the meaning of a research methodology

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

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

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

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

Research Methodology 101

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

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

Psst... there’s more!

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

199 Comments

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Thanks for your comment.

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

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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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Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

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

Qualitative vs. quantitative data

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

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

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


Qualitative		to broader populations. .
Quantitative		.

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

Primary vs. secondary research

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

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


Primary	.	methods.
Secondary

Descriptive vs. experimental data

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

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

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


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

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

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

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

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

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

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

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What Is a Research Design | Types, Guide & Examples
Data Collection | Definition, Methods & Examples

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What Is Research Methodology? Types, Process, Examples In Research Design

Research methodology is the backbone of any successful study, providing a structured approach to collecting and analysing data. It encompasses a broad spectrum of methods, each with specific processes and applications, tailored to answer distinct research questions.

This article will explore various types of research methodologies, delve into their processes, and illustrate with examples how they are applied in real-world research.

Understanding these methodologies is essential for any researcher aiming to conduct thorough and impactful studies.

Types Of Research Methodology

Research methodology contains various strategies and approaches to conduct scientific research, each tailored to specific types of questions and data.

Think of research methodology as the master plan for your study. It guides you on why and how to gather and analyse data, ensuring your approach aligns perfectly with your research question.

This methodology includes deciding between qualitative research, which explores topics in depth through interviews or focus groups, or quantitative research, which quantifies data through surveys and statistical analysis.

There is even an option to mix both, and approach called the mixed method.

If you’re analysing the lived experiences of individuals in a specific setting, qualitative methodologies allow you to capture the nuances of human emotions and behaviours through detailed narratives.

Quantitative methodologies would enable you to measure and compare these experiences in a more structured, numerical format.

Choosing a robust methodology not only provides the rationale for the methods you choose but also highlights the research limitations and ethical considerations, keeping your study transparent and grounded.

It’s a thoughtful composition that gives research its direction and purpose, much like how an architect’s plan is essential before the actual construction begins.

Qualitative Research Methodology

Qualitative research dives deep into the social context of a topic. It collects words and textual data rather than numerical data.

Within the family, qualitative research methodologies can be broken down into several approaches:

Ethnography: Deeply rooted in the traditions of anthropology, you immerse yourself in the community or social setting you’re studying when conducting an ethnography study.

Case Study Research: Here, you explore the complexity of a single case in detail. This could be an institution, a group, or an individual. You might look into interviews, documents, and reports, to build a comprehensive picture of the subject.

Grounded Theory: Here, you try to generate theories from the data itself rather than testing existing hypotheses. You might start with a research question but allow your theories to develop as you gather more data.

Narrative Research: You explore the stories people tell about their lives and personal experiences in their own words. Through techniques like in-depth interviews or life story collections, you analyse the narrative to understand the individual’s experiences.

Discourse Analysis: You analyse written or spoken words to understand the social norms and power structures that underlie the language used. This method can reveal a lot about the social context and the dynamics of power in communication.

These methods help to uncover patterns in how people think and interact. For example, in exploring consumer attitudes toward a new product, you would likely conduct focus groups or participant observations to gather qualitative data.

This method helps you understand the motivations and feelings behind consumer choices.

Quantitative Research Methodology

Quantitative research relies on numerical data to find patterns and test hypotheses. This methodology uses statistical analysis to quantify data and uncover relationships between variables.

There are several approaches in quantitative research:

Experimental Research: This is the gold standard when you aim to determine causality. By manipulating one variable and controlling others, you observe changes in the dependent variables.

Survey Research: A popular approach, because of its efficiency in collecting data from a large sample of participants. By using standardised questions, you can gather data that are easy to analyse statistically.

Correlational Research: This approach tries to identify relationships between two or more variables without establishing a causal link. The strength and direction of these relationships are quantified, albeit without confirming one variable causes another.

Longitudinal Studies: You track variables over time, providing a dynamic view of how situations evolve. This approach requires commitment and can be resource-intensive, but the depth of data they provide is unparalleled.

Cross-sectional Studies: Offers a snapshot of a population at a single point in time. They are quicker and cheaper than longitudinal studies.

Mixed Research Methodology

Mixed methods research combines both approaches to benefit from the depth of qualitative data and the breadth of quantitative analysis.

You might start with qualitative interviews to develop hypotheses about health behaviours in a community. Then, you could conduct a large-scale survey to test these hypotheses quantitatively.

This approach is particularly useful when you want to explore a new area where previous data may not exist, giving you a comprehensive insight into both the empirical and social dimensions of a research problem.

Factors To Consider When Deciding On Research Methodology

When you dive into a research project, choosing the right methodology is akin to selecting the best tools for building a house.

It shapes how you approach the research question, gather data, and interpret the results. Here are a couple of crucial factors to keep in mind.

Research Question Compatibility

The type of research question you pose can heavily influence the methodology you choose. Qualitative methodologies are superb for exploratory research where you aim to understand concepts, perceptions, and experiences.

If you’re exploring how patients feel about a new healthcare policy, interviews and focus groups would be instrumental.

Quantitative methods are your go-to for questions that require measurable and statistical data, like assessing the prevalence of a medical condition across different regions.

Data Requirements

Consider what data is necessary to address your research question effectively. Qualitative data can provide depth and detail through:

images, and

This makes qualitative method ideal for understanding complex social interactions or historical contexts.

Quantitative data, however, offers the breadth and is often numerical, allowing for a broad analysis of patterns and correlations.

If your study aims to investigate both the breadth and depth, a mixed methods approach might be necessary, enabling you to draw on the strengths of both qualitative and quantitative data.

Resources and Constraints

While deciding on research methodology, you must evaluate the resources available, including:

funding, and

Quantitative research often requires larger samples and hence, might be more costly and time-consuming.

Qualitative research, while generally less resource-intensive, demands substantial time for data collection and analysis, especially if you conduct lengthy interviews or detailed content analysis.

If resources are limited, adapting your methodology to fit these constraints without compromising the integrity of your research is crucial.

Skill Set and Expertise

Your familiarity and comfort level with various research methodologies will significantly affect your choice.

Conducting sophisticated statistical analyses requires a different skill set than carrying out in-depth qualitative interviews.

If your background is in social science, you might find qualitative methods more within your wheelhouse; whereas, a postgraduate student in epidemiology might be more adept at quantitative methods.

It’s also worth considering the availability of workshops, courses, or collaborators who could complement your skills.

Ethical and Practical Considerations

Different methodologies raise different ethical concerns.

