
Surveys (online or physical surveys)
Observations (watching and recording activities)
Biophysical measurements (e.g., blood pressure, heart rate, etc.)
Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

Qualitative content analysis
Thematic analysis
Discourse analysis
Narrative analysis
Interpretative phenomenological analysis (IPA)
Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some common qualitative analysis methods, along with practical examples.

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

Descriptive statistics (e.g. means, medians, modes )
Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis).

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components.

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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199 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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Thanks for the feedback, Matobela. Good luck with your research methodology.

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Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

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Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

Thanks a lot I am relieved of a heavy burden.keep up with the good work

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I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

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Short but sweet.Thank you

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I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

great article for someone who does not have any background can even understand

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

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Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

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how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

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As a researcher, I commend you for the detailed and simplified information on the topic in question. I would like to remain in touch for the sharing of research ideas on other topics. Thank you

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Thank you very much for the presentation. I am an MPH student with the Adventist University of Africa. I have successfully completed my theory and starting on my research this July. My topic is “Factors associated with Dental Caries in (one District) in Botswana. I need help on how to go about this quantitative research

I am so grateful to run across something that was sooo helpful. I have been on my doctorate journey for quite some time. Your breakdown on methodology helped me to refresh my intent. Thank you.

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I am nkasa lizwi doing my research proposal on honors with the university of Walter Sisulu Komani I m on part 3 now can you assist me.my topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

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What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base.

This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

Implement robust data management practices to maintain the integrity and security of research data.
Transparently document data analysis procedures, including software and statistical methods used.
Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

Be aware of potential biases in the research process and take steps to minimize them.
Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

Seek peer review from experts in your field before publishing or presenting your research findings.
Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

Strive to make your research findings replicable, allowing other researchers to validate your results independently.
Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

Identify potential and new customers
Understand existing customers
Set pragmatic goals
Develop productive market strategies
Address business challenges
Put together a business expansion plan
Identify new business opportunities
Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
The analysis is based on logical reasoning and involves both inductive and deductive methods.
Real-time data and knowledge is derived from actual observations in natural settings.
There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
It creates a path for generating new questions. Existing data helps create more research opportunities.
It is analytical and uses all the available data so that there is no ambiguity in inference.
Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

One-to-one Interview
Focus Groups
Ethnographic studies
Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

Survey research
Descriptive research
Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

Valid – founded, logical, rigorous, and impartial.
Accurate – free of errors and including required details.
Reliable – other people who investigate in the same way can produce similar results.
Timely – current and collected within an appropriate time frame.
Complete – includes all the data you need to support your business decisions.

Gather insights

Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
Keep track of the frequency with which each of the main findings appears.
Make a list of your findings from the most common to the least common.
Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
Prepare conclusions and recommendations about your study.
Act on your strategies
Look for gaps in the information, and consider doing additional inquiry if necessary
Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Types of Research – Methods Explained with Examples

In the ever-evolving world of academia and professional inquiry, understanding the various types of research is crucial for anyone looking to delve into a new study or project. Research, a systematic investigation aimed at discovering and interpreting facts, plays a pivotal role in expanding our knowledge across various fields.

From qualitative research , known for its in-depth analysis of non-numerical data, to quantitative research , which focuses on numerical data and statistical approaches, the spectrum of research types is broad and diverse. We also explore descriptive research , which aims to accurately and systematically describe a population, situation, or phenomenon, and analytical research, which goes a step further to understand the ‘why’ and ‘how’ of a subject.

What is Research?

Research is the process of studying a subject in detail to discover new information or understand it better. This can be anything from studying plants or animals, to learning how people think and behave, to finding new ways to cure diseases. People do research by asking questions, collecting information, and then looking at that information to find answers or learn new things.

Types of Researches Glance

This table provides a quick reference to understand the key aspects of each research type.

Types of Researches Methodology

1. qualitative.

Qualitative research is a methodological approach primarily used in fields like social sciences, anthropology, and psychology. It’s aimed at understanding human behavior and the motivations behind it. Unlike quantitative research that focuses on numbers and statistics, qualitative research delves into the nature of phenomena through detailed, in-depth exploration. Here’s a more detailed explanation:

Definition and Approach: Qualitative research focuses on understanding human behavior and the reasons that govern such behavior. It involves in-depth analysis of non-numerical data like texts, videos, or audio recordings.

Key Features:

Emphasis on exploring complex phenomena
Involves interviews, focus groups , and observations
Generates rich, detailed data that are often subjective

Applications: Widely used in social sciences, marketing, and user experience research.

2. Quantitative Research

Quantitative research is a systematic approach used in various scientific fields to quantify data and generalize findings from a sample to a larger population. This type of research is fundamentally different from qualitative research in several ways:

Definition and Approach: Quantitative research is centered around quantifying data and generalizing results from a sample to the population of interest. It involves statistical analysis and numerical data .

Relies on structured data collection instruments
Large sample sizes for generalizability
Statistical methods to establish relationships between variables

Applications: Common in natural sciences, economics, and market research.

3. Descriptive Research

Definition and Approach: This research type aims to accurately describe characteristics of a particular phenomenon or population.

Provides detailed insights without explaining why or how something happens
Involves surveys and observations
Often used as a preliminary research method

Applications: Used in demographic studies, census, and organizational reporting.

4. Analytical Research

Definition and Approach: Analytical research goes beyond description to understand the underlying reasons or causes.

Involves comparing data and facts to make evaluations
Critical thinking is a key component
Often hypothesis-driven

Applications: Useful in scientific research, policy analysis, and business strategy.

5. Applied Research

Definition and Approach: Applied research focuses on finding solutions to practical problems.

Direct practical application
Often collaborative , involving stakeholders
Results are immediately applicable

Applications: Used in healthcare, engineering, and technology development.

6. Fundamental Research

Definition and Approach: Also known as basic or pure research, it aims to expand knowledge without a direct application in mind.

Theoretical framework
Focus on understanding fundamental principles
Long-term in nature

Applications: Foundational in fields like physics, mathematics, and social sciences.

7. Exploratory Research

Definition and Approach: This type of research is conducted for a problem that has not been clearly defined.

Flexible and unstructured
Used to identify potential hypotheses
Relies on secondary research like reviewing available literature

Applications: Often the first step in social science research and product development.

8. Conclusive Research

Definition and Approach: Conclusive research is designed to provide information that is useful in decision-making.

Structured and methodical
Aims to test hypotheses
Involves experiments, surveys, and testing

Applications: Used in market research, clinical trials, and policy evaluations.

Difference between Qualitative And Quantitative Research

Here is detailed difference between the qualitative and quantitative research –

Understanding the different types of research is crucial for anyone embarking on a research project. Each type has its unique approach, methodology, and application area, making it essential to choose the right type for your specific research question or problem. This guide serves as a starting point for researchers to explore and select the most suitable research method for their needs, ensuring effective and reliable outcomes.

Types of Research – FAQs

What are the 4 main types of research.

There are four main types of Quantitative research: Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research . attempts to establish cause- effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences.

What are the types of research PDF?

APPLIED RESEARCH BASIC RESEARCH CORRELATIONAL RESEARCH DESCRIPTIVE RESEARCH ETHNOGRAPHIC RESEARCH EXPERIMENTAL RESEARCH. EXPLORATORY RESEARCH

What are the 5 main purpose of research?

The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge .

Can I be sure that my assignment paper will be plagiarism-free?

You can be 100% sure about the content of your assignment when done by a professional and genuine assignment maker. Most companies focus on providing the best and unique content so that they can attract more returning customers.

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→ The 8 types of market research and ho...

The 8 types of market research and how to use them

There are eight types of marketing research you can try to stay ahead of the competition. Learn more about marketing research methods and how to use them.

Person conducting different types of market research.

Latest posts on Tips

Typeform | 05.2024

Typeform | 04.2024

“If you keep doing what you’ve always done, you’ll keep getting what you’ve always got.”

Doesn’t sound too threatening if you’ve always been successful, right?

Continuing to do what you’ve always done means you’ll fall behind—and probably fade to darkness—to where all the forgotten brands go.

Take Kodak. They were a major player in photography for decades—remember? When digital photography boomed, Kodak kept doing what they always did. Their business floundered and people forgot about them. Well, everyone apart from Pitbull.

Now, look at Fujifilm, one of Kodak’s biggest competitors. They did the opposite and looked for ways to apply their expertise in film to the technology of the new millennium instead. Their company is still going strong.

