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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.

Table of contents

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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15 Types of Research Methods

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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.

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.

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.

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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

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Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

Why is data validation important in research?

When Data Speak, Listen: Importance of Data Collection and Analysis 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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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• 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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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

There are four types of hypotheses :

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

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

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

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

Sampling techniques

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

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

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

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

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

Experiments always have an independent and dependent variable .

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

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

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

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

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

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

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

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

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

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

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

Experimental Design

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

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

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

Experimental Methods

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

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

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

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

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

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

Correlational Studies

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

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

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

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

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

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

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

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

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

Interview Methods

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

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

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

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

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

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

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

Questionnaire Method

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

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

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

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

Observations

There are different types of observation methods :

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

Pilot Study

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

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

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

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

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

Research Design

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

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

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

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

Reliability

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

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

Meta-Analysis

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

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

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

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

Peer Review

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

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

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

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

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

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

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

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

Types of Data

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

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

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

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

Features of Science

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

Statistical Testing

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

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

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

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

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

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

Ethical Issues

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

Research Methodologies

• What are research designs?

What are research methodologies?

Quantitative research methodologies, qualitative research methodologies, mixed method methodologies, selecting a methodology.

• What are research methods?
• Additional Sources

According to Dawson (2019),a research methodology is the primary principle that will guide your research.  It becomes the general approach in conducting research on your topic and determines what research method you will use. A research methodology is different from a research method because research methods are the tools you use to gather your data (Dawson, 2019).  You must consider several issues when it comes to selecting the most appropriate methodology for your topic.  Issues might include research limitations and ethical dilemmas that might impact the quality of your research.  Descriptions of each type of methodology are included below.

Quantitative research methodologies are meant to create numeric statistics by using survey research to gather data (Dawson, 2019).  This approach tends to reach a larger amount of people in a shorter amount of time.  According to Labaree (2020), there are three parts that make up a quantitative research methodology:

• Sample population
• How you will collect your data (this is the research method)
• How you will analyze your data

Once you decide on a methodology, you can consider the method to which you will apply your methodology.

Qualitative research methodologies examine the behaviors, opinions, and experiences of individuals through methods of examination (Dawson, 2019).  This type of approach typically requires less participants, but more time with each participant.  It gives research subjects the opportunity to provide their own opinion on a certain topic.

Examples of Qualitative Research Methodologies

• Action research:  This is when the researcher works with a group of people to improve something in a certain environment.  It is a common approach for research in organizational management, community development, education, and agriculture (Dawson, 2019).
• Ethnography:  The process of organizing and describing cultural behaviors (Dawson, 2019).  Researchers may immerse themselves into another culture to receive in "inside look" into the group they are studying.  It is often a time consuming process because the researcher will do this for a long period of time.  This can also be called "participant observation" (Dawson, 2019).
• Feminist research:  The goal of this methodology is to study topics that have been dominated by male test subjects.  It aims to study females and compare the results to previous studies that used male participants (Dawson, 2019).
• Grounded theory:  The process of developing a theory to describe a phenomenon strictly through the data results collected in a study.  It is different from other research methodologies where the researcher attempts to prove a hypothesis that they create before collecting data.  Popular research methods for this approach include focus groups and interviews (Dawson, 2019).

A mixed methodology allows you to implement the strengths of both qualitative and quantitative research methods.  In some cases, you may find that your research project would benefit from this.  This approach is beneficial because it allows each methodology to counteract the weaknesses of the other (Dawson, 2019).  You should consider this option carefully, as it can make your research complicated if not planned correctly.

What should you do to decide on a research methodology?  The most logical way to determine your methodology is to decide whether you plan on conducting qualitative or qualitative research.  You also have the option to implement a mixed methods approach.  Looking back on Dawson's (2019) five "W's" on the previous page , may help you with this process.  You should also look for key words that indicate a specific type of research methodology in your hypothesis or proposal.  Some words may lean more towards one methodology over another.

Quantitative Research Key Words

• How satisfied

Qualitative Research Key Words

• Experiences
• Thoughts/Think
• Relationship
• << Previous: What are research designs?
• Next: What are research methods? >>
• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

• > General Analytics

Different Types of Research Methods

• Mallika Rangaiah
• Dec 22, 2021
• Updated on: Nov 21, 2023

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth. 

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means. 

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it? 

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same.  Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data. 

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue. 

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition),  the basic types of Research Methods are the following : 

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research. 