In qualitative research, maintaining anonymity and dealing with sensitive information can be challenging, especially when using direct quotes or detailed descriptions from participants.

Quantitative research might involve considerations around participant consent for large surveys or experiments.

Practically, you need to think about the sampling design to ensure it is representative of the population studied. Non-probability sampling might be quicker and cheaper but can introduce bias, limiting the generalisability of your findings.

By meticulously considering these factors, you tailor your research design to not just answer the research questions effectively but also to reflect the realities of your operational environment.

This thoughtful approach helps ensure that your research is not only robust but also practical and ethical, standing up to both academic scrutiny and real-world application.

What Is Research Methodology? Answered

Research methodology is a crucial framework that guides the entire research process. It involves choosing between various qualitative and quantitative approaches, each tailored to specific research questions and objectives.

Your chosen methodology shapes how data is gathered, analysed, and interpreted, ultimately influencing the reliability and validity of your research findings.

Understanding these methodologies ensures that researchers can effectively write research proposal, address their study’s aims and contribute valuable insights to their field.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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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.

Data type	What is it?	Methodology
Quantitative	This methodology focuses more on measuring and testing numerical data. What is the aim of quantitative research? When using this form of research, your objective will usually be to confirm something.	Surveys, tests, existing databases. For example, you may use this type of methodology if you are looking to test a set of hypotheses.
Qualitative	Qualitative research is a process of collecting and analyzing both words and textual data. This form of research methodology is sometimes used where the aim and objective of the research are exploratory.	Observations, interviews, focus groups. Exploratory research might be used where you are trying to understand human actions i.e. for a study in the sociology or psychology field.
Mixed-method	A mixed-method approach combines both of the above approaches. The quantitative approach will provide you with some definitive facts and figures, whereas the qualitative methodology will provide your research with an interesting human aspect.	Where you can use a mixed method of research, this can produce some incredibly interesting results. This is due to testing in a way that provides data that is both proven to be exact while also being exploratory at the same time.

➡️ 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.

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No dissertation or research paper is complete without the research methodology section. Since this is the chapter where you explain how you carried out your research, this is where all the meat is! Here’s where you clearly lay out the steps you have taken to test your hypothesis or research problem.

Through this blog, we’ll unravel the complexities and meaning of research methodology in academic writing , from its fundamental principles and ethics to the diverse types of research methodology in use today. Alongside offering research methodology examples, we aim to guide you on how to write research methodology, ensuring your research endeavors are both impactful and impeccably grounded!

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Let’s first take a closer look at a simple research methodology definition:

Defining what is research methodology

Research methodology is the set of procedures and techniques used to collect, analyze, and interpret data to understand and solve a research problem. Methodology in research not only includes the design and methods but also the basic principles that guide the choice of specific methods.

Grasping the concept of methodology in research is essential for students and scholars, as it demonstrates the thorough and structured method used to explore a hypothesis or research question. Understanding the definition of methodology in research aids in identifying the methods used to collect data. Be it through any type of research method approach, ensuring adherence to the proper research paper format is crucial.

Now let’s explore some research methodology types:

Types of research methodology

1. qualitative research methodology.

Qualitative research methodology is aimed at understanding concepts, thoughts, or experiences. This approach is descriptive and is often utilized to gather in-depth insights into people’s attitudes, behaviors, or cultures. Qualitative research methodology involves methods like interviews, focus groups, and observation. The strength of this methodology lies in its ability to provide contextual richness.

2. Quantitative research methodology

Quantitative research methodology, on the other hand, is focused on quantifying the problem by generating numerical data or data that can be transformed into usable statistics. It uses measurable data to formulate facts and uncover patterns in research. Quantitative research methodology typically involves surveys, experiments, or statistical analysis. This methodology is appreciated for its ability to produce objective results that are generalizable to a larger population.

3. Mixed-Methods research methodology

Mixed-methods research combines both qualitative and quantitative research methodologies to provide a more comprehensive understanding of the research problem. This approach leverages the strengths of both methodologies to provide a deeper insight into the research question of a research paper .

Research methodology vs. research methods

The research methodology or design is the overall strategy and rationale that you used to carry out the research. Whereas, research methods are the specific tools and processes you use to gather and understand the data you need to test your hypothesis.

Research methodology examples and application

To further understand research methodology, let’s explore some examples of research methodology:

a. Qualitative research methodology example: A study exploring the impact of author branding on author popularity might utilize in-depth interviews to gather personal experiences and perspectives.

b. Quantitative research methodology example: A research project investigating the effects of a book promotion technique on book sales could employ a statistical analysis of profit margins and sales before and after the implementation of the method.

c. Mixed-Methods research methodology example: A study examining the relationship between social media use and academic performance might combine both qualitative and quantitative approaches. It could include surveys to quantitatively assess the frequency of social media usage and its correlation with grades, alongside focus groups or interviews to qualitatively explore students’ perceptions and experiences regarding how social media affects their study habits and academic engagement.

These examples highlight the meaning of methodology in research and how it guides the research process, from data collection to analysis, ensuring the study’s objectives are met efficiently.

Importance of methodology in research papers

When it comes to writing your study, the methodology in research papers or a dissertation plays a pivotal role. A well-crafted methodology section of a research paper or thesis not only enhances the credibility of your research but also provides a roadmap for others to replicate or build upon your work.

How to structure the research methods chapter

Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:

Step 1: Explain your research methodology

At the start of a research paper , you would have provided the background of your research and stated your hypothesis or research problem. In this section, you will elaborate on your research strategy.

Begin by restating your research question and proceed to explain what type of research you opted for to test it. Depending on your research, here are some questions you can consider:

a. Did you use qualitative or quantitative data to test the hypothesis?

b. Did you perform an experiment where you collected data or are you writing a dissertation that is descriptive/theoretical without data collection?

c. Did you use primary data that you collected or analyze secondary research data or existing data as part of your study?

These questions will help you establish the rationale for your study on a broader level, which you will follow by elaborating on the specific methods you used to collect and understand your data.

Step 2: Explain the methods you used to test your hypothesis

Now that you have told your reader what type of research you’ve undertaken for the dissertation, it’s time to dig into specifics. State what specific methods you used and explain the conditions and variables involved. Explain what the theoretical framework behind the method was, what samples you used for testing it, and what tools and materials you used to collect the data.

Step 3: Explain how you analyzed the results

Once you have explained the data collection process, explain how you analyzed and studied the data. Here, your focus is simply to explain the methods of analysis rather than the results of the study.