The same goes for research. If you’re doing the same old types of market research, speaking to the same old people, and doing the same old tired surveys—you’re already behind.

How do you decide what kind of market research you need to do? It all comes down to what you need to know and what your business goals are.

In this article, we’ll explain the various types of market research you can use to solve issues and challenges in your business. We’ll throw you a freebie, too, and provide some market research tips about when to use each strategy.

Let’s get you ahead of the curve.

1. Brand research

Brand research helps with creating and managing a company’s brand, or identity. A company’s brand is the images, narratives, and characteristics people associate with it.

When to use it

Brand research can be used at every stage in a business’s lifecycle, from creation to new product launches and re-branding. There are at least seven types of brand research:

Brand advocacy: How many of your customers are willing to recommend your brand?

Brand awareness : Does your target market know who you are and consider you a serious option?

Brand loyalty: Are you retaining customers?

Brand penetration: What is the proportion of your target market using your brand?

Brand perception : What do people think of as your company’s identity or differentiating qualities?

Brand positioning: What is the best way to differentiate your brand from others in the consumer’s mind and articulate it in a way that resonates?

Brand value: How much are people willing to pay for an experience with your brand over another?

How to do it

A researcher will use several types of market research methods to assess your and your competitors’ strengths and weaknesses. Generally, they will conduct competitor research, both qualitative and quantitative, to get a picture of the overall marketplace. Focus groups and interviews can be used to learn about their emotions and associations with certain brands.

Market research surveys are useful to determine features and benefits that differentiate you from competitors . These are then translated into emotionally compelling consumer language.

2. Campaign effectiveness

This type of market research is designed to evaluate whether your advertising messages are reaching the right people and delivering the desired results. Successful campaign effectiveness research can help you sell more and reduce customer acquisition costs.

It’s estimated people see up to 5,000 advertising messages each day. That means attention is a scarce resource, so campaign effectiveness research should be used when you need to spend your advertising dollars effectively.

Campaign effectiveness research depends on which stage of the campaign you use it in (ideally, it’s all of them!). Quantitative research can be conducted to provide a picture of how your target market views advertising and address weaknesses in the advertising campaign.

3. Competitive analysis

Different companies are conducting competitor analysis.

Competitive analysis allows you to assess your competitors’ strengths and weaknesses in the marketplace, providing you with fuel to drive a competitive advantage.

No business exists in a vacuum—competitive analysis is an integral part of any business and market plan. Whether you’re just getting started, moving into a new market, or doing a health check of your business, a competitive analysis will be invaluable.

A researcher will typically choose a few of your main competitors and analyze things like their marketing strategy, customer perceptions, revenue or sales volume, and so on.

Secondary sources such as articles, references, and advertising are excellent sources of competitive information; however, primary research, such as mystery shopping and focus groups, can offer valuable information on customer service and current consumer opinions.

4. Consumer insights

Consumer insights research does more than tell you about who your customers are and what they do. It reveals why customers behave in certain ways and helps you leverage that to meet your business goals.

Knowing your customers deeply is integral to creating a strategic marketing plan. This type of market research can help you anticipate consumer needs, spark innovation, personalize your marketing, solve business challenges, and more.

Consumer insights research should be specific to your business—it’s about getting to know your target audience and customers. Various market research methods can be used, such as interviews, ethnography, survey research, social monitoring, and customer journey research.

Here are some of the characteristics you should understand through consumer insights research:

Purchase habits

Interests, hobbies, passions

Personal and professional information

How they consume media and advertising

5. Customer satisfaction research

Customer satisfaction research is a type of market research that measures customers’ experiences with products or services, specifically looking at how those meet, exceed, or fail to live up to their expectations.

Customer satisfaction is a strong indicator of customer retention and overall business performance. Successful customer satisfaction research should help you understand what your customers like, dislike, and feel needs improvement. You can use this type of market research to look at the quality and design of products, speed and timeliness of delivery, staff and service reliability, knowledge, and friendliness, market price, and value for money.

There are several ways to measure customer satisfaction, most commonly using surveys. An NPS or Voice of the Customer Survey can help you measure customer loyalty. Customer Effort Scoring measures how satisfied people are with customer service or problem resolution. CSAT is any survey that measures customer satisfaction , typically measured using Likert scale surveys . They can be conducted at different points in the customer experience, allowing deeper insight into that moment.

6. Customer segmentation research

Customer segmentation studies aim to divide markets or customers into smaller groups or personas with similar characteristics to enable targeted marketing. By understanding how people in each category behave, you can understand how each influences revenue.

Customer segmentation research is best used if you’re ready to give customers individualized experiences. Not every customer in your target market is the same. The more you understand each specific persona, the easier it is to focus on delivering personalized marketing, build loyal relations, price products effectively, and forecast how new products and services will perform in each segment.

Market researchers use four characteristics to segment customers.

Demographics: demographic information such as age, gender, family status, education, household income, occupation and so on

Geography: where people live, from cities and countries to whether they are city dwellers or suburbanites

Psychographics: socioeconomic status, class, lifestyle, personality traits, generation, interests, hobbies, etc.

Behavior: brand affinity, consumption and shopping habits, spending, etc.

A researcher will identify your current customers and collect data about them through various market research methods, such as surveys, database research, website analytics, interviews, and focus groups. The aim is to gather as much information as possible.

7. Product development

Market research for product development involves using customer knowledge to inform the entire process of creating or improving a product, service, or app and bringing it to market.

Innovation is hard work. A quick Google will tell you that 80–95% of new products fail every year. Conducting market research for product and app development helps minimize the risk of a new product or change going bust as it enters the market. There are three stages where you can use market research:

Conception: The moment you’re thinking about adding something new, market research can find market opportunities and provide insights into customer challenges or their jobs-to-be-done, so you can find a way to fill the gap.

Formation: Once you have an idea, market researchers can help you turn it into a concept that can be tested. You can learn more about strategizing pricing, testing advertising and packaging, value proposition, and so on.

Introduction: Market research can help you gauge attitudes toward the product once it’s in the market and adapt your messaging as it rolls out.

Keep making the product better or find opportunities to introduce it to new markets.

Product development research will utilize different market research methods, depending on the goal of the research. A researcher could present focus groups with product concepts and listen to their opinions, conduct interviews to learn more about their pain points, or perform user testing to see how they interact with an app or website.

8. Usability testing

Usability testing is concerned with understanding how customers use your products in real time. It can involve physical products, like a new blender, or digital products like a website or app.

Usability testing is helpful when you need to detect problems or bugs in early prototypes or beta versions before launching them. It typically costs far less to test a product or service beforehand than to pull a flawed product off the shelves or lose sales because of poor functionality.

There are several types of usability tests, which vary based on whether you’re testing a physical or digital product.

Journey testing involves observing the customer experience on an app or website and monitoring how they perform. This type of study can be done online

Eye tracking studies monitor where people’s eyes are drawn. Generally, they are conducted on websites and apps, but can also be done in stores to analyze where people look while shopping

Learn ability studies quantify the learning curve over time to see which problems people encounter after repeating the same task

Click tracking follows users’ activity on websites to evaluate the linking structure of a website

Checklist testing involves giving users tasks to perform and recording or asking them to review their experience

Combining types of market research with Typeform

When it comes to market research, you need to ask yourself what business challenge or question you’re trying to address. Then, select the appropriate methods and tools, such as market research automation , to simplify your process.From there, the world of useful data and actionable insights will open to you.

About the author

We're Typeform - a team on a mission to transform data collection by bringing you refreshingly different forms.

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Introduction to Bioprocessing

Bioprocessing employs the transformative power of biochemistry to drive sustainable production and pharmaceutical development..

Amielle Moreno earned her doctorate in neuroscience from Emory University and has dedicated her career to science communication, news coverage, and medical writing and editing. She is a published researcher...

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Stay up to date on the latest science with Brush Up Summaries.

What Is Bioprocessing?

Bioprocessing is the manipulation of naturally occurring living organisms and systems by bioengineers to achieve research and industrial goals. 1 Bioprocesses harness a diverse array of biological components including microbial, animal, plant, and fungal cells, as well as enzymes to act as catalysts or source materials in chemical production. 2,3

A flow chart of the major steps of the average bioprocess. Scientists select biological systems, substrates, and desired products; perform genetic enhancement, kinetic modeling, and small-scale experimentation; scale up operational units and catalyzation in bioreactors; filter and purify samples; and recover products and eliminate waste.

