what are research methods essay

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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Exam Tips: How to write a research methods essay

Travis Dixon March 17, 2019 Research Methodology , Revision and Exam Preparation

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Updated Feb 2021

One of the most difficult of the five types of exam questions to write about in IB Psychology is research methods. Like most other essay questions, students tend to focus on studies and miss other important aspects of the essay. In this post, I offer my best tips for how to write an excellent essay on research methods.

What-How-Why

Our unit on Quantitative Methods is designed to help students answer these challenging questions.

These three simple questions are an excellent guideline for most of the IB Psych’ course. Research methods essays are no different. You want to show you:

• Know what the research methods are
• Understand  how  and  why they’re used

The following helps you figure out how to address these key questions. However, the structure and content of your answer will depend on the specific question you’re answering and the materials you’ve used in your course.

• Exam tips for writing about research methods and ethics
• Biological Research Methods Example Essay (ERQ)
• How to explain the use of a research method

Step 1: What?

The first thing to get right is the definition . It’s really important to have a clear, precise and accurate definition of the research method/s you’re writing about. This could be either in the introduction or in the first body paragraph of the essay (i.e. the central argument). After defining the method, the explanation of how and why should begin.

Step 2: How?

Every essay answer should have a central argument that shows knowledge and understanding of the topic in the question (watch this video for more essay advice). In the case of research methods questions, the central argument should be an explanation of how and why the particular method is used in that particular topic . This is a key part of the answer that many students miss.

For example, if you’re explaining the use of a true experiment the “how” would include the nature of the independent variables and perhaps common ways the dependent variable is measured. For instance, in true experiments on the brain the IV is commonly a factor hypothesized to affect brain activity like chemical messengers such as serotonin and the effects of the manipulation of this IV on the brain is measured using fMRI.

Tip:  A detailed central argument explaining how and why the method is used is the key to scoring top marks in the “knowledge and understanding” section of the essay rubric. Your explanation of the method would be identical in an SAQ and an essay.

Step 3: Why?

This is straightforward – give reasons why this method is useful. Weak answers will be generic, such as “true experiments are helpful because they can establish causal relationships.” Stronger answers will focus on specific details of the field of psychology in question. For instance, an essay on true experiments might focus on animal experimentation and the benefits of manipulating brain activity in rodents and animals in ways that couldn’t be done in humans but provide valuable additional information alongside human quasi experiments and correlational studies.

Scroll on for more exam tips.

DOWNLOAD Example Research Methods Essay

Common Error: What is a “method”?

One of the biggest mistakes a student can make in a research method essay is writing about a method that actually isn’t a “ research method .” For example, if a student wrote about animal studies, twin studies, or brain imaging technology as a research method they would score very low marks because these are not research methods (according to the IB).

To quote the official IB FAQs document, “…the different research methods for the study of psychology at this level: case studies, naturalistic observations, interviews, experiments, field experiments, quasi-experiments, natural experiments, correlations studies.” Therefore, make sure the method/s you are writing about is from this list.

Tip:  Prepare to write about true experiments and/or correlational studies for Paper 1 and 2. These are used in every topic and it simplifies the preparation for this tricky question. Our IB Psychology Revision Textbook follows this approach.

Research Methods from the IB Guide

This screen shot of the online IB Psychology guide shows the list of “research methods” that you have to choose from.

Link to the topic!

A common weakness in research methods essays is they do not fully address the question by explaining the use of the method in relation to the approach or the topic in the question. A good way to check if the answer has applied the explanation to the topic is to read the explanation and see if it could also be true of any topic in IB Psychology. If it’s generic enough so it could be used across multiple topics, then the application needs to be clearer. Another even easier way to check is to see if the topic or approach is even mentioned at all in the explanation!

Explain studies, don’t just describe them!

Examiner’s reports always complain that “there’s too much description in essays”. This is actually the wrong complaint. A lot of description is great, but  it needs to be matched with explanation . So the real critique should be “there’s not enough explanation in essays.”

So how do you go beyond describing so you’re explaining a study? The key is in the final 1-3 sentences after the results when you make it clear what the results show about the question, i.e. what conclusions are we drawing from those results?

In research methods essays, however, it’s a little different. Many students make the mistake of explaining what the results show about behaviour , whereas in research methods essays you need to explain how the study shows the use of the method . For example, if using a study that’s a true experiment you explain how the manipulation of the IV in a controlled environment enabled causal conclusions to be drawn, or why it was useful in a correlational study to show the strength of a relationship between the two variables in the study, etc.

Remember : In ThemEd’s three levels of learning, describing means to summarize individual things and explaining means to show how they’re related. In this type of essay, this means explaining how the procedures of a research method are applied in a particular topic (see the example essay for a demonstration of this being done).

Evaluate the method, not the studies

To show critical thinking in a research methods essay, it’s important you can explain strengths and limitations of the research method you are writing about. So when you’re evaluating the studies, it’s not a good idea to explain the limitations of the study (e.g. lacking generalizability) without focusing on how the methods are limited in some way.

For example, if I was writing about Loftus and Palmer’s car crash study to show the use of a true experiment to study cognitive processes, it would not be that relevant to the question to explain a limitation of the study based on the characteristics of the participants (e.g. they had limited driving experience) because this isn’t really linked to the true experimental method. A far better evaluative point to make is to explain that maybe these results in a controlled environment might not be reflective of what happens in real life, possibly due to the fact that in this scenario there were no consequences for a wrong answer and the level of emotion is much lower than when witnessing real crimes. These are better points to make because they are focused on a key aspect of the true experiment – the controlled environment.

When explaining limitations of correlational studies, don’t just say “correlational doesn’t mean causation” but actually provide examples. For example, how could the relationship work in both directions, or what are some other factors that might explain the relationship? For example, if I was critiquing the use of correlational studies to study personal relationships, I might say that correlational studies (e.g. Gottman and Levenson) that show a correlational between marital satisfaction and communication could be explained in either direction (what we call bidirectional ambiguity): it could be that poor communication is leading to decreased marital satisfaction (being unhappy in a marriage), or that being unhappy in the marriage is causing more poor communication.

Tip:  Go beyond one sentence explanations of limitations like “ecological validity.” This post explains exactly how to explain limitations of ecological in detail.

Often a third variable can explain the connection between two variables in a correlational study, like in this example of crime and ice-cream. Explaining possible links like this is an excellent way of highlighting the limitations in correlational studies.

Study one method in depth

My advice to students is to prep true experiments and correlational studies, but for every topic they should choose one of these to prep in depth. This is because there is a good chance you might be asked to write an essay about just one method. For example, if the question was “Evaluate the use of one method to study emotion and cognition.” If you revised two or more methods for this topic, then that would have been a wasted effort. By preparing to write about one in-depth (and perhaps having a second with just one study as an example), you are best-prepared to write an excellent essay.

This post about why depth is better than breadth in IB Psychology might help clarify this point.

Tip:  Use one method to critique the other. This is another reason why I like true experiments and correlational studies – their strengths and limitations can be used to evaluate one-anothers.

Discuss vs evaluate – what’s the difference?

There is none. While some teachers will argue that these command terms require different content, I argue (and can demonstrate) that this is simply not the case, especially when it comes to research methods. You can read more about this on this blog post. 

One method or two?

A common question is “do quasi and true experiments count as one method or two?” The truth is no-one yet knows how the IB is going to mark this in the exams. It appears from the IB materials that they are considered two (see quote and image above). However, this is another reason why prepping correlational studies and true experiments is a good idea because they are clearly two separate methods.

Example Essay

You can read an example essay that (I hope) follows the advice given in this blog pots. This essay has been taken from the resources in the  Quantitative Methods Teacher Support Pack.   Even if you’re not teaching using our textbooks, this TSP would still be useful.

Note to students: This has been submitted to turnitin and will be known by your teachers, so it would be very unwise to try to submit this as your own work.

Example Questions

You might be asked about research methods in relation to any of the  topics   in the IB Psychology curriculum. For example, you might be asked to evaluate the use of one research method used in the study of the brain and behaviour. However, you would not be asked to discuss the method used to study neuroplasticity, since this is a sub-content point related to the broader topic. It’s possible that the question might be linked to the approach as well.

Remember that essay questions will be either discuss, evaluate, to what extent or contrast. It is unlikely that the “to what extent” command term will be used for a research methods essay (but not impossible) and it’s also unlikely that “contrast” will be used as well because it is more likely you’ll be asked to write about “one or more” methods. So here are some example essay questions:

• Evaluate the use of one research method used to study cognitive processes. 
• Discuss how and why one research method is used to study cultural origins of behaviour. 
• Discuss the use of one or more research methods used to study genes and behaviour.
• Evaluate the use of one or more research methods used in the biological approach to understanding human behaviour.
• Evaluate the use of one or more research methods used to study etiologies of psychological disorders. 
• Discuss the use of one research method used to study personal relationships. 

Check the IB Guide or your textbooks for a list of the topics in the course.

Good luck and I hope this post was helpful. Remember to check out our store to find the latest revision materials and resources.

Was this helpful? Feel free to leave questions and comments.