Here are some questions you can answer at this stage:

a. What tools or software did you use to analyze your results?

b. What parameters or variables did you consider while understanding and studying the data you’ve collected?

c. Was your analysis based on a theoretical framework?

Your mode of analysis will change depending on whether you used a quantitative or qualitative research methodology in your study. If you’re working within the hard sciences or physical sciences, you are likely to use a quantitative research methodology (relying on numbers and hard data). If you’re doing a qualitative study, in the social sciences or humanities, your analysis may rely on understanding language and socio-political contexts around your topic. This is why it’s important to establish what kind of study you’re undertaking at the onset.

Step 4: Defend your choice of methodology

Now that you have gone through your research process in detail, you’ll also have to make a case for it. Justify your choice of methodology and methods, explaining why it is the best choice for your research question. This is especially important if you have chosen an unconventional approach or you’ve simply chosen to study an existing research problem from a different perspective. Compare it with other methodologies, especially ones attempted by previous researchers, and discuss what contributions using your methodology makes.

Step 5: Discuss the obstacles you encountered and how you overcame them

No matter how thorough a methodology is, it doesn’t come without its hurdles. This is a natural part of scientific research that is important to document so that your peers and future researchers are aware of it. Writing in a research paper about this aspect of your research process also tells your evaluator that you have actively worked to overcome the pitfalls that came your way and you have refined the research process.

Tips to write an effective methodology chapter

1. Remember who you are writing for. Keeping sight of the reader/evaluator will help you know what to elaborate on and what information they are already likely to have. You’re condensing months’ work of research in just a few pages, so you should omit basic definitions and information about general phenomena people already know.

2. Do not give an overly elaborate explanation of every single condition in your study.

3. Skip details and findings irrelevant to the results.

4. Cite references that back your claim and choice of methodology.

5. Consistently emphasize the relationship between your research question and the methodology you adopted to study it.

To sum it up, what is methodology in research? It’s the blueprint of your research, essential for ensuring that your study is systematic, rigorous, and credible. Whether your focus is on qualitative research methodology, quantitative research methodology, or a combination of both, understanding and clearly defining your methodology is key to the success of your research.

Once you write the research methodology and complete writing the entire research paper, the next step is to edit your paper. As experts in research paper editing and proofreading services , we’d love to help you perfect your paper!

Here are some other articles that you might find useful:

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Frequently Asked Questions

What does research methodology mean, what types of research methodologies are there, what is qualitative research methodology, how to determine sample size in research methodology, what is action research methodology.

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One comment on “ Research Methodology Guide: Writing Tips, Types, & Examples ”

This is very simplified and direct. Very helpful to understand the research methodology section of a dissertation

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one.

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount.

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions.

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
Interviews: In-depth conversations with participants, offering qualitative insights.
Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions.

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
Random Assignment: Participants are randomly assigned to different experimental conditions.
Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
Large Sample Size: Surveys often target a large and diverse group of participants.
Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

Observation and Data Collection: This involves observing and documenting without manipulating variables.
Objective Description: Aim to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
Contextual Understanding: Aims to understand the context and unique characteristics of the case.
Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

Participant Observation: Researchers actively participate in the community or setting.
Holistic Perspective: Focuses on the interconnectedness of cultural elements.
Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

Constant Comparison: Data is continually compared and analyzed during the research process.
Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

Flexibility: Allows for flexibility in data collection and analysis.
Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

Structured Data Collection: Involves systematic and structured data collection methods.
Objective Presentation: Aims to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

Causal Relationships: Seeks to establish causal relationships between variables.
Controlled Variables : Often involves controlling certain variables to isolate causal factors.
Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

Snapshot View: Provides a snapshot of a population at a specific moment.
Efficiency: More efficient in terms of time and resources.
Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

Temporal Sequence: Allows for the examination of changes over time.
Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

Causality Inference: Enables the inference of cause-and-effect relationships.
Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

Natural Settings: Data is often collected in natural settings without intervention.
Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour.

Designing Effective Surveys

Objective Design:

Clearly define the research objectives to guide the survey design.
Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

Use a structured format with standardized questions for consistency.
Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

Conduct pilot tests to identify and rectify potential issues with survey design.
Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

Develop a robust sampling strategy to ensure a representative participant group.
Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

Build rapport with participants to create a comfortable and open environment.
Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

Frame open-ended questions to encourage detailed responses.
Allow participants to express their thoughts and perspectives freely.

Active Listening:

Practice active listening to understand areas and gather rich data.
Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

Obtain informed consent and assure participants of confidentiality.
Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

Actively immerse yourself in the setting or group being observed.
Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

Maintain detailed and reflective field notes during observations.
Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

Be conscious of ethical considerations, ensuring respect for participants.
Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

Maintain a more detached and objective stance during non-participant observation.
Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

Enhance the reliability of data by reducing observer bias.
Develop clear observation protocols and guidelines.

Contextual Understanding:

Strive for a thorough understanding of the observed context.
Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

Locate and access archives relevant to the research topic.
Collaborate with institutions or repositories holding valuable data.

Data Verification:

Verify the accuracy and reliability of archived data.
Cross-reference with other sources to ensure data integrity.

Ethical Use:

Adhere to ethical guidelines when using existing data.
Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

Address the possibility of incomplete or inaccurate archival records.
Acknowledge limitations and uncertainties in the data.

Temporal Bias:

Recognize potential temporal biases in archived data.
Consider the historical context and changes that may impact interpretation.

Access Limitations:

Address potential limitations in accessing certain archives.
Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

Limited timeframes can compromise the depth and thoroughness of the research process.
Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

Inadequate resources, whether financial or human, can impede the execution of research activities.
Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

Selecting participants in a way that systematically skews the sample can introduce selection bias.
Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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Here's What You Need to Understand About Research Methodology

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

Analyze and understand research methodologies faster with SciSpace Copilot

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

When someone tries to duplicate or expand upon your research after few years.
If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

Observation
Experimental
Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

Research Methodology: An Introduction

First Online: 31 March 2018

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Vida Davidavičienė 3

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Digital age brings the most dramatic changes in this study and research discipline as well as in other fields of human activities. Scientific research is known for a very long time, however in comparison with other research fields the business and management researches are a little bit younger. The information technologies and new research methodologies that have recently emerged, dramatically change the nature of the research. Therefore, researchers should be ready to absorb new possibilities and follow basic roles coming from earlier stages of the discipline. The intention of this chapter is to provide a brief introduction to those aspects of pertinent research to beginner researchers. The chapter presents the nature of scientific research so that it may be clearly understood and uses, as its basic approach, the fundamental principles of problem solving. The scope of the research provides an overviews the entire assumptions about reality, knowledge and human nature, key terms of theory and research presented. Main concepts of the research are discussed and all this is oriented to business, management and economic science specific.