Upstream and Downstream Bioprocessing

Bioprocesses encompass a sequence of activities that facilitate biochemical changes , with upstream processing steps that prepare biomass and substrates for catalytic reactions, followed by downstream processing to handle the resulting materials. 1 To change the phase or composition of raw materials, most systems employ a series of methods called unit operations . 2 Scientists tailor and optimize the order of these unit operations for specific biological catalysts and industrial production.

Unit operations commonly employed in bioprocessing include the following: 2

Centrifugation
Chromatography
Crystallization
Distillation
Evaporation
Filtration (e.g., microfiltration and ultrafiltration)
Flocculation
Homogenization
Humidification
Precipitation
Sedimentation
Solvent extraction

What is upstream processing?

The first phase of bioprocessing is known as upstream processing . 1 Bioengineers first identify a biological catalyst or system that can be reengineered for industrial purposes. 1 Then they design an industrial environment to support the desired metabolic events. 3

Once a researcher selects the ideal biological system, genetic engineering equips host organisms with the traits essential for industrial production. 2 The researchers then create a kinetic analysis of how the product will be manufactured. This analysis uses mathematical models designed to describe the various features of the biological system such as metabolic rates , substrate utilization, and product formation . 2,4,5

A project's economic viability hinges on the cost of obtaining necessary biomass and substrate, as well as the parameters essential for catalysis support. 2 Engineers run small scale experiments and perform comparative analysis between different experimental conditions to make informed decisions on the order of unit operations and manufacturing methods crucial for financial feasibility of a given process. 2,5

Bioreactors , also known as fermenters , serve as the heart of upstream bioprocessing, facilitating major biochemical transformations of substrate and biomass. 1,6 These containers optimize conditions for biocatalysts such as enzymes or whole cells to convert biochemicals into desired products while minimizing manufacturing costs. 2,3

Bioreactors are versatile instruments with applications across diverse areas of production , including the following: 3

Cell growth
Enzyme production
Food production
Tissue generation
Algae production
Protein synthesis
Anaerobic digestion

Product yields from a bioreactor depend on the instrument’s ability to do the following: 7

Maintain high cell concentrations and metabolic activities
Achieve and preserve sterile conditions
Provide adequate agitation to achieve uniformity of the microenvironment

What is downstream processing?

Downstream processing recovers the final product once catalyzation is complete. 2,3 The series of unit operations used in downstream recovery depend on the product’s location in the source material and its format , such as extracellular, intracellular, or whole cell biologics. 3

Table 1: Different product types and associated downstream unit operations 3

Filtration, purification, and other product collection techniques that succeed in small scale laboratory workflows might be difficult to implement on a larger scale. 2 In many bioprocesses, final product retrieval poses the biggest challenge and incurs significant costs. 3 For example, in the case of specific recombinant DNA-derived products, the extensive process necessary for purification may constitute 80 to 90 percent of the total processing expenses . 2 Methodological advancements in downstream processing can significantly decrease processing time and costs while offering higher yields. 3

Major considerations in bioprocessing workflows

In bioprocessing, maintaining efficacy while upscaling from laboratory operations to industrial-scale bioreactors requires careful management of biological components, including biomass, gas, and liquid constituents of a bioprocess. 2

Instruments such as meters and sensors collect data throughout the bioprocess system. Most bioprocessing occurs in a liquid medium, which makes it vital to monitor the system’s fluid dynamics , such as viscosity, density, and concentration, and maintain mixers and tubing connectors. 2 Other parameters subject to routine data collection include UV exposure, conductivity, pH, temperature, air, and pressure. 2

Additionally, products must meet final purity standards before they are released on the market . 2,3 Instrument and environment sterilization are essential for cultivating only the desired cells or products; scientists often use stainless steel equipment, which is heat or chemically treated between batches. 3 Purification techniques such as filtration, chromatography, and precipitation remove impurities and optimize productivity. 3

Bioprocessing Methods and Technologies

Advancements in research technologies, such as recombinant DNA , gene probes, cell fusion, and tissue culture continually expand the horizons of bioprocessing, enabling scientists to refine methods and develop new products . 8,9

Bioprocessing culture types

The cellular cultures used in bioprocessing encompass a diverse array of organisms, including bacterial, algal, fungal, and mammalian systems. 1 Each organism's proliferation is regulated differently, leading to variations in the time required to complete cell cycles or enzymatic actions, even within the same species or cell lines. 5 Recent developments in cell culture media , feeding techniques, and bioreactor controls help scientists create highly capable systems. 5

Human and nonhuman eukaryotic cell cultures can either adhere to surfaces or float freely, with recent advancements enabling the creation of continuous cell lines through transformation processes. 5

A scientist cultures organoids in a multi-well plate filled with red cell culture media

Microbial systems offer advantages such as fast, predictable growth rates and high product output with low-cost growth media. 3 Microbial bioprocessing is particularly suitable for less complex biotherapeutics that do not require extensive post-translational modifications. 5

Microalgae are a renewable bioprocessing resource with substantial economic potential, capable of producing biofuels and various bioactive compounds. 3,6

Fungi abundantly generate enzymes and small molecules, and are well established bioprocessing systems for drug discovery and pharmaceutical applications. 6,10

Single-use bioprocessing

Single-use technologies are increasingly common in industrial settings, offering advantages such as eliminating the need for cleaning or sterilization, thereby minimizing risks of contamination. 11 Scientists adopt single-use systems to reduce investment and operational costs while enhancing process flexibility. 11 Although single-use systems for commercial manufacturing are projected to become more popular in the coming years, they will likely remain second to stainless steel equipment . 11

Applications

Bioprocessing offers diverse applications in sustainability, economic production, and medical discovery. 1

Bioprocessing in food science

Bioprocessing is commonly found in everyday food production. Depriving anaerobic bacteria of oxygen leads to the metabolic process of fermentation , which people have employed for centuries to enhance and preserve various plant-based components, yielding food staples such as sake, bread, beer, soy sauce, and rice vinegar. 6

Outside the common kitchen, the food industry is a major buyer on the billion dollar enzyme market . 9 Conventional enzymes utilized in food processing boast biodegradability and minimal environmental impact. 3 Advancements in enzyme-related techniques offer enhanced preservation and novel food components, including flavors, colorants, and phytochemicals . 9,12

Bioprocessing in the pharmaceutical industry

In 1928, penicillin emerged as a groundbreaking fungal metabolite, marking the inception of biologically-sourced pharmaceuticals. 6 Now, bioprocessing is the primary source of many drugs and biologics necessary for medical treatments and scientific research. 1

Pharmaceuticals derived from living organisms include recombinant proteins, tissues , cells, genes, allergens, blood components, and vaccines. 1,3,8

For example, cell therapy treats various diseases by introducing cellular material into a patient's body through injection, grafting, or implantation. 13 Additionally, scientists and clinicians use human mesenchymal stem cells to secrete bioactive factors for regenerative medicine. 14 Optimizing the bioprocessing techniques involved in these and other therapeutics holds immense potential for enhancing human health . 1

Bioprocessing to produce biofuels

Bioengineers convert various organic materials such as forestry waste, agricultural residues, and algal biomass into oil-based plastics and biofuels . 15 These bioprocessing applications offer solutions to energy demands and reduce reliance on fossil fuels. 16

Scientists generate bioethanol , a popular liquid biofuel, through microbial anaerobic fermentation of plants such as corn. 15 Alternatively, utilizing marine vegetation such as algae as a biofuel source addresses challenges in renewable energy and land use allocation. 6 Despite obstacles in algal biofuel production , such as high energy cost and complicated product harvesting, technological advancements are steadily enhancing its feasibility. 15

1. Liu S. Bioprocess Engineering: Kinetics, Sustainability, and Reactor Design . Third ed: Elsevier; 2020.

2. Doran PM. Bioprocess Engineering Principles . Second ed: London: Academic Press; 2013:1-919.

3. Moo-Young M. Comprehensive Biotechnology . Second ed: Elsevier Science; 2011.

4. Koutinas M, et al. Bioprocess systems engineering: Transferring traditional process engineering principles to industrial biotechnology . Comput Struct Biotechnol J . 2012;3(4):e201210022.