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

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

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

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

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

Research Methodology 101

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

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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198 Comments

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Thanks for your comment.

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

All the best with your research.

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

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

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

Table of Contents

Collaboration, information literacy, writing process, research methods.

• © 2023 by Joseph M. Moxley - University of South Florida

Understand how to identify appropriate research methods for particular methodological communities , rhetorical situations , and research questions .

Research Methods are the tools and techniques (aka protocols , processes , strategies ) that investigators and methodological communities use to conduct research .

Research methods may be empirical (aka the scientific method), informal , or textual .

Key Terms: Methodological Community ; Research Methodology

If you are doing more than writing an essay that relies on sources, then you can benefit from understanding why there are different research methods.  Learn more about how academic and professional researchers employ diverse research methods.  Understand the philosophical assumptions that inform researchers in different disciplines.

Academic disciplines—for example, mathematics, psychology, physics, engineering, or business—have different ways of conducting and evaluating research. An anthropologist’s account of kinship patterns in a tribe of Native Americans bears almost no resemblance to a cognitive psychologist’s investigation of sensory responses to light stimuli. Even within a particular academic discipline, researchers may disagree over what makes good research.  Different researchers employ different research methodologies because they have opposing, sometimes contradictory ideas, about what constitutes a valid knowledge claim.  Not only do people disagree about appropriate methods of research, but their ideas may change over time. Conceptions about knowledge, available technologies, and research practices influence each other and change constantly. For example, capturing gorillas and studying them in cages might have been considered good research in the 1920s. The work of later researchers like Dian Fossey, however, demonstrated how animals might be better understood in their natural environment. Today, research based on observations of wild animals in captivity would gain little support or interest.

Research methods are a social, rhetorical construct . Different academic and professional communities —e.g., mathematics, psychology, physics, engineering, or business—employ unique research methods. A primary focus of training in academic and professional disciplines concerns learning how to use disciplinary-specific methods, tools, protocols, and processes for gathering and assessing information. For instance, an anthropologist’s account of kinship patterns in a tribe of Native Americans bears almost no resemblance to a cognitive psychologist’s investigation of sensory responses to light stimuli. 

Whether research results, truth claims , are understood or judged to be valid or convincing depends to a great extent on whether the investigator follows the tacit and explicit guidelines a discourse community considers appropriate for a particular research question and rhetorical situation . This is why rhetorical reasoning (especially audience awareness ) plays such a formative role in the selection of research methods.

Research methods are not necessarily paired with particular methodologies , epistemological values , such as the rejection of positivism . Rather, methodological communities may employ the same methods yet hold contrary assumptions about the sort of knowledge those methods produce. For instance, a researcher could argue a case study creates universal knowledge —insights that transcend individuals, cultures, and historical periods. For instance, based on his therapy notes, Freud theorized we all have an id, ego, and superego. Jung suggested we all play archetypal roles including the hero, the shadow, the anima and animus. In contrast, another researcher could conduct a case study with similar subjects and yet argue the insights gleaned from the research do not illustrate universal knowledge—i.e., the results are stories, narratives, that provide robust details about the subjects interviewed . . . and nothing more.

Research Methods are constantly changing in response to new technologies. Eager to develop new knowledge or test knowledge claims , investigators experiment with new methods as technologies evolve. For example, the internet enables investigators to conduct worldwide research with online survey tools and video conferencing tools. Businesses are working with big data and analytics to commercialize data about consumers habits as they traverse the internet or purchase items in the world. Machine learning theorists are looking at how people write to develop artificial intelligence so technologies like Amazon’s Alexa can speak with humans.

Research Methods are evolving in response to new cultural mores. Communities of practice reconsider ethical principles and engage in dialectics regarding best practices. For example, capturing gorillas and studying them in cages might have been considered good research in the 1920s. The work of later researchers like Dian Fossey, however, demonstrated how animals might be better understood in their natural environment. Today, research based on observations of wild animals in captivity would gain little support or interest.

Consumers of research studies are wise to evaluate research methods. As a consumer of research, you are wise to critically evaluate a researcher’s methods. You would, for example, take your doctor’s diagnosis of a life-threatening disease more seriously than a fortune teller’s prediction of an early death. What distinguishes a physician’s prognosis from a fortune teller’s prophecy are research methods: the doctor may be looking at the results of your physical, blood work, x rays, CT Scans, MRI, or family history, whereas the fortune teller may be gazing into a crystal ball, pendulum, Tarot cars, or astrological charts.

[ The CRAAP Test (Currency, Relevance, Authority, Accuracy, Purpose) ]

Taxonomy of Research Methods

Research methods & mindset.

How deeply we engage in Informal Research, Textual Research, or Primary Research is tied to our mindset as an investigator, the importance of the occasion and exigency, and our judgment as to whether it’s the best possible kairotic moment.

Our engagement with research methods is also tied to our training. In high schools and colleges in the U.S., students learn about information literacy perspectives & practices and write with sources . Additionally, some fortunate students learn to conduct lab experiments in high schools. But it really isn’t until college — and, for some, graduate school — that students receive training from experts in empirical research methods.

Our information seeking behaviors are also shaped by the seriousness of the occasion to ourselves and others. For instance, when COVID-19 virus became a pandemic in the spring of 2020, many scientists from throughout the world dropped what they had been working on and turned to finding a vaccine or medications to ameliorate the virus.

Related Articles:

Empirical Research Methods

Informal research methods, mixed research methods, qualitative research methods, quantitative research methods.

Textual Research Methods

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Research Methods Essays – How to Write Them

Last Updated on April 17, 2017 by

Essay planning and writing for the AS and A Level sociology exams – hints and tips

The research methods section of the AS sociology 7191 (2) exam (research methods and topics in sociology) consists of one short answer question (out of 4 marks) and one essay question (out of 16 marks).

You should aim to spend approximately 20-25 minutes answering this essay question

This longer methods question will nearly always ask you to evaluate either the strengths or limitations of a particular method, for example ‘Evaluate the strengths of using social surveys in Social Research’.

This means that you will need to evaluate either the strengths or the limitations of the particular method as directed in the question.

You should always use the following structure whether talking about strengths or limitations of the method. Remember that you will need to emphasis the relevant sections depending on whether you are asked to evaluate strengths or limitations.

Define the method

Explain why Positivists like or dislike the method

Explain why Interpretivists like or dislike the method

Validity – explain why the method has good or bad validity

Reliability – explain why the method has good or bad reliability

Representativeness – explain how easy it is to get a large, representative sample

Practical factors – explain what practical strengths or limitations the method has

Ethical issues – explain any ethical problems associated with the method, or talk about the ethical strengths as appropriate

Say what kind of topics this method is useful for researching and why

Say when you wouldn’t use this method and why

Compare the relative strengths and weaknesses of different types of the method.

It is good practice to use examples of actual examples of research studies that have used the method under examination, preferably woven into the body of the essay.

It is also good practice to distinguish between different ways of doing the method throughout, as you are asked to do in number 11.

You can remember the above 11 point plan by memorizing the handy acronym DPIVRRPETTC

If you like this sort of thing, then you might like to purchase more of the same…

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Methods in Context Essay Template

Assessment Objectives and Key Skills in A Level Sociology – for an explanation of what ‘evaluation’ means

AQA Assessment Resources – AS paper 2 has an example of a pure research methods question.  

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15 Types of Research Methods

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.

Chris Drew (PhD)

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

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• How to Do Research for an Excellent Essay: The Complete Guide

One of the biggest secrets to writing a good essay is the Boy Scouts’ motto: ‘be prepared’. Preparing for an essay – by conducting effective research – lays the foundations for a brilliant piece of writing, and it’s every bit as important as the actual writing part. Many students skimp on this crucial stage, or sit in the library not really sure where to start; and it shows in the quality of their essays. This just makes it easier for you to get ahead of your peers, and we’re going to show you how. In this article, we take you through what you need to do in order to conduct effective research and use your research time to best effect.

Allow enough time

First and foremost, it’s vital to allow enough time for your research. For this reason, don’t leave your essay until the last minute . If you start writing without having done adequate research, it will almost certainly show in your essay’s lack of quality. The amount of research time needed will vary according to whether you’re at Sixth Form or university, and according to how well you know the topic and what teaching you’ve had on it, but make sure you factor in more time than you think you’ll need. You may come across a concept that takes you longer to understand than you’d expected, so it’s better to allow too much time than too little.

Read the essay question and thoroughly understand it

If you don’t have a thorough understanding of what the essay question is asking you to do, you put yourself at risk of going in the wrong direction with your research. So take the question, read it several times and pull out the key things it’s asking you to do. The instructions in the question are likely to have some bearing on the nature of your research. If the question says “Compare”, for example, this will set you up for a particular kind of research, during which you’ll be looking specifically for points of comparison; if the question asks you to “Discuss”, your research focus may be more on finding different points of view and formulating your own.