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place. 

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends. 

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies. 

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data. 

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based. 

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done. 

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue. 

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business. 

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company. 

The crux of Applied Research is to figure out the solution to a certain growing practical issue. 

The 3 Types of Applied Research are mainly 

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research. 

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry. 

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science. 

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression. 

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it. 

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed. 

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists. 

Quantitative research methods

• Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables. 
• Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone. 
• (Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting. 
• Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey. 

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men. 

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts. 

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association. 

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

• Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
• Interviews: Personally asking people questions in one-on-one conversations.
• Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same. 
• Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers. 
• Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts. 

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments. 

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective. 

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available. 

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.   

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment. 

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research. 

One time Research : The research is restricted to a single time period. 

Longitudinal Research : The research is executed over multiple time periods. 

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on. 

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time. 

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships. 

Research can also be Exploratory or Formalized. 

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results. 

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing. 

We can also classify Research as conclusion-oriented and decision-oriented. 

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements. 

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases. 

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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Education: Research Methods & Lit Reviews

Research Methods

Sage Research Methods (SRM) is a collection of resources and tools intended to assist researchers as they plan, conduct and analyze their research SRM is the best place to start and covers the entire research process from developing a research question, to conducting literature reviews, gathering and analyzing data, and publishing your work. See the SAGE Research Methods guide for more information.

Developing Impactful Interdisciplinary Research Projects

Finding articles using specific research methods.

• Strategies to Use When Searching Databases Combine your topic with specific research methods such as qualitative, quantitative, case study, interview, survey, statistical, grounded theory, focus group, T-test, mixed-methods, etc. For example, to find articles using case studies dealing with equity in higher education in ERIC : "case studies" AND equity AND "higher education"

Additional Guides for Educational Research

Literature reviews.

Literature reviews are specialized articles that provide an overview and synthesis of the current state of research on a specific topic.

Finding Literature Reviews

• Review of Research in Education 1973-
• Strategies to Use When Searching Databases Combine your topic with phrases such as "review of research" or "literature review" or "systematic review." For example, to find literature reviews on special education in the Education Source database: "special education" AND "literature review"

Guides for Writing Literature Reviews

• Literature Reviews for Education and Nursing Graduate Students
• Systematic Reviews in Educational Research
• Writing for Inquiry and Research
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What are the Market Research Methods?

• August 15, 2023
• Topic: Brand Strategy , Corporate , Corporate Trends , Customer Experience , Market Analysis , Product Lifecycle
• Resource type: Insights Blog

So, you have a question, but you are unsure of how to get your answer. Maybe you are wondering who your target audience is or why you lost out on a deal to your competition. Maybe you are looking to expand into a new market and want to know more about the customers and competitors in the industry. While these examples are similar in the way they help you understand your business better, they all require different market research methodologies to arrive at the answer.  

What are Market Research Methodologies?  

Research methodologies are various ways to perform research to understand your problem. The correct type to employ depends on the answers you are seeking, the information you have, and the information you need to gather. There are many different methods, but most fall into four categories: data analytics, survey, qualitative, and secondary.

In this post, we will provide an overview of the four main research methodologies along with the benefits and challenges of each.  

Custom or Syndicated Research  

In addition to the types of methodologies, there are two types of funded research: custom and syndicated.   

Custom research is funded by a single company and is focused on answering the key questions the business seeks to understand. Though more costly, the research design, implementation, and results are unique and targeted toward addressing the funding company’s needs.  

On the other hand, syndicated research is not curated or funded by a specific client; a market research company conducts it to offer data such as industry statistics, current best practices, or recent trends. Though not directly tied to a single company’s situation, businesses often buy syndicated research to gather perspective on their performance and identify areas where custom research can help provide more insight.   

The Four Types Of Market Research   

Data analytics  .

Data analytics research involves collecting and analyzing large sets of data to derive answers, uncover patterns, and predict future outcomes. This method helps you identify and understand things you are aware of but don’t yet understand.

Data can come from a variety of sources including CRM data, historical transactional data, survey data, a third-party publisher, and more to build a holistic map of the situation, identify gaps and discern trends. Data analytics is the most common research method with almost 70% of companies using it in at least one market research project in the past year.  

For example, you might have large sets of historical data and know there is a data-backed answer for how to segment your customers, but you have yet to compile all your information together to identify the answer.  