5. Jagschies G, et al. Biopharmaceutical Processing . Elsevier; 2018.

6. Dunford NT. Food and Industrial Bioproducts and Bioprocessing . John Wiley & Sons; 2012.

7. Zydney AL. Perspectives on integrated continuous bioprocessing—opportunities and challenges . Curr Opin Chem Eng . 2015;10:8-13.

8. Tripathi NK, Shrivastava A. Recent developments in bioprocessing of recombinant proteins: expression hosts and process development . Front Bioeng Biotechnol . 2019;7:420.

9. Zhang Y, et al. Enzymes in food bioprocessing—novel food enzymes, applications, and related techniques . Curr Opin Food Sci . 2018;19:30-35.

10. El Enshasy HA. Fungal morphology: a challenge in bioprocess engineering industries for product development . Curr Opin Cheml Eng . 2022;35:100729.

11. Eibl R, Eibl D. Single-Use technology in biopharmaceutical manufacture . In: Wiley eBooks ; 2019.

12. Srivastava PS. Plant Biotechnology and Molecular Markers . Springer; 2005.

13. Kirouac DC, Zandstra PW. The systematic production of cells for cell therapies . Cell Stem Cell . 2008;3(4):369-381.

14. Panchalingam KM, et al. Bioprocessing strategies for the large-scale production of human mesenchymal stem cells: A review . Stem Cell Res Ther . 2015;6:1-10.

15. Yusup S, Rashidi NA. Value-Chain of Biofuels . Elsevier; 2022.

16. Argin-Soysal S, et al. Bioprocessing for Value-Added Products from Renewable Resources . Amsterdam: Elsevier; 2007.

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Home » Research Approach – Types Methods and Examples

Research Approach – Types Methods and Examples

Table of Contents

Research Approach

Definition:

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

Types of Research Approach

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

Deductive Approach

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

Inductive Approach

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

Abductive Approach

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

Research Approach Methods

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

Deductive approach methods:

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

Inductive approach methods:

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

Abductive approach methods:

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

Applications of Research Approach

Here are some common applications of research approach:

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

Examples of Research Approach

Examples Deductive approach:

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

Examples Inductive approach:

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

Examples Abductive approach:

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

Purpose of Research Approach

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

Here are some specific purposes of different research approaches:

Deductive approach:

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

Inductive approach:

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

Abductive approach:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Article

Volume 15, issue 1
MS, 15, 315–330, 2024
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Zhiyong yang, ruixiang zhang.

With the advancement of vehicle technology, there is a growing demand for vehicle comfort in addition to the focus on safety and functionality. On certain accident-prone sections of highways, such as entrance and exit ramps, tunnels, and downhill stretches, continuous speed bumps are typically installed to remind vehicles to reduce their speed. However, while enhancing safety, these measures also introduce a degree of discomfort for passengers and drivers alike. Vehicle speed and the type of road speed bump are key factors influencing vehicle comfort. In order to improve the ride comfort, this paper investigates the problem of adaptive speed control for vehicles passing over different types of continuous speed bumps and proposes a method for solving the optimal speed. In this research, a 4-degree-of-freedom vehicle suspension model and a road excitation model are employed to simulate vehicle vibrations. Simulation optimisation is performed using MATLAB in conjunction with an immune algorithm to obtain the optimal vehicle speeds for traversing three types of continuous speed bumps – sinusoidal, rectangular, and trapezoidal – while adhering to specified constraints. The simulation results demonstrate that this optimisation algorithm effectively enhances the ride comfort of vehicles when navigating speed bumps. The algorithm, when applied, reduces vehicle vertical displacement, acceleration, suspension deflection, and tyre load to varying degrees when crossing speed bumps. It also reduces tyre ground clearance to some extent, achieving a balance between comfort and safety. Furthermore, the study identifies the range of comfortable vehicle speeds for traversing these three types of speed bumps, providing valuable insights for selecting the appropriate speed bump design on roads with varying speed limits.

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Yang, Z., Zhang, R., Guo, Z., Guo, J., and Zhou, Y.: Research on the optimal speed of vehicles passing speed bumps on the highway based on an immune algorithm, Mech. Sci., 15, 315–330, https://doi.org/10.5194/ms-15-315-2024, 2024.

Intelligent driving technology has progressively matured with the rapid development of a new generation of information technology and has developed into a practical application. The driving comfort and safety of the vehicle are crucial as a product for real-world applications. However, the majority of current research on the comfort of vehicles has been carried out with vehicles travelling on flat surfaces, such as in route planning (Motallebi et al., 2020) and suspension optimisation (Gao and Qi, 2021). There are few studies on the comfort and safety of vehicles when passing over uneven surfaces, especially when it comes to passing speed bumps. When drivers encounter speed bumps while driving, they rapidly identify the type of speed bump and adjust their vehicle's speed based on their subjective experience to minimise discomfort during passage (Barreno et al., 2022). In the case of semi-automated driving, forward-facing cameras are used to detect speed bumps on the road ahead, and the driving assistance system assists the driver in adjusting the vehicle's speed to reduce vehicle vibrations (Zein and Darwiche, 2020). However, vehicles lack the capability for subjective evaluation, making it challenging to adjust to an appropriate speed based on comfort requirements when encountering speed bumps. This presents a significant hurdle in achieving the optimal balance between driving comfort and safety.

The discomfort experienced during rides primarily arises from vertical vibrations induced by uneven road surfaces, and vehicle speed also affects the intensity of the vibrations. Therefore, the enhancement of ride comfort predominantly focuses on the suppression of vertical vibrations due to the presence of the driver. The suspension system, as a crucial component ensuring comfort, can absorb vibrations generated by road disturbances and has become an active area of research. To date, numerous studies have been conducted on suspension systems. Researchers such as Yan et al. (2019) have applied H ∞ suspension control to quarter-vehicle active-suspension systems to dampen vibrations, thereby enhancing vehicle handling stability and driver safety; however, considering road incentives to be disturbances has limited the guidance of road information. Mahmoodabadi et al. (2020) proposed a method based on optimal fuzzy adaptive robust proportional–integral–derivative (PID) controllers, aiming to minimise the relative displacement between the vehicle body acceleration and tyre spring mass, thereby improving active-suspension performance and ride comfort. Nguyen and Nguyen (2022a) proposed a state-multivariable-based sliding-mode PID-integrated suspension control method combined with a quarter-dynamics model, significantly reducing spring mass displacement and acceleration and thus enhancing vehicle stability and comfort. Further research introduced the optimised sliding-mode control (OSMC) algorithm to control the operation of active-suspension systems, substantially improving vehicle oscillations on uneven road surfaces while effectively preventing wheel separation, further enhancing ride comfort (Nguyen and Nguyen, 2022b). Additionally, an AFSPIDF active-suspension control algorithm, blending PID, SMC, and various fuzzy algorithms, successfully suppressed vehicle vibrations, significantly reducing vertical body displacement and acceleration and ultimately enhancing vehicle stability and comfort (Nguyen and Nguyen, 2023). However, due to the complexity of vehicle systems, the above researchers mainly utilised a quarter-vehicle model in studying ride comfort and safety. Nonetheless, the quarter-vehicle model can only investigate vehicle motion in the vertical direction, overlooking many crucial vehicle vibration characteristics and thus making it challenging to fully capture vehicle system information (Yang et al., 2015). Moreover, the use of more complex models would increase the difficulty of mathematical modelling and dynamic analysis. Therefore, employing the 4-degree-of-freedom (4-DOF) nonlinear vehicle suspension model is a moderately complex yet closer-to-reality approach, enabling more accurate acquisition of vehicle feedback information and providing precise adjustment solutions for vehicles navigating different speed bumps.

For advanced intelligent vehicles, their sensors and network systems have the capability to acquire information about the road ahead. Consequently, based on this context, researchers have proposed novel approaches. For instance, drawing upon road information obtained from networks and forward sensors, Wu et al. (2020) introduced a comfort optimisation strategy capable of coordinating speed variations and suspension vibrations. This approach utilised a hybrid horizon variance (HV) model predictive control (MPC) method, resulting in enhanced comfort for passive suspension vehicles operating at a fixed speed. Building upon this foundation, Liu et al. (2023) developed an integrated approach based on road information, combining preview active-suspension control and longitudinal velocity planning. This integrated approach employed a road-information-based semi-explicit model predictive controller (SE-MPC) for active-suspension control, further elevating the ride comfort of autonomous vehicles. Huang et al. (2023) and others approached the problem from the perspective of adaptive nonlinear control, introducing a novel suspension control method. Leveraging X-shaped biomimetics inspired by the skeletal structures of animals or insects and utilising road information to solve multi-objective optimisation problems, this method significantly enhanced vehicle comfort.