Begin with a brainstorm

Start your research time by brainstorming what you already know. Doing this means that you can be clear about exactly what you’re already aware of, and you can identify the gaps in your knowledge so that you don’t end up wasting time by reading books that will tell you what you already know. This gives your research more of a direction and allows you to be more specific in your efforts to find out certain things. It’s also a gentle way of introducing yourself to the task and putting yourself in the right frame of mind for learning about the topic at hand.

Achieve a basic understanding before delving deeper

If the topic is new to you and your brainstorm has yielded few ideas, you’ll need to acquire a basic understanding of the topic before you begin delving deeper into your research. If you don’t, and you start by your research by jumping straight in at the deep end, as it were, you’ll struggle to grasp the topic. This also means that you may end up being too swayed by a certain source, as you haven’t the knowledge to question it properly. You need sufficient background knowledge to be able to take a critical approach to each of the sources you read. So, start from the very beginning. It’s ok to use Wikipedia or other online resources to give you an introduction to a topic, though bear in mind that these can’t be wholly relied upon. If you’ve covered the topic in class already, re-read the notes you made so that you can refresh your mind before you start further investigation.

Working through your reading list

If you’ve been given a reading list to work from, be organised in how you work through each of the items on it. Try to get hold of as many of the books on it as you can before you start, so that you have them all easily to hand, and can refer back to things you’ve read and compare them with other perspectives. Plan the order in which you’re going to work through them and try to allocate a specific amount of time to each of them; this ensures that you allow enough time to do each of them justice and that focus yourself on making the most of your time with each one. It’s a good idea to go for the more general resources before honing in on the finer points mentioned in more specialised literature. Think of an upside-down pyramid and how it starts off wide at the top and becomes gradually narrower; this is the sort of framework you should apply to your research.

Ask a librarian

Library computer databases can be confusing things, and can add an extra layer of stress and complexity to your research if you’re not used to using them. The librarian is there for a reason, so don’t be afraid to go and ask if you’re not sure where to find a particular book on your reading list. If you’re in need of somewhere to start, they should be able to point you in the direction of the relevant section of the library so that you can also browse for books that may yield useful information.

Use the index

If you haven’t been given specific pages to read in the books on your reading list, make use of the index (and/or table of contents) of each book to help you find relevant material. It sounds obvious, but some students don’t think to do this and battle their way through heaps of irrelevant chapters before finding something that will be useful for their essay.

Taking notes

As you work through your reading, take notes as you go along rather than hoping you’ll remember everything you’ve read. Don’t indiscriminately write down everything – only the bits that will be useful in answering the essay question you’ve been set. If you write down too much, you risk writing an essay that’s full of irrelevant material and getting lower grades as a result. Be concise, and summarise arguments in your own words when you make notes (this helps you learn it better, too, because you actually have to think about how best to summarise it). You may want to make use of small index cards to force you to be brief with what you write about each point or topic. We’ve covered effective note-taking extensively in another article, which you can read here . Note-taking is a major part of the research process, so don’t neglect it. Your notes don’t just come in useful in the short-term, for completing your essay, but they should also be helpful when it comes to revision time, so try to keep them organised.

Research every side of the argument

Never rely too heavily on one resource without referring to other possible opinions; it’s bad academic practice. You need to be able to give a balanced argument in an essay, and that means researching a range of perspectives on whatever problem you’re tackling. Keep a note of the different arguments, along with the evidence in support of or against each one, ready to be deployed into an essay structure that works logically through each one. If you see a scholar’s name cropping up again and again in what you read, it’s worth investigating more about them even if you haven’t specifically been told to do so. Context is vital in academia at any level, so influential figures are always worth knowing about.

Keep a dictionary by your side

You could completely misunderstand a point you read if you don’t know what one important word in the sentence means. For that reason, it’s a good idea to keep a dictionary by your side at all times as you conduct your research. Not only does this help you fully understand what you’re reading, but you also learn new words that you might be able to use in your forthcoming essay or a future one . Growing your vocabulary is never a waste of time!

Start formulating your own opinion

As you work through reading these different points of view, think carefully about what you’ve read and note your own response to different opinions. Get into the habit of questioning sources and make sure you’re not just repeating someone else’s opinion without challenging it. Does an opinion make sense? Does it have plenty of evidence to back it up? What are the counter-arguments, and on balance, which sways you more? Demonstrating your own intelligent thinking will set your essay apart from those of your peers, so think about these things as you conduct your research.

Be careful with web-based research

Although, as we’ve said already, it’s fine to use Wikipedia and other online resources to give you a bit of an introduction to a topic you haven’t covered before, be very careful when using the internet for researching an essay. Don’t take Wikipedia as gospel; don’t forget, anybody can edit it! We wouldn’t advise using the internet as the basis of your essay research – it’s simply not academically rigorous enough, and you don’t know how out of date a particular resource might be. Even if your Sixth Form teachers may not question where you picked up an idea you’ve discussed in your essays, it’s still not a good habit to get into and you’re unlikely to get away with it at a good university. That said, there are still reliable academic resources available via the internet; these can be found in dedicated sites that are essentially online libraries, such as JSTOR. These are likely to be a little too advanced if you’re still in Sixth Form, but you’ll almost certainly come across them once you get to university.

Look out for footnotes

In an academic publication, whether that’s a book or a journal article, footnotes are a great place to look for further ideas for publications that might yield useful information. Plenty can be hidden away in footnotes, and if a writer is disparaging or supporting the ideas of another academic, you could look up the text in question so that you can include their opinion too, and whether or not you agree with them, for extra brownie points.

Don’t save doing all your own references until last

If you’re still in Sixth Form, you might not yet be required to include academic references in your essays, but for the sake of a thorough guide to essay research that will be useful to you in the future, we’re going to include this point anyway (it will definitely come in useful when you get to university, so you may as well start thinking about it now!). As you read through various books and find points you think you’re going to want to make in your essays, make sure you note down where you found these points as you go along (author’s first and last name, the publication title, publisher, publication date and page number). When you get to university you will be expected to identify your sources very precisely, so it’s a good habit to get into. Unfortunately, many students forget to do this and then have a difficult time of going back through their essay adding footnotes and trying to remember where they found a particular point. You’ll save yourself a great deal of time and effort if you simply note down your academic references as you go along. If you are including footnotes, don’t forget to add each publication to a main bibliography, to be included at the end of your essay, at the same time.

Putting in the background work required to write a good essay can seem an arduous task at times, but it’s a fundamental step that can’t simply be skipped. The more effort you put in at this stage, the better your essay will be and the easier it will be to write. Use the tips in this article and you’ll be well on your way to an essay that impresses!

To get even more prepared for essay writing you might also want to consider attending an Oxford Summer School .

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• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

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Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

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

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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.

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.

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Extended Essay Writers

What Are Research Methods in IB Extended Essays? A Student’s Guide

Luke MacQuoid

Hello, fellow IB scholars! As a seasoned IB writer, I’ve mastered all the intricacies of the International Baccalaureate (IB) extended essay. This task is much easier than you think, but only if you use the right approach. In this article, I’ll share my insights on effective extended essay research methods, blending my experience with the general IB criteria. So, if you’re beginning to work on your paper, you’re in the right place to gather some valuable tips.

Original Research IB EE: Why It Matters?

Original research in the context of an IB EE refers to independently conducting investigations, analysis, and synthesis to contribute new insights or perspectives on a chosen topic. Why is it so crucial? First and foremost, it fosters critical thinking skills. Students learn to evaluate sources, weigh evidence, and develop reasoned arguments. In my view, these are not just academic skills; they are essential life skills. Thinking critically and evaluating information is invaluable in today’s information-rich world.

Moreover, according to general IB criteria, the extended essay demands high organization and self-discipline. Students must plan, research, write, and edit a significant piece of work, managing their time and resources effectively. This process instills a sense of responsibility and self-motivation, qualities that are highly regarded in higher education and beyond.

From my perspective, the extended essay also encourages intellectual curiosity and creativity. Students have the freedom to choose their topic, allowing them to investigate an area of personal interest deeply. This autonomy can ignite a passion for learning that lasts well beyond the project’s duration.

In my role guiding IB students, I’ve observed how the extended essay research process enhances students’ understanding of academic integrity and ethics. They learn the importance of original thought and the rigor required in citing sources and presenting their work. This understanding is crucial in a world where the authenticity and accuracy of information are frequently questioned.

Finally, the extended essay provides a valuable preview of college-level research. Students who have completed this project often find themselves better prepared for the demands of university studies. They enter higher education with a clear understanding of the research process and the ability to manage large-scale projects.

Effective Research Methods in the IB Extended Essay

In the IB extended essay, selecting the proper research methods is crucial. It’s a balancing act between gathering comprehensive data and interpreting it in a way that supports your thesis. In my years guiding IB students, I’ve appreciated the power of qualitative and quantitative research methods. Here’s a deeper look at these approaches and how they can enrich your extended essay.