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BMC Med Res Methodol

A tutorial on methodological studies: the what, when, how and why

Lawrence mbuagbaw.

1 Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON Canada

2 Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, Ontario L8N 4A6 Canada

3 Centre for the Development of Best Practices in Health, Yaoundé, Cameroon

Daeria O. Lawson

Livia puljak.

4 Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia

David B. Allison

5 Department of Epidemiology and Biostatistics, School of Public Health – Bloomington, Indiana University, Bloomington, IN 47405 USA

Lehana Thabane

6 Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, ON Canada

7 Centre for Evaluation of Medicine, St. Joseph’s Healthcare-Hamilton, Hamilton, ON Canada

8 Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON Canada

Associated Data

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 – 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 – 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig. 1 .

An external file that holds a picture, illustration, etc.
Object name is 12874_2020_1107_Fig1_HTML.jpg

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 – 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups
Determining a proportion, mean or another quantifier
Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.
Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].
Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]
Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 – 67 ].
Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].
Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].
Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].
Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

2. What is the design?

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

3. What is the sampling strategy?

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n = 103) [ 30 ].

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

4. What is the unit of analysis?

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig. 2 .

An external file that holds a picture, illustration, etc.
Object name is 12874_2020_1107_Fig2_HTML.jpg

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Acknowledgements

Abbreviations.

CONSORT	Consolidated Standards of Reporting Trials
EPICOT	Evidence, Participants, Intervention, Comparison, Outcome, Timeframe
GRADE	Grading of Recommendations, Assessment, Development and Evaluations
PICOT	Participants, Intervention, Comparison, Outcome, Timeframe
PRISMA	Preferred Reporting Items of Systematic reviews and Meta-Analyses
SWAR	Studies Within a Review
SWAT	Studies Within a Trial

Authors’ contributions

LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

This work did not receive any dedicated funding.

Availability of data and materials

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

DOL, DBA, LM, LP and LT are involved in the development of a reporting guideline for methodological studies.

Publisher’s Note

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

Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

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

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

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

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

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

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

Finding Mixed Methods research in the Databases

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

Data collection tools

Techniques or tools used for gathering research data include:

Qualitative Techniques or Tools	Quantitative Techniques or Tools
: these can be structured, semi-structured or unstructured in-depth sessions with the researcher and a participant.	Surveys or questionnaires: which ask the same questions to large numbers of participants or use Likert scales which measure opinions as numerical data.
: with several participants discussing a particular topic or a set of questions. Researchers can be facilitators or observers.	Observation: which can either involve counting the number of times a specific phenomenon occurs, or the coding of observational data in order to translate it into numbers.
: On-site, in-context or role-play options.	Document screening: sourcing numerical data from financial reports or counting word occurrences.
: Interrogation of correspondence (letters, diaries, emails etc) or reports.	Experiments: testing hypotheses in laboratories, testing cause and effect relationships, through field experiments, or via quasi- or natural experiments.
: Remembrances or memories of experiences told to the researcher.

SAGE research methods

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

Help and Information

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Past presentations

Prof. D. Walwyn: 2017

Lecture 1: Introduction
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Lecture 3: Research Design
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Mixed Methods
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Prof. C.M.E. McCrindle: 2017

Quantitative Research Tools

Prof. D. Walwyn: 2016

Lecture 2: Research Design
Lecture 3: Mixed Methods

Prof. J. Burnett: 2009

Qualitative Data Collection Methods

External Resources

Methodology

Research methodology can be understood as a way to systemically solve or answer the research problem. Thus essentially, it can be understood as the process of studying how research is done in a scientific manner. Through the methodology, we study the various steps that are generally adopted by a researcher in studying his/her research problem and the underlying logic behind them. The selection of the research method is crucial for what conclusions you can make about a phenomenon. It affects what you can say about the cause and factors influencing the phenomenon.

Research methods

Research methods refers to the tools that one uses to do research. These can either be qualitative or quantitative or mixed. Quantitative methods examines numerical data and often requires the use of statistical tools to analyse data collected. This allows for the measurement of variables and relationships between them can then be established. This type of data can be represented using graphs and tables. Qualitative data is non-numerical and focuses on establishing patterns. Mixed methods are composed of both qualitative and quantitative research methods. Mixed methods allow for explanation of unexpected results.

Determine what kind of knowledge you are trying to uncover (is it subjective or objective? experimental or interpretive?).
Let the literature be your guide: A thorough literature review is the best starting point for choosing your methods. Due to the fact that evaluating previous researchers' efforts can suggest a direction to answer your own research question.
Align your chosen methodology with research questions, aims and objectives (in other words, make sure your research questions and objectives can be answered through your chosen methodology).
The authenticity of your research depends upon the validity of the research data, the reliability of measures taken to amass the data, as well as the time taken to conduct the analysis, so it is essential to ensure that there is continuity throughout the research process.
It is also important to choose a research method which is within the limits of what the researcher can do. Time, money, feasibility, ethics and availability to measure the phenomenon correctly are examples of issues constraining the research.
When confused, ask! Do not be afraid to lean on the expertise of your supervisor, departmental research specialists etc. They are all there to help you.

Recommended Quantitative Methods books

Recommended Qualitative Methods books

Recommended Mixed Methods research sources

Advances in Mixed Methods Research by Prof J. W. Creswell
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Research Methodologies

What are research designs?
What are research methodologies?

What are research methods?

Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.

Additional Sources

Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project. The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data. The following boxes below contain a list of quantitative, qualitative, and mixed research methods.

Closed-ended questionnaires/survey: These types of questionnaires or surveys are like "multiple choice" tests, where participants must select from a list of premade answers. According to the content of the question, they must select the one that they agree with the most. This approach is the simplest form of quantitative research because the data is easy to combine and quantify.
Structured interviews: These are a common research method in market research because the data can be quantified. They are strictly designed for little "wiggle room" in the interview process so that the data will not be skewed. You can conduct structured interviews in-person, online, or over the phone (Dawson, 2019).