It is evident that forward road information is paramount for speed adjustment, particularly when considering ride comfort. However, current research has not adequately addressed whether speed planning can achieve optimal comfort when navigating uneven road surfaces, especially over speed bumps. In various road segments, such as tunnel entrances, downhill slopes, and pedestrian areas, speed bumps are commonly deployed to control vehicle speed and to enhance the safety of both vehicles and pedestrians. These speed bumps come in different shapes, including rectangular, semi-sinusoidal, and trapezoidal designs. However, current research concerning vehicle traversal of speed bumps predominantly focuses on trapezoidal speed bumps, with limited exploration of other shapes (Walavalkar et al., 2021; Miracle et al., 2021). In fact, different shapes of speed bumps have different optimal passing speeds, and the study of trapezoidal speed bumps alone is relatively homogeneous and lacks universality. In addition, there are different speed limits at different road locations, and the study of what kind of speed bumps should be laid on different speed-limited sections is relatively limited. Hence, conducting research on the optimal vehicle speeds for driving vehicles to navigate different speed bump shapes holds significant importance. Such research endeavours contribute to enhancing the comfort and safety of vehicles, facilitating their adaptation to the diverse road conditions and speed limit requirements.

When adjusting vehicle speed, it is imperative not only to ascertain the optimal speed for comfort but also to comprehensively consider the influence of other factors on comfort. While the immune algorithm (IA) (Gong et al., 2009) is a multi-objective optimisation method constructed by mimicking the biological immune mechanism and integrating the incentive of gene evolution, it has the advantages of self-adaptation, stochasticity, and population diversity, as well as superior global search capabilities, parallelism, and robustness, which overcomes the phenomenon of prematurity that exists in general optimisation. Significantly, the IA has found successful application in the realm of automotive mechatronic systems. For example, Chen (2020) used the IA to optimise the front- and rear-suspension parameters of an off-road vehicle, which enhanced the suspension performance of the off-road vehicle, thus improving the ride comfort and stability of the whole vehicle. Similarly, Shieh et al. (2014) combined the IA with adaptive fuzzy control and finally developed an integrated adaptive fuzzy controller which was integrated into the vehicle suspension system to achieve a balance between comfort and operability. However, it is noteworthy that these studies predominantly treated vehicle speed as a quantitative parameter, focusing on the enhancement of comfort through suspension system parameter improvements. Regrettably, the pivotal role of vehicle speed as a determinant of driving and riding comfort has often been overlooked.

Based on the aforementioned statements, this paper approaches the topic from a different perspective. Utilising the information obtainable with regard to road speed bumps through networks and sensors, the vehicle speed is considered to be an unknown condition. The research focuses on a 4-degree-of-freedom nonlinear vehicle suspension model. It employs the immune algorithm for optimising the speed of vehicles when traversing various speed bumps. This optimisation aims to achieve the ideal speed for navigating speed bumps, thereby reducing vibrations in vehicles. This approach facilitates adaptive speed adjustment in vehicles, ultimately enhancing both ride comfort and safety. Simultaneously, determining the optimal speed for traversing speed bumps provides valuable guidance for the installation of speed bumps in different speed limit zones.

The structure of this paper is as follows: the first part analyses the current state of research on vehicle suspension systems and comfort; the second part introduces the 4-degree-of-freedom nonlinear vehicle suspension model, as well as the trapezoidal, half-sine, and rectangular-wave speed bump models, and also describes the relevant parameters of the models; the third part outlines a multi-objective optimisation algorithm based on the IA; and the fourth part details the experiments on applications in different speed bump scenarios, and the results are analysed.

2.1 The 4-degree-of-freedom nonlinear vehicle suspension model

Figure 1 depicts the simplified diagram of the 4-DOF nonlinear vehicle suspension model applied in this paper (Yang et al., 2016). The nonlinear suspension model consists mainly of the vehicle body, unsprung masses of the front and rear suspension, springs, front and rear suspensions, tyres, and dampers. It enables the study not only of the vertical and pitch motion of the vehicle body but also of the vertical motion of the front and rear wheels (Yang et al., 2014). Table 1 shows the definitions of the symbols used in the model shown in Fig. 1.

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Figure 1 Nonlinear vehicle suspension model of 4-DOF.

Table 1 Symbolic interpretation of the model.

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From the d'Alembert principle, the system equation of motion can be expressed as in Eq. (1):

In some simplified nonlinear dynamics studies of vehicle suspension, the suspension spring is regarded to be linear; that is, the deformation amount of the spring has a linear relationship with the spring force range; f s = k Δ x holds, where k is the stiffness coefficient, and Δ x is the deformation amount of the spring. However, in actual situations, the suspension spring will only be approximately linear when there is a small deformation, and nonlinear motion characteristics will appear when the deformation is large. The suspension spring will exhibit nonlinear deformation under road excitation. To better fit the actual situation, the nonlinear spring characteristics are expressed as follows (Yang et al., 2016):

where f s represents the dynamic spring force, sgn(⋅) denotes the signum function, Δ s signifies the deformation of the spring, k s represents the stiffness coefficient of the spring, and n denotes the nonlinearity coefficient of the spring. When n ≠1 , the spring exhibits nonlinear characteristics; otherwise, it demonstrates linear characteristics. Consequently, the nonlinear characteristics of each spring in the vehicle can be expressed as follows:

We express the damping force of the nonlinear suspension system as follows (Yang et al., 2016):

where f sc represents the damping force, Δ x ˙ s signifies the relative velocity of the damper, and c s represents the damping coefficient, which exhibits different characteristics when the damper is stretched and compressed. Therefore, the nonlinear damping forces of various vehicle suspension systems are expressed as follows:

where c f1 and c r1 denote the front- and rear-tyre damping coefficients, and c f2 and c r2 denote the front- and rear-tyre damping coefficients. Furthermore,

Let the state variable x 1 = x b , x 2 = x ˙ b , x 3 = θ , x 4 = θ ˙ , x 5 = x f , x 6 = x ˙ f , x 7 = x r , and x 8 = x ˙ r . The equation of state of the 4-DOF nonlinear suspension system is expressed as in Eq. (8):

When the suspension system is in relative static equilibrium, it can be calculated using the following equation:

where Δ sfi i = 1 , 2 and Δ sri i = 1 , 2 denote the static deformations, which can be obtained from Eq. (4); the masses of the spring load mass m b at the front and rear axes, m bf and m br , respectively, are expressed as follows:

2.2 Periodic speed bump excitation model

Accurately obtaining road surface information is crucial for analysing and evaluating vehicles. The uneven road surface between speed bumps also serves as a source of vehicle vibrations. When the vehicle's speed changes, the road surface excitation experienced by the vehicle also varies. In other words, the road surface excitation encountered by the vehicle when passing over speed bumps is influenced jointly by the speed bumps and the uneven road surface. Hence, this study takes into consideration the unevenness excitation that the uneven road surface between speed bumps imposes on vehicles and simulates the road surface's unevenness excitation using a sine wave model. Let x h (t) be the excitation function of the speed bumps, let A be the average amplitude of the uneven road surface, and let f r represent the excitation frequency of the uneven road surface. Then the excitations x fd and x rd of the front and rear wheels of the vehicle are represented as follows:

with Δ t being the time difference between the front and rear wheels of the vehicle through the road point, approximately equal to l f + l r / v .

2.2.1 Periodic trapezoidal speed bumps

Figure 2 shows the excitation model for the periodic trapezoidal speed bumps (Yang et al., 2022); d is the separation between the speed bumps, and w and h represent the width and height of the speed bumps, respectively. The vehicle passes over the speed bumps with speed v , and then the excitation period of the periodic trapezoidal speed bumps is T = w + d / v .

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Figure 2 Periodic trapezoidal speed bump excitation model.