Qualitative Research Methods

Qualitative research methods are excellent for gaining in-depth understanding and insight. Here are some key options:

• Interviews . Conducting interviews allows for gathering detailed information and personal experiences. It’s useful for topics that require subjective analysis or opinions.
• Case Studies . Analyzing specific instances or examples in detail can provide a deeper understanding of your topic. It’s a great way to illustrate theories or concepts in a real-world context.
• Ethnographic Research . It involves immersing yourself in a particular community or environment to understand its dynamics. It’s particularly effective for cultural or sociological topics.
• Content Analysis . This involves analyzing texts, media, or documents to identify patterns or themes. It’s useful for literature, media studies, or history topics.

These methods are beneficial when investigating complex issues where insufficient numerical data exists.

Quantitative Research Methods

Quantitative methods, on the other hand, involve numerical data and statistical analysis. Some common options include:

• Surveys and Questionnaires . These are fantastic for collecting data from a large number of people. They can provide a broad overview of trends, attitudes, or behaviors.
• Experiments . Controlled experiments can be used to establish cause-and-effect relationships. This method is particularly relevant for scientific or psychological topics.
• Statistical Analysis . Applying statistical techniques to analyze data can reveal patterns and relationships. This method is crucial for validating your findings and making them reliable.
• Data Mining . It involves extracting information from large datasets. It’s becoming increasingly important in economics, biology, and social sciences.

These methods are ideal for testing hypotheses or examining relationships between variables.

Combining Qualitative and Quantitative Methods

In my experience, the most compelling extended essays often employ qualitative and quantitative research methods. This mixed-methods approach provides a comprehensive view of the topic. For instance, you might start with a survey to gather broad data and conduct interviews for more detailed insights.

Remember, the choice of research methods should align with your research question and the objectives of your essay. The methods you choose will shape the direction of your research and, ultimately, the conclusions you draw. Thus, it’s vital to carefully consider which options will best serve your essay’s purpose and help you build a robust and well-supported argument.

How to Choose the Right Extended Essay Research Methods?

As an experienced IB tutor and writer, I’m eager to share insights on choosing effective research methods for your extended essay. The process is nuanced, requiring careful consideration of various factors.

1. Understand Your Research Question

Your EE research question is the cornerstone of your essay. It should directly influence your choice of research methods. For example, qualitative methods like interviews or narrative analysis are ideal for exploratory questions, while quantitative methods like surveys and statistical analysis suit questions seeking measurable trends.

2. Assess Resource Availability

Consider the resources at your disposal. It includes access to primary sources , software for data analysis, and even the amount of time you can dedicate to your research. Align your methods with these resources to ensure feasibility.

3. Align with Your Strengths and Interests

Choose extended essay research methods that play to your strengths and interests. If you’re mathematically inclined, you might gravitate toward quantitative methods. Qualitative methods might appeal more if you’re interested in personal stories or subjective experiences.

4. Match the Scope and Depth of Research

Your methods should reflect the desired scope and depth of your extended essay. Broad topics may require a wider, more general approach, while niche themes might benefit from in-depth, focused methods.

5. Prioritize Ethical Considerations

Ensure your research methods adhere to ethical standards. This is about respecting privacy, ensuring confidentiality, and seeking permission, especially when conducting surveys or interviews.

6. Seek Guidance and Feedback

Don’t underestimate the value of mentorship . Consult with your supervisor or other experienced individuals. They can provide critical insights into the suitability and effectiveness of your chosen extended essay research methods.

7. Time Management

Lastly, effective extended essay research involves good time management . It’s incredibly important. So, if you choose a qualitative or quantitative research method, ensure you have enough time for it.

Data Analysis and Interpretation in IB EE

Analysis is where your research skills shine. When dealing with qualitative data, such as interview transcripts, textual responses, or observational notes, your focus should be identifying patterns and themes. Here’s how you can approach it:

• Thematic Analysis . Look for recurring themes or ideas in your data. It involves reading your data multiple times and coding it to categorize these themes.
• Contextual Understanding . Remember, qualitative data is often rich in context. Pay attention to the nuances and subtleties in responses or observations. 
• Narrative Analysis . Analyzing the story or narrative can sometimes offer insights, especially in humanities or social sciences research. How do individuals describe their experiences, and what does this reveal about your topic?
• Comparative Analysis . If you have data from different sources, compare and contrast these to draw broader conclusions. It highlights differences or similarities that are significant to your research question.

Need help with your IB extended essay?

From research and analysis to structuring and editing, our skilled mentors will be by your side, helping you write an exceptional extended essay that meets the word count and stringent IB criteria and reflects your passion for the selected IB group .

Quantitative data analysis in EE involves working with numerical data, which can be managed and interpreted through various statistical methods:

• Descriptive Statistics . Start with statistics like mean, median, mode, and standard deviation. These give you an overview of your data’s distribution and central tendencies.
• Inferential Statistics . If you want to draw conclusions or make predictions, use inferential statistics. Techniques like regression analysis, hypothesis testing, or correlation analysis can be helpful here.
• Data Visualization . Graphs, charts, and tables are invaluable for making sense of quantitative data. They provide a visual representation that can make trends and patterns more apparent.
• Significance Testing . Determine the statistical significance of your findings. It helps to understand whether your results are likely due to chance or represent a genuine effect or relationship.

A critical part of data analysis and interpretation in your extended essay is tying your findings to your research question. Each piece of analysis should contribute to answering this question or investigating its facets.

In conclusion, mastering the extended essay research method is worth taking. It will hone your research skills and prepare you for future academic challenges. As someone who has been there, I can assure you that the effort you put into your original research in IB EE will be both rewarding and enlightening. Keep curious, stay focused, and enjoy the ride! And remember, if you need help, our team of Extended Essay Writers is always here to assist you.

Luke MacQuoid has extensive experience teaching English as a foreign language in Japan, having worked with students of all ages for over 12 years. Currently, he is teaching at the tertiary level. Luke holds a BA from the University of Sussex and an MA in TESOL from Lancaster University, both located in England. As well to his work as an IB Examiner and Master Tutor, Luke also enjoys sharing his experiences and insights with others through writing articles for various websites, including extendedessaywriters.com blog

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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.

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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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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

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Researcher, Academic Writer, Web developer

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• Published: 25 March 2024

Methods, techniques, assays and protocols

Nature Biomedical Engineering volume  8 ,  pages 201–202 ( 2024 ) Cite this article

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Applied biomedical research needs more of them to be more broadly useful, reproducible and robust.

The scientific process is a structured method, yet not one that is defined in detail. Scientific knowledge and scientific applications advance through rational guesses, observation, the formulation of hypotheses or theories, experimentation and computation, as well as recurrent testing, data analyses, validation and replication (not necessarily in this order).

Acquiring and refining scientific knowledge, and devising science-based solutions to problems, thus requires rationality, objectivity, empiricism, scepticism, peer review and many other scientific practices and values (with accountability and transparency becoming increasingly crucial). Robust methods are also required as well as painstakingly detailed protocols — that is, step-by-step instructions for carrying out a specific method or technique. Regardless of whether a method is referred to as an ‘approach’, ‘a set of techniques’, a ‘workflow’ or an ‘assay’ (or analogous wording, depending on the customs of the research area and the method’s purpose; for instance, an assay usually refers to a test for the detection or quantification of molecules or substances or their activities), a method tends to be broader in scope than a protocol, and sufficiently flexible or modifiable to fit the actual research hypothesis, problem or set-up.

In applied biomedical research, methods and protocols are indispensable for unravelling the workings of biomedically relevant biological systems (molecular, cellular, and at the organ and whole-organism levels) and of mechanisms of disease, and for diagnosing conditions and devising treatments. Biomedical methods and protocols (including laboratory and clinical-trial protocols as well as standard operating procedures) can also serve as common communication and collaboration tools across disciplines, and the most widely used methods have contributed considerably to the most-cited research of all time ( R. Van Noorden et al. Nature 514 , 550–553; 2014 ).

The degree of utility is an essential editorial consideration in how manuscripts that report methods are assessed at Nature Biomedical Engineering . We pursue the publication of methods (but not protocols, which editors at Nature Protocols often commission for recent papers reporting methods) that enable the acquisition of biomedical data or knowledge that were otherwise difficult to capture, that facilitate the efficient analysis of big biomedically relevant datasets, that address a clear biomedical, translational or clinical need, that would seem to have a broader appeal (because they would be applicable to multiple research areas, for instance), that advantageously surpass existing procedures (for example, they are easier, cheaper or more efficient to run or implement), or that enhance the utility of already broadly used methods. (Some of these considerations are also relevant to papers published in Nature Methods , yet the journal focuses on serving researchers actively involved in laboratory practice.)

In this issue of Nature Biomedical Engineering , we highlight eight methods that exemplify these utility considerations.

In one Article, Philipp Holliger and colleagues describe a method for the rapid discovery of antibodies with binding affinities in the low-nanomolar to mid-picomolar range, as they show for the antigens human interleukin-7 and human epidermal growth factor receptor 2. The method leverages array-based assays, next-generation sequencing and high-throughput screening of antibody libraries to probe of the order of 10 8 antibody–antigen interactions, in 3 days. The generated datasets can also be used to train machine-learning models that accelerate the antibody-discovery process. The method has clear broad utility in helping accelerate antibody discovery and the exploration of genotype–phenotype relationships.