Constructing Questionnaires

When constructing your questions for a survey or questionnaire, there are things you can do to ensure that your questions are accurate and easy to understand (Dawson, 2019):

Keep the questions brief and simple.
Eliminate any potential bias from your questions. Make sure that they do not word things in a way that favor one perspective over another.
If your topic is very sensitive, you may want to ask indirect questions rather than direct ones. This prevents participants from being intimidated and becoming unwilling to share their true responses.
If you are using a closed-ended question, try to offer every possible answer that a participant could give to that question.
Do not ask questions that assume something of the participant. The question "How often do you exercise?" assumes that the participant exercises (when they may not), so you would want to include a question that asks if they exercise at all before asking them how often.
Try and keep the questionnaire as short as possible. The longer a questionnaire takes, the more likely the participant will not complete it or get too tired to put truthful answers.
Promise confidentiality to your participants at the beginning of the questionnaire.

Quantitative Research Measures

When you are considering a quantitative approach to your research, you need to identify why types of measures you will use in your study. This will determine what type of numbers you will be using to collect your data. There are four levels of measurement:

Nominal: These are numbers where the order of the numbers do not matter. They aim to identify separate information. One example is collecting zip codes from research participants. The order of the numbers does not matter, but the series of numbers in each zip code indicate different information (Adamson and Prion, 2013).
Ordinal: Also known as rankings because the order of these numbers matter. This is when items are given a specific rank according to specific criteria. A common example of ordinal measurements include ranking-based questionnaires, where participants are asked to rank items from least favorite to most favorite. Another common example is a pain scale, where a patient is asked to rank their pain on a scale from 1 to 10 (Adamson and Prion, 2013).
Interval: This is when the data are ordered and the distance between the numbers matters to the researcher (Adamson and Prion, 2013). The distance between each number is the same. An example of interval data is test grades.
Ratio: This is when the data are ordered and have a consistent distance between numbers, but has a "zero point." This means that there could be a measurement of zero of whatever you are measuring in your study (Adamson and Prion, 2013). An example of ratio data is measuring the height of something because the "zero point" remains constant in all measurements. The height of something could also be zero.

Focus Groups

This is when a select group of people gather to talk about a particular topic. They can also be called discussion groups or group interviews (Dawson, 2019). They are usually lead by a moderator to help guide the discussion and ask certain questions. It is critical that a moderator allows everyone in the group to get a chance to speak so that no one dominates the discussion. The data that are gathered from focus groups tend to be thoughts, opinions, and perspectives about an issue.

Advantages of Focus Groups

Only requires one meeting to get different types of responses.
Less researcher bias due to participants being able to speak openly.
Helps participants overcome insecurities or fears about a topic.
The researcher can also consider the impact of participant interaction.

Disadvantages of Focus Groups

Participants may feel uncomfortable to speak in front of an audience, especially if the topic is sensitive or controversial.
Since participation is voluntary, not every participant may contribute equally to the discussion.
Participants may impact what others say or think.
A researcher may feel intimidated by running a focus group on their own.
A researcher may need extra funds/resources to provide a safe space to host the focus group.
Because the data is collective, it may be difficult to determine a participant's individual thoughts about the research topic.

Observation

There are two ways to conduct research observations:

Direct Observation: The researcher observes a participant in an environment. The researcher often takes notes or uses technology to gather data, such as a voice recorder or video camera. The researcher does not interact or interfere with the participants. This approach is often used in psychology and health studies (Dawson, 2019).
Participant Observation: The researcher interacts directly with the participants to get a better understanding of the research topic. This is a common research method when trying to understand another culture or community. It is important to decide if you will conduct a covert (participants do not know they are part of the research) or overt (participants know the researcher is observing them) observation because it can be unethical in some situations (Dawson, 2019).

Open-Ended Questionnaires

These types of questionnaires are the opposite of "multiple choice" questionnaires because the answer boxes are left open for the participant to complete. This means that participants can write short or extended answers to the questions. Upon gathering the responses, researchers will often "quantify" the data by organizing the responses into different categories. This can be time consuming because the researcher needs to read all responses carefully.

Semi-structured Interviews

This is the most common type of interview where researchers aim to get specific information so they can compare it to other interview data. This requires asking the same questions for each interview, but keeping their responses flexible. This means including follow-up questions if a subject answers a certain way. Interview schedules are commonly used to aid the interviewers, which list topics or questions that will be discussed at each interview (Dawson, 2019).

Theoretical Analysis

Often used for nonhuman research, theoretical analysis is a qualitative approach where the researcher applies a theoretical framework to analyze something about their topic. A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study. This is a popular research method for analyzing works of literature, films, and other forms of media. You can implement more than one theoretical framework with this method, as many theories complement one another.

Common theoretical frameworks for qualitative research are (Grant and Osanloo, 2014):

Behavioral theory
Change theory
Cognitive theory
Content analysis
Cross-sectional analysis
Developmental theory
Feminist theory
Gender theory
Marxist theory
Queer theory
Systems theory
Transformational theory

Unstructured Interviews

These are in-depth interviews where the researcher tries to understand an interviewee's perspective on a situation or issue. They are sometimes called life history interviews. It is important not to bombard the interviewee with too many questions so they can freely disclose their thoughts (Dawson, 2019).

Open-ended and closed-ended questionnaires: This approach means implementing elements of both questionnaire types into your data collection. Participants may answer some questions with premade answers and write their own answers to other questions. The advantage to this method is that you benefit from both types of data collection to get a broader understanding of you participants. However, you must think carefully about how you will analyze this data to arrive at a conclusion.

Other mixed method approaches that incorporate quantitative and qualitative research methods depend heavily on the research topic. It is strongly recommended that you collaborate with your academic advisor before finalizing a mixed method approach.

How do you determine which research method would be best for your proposal? This heavily depends on your research objective. According to Dawson (2019), there are several questions to ask yourself when determining the best research method for your project:

Are you good with numbers and mathematics?
Would you be interested in conducting interviews with human subjects?
Would you enjoy creating a questionnaire for participants to complete?
Do you prefer written communication or face-to-face interaction?
What skills or experiences do you have that might help you with your research? Do you have any experiences from past research projects that can help with this one?
How much time do you have to complete the research? Some methods take longer to collect data than others.
What is your budget? Do you have adequate funding to conduct the research in the method you want?
How much data do you need? Some research topics need only a small amount of data while others may need significantly larger amounts.
What is the purpose of your research? This can provide a good indicator as to what research method will be most appropriate.
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methodology

Definition of methodology

Did you know.

Methodology and Science

The methodology employed in an experiment is essential to its success, and bad methodology has spoiled thousands of research projects. So whenever a piece of research is published in a scientific or medical journal, the researchers always carefully describe their methodology; otherwise, other scientists couldn't possibly judge the quality of what they've done.