The excitation of the wheels in Fig. 2 is represented as in Eq. (5):

Thus, the excitation of the front and rear wheels of the vehicle on the periodic trapezoidal speed bumps is respectively expressed as follows:

2.2.2 Periodic half-sine speed bumps

Figure 3 shows the excitation model for the periodic half-sine speed bumps (Zhang and Zheng, 2022); the speed bump's height is h , its width is w , and the separation between the speed bumps is d . After measurement, the width of the speed bump is roughly equal to the separation; that is, w = d . When the vehicle passes over the speed bump with speed v , the excitation of the front and rear wheels of the vehicle on the periodic half-sine speed bump road surface is defined as follows:

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Figure 3 Periodic half-sine speed bump excitation model.

2.2.3 Periodic rectangular-wave speed bumps

Figure 4 shows the excitation model for the periodic rectangular-wave speed bumps (Wu et al., 2014). The height and width of the speed bumps are h and w , respectively, and the separation between the speed bumps is d . The speed over the speed bumps of the vehicle is v , and then the excitation of the periodic rectangular-wave speed bump road surface to the front and rear wheels of the vehicle can be expressed as follows:

where square (⋅) denotes the rectangular-wave function.

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Figure 4 Periodic rectangular-wave speed bump excitation model.

2.3 Simulation parameters

The 4-DOF vehicle suspension parameters used in the simulation are shown in Table 2 (Zhu and Ishitobi, 2004). The simulation takes the static equilibrium point x b , x ˙ b , θ , θ ˙ b , x f , x ˙ f , x r , x ˙ r = 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 in the vertical direction of the vehicle as the initial condition. Due to the nonlinearity of the differential equations, a numerical investigation of the dynamics of the vehicle model was conducted using a fourth-order fixed-step Runge–Kutta algorithm (Yang et al., 2022).

Table 2 Parameters of 4-DOF vehicle suspension.

This chapter aims to discuss the problem of speed adaptive adjustment of vehicles and optimises multiple objectives, such as the speed- and suspension-damping coefficients, simultaneously. Firstly, objective functions are established by combining the optimisation objectives with the vehicle indexes to evaluate the comfort of the optimisation objectives. Then, the optimisation objectives are regarded to be a set of antibodies, and we use the immune algorithm to establish the multi-objective optimisation algorithm for optimisation, which can achieve the purpose of improving the comfort of the vehicle.

3.1 Establish the objective function

The vertical movement of the vehicle body, the dynamic deflection of the front and rear suspension, and the front- and rear-wheel dynamic loads are used as evaluation indexes of the vehicle to assess the overall performance and balance response. In addition, all indexes are combined for a comprehensive assessment of comfort.

The vertical displacement of the body is usually proportional to the vehicle speed when the vehicle passes over speed bumps. Appropriate vertical body vibration displacement can effectively protect the driver and occupants from unevenness excitation of the road surface; thus, this paper takes the vertical body displacement to be the evaluation index of vehicle comfort (Pan and Sun, 2019); combined with Eq. (8), let

The vehicle's suspension deflection directly affects the handling stability of the vehicle. If the dynamic suspension deflection exceeds the design stroke of the vehicle, it will cause damage to the vehicle's suspension components. Therefore, the paper selects the front- and rear-suspension deflection f 2f and f 2r as indicators for assessing the vehicle's smoothness (Sha et al., 2020); these are expressed as follows:

Vibrations generated by the ground excitation on the wheels will cause discomfort to the driver, and the dynamic loads generated by the vibrations will aggravate the wear of the vehicle and even cause damage. Therefore, the dynamic loads f 3f and f 3r of the front and rear wheels are used to evaluate the vehicle's safety when driving on the road (Yu et al., 2019). The stiffness coefficients for the front and rear wheels are expressed in terms of k f1 and k r1 . Moreover, x fd and x rd represent the road excitations to the front and rear wheels; thus, the dynamic loads of the vehicle are expressed, respectively, as follows:

We use the root-mean-square (rms) value to dimensionlessly process the sub-objectives, such as the vertical displacement of the vehicle body, the dynamic deflection of the suspension, and the dynamic load of the wheels, thereby reducing the influence of incidental factors such as data inequality. Based on the influence level of each sub-objective, the linear weighting method is used to sum up the sub-objectives, and the objective function is expressed as follows (Wang et al., 2022):

Due to the varying impact of different influencing factors on the overall comfort of the vehicle, this study takes the relative weights ω 1 , ω 2f , ω 2r , ω 3f , and ω 3r of each sub-objective as an example, setting them at 0.20, 0.15, 0.15, 0.25, and 0.25, respectively, as examples for investigation. This paper simulates the scenario where the vehicle passes over trapezoidal, semi-sinusoidal, and rectangular speed bumps when exiting a highway ramp with a speed limit of 60 km h −1 . The initial speed of the vehicle before optimisation is 40 km h −1 . To ensure the accuracy of the simulation, the duration of vehicle motion in the simulation is set to 120 s (Yang et al., 2022).

3.2 Optimisation process based on the immune algorithm

Establishing the objective function facilitates the evaluation of the optimised comfort, which indicates the degree of optimisation. While this part describes the implementation process of the optimisation objective through the immune algorithm, which is to achieve the practical application of the algorithm by optimising the speed of the vehicle passing over the speed bumps and the relevant parameters, the specific flowchart is shown in Fig. 5.

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Figure 5 Basic flow of IA optimisation.

In order to facilitate the acquisition of the vehicle balance response, the relevant vehicle parameters should be initialised to achieve better optimisation of the vehicle's performance. This paper addresses the problem of adaptive control of vehicle speed when passing over speed bumps. It considers a set of antibodies, x = v , C f 2 , C r 2 , where v represents the vehicle speed, and C f2 and C r2 denote the front- and rear-suspension damping coefficients. These antibodies form the initial generation of the antibody population X = x 1 , x 2 , ⋯ , x N using an immune algorithm. Simulations are conducted using the vehicle model to obtain performance feedback, and the objective function (19) is computed accordingly. The affinity of the antibodies is calculated as aff x = 1 / F , where F is the value of the obtained objective function. This affinity assessment is used to evaluate the comfort of the vehicle when passing over speed bumps in conjunction with the reciprocal of the objective function (19) from Sect. 3.1. A higher affinity value implies better comfort when the vehicle traverses speed bumps. While ensuring the algorithm's global search capability, this paper sets the population size N to be 30.

In optimisation problems, it is generally considered to be advantageous to initiate optimisation from the current best data point and to select the nearest data point to the current one for further optimisation. The introduction of heuristic information plays a significant role in aiding the optimisation process. By combining the affinity and concentration of antibodies, the excitation level of each group of antibodies is computed to perform the final assessment of antibody quality. A higher excitation level of antibodies indicates better quality, signifying that the group of antibodies can enhance the comfort of vehicles when traversing speed bumps. The concentration of antibodies is defined as

where N is the population size, and S ( x i , x j ) is the similarity between the antibodies; the incentive of the antibodies is denoted as

The calculation of the excitation degree suppresses the high antibody concentration, which ensures the diversity of the antibody population and ensures that the algorithm can be optimised to obtain the optimal speed and related parameters after optimisation. Optimising the speed of the vehicle has to satisfy a variety of constraints, such as smoothness and safety, in addition to the optimal comfort of the vehicle over the speed bump; however, the optimal solutions obtained so far do not meet the requirements of the various constraints. In order to obtain the optimal vehicle speed and related parameter solutions for the current problem as much as possible, after multiple experiments and verifications, it was found that the affinity of the antibody in the paper had almost converged before the evolution of 200 generations, and good results could be obtained. However, after the number G was set to 200 generations, the affinity of the antibody rarely continued to increase. Therefore, the maximum generation was set to 200.

In each round of the evolutionary process, immune operations play a crucial role in ensuring the continuous improvement of vehicle comfort. Various immune operations from the biological immune response, including immune selection, cloning, mutation, and clone suppression, are employed to enhance the quality of antibody parameters affecting vehicle comfort and to optimise the comfort of vehicle passage over speed bumps. The immune selection operator filters antibodies in the population, activating high-quality antibodies that improve vehicle comfort, with the immune selection ratio set to 50 % of the population (50 % NP). The cloning operator replicates activated parameter antibodies to generate several copies. In order to ensure that changes in antibodies' variations lead to alterations in vehicle comfort, the cloning quantity ( M ) is set at 10. The mutation operator is applied to copies of vehicle speed and suspension damping coefficients, using a real-number-encoding algorithm with a certain probability. This mutation is aimed at modifying the vehicle's comfort while traversing speed bumps, maintaining population diversity, and enhancing local search capabilities, as illustrated below:

where the symbol x i , j , m represents the j th dimension of the m th clone of antibody x i , where δ denotes the defined neighbourhood range, and the mutation rate p m is set to 0.7. The clone suppression operator performs a reselection on the results of mutation, suppressing and eliminating parameter antibodies with low vehicle comfort and ensuring that high-quality antibodies that improve vehicle comfort are retained for the next generation. This ensures that the next generation of antibodies has a rich diversity in terms of vehicle comfort results, maintaining diversity in the new antibody population.