Benefits and Challenges

Benefits: Analyzing historical data provides a holistic view of a situation by combining different sets of data and modeling potential scenarios and outcomes. You can confirm hypotheses, break biases, and help build cases internally.

Challenges: This method requires a lot of data, and some of that data may be hard to access, hard to generate, or not easily analyzed. This method also requires a lot of time, money, and resources to acquire and parse the correct data.   

Survey research involves gathering opinions, preferences, and experiences by asking a set of questions to a targeted group of people. The focus of survey research is to test theories, assumptions, and hypotheses. The answers are collected from a representative sample of a targeted audience, allowing the researchers to quantify data and generalize the results to the wider population with a reasonable margin of error and strong confidence level.

Survey data can be collected from consumers, other business decision-makers, or your customer lists. Surveys are a very popular market research methodology with over 60% of companies performing at least one survey in the last year.  

For example, you may be wondering how satisfied your customers are, what factors drive satisfaction, and how you compare to key competitors in the market. By surveying your customers and those of key competitors you can understand the drivers of satisfaction and your relative strengths and opportunity areas in the market.  

Benefits and Challenges 

Benefits : Surveys provide an aggregate but statistically significant picture that companies can leverage to make decisions that align with their audiences’ preferences. Surveys also offer the ability to segment answers based on segments of the audience to analyze how different groups respond to the same questions.   

Challenges: Surveys are a fixed set of questions and cannot be adjusted once the survey has been deployed. Responses are limited to the questions posed by the researcher and don’t allow for open-ended qualitative responses. Surveys require many respondents, and depending on the target audience, it can be challenging to find a large enough sample size to provide statistically significant results. Lastly, surveys need to incentivize respondents, which could lead to a high price tag.  

Qualitative Methodologies   

Qualitative research focuses on targeted insights around concepts, opinions, and preferences. Unlike quantitative methods, these market research methodologies leverage a smaller set of data and respondents but allow for more in-depth answers. It also allows for companies to gather follow-up data that delves deeper into the reasoning behind responses  

This method is exploratory in nature to help you formulate hypothesis and establish directional themes or trends. Qualitative research also helps you understand the underlying motivations, attitudes, and perceptions of respondents.  

 The two most common qualitative research methodologies are in-depth interviews and focus groups.  

In-depth interviews  

This market research methodology involves one-on-one conversations between interviewers and those from the target audience. The interview follows a pre-determined set of questions to reveal sentiment, decision-making processes, and unmet needs. With only 40% of companies conducting them, interviews are the least used methodology, likely a result of the challenges mentioned below.  

Benefits : Interviews provide the ability to gather more in-depth answers on customer preferences by allowing researchers to ask follow-up questions to probe deeper and further clarify responses. It also allows respondents to answer in their own words rather than be bound by the available responses offered by a survey. 

Challenges: Interviews are responses from a small group of people and the results cannot be generalized to a wider audience. They are also very challenging to implement. Often, it is a struggle to identify and incentivize enough participants, and the price per respondent can be costly depending on their rarity and level of expertise. It is also critical to enlist an experienced interviewer to ensure that both the initial and follow-up questions are tailored to gather accurate information that fully addresses your target questions.   

  Focus groups   

These facilitator-led group discussions reveal perceptions of or reception to a concept or idea. While the facilitator guides the meeting, the direction of the conversation is determined by the participants creating organic responses that stem from participant perception. Just over half of companies have conducted a focus group in the last year.   

Benefits: F ocus groups allow for exploration of concepts and physical products beyond set responses like those available in through a survey. The social aspect of the focus group can also gather multiple points of view on a topic in one setting. This can add additional insight for both participants in their ongoing feedback and facilitators for their final analysis.

Challenges: Focus groups are kept small to gather meaningful insights from a group of people, something that would be difficult if the group was too large. As such, the sample size is very small, and the responses can‘t be extrapolated to a larger audience.  It is also challenging to find a group of qualified participants that are all available at the same time.

Traditionally, focus groups were conducted in person and there was a higher cost to host the group live. Now depending on the product or concept being reviewed, focus groups can be conducted over video calls, lessening the burden of cost and logistics, however the cost to incentivize members to take part remains. Similar to interviews, you will need to enlist an experienced moderator that can facilitate the conversation and help direct it as needed to ensure the target questions are addressed  

Secondary Research Methodologies     

Secondary research, a lso known as desk research, is leveraging data that already exists to answer your questions. This market research method is helpful for answering questions or deepening your understanding of things you are not directly familiar with but understand. It can be used to understand what others in your market are doing, identify potential markets for growth or expansion, or allow you to compare your organization to others on key performance indicators.  