Examples of methodology in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'methodology.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

New Latin methodologia , from Latin methodus + -logia -logy

1800, in the meaning defined at sense 1

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Dictionary Entries Near methodology

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“Methodology.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/methodology. Accessed 18 Aug. 2024.

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A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

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The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

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Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
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Assignment due date	Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country.	October 18, 2020 18 October 2020
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Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

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Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
Author names	Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name.	Francesca Humboldt
Author names	When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations).	Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams
Author affiliation	For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.	Department of Nursing, Morrigan University
Author affiliation	When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more).	Department of Psychology, Princeton University Department of Speech, Language, and Hearing Sciences, Purdue University
Author note	Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the .	n/a
	The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head.	Prediction errors support children’s word learning
	Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header.	1

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

Providing a clear and concise plan of action for the research study.
Ensuring that the research is conducted ethically and with rigor.
Maximizing the accuracy and reliability of the research findings.
Minimizing the possibility of errors, biases, or confounding variables.
Ensuring that the research is feasible, practical, and cost-effective.
Determining the appropriate research methodology to answer the research question(s).
Identifying the sample size, sampling method, and data collection techniques.
Determining the data analysis method and statistical tests to be used.
Facilitating the replication of the study by other researchers.
Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

Research Design	Research Methodology
The plan and structure for conducting research that outlines the procedures to be followed to collect and analyze data.	The set of principles, techniques, and tools used to carry out the research plan and achieve research objectives.
Describes the overall approach and strategy used to conduct research, including the type of data to be collected, the sources of data, and the methods for collecting and analyzing data.	Refers to the techniques and methods used to gather, analyze and interpret data, including sampling techniques, data collection methods, and data analysis techniques.
Helps to ensure that the research is conducted in a systematic, rigorous, and valid way, so that the results are reliable and can be used to make sound conclusions.	Includes a set of procedures and tools that enable researchers to collect and analyze data in a consistent and valid manner, regardless of the research design used.
Common research designs include experimental, quasi-experimental, correlational, and descriptive studies.	Common research methodologies include qualitative, quantitative, and mixed-methods approaches.
Determines the overall structure of the research project and sets the stage for the selection of appropriate research methodologies.	Guides the researcher in selecting the most appropriate research methods based on the research question, research design, and other contextual factors.
Helps to ensure that the research project is feasible, relevant, and ethical.	Helps to ensure that the data collected is accurate, valid, and reliable, and that the research findings can be interpreted and generalized to the population of interest.

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Muhammad Hassan

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The Root Mean Square Intersect Method to Identify Vortex Cores

University of Alabama

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Abstract and Figures

Leading edge vortex experiment. The plate was represented by the black rectangle. The flow was from bottom to top. a) The streamwise velocity field. b) Vorticity field. c) The curves represent the 〖AL〗_1 in the streamwise (pink) and 〖AL〗_2 in the perpendicular (green) directions to the flow. The intersection represented the center of the vortex. d) The intersection area was isolated. e) The velocity vector field superimposed on the intersection of the curves. f) It showed the Q-criteria without specifying a pressure threshold.

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Genomic reproducibility in the bioinformatics era

Pelin Icer Baykal ORCID: orcid.org/0000-0002-9542-5292 1 , 2 ,
Paweł Piotr Łabaj ORCID: orcid.org/0000-0002-4994-0234 3 , 4 ,
Florian Markowetz ORCID: orcid.org/0000-0002-2784-5308 5 , 6 ,
Lynn M. Schriml ORCID: orcid.org/0000-0001-8910-9851 7 ,
Daniel J. Stekhoven ORCID: orcid.org/0000-0003-3163-3161 2 , 8 ,
Serghei Mangul ORCID: orcid.org/0000-0003-4770-3443 9 , 10 na1 &
Niko Beerenwinkel ORCID: orcid.org/0000-0002-0573-6119 1 , 2 na1

Genome Biology volume 25 , Article number: 213 ( 2024 ) Cite this article

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In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.

Reproducibility is a cornerstone principle across various scientific disciplines, each adapting the concept to suit its specific nuances [ 1 , 2 , 3 , 4 ]. The topic of reproducibility has garnered significant attention as experts across fields highlight the need to establish standards for validating scientific findings. Definitions of reproducibility and related concepts, such as replicability and robustness often vary by discipline. In computational research, these concepts are often defined based on whether the code and data utilized are identical. For instance, Whitaker’s matrix [ 5 ] organized the concepts of reproducibility into a framework, where the interplay between code and data determines whether the findings are reproducible, replicable, robust, or generalizable. The matrix categorizes outcomes based on the consistency of the code and data used in research. On the other hand, Essawy et al. [ 2 ] present a hierarchical pyramid model of the reproducibility taxonomy for complex computational studies, outlining the progression from repeatability, runnability, reproducibility to replicability, each requiring increasing levels of effort and time (Additional file 1 : Table S1).

In genomics, reproducibility hinges on both experimental procedures and computational methods, facilitating recent strides toward precision medicine [ 6 ]. The analysis of genomic data fuels tailored treatments and improved patient outcomes. Yet, ensuring the credibility and progress of genomic medicine demands reproducible results across laboratories.

The multifaceted nature of reproducibility in genomics research is reflected in its dependence on both experimental procedures and computational methods. This complexity is underscored by the diverse steps involved in data production and analysis, spanning experimental procedures such as sample preparation and sequencing, as well as computational tasks like read alignment, variant calling, and gene expression analysis. Furthermore, the experimental variability occurring during the production of genomic data poses a considerable challenge for bioinformatics tools, as they are supposed to generate consistent genomic results under such variation.

This aspect is commonly referred to as methods reproducibility in experimental studies [ 4 ]. Methods reproducibility, as defined by Goodman et al. [ 4 ], pertains to the ability of precisely executing, to the highest degree possible, the experimental and computational procedures, using the same data and tools, in order to yield identical results [ 4 ]. In the context of genomics, methods reproducibility refers to obtaining the same results across multiple runs of the bioinformatics tools using the same parameters and genomic data (Fig. 1 ). Ideally, bioinformatics tools should also provide consistent results when analyzing genomic data obtained from different sequencing runs, including in different laboratories, but using the same protocols. A single, universally recognized term that describes the impact of bioinformatics tools on genomic results across such technical replicates is currently lacking. Pan et al. discuss reproducibility in the context of specific bioinformatics tasks. For instance, the reproducibility impact of read alignment tools is referred to as “aligner reproducibility,” while the reproducibility of structural variant callers is termed “caller reproducibility” [ 7 ]. The authors assess the consistency of these bioinformatics tasks across multiple tools and datasets. The closest definitions for this assessment were introduced by Goodman et al. [ 4 ] as results reproducibility and by Gundersen [ 8 ] as outcome reproducibility. Results reproducibility is the ability to obtain the same results when independent studies on different datasets are conducted with procedures closely resembling the original study [ 8 ]. However, the concept of results reproducibility was defined to target the reproduction of an experiment including a handful of statistical tests, rather than the analysis of high-dimensional and heterogeneous multi-omics data produced regularly by large collaborative genomics initiatives today. Therefore, we propose the term genomic reproducibility which measures ability to obtain consistent outcomes from bioinformatics tools using genomic data obtained from different library preparations and sequencing runs, but for fixed experimental protocols (Fig. 1 ).