To enhance the global search capability of the optimisation algorithm and to obtain new antibodies with different vehicle comfort levels, a population refresh strategy is employed after each round of evolution. This strategy randomly generates new antibodies and eliminates half of the antibodies with low stimulation levels to ensure that the vehicle comfort of the next generation of antibodies entering the new round of evolution is better than the previous generation. When the specified number of evolution generations is reached in the algorithm, the current best antibody, X best , is output, which includes the optimal vehicle speed and front- and rear-suspension damping coefficients for passing over speed bumps.

This paper details an optimisation algorithm considering vehicle speed v and front- and rear-suspension damping coefficients C f2 and C r2 . Simulating the vehicle passing through the highway exit ramp with a speed limit of 40 km h −1 , combining the reality and hardware conditions, the constraint ranges of each variable are set to be 0 km h −1 < v ≤60 km h −1 , 0 kg s −1 < C f2 ≤2000 kg s −1 , and 0 kg s −1 < C r2 ≤2000 kg s −1 .

4.1 Application effects of vehicles passing over periodic trapezoidal speed bumps

After applying the multi-objective optimisation algorithm to the trapezoidal speed bumps, the convergence began in the 173rd generation. The affinity of the best antibody was 1.7419, the vehicle speed v was 17.06 km h −1 , the front-suspension damping coefficient C f2 was 1997 kg s −1 , and the rear-suspension damping coefficient C r2 was 1575 kg s −1 .

From Fig. 6, it can be observed that the optimal affinity stabilises and gradually increases after the 54th generation, ultimately converging. Between the 54th and 172nd generations, when both v and C f2 remain stable and mostly unchanged, only variations in C r2 contribute to a slight improvement in the optimal antibody's affinity. After the 173rd generation, due to the presence of mutation operators and population refreshing in the optimisation model, mutations in v , C f2 , and C r2 lead to a sudden change in antibody affinity, yielding antibodies with higher affinities that subsequently stabilise. Afterwards, it becomes increasingly challenging to generate new antibodies that would lead to an improvement in affinity. Affinity has reached a state of near-convergence, indicating that the optimal vehicle speed and related parameters have been obtained.

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Figure 6 Iterative process for each variable of vehicle passing over the periodic trapezoidal speed bumps. (a) Iterative process for optimal affinity. (b) Iterative process for individual optimal v . (c) Iterative process for individual optimal C f2 . (d) Iterative process for individual optimal C r2 .

The response curves of the vehicle suspension system before and after optimisation are plotted in Fig. 7.

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Figure 7 Comparison of vehicle response curves over periodic trapezoidal speed bumps before and after optimisation. (a) Response curve for objective function values F . (b) Response curve for vertical body displacement x b . (c) Response curve for front-suspension dynamic deflection d df . (d) Response curve for front-wheel dynamic load d lf .

Before optimisation, the vehicle speed was 40.00 km h −1 , and after optimisation, it was reduced to 17.06 km h −1 . Observing Fig. 7, it is evident that the optimised objective function value is significantly lower than before optimisation. The suspension deflection response has decreased from 0.0119 to 0.0054 m, and the wheel dynamic load response has been reduced from 2510.5 to 1106.9 N, both lower than their respective values before optimisation. Additionally, the maximum vertical displacement of the vehicle body has decreased from 0.0816 to 0.0771 m, and according to the rms criterion, the average value of the vehicle body displacement has decreased from 0.0105 to 0.0092 m. This indicates the effectiveness of the optimisation algorithm. These improvements reflect that the optimisation has enhanced the comfort and safety of the vehicle when passing over speed bumps. The spring mass acceleration curves before and after optimisation are shown in Fig. 8.

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Figure 8 Curves of acceleration a before and after optimisation over trapezoidal speed bumps.

Figure 8 shows the acceleration curves before and after optimisation when passing over the trapezoidal speed bump. Prior to optimisation, the maximum acceleration value for the spring mass reached 0.9088 m s −2 , which was reduced to 0.7264 m s −2 after optimisation. The average values were 0.0597 m s −2 before optimisation and 0.0557 m s −2 after optimisation. These findings further confirm the improvement in comfort after optimisation.

4.2 Application effects of vehicles passing over periodic half-sine speed bumps

For the half-sine speed bumps, after 200 generations of optimisation, the affinity of the best antibody converges to 1.4321 when the vehicle speed v takes 15.33 km h −1 , the front-suspension damping coefficient C f2 takes 1971 kg s −1 , and the rear-suspension damping coefficient C r2 takes 1819 kg s −1 .

From Fig. 9, it can be observed that, between the 51st and 123rd generations, the affinity of the best antibodies remains essentially stable, with minimal variation. During this evolutionary phase, changes in speed v are relatively small. However, there was a notable exchange in the values of the front and rear damping coefficients ( C r2 and C r2 ) at the 97th generation due to a mutation, which essentially corresponds to an interchange of front- and rear-wheel damping coefficients, resulting in relatively minor effects on affinity. Between the 130th and 140th generations, vehicle speed gradually starts to converge towards its optimum value. During this period, as the front and rear damping coefficients need to converge continuously around the optimal values, they start to oscillate around this optimum. Consequently, the affinity of the antibodies increases rapidly, leading to improved vehicle comfort. This trend continues until approximately the 170th generation, when the affinity of the antibodies stabilises. At this point, there is little room for further improvement, signifying that the optimal vehicle speed for passing over speed bumps has been obtained.

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Figure 9 Iterative process for each variable of the vehicle passing over periodic half-sine speed bumps. (a) Iterative process for optimal affinity. (b) Iterative process for individual optimal v . (c) Iterative process for individual optimal C f 2 . (d) Iterative process for individual optimal C r 2 .

Figure 10 shows the system response curves of the vehicle suspension system at steady state; these are contrasted to the response curves prior to optimisation.

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Figure 10 Comparison of vehicle response curves over periodic half-sine speed bumps before and after optimisation. (a) Response curve for objective function values F . (b) Response curve for vertical body displacement x b . (c) Response curve for front-suspension dynamic deflection d df . (d) Response curve for front-wheel dynamic load d lf .

Before optimisation, the vehicle speed was 40.00 km h −1 , which was reduced to 15.33 km h −1 after optimisation. Observing Fig. 10, it can be noted that the objective function value after optimisation is significantly lower than before, halving the value from the pre-optimisation state. This indicates an overall improvement in comfort. However, there is a slight increase in vertical body displacement, rising from 0.0097 to 0.0109 m, which has a negligible impact. On the other hand, suspension dynamic deflection decreased from 0.0112 to 0.0086 m, and the wheel dynamic load response was reduced from 2208.8 to 1313.6 N. Further analysis reveals that, before optimisation, there was a risk of wheel lift due to excessive speed, which has been significantly mitigated after optimisation. The spring mass acceleration curve at this stage is shown in the Fig. 11.

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Figure 11 Curves of acceleration a before and after optimisation over half-sine speed bumps.

The spring mass acceleration has shown a slight increase due to the increase in vertical body displacement. The maximum acceleration value has increased from 0.8828 to 0.9301 m s −2 , and the average acceleration has increased from 0.0593 to 0.0645 m s −2 . While there is a slight sacrifice in comfort when passing over the semi-sinusoidal speed bump, it ensures the safety and stability of the vehicle.

4.3 Application effects of vehicles passing over periodic rectangular-wave speed bumps

After applying the optimisation algorithm to the rectangular-wave speed bumps, the optimal antibody has an affinity of 1.5087 after convergence, the vehicle speed v is 25.40 km h −1 , the front-suspension damping coefficient C f2 is 1623 kg s −1 , and the rear-suspension damping coefficient C r2 is 1617 kg s −1 .