For example, you might understand that customer preferences have affected your market, but you don’t know the exact changes. However, others have already done related research that can provide context or direct answers to your question. Secondary research is a very popular method with over half ( 55% ) of companies conducting secondary research to get insights they need for their strategies.   

Benefits: Secondary research is one of the quicker methodologies as it leverages existing data. The bulk of the time is spent identifying the problem, accessing existing data, and consolidating it for analysis and insights.   

Challenges: Some of the data you need might require payment, which would increase the cost of the overall project. There is also the risk that a data point needed for your analysis does not exist, requiring you to either speak to an expert or conduct your own research to fill in the gap.  

Picking the Right Research Methodology  

Though there are many options to choose from, the correct market research methodology to implement will be guided by the information you already have and the questions you are trying to answer.

Before you start your research, begin by listing what you know and what you are looking to learn. Some choices are very clear cut. For example, are you looking to learn more about your company’s operations in the hopes of identifying a better strategy? Since you have access to your own data and are looking to learn more, data analytics would be your best path forward.  

Sometimes choosing the right market research methodology might require more thought. For example, are you looking to launch a new product and want to learn more about customer preferences? You could interview customers or launch a focus group, but do you know what questions to ask? And as the sample pool is so small, the results from qualitative methods should not be used to make assumptions about a larger customer base.

The best place to start would be to conduct a survey to the target audience to get a basic understanding of the market and potential customer preferences. If it is a well-known customer base, you may be able to through secondary research by leveraging existing data to analyze the market.  

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• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Q&A: What is the American Trends Panel, and how has it changed?

At Pew Research Center, we mostly survey the U.S. public through the American Trends Panel (ATP) – a randomly selected, nationally representative group of more than 12,000 adults who agree to take our polls on a regular basis. We created the panel in 2014 and have made several improvements to it since then.

In this Q&A, we chat with Dorene Asare-Marfo, senior panel manager of the ATP, about the fundamentals of the panel, some recent changes we’ve made to it, and more. (Read more about the ATP’s methodology .)

Let’s start with the basics. What is a survey panel? How do you get people to join one? How do participants take the surveys?

A survey panel is made up of a group of people who have agreed to regularly take surveys.

At the Center, we recruit people to the American Trends Panel using printed mailers that we send to a random sample of U.S. addresses. This way, nearly every American has a chance to be included. We ask a person who lives at the sampled address to take one of our surveys, and at the end of that survey, we invite them to take additional ones in the future.

To help persuade people to join the panel, we include a small amount of money in the mailers we send out. And for those who end up joining, we provide compensation for every survey they complete.

We typically send out one or two surveys each month, but not every panelist is asked to take each one. Rather than invite every panelist to every survey, we’ll often send a survey to a subset of panelists (e.g., to 5,000 or 10,000 of them).

The vast majority of ATP participants take our surveys online, using a smartphone, laptop or any internet-enabled device they prefer. But we also now give people the option to take surveys over the phone with an interviewer. This is to account for the fact that not everyone is online or feels comfortable taking surveys online.

Why did you create the American Trends Panel in the first place? 

It was very much a response to the challenges facing the survey research industry.

The model for most polling in the past few decades was “one-off” surveys. That is, each survey relied on a new sample of respondents who we interviewed once and never again. This was a workable model when response rates to surveys – particularly phone surveys – were a lot higher than they are now. But response rates have plummeted over the years, so the benefits of a panel approach became more apparent.

Panels are also cost-effective. While we devote a lot of resources to recruiting panelists and getting a good representative sample of the country, the people who ultimately join the panel tend to participate at high rates. Typically, more than 90% of the panelists we invite to take a particular survey end up doing so. Over time, this is a much more efficient and economical way to conduct high-quality surveys.

Having a panel also allows us to avoid asking some of the same questions every time we conduct a survey. For example, once we’ve collected our panelists’ basic demographic information – race, gender, age and so forth – we don’t need to ask those questions again in each survey.

One other important bonus is that a panel allows us to see how people’s attitudes and behaviors change – or don’t change – over time. For example, a few years ago, we  published a study  showing that most people who had voted for Donald Trump in 2016 continued to have very warm feelings about him in early 2018.