Another high-throughput method for the discovery of biomolecules included in this issue serves a clear clinical need. The method allows for the large-scale mass spectrometric quantification of glycopeptides in blood plasma samples as potential disease biomarkers, as Markus Ralser, Christoph Messner and colleagues show by using it to quantify about 1,000 glycopeptide features in the plasma glycoproteomes from patients with COVID-19.

The discovery and development of molecular drugs benefit from knowledge of interactions between the drugs and drug transporters. In another Article in this issue, Giovanni Traverso and co-authors report a method for acquiring interaction profiles between orally administered drugs and intestinal drug transporters. The method requires the modulation of the expression of drug transporters in intact porcine tissue explants via the ultrasound-enhanced delivery of small interfering RNAs. Moreover, the authors used the drug–transporter relationships that they obtained to train a random forest model for the classification of the interaction profiles. Because drug transporters determine the rates of absorption and elimination of therapeutics, by taking into account their interactions with the intestinal transportome, this type of method combining ex vivo tissue and machine learning may help to accelerate the development and formulation of oral drugs.

Technologies for single-cell sequencing allow for the classification of cells into subgroups according to their characteristics and functionality. Yet, the functional profiling of single cells has been a methodological bottleneck, particularly for highly heterogenous immune cells. Lih Feng Cheow and co-authors report an assay for the profiling of the cytotoxicity of killer cells in relation to their cellular phenotype and cytokine secretion at single-cell resolution. It relies on the detection of an initially intracellular fluorescent protein that has been ‘painted’ by a nearby lysed cell on the surface of the lysing killer cell. The assay can be integrated with flow cytometry and single-cell RNA sequencing, and could also be used to analyse molecular pathways associated with cell cytotoxicity and to seek correlates of immune responses.

The secretions of immune cells can affect them and their neighbouring cells, yet identifying genetic regulators of the secretions involves the sorting of a large number of cells according to their secretion patterns. Shana Kelley, Edward Sargent and co-authors describe in an Article also included in this issue a high-throughput method leveraging microfluidics for the analysis of the secretion levels of large populations of immune cells. The method allowed the authors to discover highly co-expressed kinase-coding genes that regulate the secretion of interferon γ by helper T lymphocytes, and may facilitate the discovery of therapeutic targets for autoimmune diseases.

Another microfluidic-based high-throughput screening method, described by Alan Wong and colleagues, enables new possibilities: the discovery of genetic and cellular drivers of the formation of syncytia (multinucleated cells resulting from cell–cell fusions) induced by the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The method takes advantage of droplet microfluidics and strategies for size-exclusion selection to screen (via large-scale mutagenesis and the genome-wide generation of gene knockouts via CRISPR) libraries of spike-variant-expressing ‘sender’ cells fusing with ‘receiver’ cells expressing the receptor angiotensin-converting enzyme 2 (ACE2). This method enables the exploration of any common and unique determinants of the virus-induced formation of syncytia.

This issue also includes an Article describing an extension to the utility of a widely used method. Seok-Hyun Yun, Sheldon Kwok and collaborators show that flow cytometry can be used to track and repeatedly measure the same cells using more markers and fewer colours, as the researchers show for three back-to-back cycles with more than ten markers per cycle. Such multi-pass high-dimensional flow cytometry takes advantage of cellular barcoding via microparticles emitting near-infrared laser light.

In another example of advantageous functionality, Ulrich Keyser and co-authors used DNA barcoding and solid-state nanopores to probe, with higher specificity and speed than had been possible, binding events between catalytically inactive Cas9 (the most used ribonucleoprotein in genome editing) and any pre-defined short sequence of double-stranded DNA. The method requires barcoded linear DNA with Cas9-binding double-stranded DNA overhangs that are sensed via changes in ionic current as the DNA translocates through solid-state nanopores (Fig. 1 ). Assessing the DNA-mismatch tolerance of catalytically inactive nucleases could inform diagnostic applications relying on the detection of single base-pair changes.

The schematic shows a solid-state nanopore (grey), two different DNA nanostructures (11111 and 11001) with two DNA overhangs (green and purple) and either with bound Cas9 (left) or without bound Cas9 (right) mixed together in solution, and the ionic-current traces (bottom) resulting from the translocation of the nanostructures through the nanopore. Figure adapted from the Article by Keyser and colleagues, under a Creative Commons license CC BY 4.0 .

More important than the specific editorial rationale for why we published the methods included in this issue is the reasonable evidence of reproducibility and robustness that they provide. Indeed, validation of the findings with additional datasets or samples, the benchmarking of a new technique against established methods, the verification of the results against alternative methods, and replicability efforts by different experimenters (when possible, under blinded conditions) are a core part of the scientific method.

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Methods, techniques, assays and protocols. Nat. Biomed. Eng 8 , 201–202 (2024). https://doi.org/10.1038/s41551-024-01199-2

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Search strategy, data extraction, risk of bias and applicability, data synthesis and analysis, parent ratings, teacher ratings, youth self-reports, combined rating scales, additional clinician tools, neuropsychological tests, biospecimen, neuroimaging, variation in diagnostic accuracy with clinical setting or patient subgroup, measures for diagnostic performance, available tools, importance of the comparator sample, clinical implications, future research, conclusions, acknowledgments, tools for the diagnosis of adhd in children and adolescents: a systematic review.

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Bradley S. Peterson , Joey Trampush , Morah Brown , Margaret Maglione , Maria Bolshakova , Mary Rozelle , Jeremy Miles , Sheila Pakdaman , Sachi Yagyu , Aneesa Motala , Susanne Hempel; Tools for the Diagnosis of ADHD in Children and Adolescents: A Systematic Review. Pediatrics April 2024; 153 (4): e2024065854. 10.1542/peds.2024-065854

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Correct diagnosis is essential for the appropriate clinical management of attention-deficit/hyperactivity disorder (ADHD) in children and adolescents.

This systematic review provides an overview of the available diagnostic tools.

We identified diagnostic accuracy studies in 12 databases published from 1980 through June 2023.

Any ADHD tool evaluation for the diagnosis of ADHD, requiring a reference standard of a clinical diagnosis by a mental health specialist.

Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.

In total, 231 studies met eligibility criteria. Studies evaluated parental ratings, teacher ratings, youth self-reports, clinician tools, neuropsychological tests, biospecimen, EEG, and neuroimaging. Multiple tools showed promising diagnostic performance, but estimates varied considerably across studies, with a generally low strength of evidence. Performance depended on whether ADHD youth were being differentiated from neurotypically developing children or from clinically referred children.

Studies used different components of available tools and did not report sufficient data for meta-analytic models.

A valid and reliable diagnosis of ADHD requires the judgment of a clinician who is experienced in the evaluation of youth with and without ADHD, along with the aid of standardized rating scales and input from multiple informants across multiple settings, including parents, teachers, and youth themselves.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental conditions in youth. Its prevalence has remained constant at ∼5.3% worldwide over the years, and diagnostic criteria have remained constant when based on rigorous diagnostic procedures. 1   Clinical diagnoses, however, have increased steadily over time, 2   and currently, ∼10% of US children receive an ADHD diagnosis. 3   Higher rates of clinical compared with research-based diagnoses are because of an increasing clinician recognition of youth who have ADHD symptoms that are functionally impairing but do not fully meet formal diagnostic criteria. 4   The higher diagnostic rates over time in clinical samples also results from youth receiving a diagnosis incorrectly. Some youth, for example, are misdiagnosed as having ADHD when they have symptoms of other disorders that overlap with ADHD symptoms, such as difficulty concentrating, which occurs in many other conditions. 5   Moreover, ADHD is more than twice as likely to be diagnosed in boys than in girls, 3   in lower-income families, 6   and in white compared with nonwhite youth 7   ; differences that derive at least in part from diagnostic and cultural biases. 8   – 11  

Improving clinical diagnostic accuracy is essential to ensure that youth who truly have ADHD benefit from receiving treatment without delay. Similarly, youth who do not have ADHD should not be diagnosed since an incorrect diagnosis risks exposing them to unbeneficial treatments. 12 , 13   Clinician judgement alone, however, especially by nonspecialist clinicians, is poor in diagnosing ADHD 14   compared with expert, research-grade diagnoses made by mental health clinicians. 15   Accurately diagnosing ADHD is difficult because diagnoses are often made using subjective clinical impressions, and putative diagnostic tools have a confusing, diverse, and poorly described evidence base that is not widely accessible. The availability of valid diagnostic tools would especially help to reduce misdiagnoses from cultural biases and symptom overlap with ADHD. 12 , 16   – 19  

This review summarizes evidence for the performance of tools for children and adolescents with ADHD. We did not restrict to a set of known diagnostic tools but instead explored the range of available diagnostic tools, including machine-learning assisted and virtual reality-based tools. The review aimed to assess how diagnostic performance varies by clinical setting and patient characteristics.