From Fig. 12, it can be observed that the variations in vehicle speed v occur relatively infrequently, and their impact on the optimal affinity of the antibodies is relatively minor. The optimal affinity remains stable between the 160th and 175th generations. Similarly, the rear suspension damping coefficient C r2 converges around the 175th generation. The trends in changes for the front and rear damping coefficients are relatively similar. Upon comparison, it is evident that the overall optimal affinity increases as the absolute difference between the front and rear damping coefficients decreases. Consequently, when passing over speed bumps of rectangular shape, it is necessary to maintain closely matched front and rear damping coefficients. Ultimately, at around the 190th generation, the optimal affinity nearly converges, indicating that the optimal vehicle speed and related parameters for passing over rectangular speed bumps have been obtained.

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Figure 12 Iterative process for each variable of vehicle passing over periodic rectangular-wave speed bumps. (a) Iterative process for optimal affinity. (b) Iterative process for individual optimal v . (c) Iterative process for individual optimal C f 2 . (d) Iterative process for individual optimal C r 2 .

Figure 13 shows the steady-state response curves of the vehicle suspension system for each parameter system are plotted and contrasted to the response curves prior to optimisation.

https://ms.copernicus.org/articles/15/315/2024/ms-15-315-2024-f13

Figure 13 Comparison of vehicle response curves over periodic rectangular-wave speed bumps before and after optimisation. (a) Response curve for objective function values F . (b) Response curve for vertical body displacement x b . (c) Response curve for front-suspension dynamic deflection d df . (d) Response curve for front-wheel dynamic load d lf .

Before optimisation, the vehicle speed was 40.00 km h −1 , and after optimisation, it became 25.40 km h −1 . Upon observation of Fig. 13, it is evident that the objective function value after optimisation is significantly lower than before. The vertical body displacement has been reduced from 0.0129 to 0.0115 m. Additionally, there have been various degrees of improvement in suspension deflection and wheel load response after optimisation, decreasing from 0.0112 to 0.0054 m and from 2209.1 to 1394.9 N, respectively. These values are considerably lower than the responses before optimisation. Furthermore, it is notable that the risk of wheel lift when passing over rectangular speed bumps, which existed before optimisation, has been reduced. This indicates a substantial increase in the comfort and safety of the vehicle when crossing rectangular speed bumps after optimisation. And the acceleration variation curves of spring mass before and after optimisation are shown in Fig. 14.

https://ms.copernicus.org/articles/15/315/2024/ms-15-315-2024-f14

Figure 14 Acceleration curves before and after optimisation over rectangular-wave speed bumps.

It can be observed that, after optimisation, there has been a certain degree of reduction in the acceleration of the spring mass. The maximum accelerations before and after optimisation are 0.9807 and 0.9330 m s −2 , respectively. According to the rms criterion, the average accelerations before and after optimisation are calculated to be 0.0612 and 0.0543 m s −2 , respectively. This indicates that, through optimisation, there has been an improvement in the comfort and safety of the vehicle when crossing rectangular speed bumps.

4.4 Comparative analysis of the optimisation results for vehicles passing over different periodic speed bumps

After the optimisation of the IA, the comparison results before and after the vehicle pass over different types of periodic speed bumps are shown in Table 3.

Table 3 Analysis of optimisation results for vehicles passing over three types of speed bumps.

After optimisation of the immune optimisation algorithm, the optimum speeds and the relevant parameters for passing trapezoidal, half-sine, and rectangular-wave speed bumps are obtained. The indicators for measuring vehicle safety and comfort have been vastly improved compared to those before optimisation, and the most obvious is the trapezoidal speed bump.

The following information can be gleaned through horizontal comparison:

Under the premise of ensuring the efficiency of passing over speed bumps, the vehicle can maintain better comfort and safety when passing over rectangular-wave speed bumps at a relatively fast speed (25.40 km h −1 ) while keeping the front and rear damping coefficients of the vehicle within a relatively low range and requiring relatively little from the body suspension.

When passing over the half-sine speed bump, it is necessary to maintain a relatively low vehicle speed (15.33 km h −1 ) . However, the relatively large range of the vehicle's front and rear damping coefficients places relatively high demands on the body suspension.

The speeds and suspension damping coefficients over the trapezoidal speed bumps are between (1) and (2).

Passing over the rectangular speed bumps requires low vehicle suspension. These speed bumps can be passed over faster; they suitable for roads where the speed limits are not very high and do not need to be passed over at a very slow speed. Trapezoidal and half-sine speed bumps have relatively high requirements in terms of the body suspension, and the damage to the body suspension is more significant when passing over quickly. Therefore, these speed bumps are suitable for sections where speed limits are demanding and where passing speeds are slow to ensure the safety of the vehicle.

By comparing results before and after the optimisation, it can be observed that the vehicle has a slight increase in vertical displacement due to the influence of the shape of the speed bumps and the change in the relevant vehicle parameters when passing over the half-sine speed bumps. Although a small part of the vehicle's comfort has been sacrificed, it ensures the safety and stability of the vehicle.

The paper constructs a multi-objective optimisation algorithm based on the IA to optimise the vertical displacement of the body, the dynamic deflection of the suspension, and the dynamic wheel loads. The experiments simulate the 4-DOF vehicle passing over the periodic trapezoidal speed bumps, the periodic half-sine speed bumps, and the periodic rectangular-wave speed bumps to solve for the optimum speed and relevant parameters for passing and draw the following conclusions:

After optimisation by the immune optimisation algorithm, the optimum speed of the vehicle can be obtained when driving over various shapes of continuous speed bumps.

Various factors will affect the safety and comfort of the vehicle when passing over different speed bumps. In addition to adjusting the speed of the vehicle, the coefficients of the front- and rear-suspension dampers need to be adjusted based on an optimisation algorithm to obtain the optimum level of comfort and safety.

After optimisation by the immune optimisation algorithm, the comfort index of the vehicle over the three types of speed bumps was significantly improved, with the most remarkable improvement being in trapezoidal speed bumps, followed by rectangular-wave speed bumps, and finally half-sine wave speed bumps, with improvements of 42.59 %, 33.18 %, and 30.27 %, respectively.

Based on the premise of ensuring the efficiency of passing over speed bumps, rectangular-wave speed bumps are suitable for being passed over at a faster speed and can obtain good comfort and safety; they are suitable for road sections with relatively low speed limit requirements and do not need to be passed over very slowly. However, trapezoidal and half-sine speed bumps require relatively high suspension damping coefficients and need to be passed over at a slower speed to obtain good comfort and safety. They are suitable for road sections where the speed limit is strict and need to be passed over carefully and slowly.

The results of the paper offer a solution to the speed adaptation problem of vehicles passing over different speed bumps, provide a scientific basis for the installation of speed bumps on different-speed-limit roads, provide a reliable reference for the study of the comfort and safety of vehicles on uneven roads, and also provide reliable data for subsequent research on the comfort of speed adaptive regulation.

However, the optimisation algorithm is not sufficient to optimise the vehicle passing over the partial speed bumps; it improves the safety when the vehicle passes over the half-sine speed bumps, but the comfort is slightly sacrificed, and further research is needed. Meanwhile, the results of this study are still at the theoretical stage, and further experimental verification and optimisation on actual vehicles are required to achieve a higher level of research results.

Most the data used in this article can be obtained by request from the corresponding author ([email protected]).

ZY and RZ proposed and developed the overall concept of the paper. ZY, RZ, and YZ conducted the experimental realisation and analysis. RZ, ZG, and JG wrote the whole paper.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

The authors are grateful to the Fundamental Research Funds for the Science and Technology Research Project of Chongqing Municipal Education Commission, China (grant no. KJQN201903402); the Fundamental Research Funds for the Natural Science Foundation of Chongqing, China (grant no. cstc2021ycjh-bgzxm0088); and the Fundamental Research Funds for Science and Technology Research Project of Chongqing Municipal Education Commission, China (grant no. KJZD-M202303401), for the support.

This research has been supported by the Natural Science Foundation of Chongqing Municipality (grant no. cstc2021ycjh-bgzxm0088), the Program for Innovation Team Building at Institutions of Higher Education in Chongqing Municipality (grant no. CXQT21032), and the Chongqing Municipal Education Commission (grant no. KJZD-M202303401).

This paper was edited by Marek Wojtyra and reviewed by two anonymous referees.

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Introduction
Simulation models
Multi-objective optimisation algorithm
Application of optimisation algorithm
Conclusions
Data availability
Author contributions
Competing interests
Acknowledgements
Financial support
Review statement

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