Are there any downsides to using a survey panel?

American Trends Panel participants take a lot of surveys. Like anything you do regularly, you become familiar with the experience, which can be good or bad.

The good part is that experienced panelists better understand what’s expected of them. They may gain trust in the survey research process, making them more honest and careful with their answers.

The downside is that they might become more interested in or knowledgeable about the topics we ask about, or they may change in other ways because of the experience of being part of the panel. In other words, panelists may no longer behave the way a fresh sample of people would. We call this “panel conditioning.”

We took a closer look at panel conditioning in 2021. The good news is we didn’t find any evidence that panel conditioning led to inaccurate estimates for news consumption, discussing politics, political partisanship or voting, though we did see that joining the panel led to a slight uptick in voter registration.

Another important thing to keep in mind is that having a survey panel is not necessarily a guarantee of quality. How the panel is constructed also matters. Some panels are recruited using “opt-in” methods, meaning that people can volunteer to take part. These panels may suffer from data quality problems because of the presence of people who are there only for the compensation and are providing insincere or sloppy responses to questions.

The ATP is much less likely to have this problem because we do not recruit via opt-in methods. Instead, we’re the ones selecting people for inclusion in our panel, and we use random sampling to do that. (Read more about some of the pitfalls of opt-in polling .)

When you recruit new people to the American Trends Panel, what kinds of people are you looking for?

Our main goal is to make sure the panel represents the general public – that is, people who live in the United States and are ages 18 and older. At the same time, we’re interested in understanding the views and experiences of many smaller groups in the population, so it’s essential for us to have enough people from those groups represented on the panel.

To do this, we’ve been using our annual recruitment efforts to target certain groups that the panel may be underrepresenting. For instance, we “oversampled” Hispanic, Black and Asian adults in 2019, 2022 and 2023, respectively. This year, we oversampled young adults, people who do not have a college degree, and certain religious minorities, including Muslim and Jewish Americans. Oversampling allows us to report on the opinions of small groups.

It’s important to note that any numbers we report about the whole population are statistically adjusted so that smaller subgroups are represented in their correct proportions.

What changes have you made to the American Trends Panel recently and why?

We’re always looking for ways to improve the panel, but this year brought an especially important change. We’ll now be giving our panelists the option to take our surveys over the phone with an interviewer, in addition to online. This is mostly aimed at people who don’t have internet access or who don’t want to take our surveys online.

For the last eight years, we included these people by providing them with an internet plan and a tablet that they could use to take our surveys. But over time, we found this was not as effective at capturing offline Americans as we’d hoped. So we felt that offering a phone option would be an improvement.

Another change is that a new vendor, SSRS, has taken over the data collection and panel maintenance responsibilities this year. They’ll handle various logistical tasks, such as taking the questionnaires we write and programming them for administration on the web and phone; handling panel relations and ensuring panelists get paid for each survey they take; collecting and cleaning the data we gather and sending it to us; and so much more.

• Research Explainers
• Survey Basics
• Survey Methods

John Gramlich is an associate director at Pew Research Center .

How do people in the U.S. take Pew Research Center surveys, anyway?

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A new method to study immune cells offers hope for finding new disease treatments

by University of Surrey

Macrophages play a critical role in the immune system by fighting infections and aiding in tissue repair. Understanding how these cells are activated in different scenarios is important for developing new medical treatments. However, identifying and studying macrophage activation has been challenging due to the complex nature of these cells and their responses.

A research team has identified a protein called "colony stimulating factor 1 receptor" (CSF1R) as a reliable marker for monocytes and dendritic cells in blood and macrophages in tissues—allowing for the clear identification and separation of different sample types. The new method works reliably for people of all ages and sexes.

The study is published in the journal Cell Reports .

Dr. Fernando Martinez Estrada, who led the research project and is Senior Lecturer in Innate Immunology in the School of Biosciences at the University of Surrey, said, "We have developed a method using CSF1R that can identify all types of Mononuclear phagocyte system cells in the body.

"This marker is incredibly useful for studying these cells in both health and disease, and it unlocks exciting new possibilities for cell isolation and quantification for diagnosing and monitoring various conditions with a single cell marker."

The study developed a set of tools to understand and check how these immune cells respond when they are activated. These tools focus on signals in the body, including IL-4 (involved in healing and fibrosis), steroids (deactivation), IFNγ (fights infections), and LPS (a bacterial product that causes inflammation).