Schematic representation of three key concepts: technical replicates, methods reproducibility, and genomic reproducibility. The same sample is processed (library preparation) and sequenced multiple times, possibly in different laboratories, but using the same experimental protocols and sequencing platform. The output of these sequencing runs are technical replicates represented as FASTQ files. Data analysis is performed for each technical replicate multiple times to assess consistency of genomic results, which refers to methods reproducibility. Genomic reproducibility, on the other hand, evaluates the consistency of genomic results across technical replicates

We explore various interpretations of reproducibility before focusing on its specific application within genomics, with the goal of refining key terminology in this context. Our focus extends to the pivotal role of bioinformatics tools and their impact on genomic reproducibility, followed by an evaluation of methodologies for assessing these tools. Additionally, we examine relevant studies and technical replicate datasets as valuable resources for assessing genomic reproducibility. In conclusion, we propose actionable best practices to enhance genomic reproducibility.

Reproducibility in genomics

Genomic reproducibility faces challenges at two pivotal junctures. The initial stage involves pre-sequencing and sequencing, where technical variability might emerge. Subsequently, during computational analysis and interpretation of genomic data, stochastic algorithms can introduce uncertainties, further impacting reproducibility. In the context of DNA sequencing, technical variability can arise from the use of diverse sequencing platforms [ 9 ] and from differences between individual flow cells [ 7 , 10 , 11 ]. Even if the sequencing protocol is kept identical across multiple runs, experimental variation is still expected as a result of the random sampling variance of the sequencing process and variations in library preparation [ 12 , 13 , 14 ]. In light of this, the objective of bioinformatics tools should be to accommodate and tolerate such experimental variation, aiming to generate consistent results across different sequencing runs and library preparations, which means achieving genomic reproducibility.

In genomics, replicates are classified into two types, biological replicates and technical replicates [ 15 ]. Biological replicates utilize multiple biological samples sharing identical conditions to quantify the inherent biological variation among them. On the other hand, technical replicates are obtained from the same biological sample sequenced multiple times, using the same experimental and computational procedures. They are used to assess and account for variability arising from the experimental process itself, such as inconsistencies in sample handling, instrument performance, or measurement techniques. Below, we focus on the best practices of using and simulating technical replicates to assess genomic reproducibility. Importantly, when assessing genomic reproducibility, we do not rely on gold standards, as the focus is not on the performance of the tools but on their capacity to maintain consistent results across technical replicates.

In practice, controlling conditions of sequencing experiments is challenging and high levels of experimental variations may compromise the ability of bioinformatics tools to maintain consistent results across technical replicates. In order to evaluate the performance of bioinformatics tools in terms of genomic reproducibility, one can consider technical replicates that specifically capture the variations among sequencing runs and library preparation techniques. This approach intentionally disregards other potential factors that could confound the results, such as sequencing protocols and platforms, allowing technical replicates acquired under the same sequencing protocols to be utilized to evaluate bioinformatics tools' impact. However, generating technical replicates can escalate both the financial burden and logistical complexity of genomic experiments. In certain cases, obtaining them may be impractical or ethically prohibitive, particularly in clinical settings.

Bioinformatics tools can remove but also introduce unwanted variation

Bioinformatics tools play a crucial role in analyzing and eliminating undesired variation in genomic data. Variations in genomic data can arise due to multiple sources, such as experimental noise, sequencing errors, or biological artifacts. For example, homopolymer compression is employed to mitigate errors in regions with repeating nucleotide sequences by simplifying these sequences to enhance alignment accuracy [ 16 ]. Furthermore, normalization processes are used to remove batch effects or technical biases, ensuring that systematic errors do not confound the results [ 17 ]. Despite their critical roles, these tools are imperfect and can introduce various kinds of variation, both deterministic and stochastic [ 18 ].

Deterministic variations include algorithmic biases, leading alignment algorithms to favor certain sequences over others. For example, BWA [ 19 ] and Stampy [ 20 ] demonstrate a reference bias in favoring sequences containing reference alleles of a known heterozygous indel [ 20 ]. Additionally, data processing decisions such as setting a low threshold for quality filtering can include low-quality reads prone to sequencing errors, thus introducing further unwanted variation [ 21 ].

Stochastic variations in bioinformatics tools, on the other hand, stem from the intrinsic randomness of certain computational processes, such as Markov Chain Monte Carlo and genetic algorithms. Consequently, these variations may produce divergent outcomes even when identical datasets are analyzed under identical conditions.

Genomic reproducibility of read alignment tools and variant callers

While bioinformatics tools aim to increase the accuracy of genomic data analysis and reduce sequencing errors, they can also introduce additional variation due to their built-in biases. For example, one of the challenges of read alignment tools is capturing and reporting reads mapped to repetitive regions of the reference genome, known as multi-mapped reads [ 22 ]. There exist different strategies to deal with the uncertainty of multi-mapped reads: some tools ignore these reads entirely (e.g., SNAP [ 23 ]), and others employ a deterministic approach to identify the best possible position among all the matching positions (e.g., RazerS [ 24 ] and mrFAST [ 25 ]), and finally, BWA-MEM [ 19 ] reports these multi-mapped reads with a mapping quality of zero. In the case of multi-mapping, allowing users to set a seed for a pseudo-random generator can restore the reproducibility of stochastic alignment strategies.