The review aims were developed in consultation with the Agency for Healthcare Research and Quality (AHRQ), the Patient-Centered Outcomes Research Institute, the topic nominator American Academy of Pediatrics, key informants, a technical expert panel (TEP), and public input. The TEP reviewed the protocol and advised on key outcomes. Subgroup analyses and key outcomes were prespecified. The review is registered in PROSPERO (CRD42022312656) and the protocol is available on the AHRQ Web site as part of a larger evidence report on ADHD. The systematic review followed Methods of the (AHRQ) Evidence-based Practice Center Program. 20  

Population: age <18 years.

Interventions: any ADHD tool for the diagnosis of ADHD.

Comparators: diagnosis by a mental health specialist, such as a psychologist, psychiatrist, or other provider, who often used published scales or semistructured diagnostic interviews to ensure a reliable DSM-based diagnosis of ADHD.

Key outcomes: diagnostic accuracy (eg, sensitivity, specificity, area under the curve).

Setting: any.

Study design: diagnostic accuracy studies.

Other: English language, published from 1980 to June 2023.

We searched PubMed, Embase, PsycINFO, ERIC, and ClinicalTrials.gov. We identified reviews for reference-mining through PubMed, Cochrane Database of Systematic Reviews, Campbell Collaboration, What Works in Education, PROSPERO, ECRI Guidelines Trust, G-I-N, and ClinicalKey. The peer reviewed strategy is in the Supplemental Appendix . All citations were screened by trained literature reviewers supported by machine learning ( Fig 1 ). Two independent reviewers assessed full text studies for eligibility. The TEP reviewed studies to ensure all were captured. Publications reporting on the same participants were consolidated into 1 record.

Literature flow diagram.

The data abstraction form included extensive guidance to aid reproducibility and standardization in recording study details, results, risk of bias, and applicability. One reviewer abstracted data and a methodologist checked accuracy and completeness. Data are publicly available in the Systematic Review Data Repository.

We assessed characteristics pertaining to patient selection, index test, reference standard, flow and timing that may have introduced bias, and evaluated applicability of study results, such as whether the test, its conduct, or interpretation differed from how the test is used in clinical practice. 21 , 22  

We differentiated parent, teacher, and youth self-report ratings; tools for clinicians; neuropsychological tests; biospecimens; EEG; and neuroimaging. We organized analyses according to prespecified outcome measures. A narrative overview summarized the range of diagnostic performance for key outcomes. Because lack of reported detail in many individual studies hindered use of meta-analytic models, we created summary figures to document the diagnostic performance reported in each study. We used meta-regressions across studies to assess the effects of age, comorbidities, racial and ethnic composition, and diagnostic setting (differentiating primary care, specialty care, school settings, mixed settings, and not reported) on diagnostic performance. One researcher with experience in use of specified standardized criteria 23   initially assessed the overall strength of evidence (SoE) (see Supplemental Appendix ) for each study, then discussed it with the study team to communicate our confidence in each finding.

We screened 23 139 citations and 7534 publications retrieved as full text against the eligibility criteria. In total, 231 studies reported in 290 publications met the eligibility criteria (see Fig 1 ).

Methodological quality of the studies varied. Selection bias was likely in two-thirds of studies; several were determined to be problematic in terms of reported study flow and timing of assessments (eg, not stating whether diagnosis was known before the results of the index test); and several lacked details on diagnosticians or diagnostic procedures ( Supplemental Fig 1 ). Applicability concerns limited the generalizability of findings ( Supplemental Fig 2 ), usually because youth with comorbidities were excluded. Many different tools were assessed within the broader categories (eg, within neuropsychological tests), and even when reporting on the same diagnostic tool, studies often used different components of the tool (eg, different subscales of rating scales), or they combined components in a variety of ways (eg, across different neuropsychological test parameters).

The evidence table ( Supplemental Table 10 , Supplemental Appendix ) shows each study’s finding. The following highlights key findings across studies.

Fifty-nine studies used parent ratings to diagnose ADHD ( Fig 2 ). The most frequently evaluated tool was the CBCL (Child Behavior Checklist), alone or in combination with other tools, often using different score cutoffs for diagnosis, and evaluating different subscales (most frequently the attention deficit/hyperactivity problems subscale). Sensitivities ranged from 38% (corresponding specificity = 96%) to 100% (specificity = 4% to 92%). 24 , 25  

Diagnostic performance parent and teacher ratings. For a complete list of scales see Supplemental Appendix .

Area under the curve (AUC) for receiver operator characteristic curves ranged widely from 0.55 to 0.95 but 3 CBCL studies reported AUCs of 0.83 to 0.84. 26   – 28   Few studies reported measurement of reliability. SoE was downgraded for study limitation (lack of detailed reporting), imprecision (large performance variability), and inconsistent findings ( Supplemental Table 1 ).

Twenty-three studies used teacher ratings to diagnose ADHD ( Fig 2 ). No 2 studies reported on rater agreement, internal consistency, or test-retest reliability for the same teacher rating scale. The highest sensitivity was 97% (specificity = 26%). 25   The Teacher Report Form, alone or in combination with Conners teacher rating scales, yielded sensitivities of 72% to 79% 29   and specificities of 64% to 76%. 30 , 32   reported AUCs ranged from 0.65 to 0.84. 32   SoE was downgraded to low for imprecision (large performance variability) and inconsistency (results for specific tools not replicated), see Supplemental Table 2 .

Six studies used youth self-reports to diagnose ADHD. No 2 studies used the same instrument. Sensitivities ranged from 53% (specificity = 98%) to 86% (specificity = 70%). 35   AUCs ranged from 0.56 to 0.85. 36   We downgraded SoE for domain inconsistency (only 1 study reported on a given tool and outcome), see Supplemental Table 3 .

Thirteen studies assessed diagnostic performance of ratings combined across informants, often using machine learning for variable selection. Only 1 study compared performance of combined data to performance from single informants, finding negligible improvement (AUC youth = 0.71; parent = 0.85; combined = 0.86). 37   Other studies reported on limited outcome measures and used ad hoc methods to combine information from multiple informants. The best AUC was reported by a machine learning supported study combining parent and teacher ratings (AUC = 0.98). 38  

Twenty-four studies assessed additional tools, such as interview guides, that can be used by clinicians to aid diagnosis of ADHD. Sensitivities varied, ranging from 67% (specificity = 65%) to 98% (specificity = 100%); specificities ranged from 36% (sensitivity = 89%) to 100% (sensitivity = 98%). 39   Some of the tools measured activity levels objectively using an actometer or commercially available activity tracker, either alone or as part of a diagnostic test battery. Reported performance was variable (sensitivity range 25% to 100%, 40   specificity range 66% to 100%, 40   AUCs range 0.75–0.9996 41   ). SoE was downgraded for imprecision (large performance variability) and inconsistency (outcomes and results not replicated), see Supplemental Table 4 .

Seventy-four studies used measures from various neuropsychological tests, including continuous performance tests (CPTs). Four of these included 3- and 4-year-old children. 42   – 44   A large majority used a CPT, which assessed omission errors (reflecting inattention), commission errors (impulsivity), and reaction time SD (response time variability). Studies varied in use of traditional visual CPTs, such as the Test of Variables of Attention, more novel, multifaceted “hybrid” CPT paradigms, and virtual reality CPTs built upon environments designed to emulate real-world classroom distractibility. Studies used idiosyncratic combinations of individual cognitive measures to achieve the best performance, though many reported on CPT attention and impulsivity measures.

Sensitivity for all neuropsychological tests ranged from 22% (specificity = 96%) to 100% (specificity = 100%) 45   ( Fig 3 ), though the latter study reported performance for unique composite measures without replication. Specificities ranged from 22% (sensitivity = 91%) 46   to 100% (sensitivity = 100% to 75%). 45 , 47   AUCs ranged from 0.59 to 0.93. 48   Sensitivity for all CPT studies ranged from 22% ( specificity = 96) to 100% (specificity = 75%). 49   Specificities for CPTs ranged from 22% (sensitivity = 91%) to 100% (sensitivity = 89%) 47   ( Fig 3 ). AUCs ranged from 0.59 to 0.93. 50 , 51   SoE was deemed low for imprecise studies (large performance variability), see Supplemental Table 5.

Diagnostic performance neuropsychological tests, CPTs, activity monitors, biospecimen, EEG.

Seven studies assessed blood or urine biomarkers to diagnose ADHD. These measured erythropoietin or erythropoietin receptor, membrane potential ratio, micro RNA levels, or urine metabolites. Sensitivities ranged from 56% (specificity = 95%) to 100% (specificity = 100% for erythropoietin and erythropoietin receptors levels). 52   Specificities ranged from 25% (sensitivity = 79%) to 100% (sensitivity = 100%). 52   AUCs ranged from 0.68 to 1.00. 52   Little information was provided on reliability of markers or their combinations. SoE was downgraded for inconsistent and imprecise studies ( Supplemental Table 6 ).