The research team also described a novel concept that they call Macrophage Activation Mosaicism. This means that macrophages do not simply switch between the canonical two states previously described; instead, they can exhibit a mix of activation characteristics, reflecting the complexity of real tissue environments.

Dr. Federica Orsenigo, co-author of the study, explains, "This discovery is significant because it changes how we perceive macrophage activation.

"Recognizing that macrophages can have mixed activation status helps us better understand their roles in different diseases and could lead to more targeted and effective treatments."

Emeritus Professor Siamon Gordon, co-author of the study from the University of Oxford, said, "Therapies that seek to re-educate macrophages are widely sought. However, the tools to measure activation are underdeveloped.

"Having a robust multi-gene tool to study macrophage activation can help in drug screening , identify drugs that revert macrophage activation , and eventually help with patient characterization and personalized medicine."

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JRC methods to help detect and quantify antibiotic residues in animal feed

Official National control laboratories across the EU have until 20 May 2025 to prepare for carrying out tests to detect and to quantify trace levels of antibiotics in compound feed.

Leveraging its expertise in food and feed analysis, the JRC developed and validated cutting-edge analytical methods capable of detecting trace amounts of 24 target antibiotics in compound feed. 

This boosts the EU fight against  antimicrobial resistance (AMR) – the ability of micro-organisms to survive or grow despite treatment with antimicrobial drugs. The methods make part of  Delegated Regulation (EU) 2024/1229   which limits the presence of antibiotics in  animal feed and empowers National control labs across the EU to detect and analyse 24 antibiotics in compound feed.

Compound feed contains a mix of nutritional raw materials for farmed animals. Antibiotics can be added to restore animal health, but misuse or excessive use can drive the development of antimicrobial resistance (AMR), i.e. drug-resistant pathogens that can potentially pass to humans. AMR is responsible for more than 35,000 deaths annually in EU/EEA (EU27 and Iceland, Lichtenstein and Norway). Furthermore, it is one of the top three priority health threats in the EU.

Applicable as of 20 May 2025, the regulation establishes specific maximum levels of cross-contamination of antimicrobial active substances in compound feed and lays down the reference methods for the analysis of residues of 24 antibiotics.

By enabling the detection of antibiotics across various types of compound feed at "as low as achievable" levels, these methods play a pivotal role in safeguarding animal welfare and public health. The findings served as a crucial criterion for establishing maximum acceptable levels of cross-contamination.

This legislation supplements  Regulation (EU) 2019/4 that established stringent guidelines for the manufacture, placement on the market and use of  medicated feed . 

Anti-microbial resistance is a serious threat today and will be for decades to come. Standing at the forefront, JRC upheld the highest standards of food safety as illustrated by this specific scientific work. The JRC-developed methods constitute a significant contribution and advancement in the concerted global effort to combat antimicrobial resistance and preserve the well-being of both animals and humans alike.

Regulation (EU) 2019/4 of the European Parliament and of the Council on medicated feed is one of the major initiatives that have contributed to further strengthening the EU’s response to AMR, together with the adoption of the 2017 AMR Action Plan and the adoption of the 2023 Council Recommendation on stepping up EU actions to combat antimicrobial resistance in a One Health approach. 

In particular, the  proposal for a Council Recommendation extends and complements the  2017 EU One Health Action Plan against AMR in all three dimensions of the One Health spectrum – people, animals and ecosystems – to maximise synergies and attain a strong and effective response against AMR across the EU.

One of the objectives of this proposal for a Council Recommendation is to reinforce surveillance and monitoring of AMR and antimicrobial consumption.

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Delegated Regulation (EU) 2024/1229

Determination of 24 antibiotics at trace levels in animal feed by High Performance Liquid Chromatography - Tandem Mass Spectrometry (LC- MS/MS)

Antimicrobial resistance (AMR)  

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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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New AI algorithm flags deepfakes with 98% accuracy — better than any other tool out there right now

Recent research into methods for spotting AI-generated video looks for specific markers not found in standard digital images.

With the release of artificial intelligence (AI) video generation products like Sora and Luma, we're on the verge of a flood of AI-generated video content, and policymakers, public figures and software engineers are already warning about a deluge of deepfakes. Now it seems that AI itself might be our best defense against AI fakery after an algorithm has identified telltale markers of AI videos with over 98% accuracy.

The irony of AI protecting us against AI-generated content is hard to miss, but as project lead Matthew Stamm, associate professor of engineering at Drexel University, said in a statement : "It's more than a bit unnerving that [AI-generated video] could be released before there is a good system for detecting fakes created by bad actors."

"Until now, forensic detection programs have been effective against edited videos by simply treating them as a series of images and applying the same detection process," Stamm added. "But with AI-generated video, there is no evidence of image manipulation frame-to-frame, so for a detection program to be effective it will need to be able to identify new traces left behind by the way generative AI programs construct their videos."

The breakthrough, outlined in a study published April 24 to the pre-print server arXiv , is an algorithm that represents an important new milestone in detecting fake images and video content. That's because many of the "digital breadcrumbs" existing systems look for in regular digitally edited media aren't present in entirely AI-generated media.

Related: 32 times artificial intelligence got it catastrophically wrong

The new tool the research project is unleashing on deepfakes, called "MISLnet", evolved from years of data derived from detecting fake images and video with tools that spot changes made to digital video or images. These may include the addition or movement of pixels between frames, manipulation of the speed of the clip, or the removal of frames.

Such tools work because a digital camera's algorithmic processing creates relationships between pixel color values. Those relationships between values are very different in user-generated or images edited with apps like Photoshop.

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But because AI-generated videos aren't produced by a camera capturing a real scene or image, they don't contain those telltale disparities between pixel values.

— 3 scary breakthroughs AI will make in 2024

— Photos of Amelia Earhart, Marie Curie and others come alive (creepily), thanks to AI

— Duped by Photoshop: People Are Bad at Spotting Fake Photos  

The Drexel team's tools, including MISLnet, learn using a method called a constrained neural network, which can differentiate between normal and unusual values at the sub-pixel level of images or video clips, rather than searching for the common indicators of image manipulation like those mentioned above.

MISL outperformed seven other fake AI video detector systems, correctly identifying AI-generated videos 98.3% of the time, outclassing eight other systems that scored at least 93%.

"We've already seen AI-generated video being used to create misinformation," Stamm said in the statement. "As these programs become more ubiquitous and easier to use, we can reasonably expect to be inundated with synthetic videos."

Drew is a freelance science and technology journalist with 20 years of experience. After growing up knowing he wanted to change the world, he realized it was easier to write about other people changing it instead. As an expert in science and technology for decades, he’s written everything from reviews of the latest smartphones to deep dives into data centers, cloud computing, security, AI, mixed reality and everything in between.

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How Do Our Memories Last a Lifetime? New Study Offers a Biological Explanation

Whether it’s a first-time visit to a zoo or when we learned to ride a bicycle, we have memories from our childhoods kept well into adult years. But what explains how these memories last nearly an entire lifetime? 

A new study in the journal Science Advances , conducted by a team of international researchers, has uncovered a biological explanation for long-term memories. It centers on the discovery of the role of a molecule, KIBRA, that serves as a “glue” to other molecules, thereby solidifying memory formation.

“Previous efforts to understand how molecules store long-term memory focused on the individual actions of single molecules,” explains André Fenton, a professor of neural science at New York University and one of the study’s principal investigators. “Our study shows how they work together to ensure perpetual memory storage.”

“A firmer understanding of how we keep our memories will help guide efforts to illuminate and address memory-related afflictions in the future,” adds Todd Sacktor, a professor at SUNY Downstate Health Sciences University and one of the study’s principal investigators.

Discovery of a "glue" molecule's purpose affirms a concept introduced Nobel Laureate Francis Crick to explain the brain’s role in memory storage.

It’s been long-established that neurons store information in memory as the pattern of strong synapses and weak synapses, which determines the connectivity and function of neural networks. However, the molecules in synapses are unstable, continually moving around in the neurons, and wearing out and being replaced in hours to days, thereby raising the question: How, then, can memories be stable for years to decades?  

In a study using laboratory mice, the scientists focused on the role of KIBRA, or kidney and brain expressed protein, the human genetic variants of which are associated with both good and poor memory. They focused on KIBRA’s interactions with other molecules crucial to memory formation—in this case, protein kinase Mzeta (PKMzeta). This enzyme is the most crucial molecule for strengthening normal mammalian synapses that is known, but it degrades after a few days.

Their experiments reveal that KIBRA is the “missing link” in long-term memories, serving as a “persistent synaptic tag,” or glue, that sticks to strong synapses and to PKMzeta while also avoiding weak synapses.