Forty-five studies used EEG markers to diagnose ADHD. EEG signals were obtained in a variety of patient states, even during neuropsychological test performance. Two-thirds used machine learning algorithms to select classification parameters. Several combined EEG with demographic variables or rating scales. Sensitivity ranged widely from 46% to 100% (corresponding specificities 74 and 71%). 53 , 54   One study that combined EEG with demographics data supported by machine learning reported perfect sensitivity and specificity. 54   Specificity was also variable and ranged from 38% (sensitivity = 95%) to 100% (specificities = 71% or 100%). 53   – 56   Reported AUCs ranged from 0.63 to 1.0. 57 , 58   SoE was downgraded for study imprecision (large performance variability) and limitations (diagnostic approaches poorly described), see Supplemental Table 7 .

Nineteen studies used neuroimaging for diagnosis. One public data set (ADHD-200) produced several analyses. All but 2 used MRI: some functional MRI (fMRI), some structural, and some in combination, with or without magnetic resonance spectroscopy (2 used near-infrared spectroscopy). Most employed machine learning to detect markers that optimized diagnostic classifications. Some combined imaging measures with demographic or other clinical data in the prediction model. Sensitivities ranged from 42% (specificity = 95%) to 99% (specificity = 100%) using resting state fMRI and a complex machine learning algorithm 56   to differentiate ADHD from neurotypical youth. Specificities ranged from 55% (sensitivity = 95%) to 100% 56   using resting state fMRI data. AUCs ranged from 0.58 to over 0.99, 57   SoE was downgraded for imprecision (large performance variability) and study limitations (diagnostic models are often not well described, and the number and type of predictor variables entering the model were unclear). Studies generally did not validate diagnostic algorithms or assess performance measures in an independent sample ( Supplemental Table 8 ).

Regression analyses indicated that setting was associated with both sensitivity ( P = .03) and accuracy ( P = .006) but not specificity ( P = .68) or AUC ( P = .28), with sensitivities lowest in primary care ( Fig 4 ). Sensitivity, specificity, and accuracy were also lower when differentiating youth with ADHD from a clinical sample than from typically developing youth (sensitivity P = .04, specificity P < .001, AUC P < .001) ( Fig 4 ), suggesting that clinical population is a source of heterogeneity in diagnostic performance. Findings should be interpreted with caution, however, as they were not obtained in meta-analytic models and, consequently, do not take into account study size or quality.

Diagnostic performance by setting and population.

Supplemental Figs 3–5 in the Supplemental Appendix document effects by age and gender. We did not detect statistically significant associations of age with sensitivity ( P = .54) or specificity ( P = .37), or associations of the proportion of girls with sensitivity ( P = .63), specificity ( P = .80), accuracy ( P = .34), or AUC ( P = .90).

We identified a large number of publications reporting on ADHD diagnostic tools. To our knowledge, no prior review of ADHD diagnostic tools has been as comprehensive in the range of tools, outcomes, participant ages, and publication years. Despite the large number of studies, we deemed the strength of evidence for the reported performance measures across all categories of diagnostic tools to be low because of large performance variability across studies and various limitations within and across studies.

We required that studies report diagnoses when using the tool compared with diagnoses made by expert mental health clinicians. Studies most commonly reported sensitivity (true-positive rate) and specificity (true-negative rate) when a study-specific diagnostic threshold was applied to measures from the tool being assessed. Sensitivity and specificity depend critically on that study-specific threshold, and their values are inherently a trade-off, such that varying the threshold to increase either sensitivity or specificity reduces the other. Interpreting diagnostic performance in terms of sensitivity and specificity, and comparing those performance measures across studies, is therefore challenging. Consequently, researchers more recently often report performance for sensitivity and specificity in terms of receiver operating characteristics (ROC) curves, a plot of sensitivity versus specificity across the entire range of possible diagnostic thresholds. The area under this ROC curve (AUC) provides an overall, single index of performance that ranges from 0.5 (indicating that the tool provides no information above chance for classification) to 1.0 (indicating a perfect test that can correctly classify all participants as having ADHD and all non-ADHD participants as not having it). AUC values of 90 to 100 are commonly classified as excellent performance; 80 to 90 as good; 70 to 80 as fair; 60 to 70 as poor; and 50 to 60 failed performance.

Most research is available on parental ratings. Overall, AUCs for parent rating scales ranged widely from “poor” 58   to “excellent.” 59   Analyses restricted to the CBCL, the most commonly evaluated scale, yielded more consistent “good” AUCs for differentiating youth with ADHD from others in clinical samples, but the number of studies contributing data were small. Internal consistency for rating scale items was generally high across most rating scales. Test-retest reliability was good, though only 2 studies reported it. One study reported moderate rater agreement between mothers and fathers for inattention, hyperactivity, and impulsivity symptoms. Few studies included youth under 7 years of age.

AUCs for teacher rating scales ranged from “failed” 33   to “good.” 34   Internal consistency for scale items was generally high. Teacher ratings demonstrated very low rater agreement with corresponding parent scales, suggesting either a problem with the instruments or a large variability in symptom presentation with environmental context (home or school).

Though data were limited, self-reports from youth seemed to perform less well than corresponding parent and teacher reports, with AUCs ranging from “failed” for CBCL or ASEBA when distinguishing ADHD from other patients 33   to “good” for the SWAN in distinguishing ADHD from neurotypical controls. 36 , 37  

Studies evaluating neuropsychological tests yielded AUCs ranging from “poor” 60 , 61   to “excellent.” 50   Many used idiosyncratic combinations of cognitive measures, which complicates interpretation of the results across studies. Nevertheless, extracting specific, comparable measures of inattention and impulsivity from CPTs yielded diagnostic performance ranging from “poor” to “excellent” in differentiating ADHD youth from neurotypical controls and “fair” in differentiating ADHD youth from other patients. 42 , 60 , 62   No studies provided an independent replication of diagnosis using the same measure.

Blood biomarkers yielded AUCs ranging from “poor” (serum miRNAs) 63   to “excellent” (erythropoietin and erythropoietin receptors levels) 52   in differentiating ADHD from neurotypical youth. None have been independently replicated, and test-retest reliability was not reported. Most EEG studies used machine learning for diagnostic classification. AUCs ranged from “poor” 64   to “excellent” when differentiating ADHD youth from neurotypical controls. 65   Diagnostic performance was not prospectively replicated in any independent samples.

Most neuroimaging studies relied on machine learning to develop diagnostic algorithms. AUCs ranged from “poor” 66   to “excellent” for distinguishing ADHD youth from neurotypically developing controls. 57   Most studies used pre-existing data sets or repositories to retrospectively discriminate youths with ADHD from neurotypical controls, not from other clinical populations and not prospectively, and none assessed test-retest reliability or the independent reproducibility of findings. Reporting of final mathematical models or algorithms for diagnosis was limited. Activity monitors have the advantage of providing inexpensive, objective, easily obtained, and quantified measures that can potentially be widely disseminated and scaled.

Studies of combined approaches, such as integrating diagnostic tools with clinician impressions, were limited. One study reported increased sensitivity and specificity when an initial clinician diagnosis combined EEG indicators (the reference standard was a consensus diagnosis from a panel of ADHD experts). 67   These findings were not independently replicated, however, and no test-retest reliability was reported.

Many studies aimed to distinguish ADHD youth from neurotypical controls, which is a distinction of limited clinical relevance. In clinically referred youth, most parents, teachers, and clinicians are reasonably confident that something is wrong, even if they are unsure whether the cause of their concern is ADHD. To be informed by a tool that the child is not typically developing is not particularly helpful. Moreover, we cannot know whether diagnostic performance for tools that discriminate ADHD youth only from neurotypical controls is determined by the presence of ADHD or by the presence of any other characteristics that accompany clinical “caseness,” such as the presence of comorbid illnesses or symptoms shared or easily confused with those of other conditions, or the effects of chronic stress or current or past treatment. The clinically more relevant and difficult question is, therefore, how well the tool distinguishes youth with ADHD from those who have other emotional and behavioral problems. Consistent with these conceptual considerations that argue for assessing diagnostic performance in differentiating youth with ADHD from those with other clinical conditions, we found significant evidence that, across all studies, sensitivity, specificity, and AUC were all lower when differentiating youth with ADHD from a clinical sample than when differentiating them from neurotypical youth. These findings also suggest that the comparison population was a significant source of heterogeneity in diagnostic performance.

Despite the large number of studies on diagnostic tools, a valid and reliable diagnosis of ADHD ultimately still requires the judgement of a clinician who is experienced in the evaluation of youth with and without ADHD, along with the aid of standardized rating scales and input from multiple informants across multiple settings, including parents, teachers, and youth themselves. Diagnostic tools perform best when the clinical question is whether a youth has ADHD or is healthy and typically developing, rather than when the clinical question is whether a youth has ADHD or another mental health or behavioral problem. Diagnostic tools yield more false-positive and false-negative diagnoses of ADHD when differentiating youth with ADHD from youth with another mental health problem than when differentiating them from neurotypically developing youth.

Scores for rating scales tended to correlate poorly across raters, and ADHD symptoms in the same child varied across settings, indicating that no single informant in a single setting is a gold-standard for diagnosis. Therefore, diagnosis using rating scales will likely benefit from a more complete representation of symptom expression across multiple informants (parents, school personnel, clinicians, and youth) across more than 1 setting (home, school, and clinic) to inform clinical judgement when making a diagnosis, thus, consistent with current guidelines. 68   – 70   Unfortunately, methods for combining scores across raters and settings that improve diagnosis compared with scores from single raters have not been developed or prospectively replicated.

Despite the widespread use of neuropsychological testing to “diagnose” youth with ADHD, often at considerable expense, indirect comparisons of AUCs suggest that performance of neuropsychological test measures in diagnosing ADHD is comparable to the diagnostic performance of ADHD rating scales from a single informant. Moreover, the diagnostic accuracy of parent rating scales is typically better than neuropsychological test measures in head-to-head comparisons. 44 , 71   Furthermore, the overall SoE for estimates of diagnostic performance with neuropsychological testing is low. Use of neuropsychological test measures of executive functioning, such as the CPT, may help inform a clinical diagnosis, but they are not definitive either in ruling in or ruling out a diagnosis of ADHD. The sole use of CPTs and other neuropsychological tests to diagnose ADHD, therefore, cannot be recommended. We note that this conclusion regarding diagnostic value is not relevant to any other clinical utility that testing may have.

No independent replication studies have been conducted to validate EEG, neuroimaging, or biospecimen to diagnose ADHD, and no clinical effectiveness studies have been conducted using these tools to diagnose ADHD in the real world. Thus, these tools do not seem remotely close to being ready for clinical application to aid diagnosis, despite US Food and Drug Administration approval of 1 EEG measure as a purported diagnostic aid. 67 , 72  

All studies of diagnostic tools should report data in more detail (ie, clearly report false-positive and -negative rates, the diagnostic thresholds used, and any data manipulation undertaken to achieve the result) to support meta-analytic methods. Studies should include ROC analyses to support comparisons of test performance across studies that are independent of the diagnostic threshold applied to measures from the tool. They should also include assessment of test-retest reliability to help discern whether variability in measures and test performance is a function of setting or of measurement variability over time. Future studies should address the influence of co-occurring disorders on diagnostic performance and how well the tools distinguish youth with ADHD from youth with other emotional and behavioral problems, not simply from healthy controls. More studies should compare the diagnostic accuracy of different test modalities, head-to-head. Independent, prospective replication of performance measures of diagnostic tools in real-world settings is essential before US Food and Drug Administration approval and before recommendations for widespread clinical use.

Research is needed to identify consensus algorithms that combine rating scale data from multiple informants to improve the clinical diagnosis of ADHD, which at present is often unguided, ad hoc, and suboptimal. Diagnostic studies using EEG, neuroimaging, and neuropsychological tests should report precise operational definitions and measurements of the variable(s) used for diagnosis, any diagnostic algorithm employed, the selected statistical cut-offs, and the number of false-positives and false-negatives the diagnostic tool yields to support future efforts at synthetic analyses.

Objective, quantitative neuropsychological test measures of executive functioning correlate only weakly with the clinical symptoms that define ADHD. 73   Thus, many youth with ADHD have normal executive functioning profiles on neuropsychological testing, and many who have impaired executive functioning on testing do not have ADHD. 74   Future research is needed to understand how test measures of executive functioning and the real-world functional problems that define ADHD map on to one another and how that mapping can be improved.

One of the most important potential uses of systematic reviews and meta-analyses in improving the clinical diagnosis of ADHD and treatment planning would be identification of effect modifiers for the performance of diagnostic tools: determining, for example, whether tools perform better in patients who are younger or older, in ethnic minorities, or those experiencing material hardship, or who have a comorbid illness or specific ADHD presentation. Future studies of ADHD should more systematically address the modifier effects of these patient characteristics. They should make available in public repositories the raw, individual-level data and the algorithms or computer code that will aid future efforts at replication, synthesis, and new discovery for diagnostic tools across data sets and studies.

Finally, no studies meeting our inclusion criteria assessed the consequences of being misdiagnosed or labeled as either having or not having ADHD, the diagnosis of ADHD specifically in preschool-aged children, or the potential adverse consequences of youth being incorrectly diagnosed with or without ADHD. This work is urgently needed.

We thank Cynthia Ramirez, Erin Tokutomi, Jennifer Rivera, Coleman Schaefer, Jerusalem Belay, Anne Onyekwuluje, and Mario Gastelum for help with data acquisition. We thank Kymika Okechukwu, Lauren Pilcher, Joanna King, and Robyn Wheatley from the American Academy of Pediatrics (AAP), Jennie Dalton and Paula Eguino Medina from PCORI, Christine Chang and Kim Wittenberg from AHRQ, and Mary Butler from the Minnesota Evidence-based Practice Center. We thank Glendy Burnett, Eugenia Chan, MD, MPH, Matthew J. Gormley, PhD, Laurence Greenhill, MD, Joseph Hagan, Jr, MD, Cecil Reynolds, PhD, Le'Ann Solmonson, PhD, LPC-S, CSC, and Peter Ziemkowski, MD, FAAFP who served as key informants. We thank Angelika Claussen, PhD, Alysa Doyle, PhD, Tiffany Farchione, MD, Matthew J. Gormley, PhD, Laurence Greenhill, MD, Jeffrey M. Halperin, PhD, Marisa Perez-Martin, MS, LMFT, Russell Schachar, MD, Le'Ann Solmonson, PhD, LPC-S, CSC, and James Swanson, PhD who served as a technical expert panel. Finally, we thank Joel Nigg, PhD, and Peter S. Jensen, MD for their peer review of the data.

Drs Peterson and Hempel conceptualized and designed the study, collected data, conducted the analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Trampush conducted the critical appraisal; Ms Brown, Ms Maglione, Drs Bolshakova and Padkaman, and Ms Rozelle screened citations and abstracted the data; Dr Miles conducted the analyses; Ms Yagyu designed and executed the search strategy; Ms Motala served as data manager; and all authors provided critical input for the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

This trial has been registered at PROSPERO (identifier CRD42022312656).

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2024-065787 .

Data Sharing: Data are available in SRDRPlus.

FUNDING: The work is based on research conducted by the Southern California Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract 75Q80120D00009). The Patient-Centered Outcomes Research Institute (PCORI) funded the research (PCORI Publication No. 2023-SR-03). The findings and conclusions in this manuscript are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ or PCORI, its Board of Governors, or Methodology Committee. Therefore, no statement in this report should be construed as an official position of PCORI, AHRQ or of the US Department of Health and Human Services.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest to disclose.

attention-deficit/hyperactivity disorder

area under the curve

Child Behavior Checklist

continuous performance test

functional magnetic resonance imaging

receiver operating characteristics

strength of evidence

technical expert panel

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Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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Hawaii International Conference on System Sciences 2023

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We are so pleased to release the proceedings of the 56th Hawaii International Conference on System Sciences (HICSS).

The pandemic has at least three positive effects on the shaping of the research at HICSS. Overall, judging from the massive amount of reviews of the 1,429 papers submitted for the conference, the quality of papers have significantly increased, and the overall total score of the accepted papers have jumped to an all-time high. With a target acceptance rate set at 47%, HICSS welcomes 678 papers to its 2023 Proceedings.

As HICSS records an increase in the number of paper submissions in all tracks, and if the number of submitted papers is a relevant indicator of research interests, the following tracks have attracted a significant amount of attention:

• Organizational Systems and Technology (278)
• Internet and the Digital Economy (237)
• Decision Analytics and Service Sciences (191)
• Collaboration Systems and Technologies (140)
• Digital and Social Media (127)
• Information Technology in Healthcare (122)

HICSS continues to promote a new area of MIS research – Location intelligence. In a nutshell, location intelligence brings the context of location to business analytics and problem solving. By analyzing and visualizing spatial data on maps, dashboards, and business models, location intelligence has become an integral part of today’s decision-making and planning paradigms.

We have seen a high number of research papers on the effect of COVID-19 pandemic on a wide spectrum of MIS topics from technology-supported collaboration to the future of work. We also notice an increasing number of research on the pandemic effect on the “dark side of technology” – negative impacts on people’s health and the dissemination of misinformation. As a consequence, various aspects of cyber security are another key feature of this year’s research at HICSS.

As HICSS has returned to the island of Maui to host its annual event, we welcome more than 1,100 scientists at the Hyatt Regency Resort in the pristine Kaanapali Beach area, HICSS welcomes a host of Symposia, Workshops and Tutorials.

As you are downloading the papers in this year’s proceedings, we invite you to cite them in your research work, and we encourage you to submit your work to future HICSS.

Finally, I would like to acknowledge the co-creation of HICSS-56 by the HICSS community.

• 2,012 Authors of 678 research papers;
• 2,523 Reviewers ;
• 457 Minitrack Chairs;
• 20 Track Chairs; and
• Our sponsors including University of Hawaii at Manoa’s Shidler College of Business, National Security Agency, Association for Information Systems, University of Redlands and ESRI’s Joint Spatial Business Initiative, and University of Arkansas Sam M. Walton College of Business.

We are looking forward to continuing working with the community of researchers to advance the mission of HICSS – that is to provide a venue where ideas meet and science speaks.

Tung Bui Conference Chair Click here to download front matter and preface

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