“During memory formation the synapses involved in the formation are activated—and KIBRA is selectively positioned in these synapses,” explains Sacktor, a professor of physiology, pharmacology, anesthesiology, and neurology at SUNY Downstate. “PKMzeta then attaches to the KIBRA-synaptic-tag and keeps those synapses strong. This allows the synapses to stick to newly made KIBRA, attracting more newly made PKMzeta.”

More specifically, their experiments in the Science Advances paper show that breaking the KIBRA-PKMzeta bond erases old memory. Previous work had shown that randomly increasing PKMzeta in the brain enhances weak or faded memories, which was mysterious because it should have done the opposite by acting at random locations, but the persistent synaptic tagging by KIBRA explains why the additional PKMzeta was memory enhancing, by only acting at the KIBRA tagged sites. 

Memories are stored by the interaction of two proteins: a structural protein, KIBRA (green), that acts as a persistent synaptic tag, and a synapse-strengthening enzyme, protein kinase Mzeta (red). Drugs that disrupt the memory-perpetuating interaction (other colors) erase pre-established long-term and remote memories. Credit: Changchi Hsieh, Ph.D.

“The persistent synaptic tagging mechanism for the first time explains these results that are clinically relevant to neurological and psychiatric disorders of memory,” observes Fenton, who is also on the faculty at NYU Langone Medical Center’s Neuroscience Institute. 

The paper’s authors note that the research affirms a concept introduced in 1984 by Francis Crick. Sacktor and Fenton point out that his proposed hypothesis to explain the brain’s role in memory storage despite constant cellular and molecular changes is a Theseus’s Ship mechanism—borrowed from a philosophical argument stemming from Greek mythology in which new planks replace old ones to maintain Theseus’s Ship for years.

“The persistent synaptic tagging mechanism we found is analogous to how new planks replace old planks to maintain Theseus’s Ship for generations, and allows memories to last for years even as the proteins maintaining the memory are replaced,” says Sacktor. “Francis Crick intuited this Theseus’s Ship mechanism, even predicting the role for a protein kinase. But it took 40 years to discover that the components are KIBRA and PKMzeta and to work out the mechanism of their interaction.”

The study also included researchers from Canada’s McGill University, Germany’s University Hospital of Münster, and University of Texas Medical School at Houston.

This work was supported by grants from the National Institutes of Health (R37 MH057068, R01 MH115304, R01 NS105472, R01 MH132204, R01 NS108190), the Natural Sciences and Engineering Research Council of Canada Discovery (203523), and the Garry and Sarah S. Sklar Fund.

About New York University Founded in 1831, NYU is one of the world’s foremost research universities (with more than $1 billion per year in research expenditures, it is ranked seventh among private research universities) and is a member of the selective Association of American Universities. NYU has degree-granting university campuses in New York, Abu Dhabi, and Shanghai; has 13 other global academic sites, including London, Paris, Florence, Tel Aviv, Buenos Aires, and Accra, and US sites in Washington, DC, Los Angeles, CA, and Tulsa, OK; and both sends more students to study abroad and educates more international students than any other U.S. college or university. Through its numerous schools and colleges, NYU is a leader in conducting research and providing education in the arts and sciences, law, medicine, business, dentistry, engineering, education, nursing, the cinematic and performing arts, music and studio arts, public service, social work, public health, and professional studies, among other areas.

About SUNY Downstate Health Sciences University Downstate Health Sciences University in Brooklyn is one of four academic health centers (AMCs) in the 64-campus State University of New York (SUNY) system and the only SUNY AMC in New York City dedicated to health education, research, and patient care for the borough’s 2.7 million residents. Its flagship hospital, University Hospital at Downstate (UHD), is a teaching hospital that benefits from the expertise of Downstate’s exceptional medical school and world-class research facilities. Beyond its clinical excellence, Downstate houses a range of esteemed educational institutions, including the College of Medicine, College of Nursing, School of Health Professions, School of Graduate Studies, and School of Public Health. Downstate fosters innovation through its multifaceted biotechnology initiatives, including the Biotechnology Incubator and BioBAT, which support both early-stage and more mature biotech companies. Downstate’s research enterprise drives innovation and discovery across a wide array of disciplines. Our investigators are making discoveries that are changing the world and pushing the boundaries of what is possible in biomedicine and healthcare.

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