According to one study, random shuffling of reads affects Bowtie2 [ 26 ] and BWA-MEM [ 19 ] differently [ 27 ]. Bowtie2 is able to produce consistent alignment results irrespective of the order of the reads, while BWA-MEM [ 19 ] can show variability in results when the sequence of reads is altered. Specifically, BWA-MEM displayed variability under specific test conditions where reads were segmented and processed independently. This deviation from BWA-MEM’s typical integrated parallel processing can alter the calculated size distribution of the read inserts, as the analysis relies on smaller groups of shuffled data. This approach, although not commonly used, highlights the potential for irreproducible mapping results with BWA-MEM with respect to read order. Such variations could also influence the consistency of structural variant detection. Alkan et al. also found that structural variant calling tools produced 3.5 to 25.0% of different variant call sets with randomly shuffled data compared to the original data [ 27 ]. Furthermore, another study highlights that detecting structural variants varies significantly across different SV (structural variant) callers and even among the same callers when different read alignment tools are used [ 7 ]. It was previously shown that these variations were mainly attributed to duplications in repeat regions [ 27 ]. These studies demonstrate the potential impact of bioinformatics algorithms on the reproducibility of genomic results and emphasize the significance of assessing it with replicates.

Opportunities to assess the impact of bioinformatics tools on genomic reproducibility

Ongoing efforts in genomics include ensuring whole-genome sequencing (WGS) reproducibility, with notable initiatives including the Genome in a Bottle (GIAB) consortium, hosted by the National Institute of Standards and Technology (NIST), and the HapMap project. The complementing efforts were performed within consecutive phases of the US FDA-led MicroArray/Sequencing Quality Control Project (MAQC/SEQC), which is helping improve microarray and next-generation sequencing technologies and foster their proper applications in discovery, development, and review of FDA-regulated products. In the MAQC-IV/SEQC phase, the aim was to assess the technical performance of next-generation sequencing platforms by generating benchmark datasets with reference samples and evaluating the advantages and limitations of various bioinformatics strategies in RNA and DNA analyses. The impact of various bioinformatics approaches on the downstream biological interpretations of RNA-seq results was comprehensively examined and the utility of RNA-seq in clinical application and safety evaluation was assessed. In SEQC2, which is the next phase of SEQC, the focus has been placed on targeted DNA- and RNA-seq to develop standard analysis protocols and quality control metrics for fit-for-purpose use of NGS data to enhance regulatory science research and precision medicine. On the other hand, consortiums such as the GIAB, and the HapMap projects provide reference materials that are used to evaluate genomic reproducibility in various studies. In Table 1 , DNA and RNA-seq technical replicate datasets from major consortiums and studies are compiled, which can be used to assess genomic reproducibility.

Technical replicates of the Ashkenazi Trio dataset were generated to assess the performance of DNA sequencing platforms [ 9 ]. This involved generating triplicates of inter-laboratory and intra-laboratory paired-end and single-end DNA-seq samples using five Illumina and three ThermoFisher Ion Torrent platforms. This dataset can serve as a valuable resource for assessing genomic reproducibility by examining the performance of DNA-seq alignment tools and structural variant callers using both paired-end and single-end triplicate samples. The Chinese Quartet dataset, the HapMap Trio, and a pilot genome NA12878 are datasets with technical replicates that have been generated for structural variant detection studies [ 7 , 31 ]. Pan et al. used technical replicates from the Chinese Quartet to assess reproducibility across three different labs using different alignment and structural variant callers [ 7 , 31 ]. These technical replicates were sequenced from three different labs as triplicates representing different runs of sequencing. The same dataset was used to evaluate how sequencing centers, replicates, alignment tools, and platforms affect SV calling in NGS [ 31 ]. Additionally, The HapMap Trio and the NA12878 datasets were employed in a separate SV calling study to examine reproducibility across various factors, including sequencing platforms, labs, library preparations, alignment tools, and SV calling tools [ 7 ]. Technical replicates consist of triplicates of short-reads which can again be used to assess genomic reproducibility and the findings can be compared to the findings available in SV calling studies [ 7 , 31 ]. Lastly, we mention an RNA-seq dataset provided by the SEQC consortium [ 32 ], which has been employed to assess the reproducibility of RNA-seq experiments [ 17 , 33 ] and also the impact of RNA-seq data analysis tools on gene expression analysis [ 18 ]. Four samples were sequenced in 4 technical replicates each. The whole experiment was replicated in 6 different sites worldwide and another 5th replicate was created by a vendor and sent to labs for sequencing. All RNA-seq technical replicates used in these studies are made publicly available, serving as a valuable resource for assessing genomic reproducibility.

Synthetic replicates

In certain conditions, such as when the number of technical replicates is limited for a specific type of genomic data or when reproducibility assessment requires a more controlled environment, synthetic replicates may be employed instead of technical replicates. This approach allows for a more controlled examination of the impact of specific alterations in the data. Synthetic replicates are generated in silico to mimic the variations of sequencing output expected from technical replicates. In practice, it is impossible to computationally reproduce all variations among technical replicates, but different techniques exist to generate synthetic replicates that reflect some of the variations.

One approach to create synthetic replicates is randomly shuffling the order of the reads reported from a sequencer (Fig. 2 a), which reflects the randomness of events in a sequencing experiment, such as DNA hybridization on the flow cell [ 27 ]. Another technique is to take the reverse complement of each read (Fig. 2 b) to assess strand bias [ 34 ] when the reference genome is double-stranded. The bias arises due to a pronounced overabundance in one direction of NGS sequencing reads either forward or reverse, compared to the opposite direction [ 35 ]. This problem may lead to unwanted variation, which can impact genomic reproducibility. Yet another technique is bootstrapping (Fig. 2 c) reflecting random sampling variance, which is a widely used type of synthetic replicate employed in many genomics, transcriptomics [ 36 ], and metagenomics [ 37 ] studies. Subsampling (Fig. 2 d) is another type of synthetic replicate, which involves randomly selecting a subset of reads from the original dataset. This method simulates different levels of sequencing depth and coverage from the stochastic nature of sequencing.

Schematic representation of generating synthetic replicates. Based on a given dataset consisting of five reads R1, …, R5 (left) four different types of synthetic replicates (right) are created by either randomly shuffling the order of the five reads ( a ), or by taking the reverse complement of each read ( b ), or by bootstrapping, i.e., resampling of the five reads with replacement ( c ), or by subsampling, i.e., selecting a subset consisting of three reads from the original five reads ( d )

Both technical replicates and synthetic replicates have their own advantages and limitations. Technical replicates contribute to a more realistic and reliable assessment by accounting for inherent variability in experimental procedures, such as different sequencing runs, and enabling rigorous statistical analysis. On the other hand, synthetic replicates offer a controlled evaluation of tools since the modifications applied to the data are known, allowing for a precise assessment against a ground truth. Hence, utilizing both types of replicates can be useful in assessing genomic reproducibility.

Challenges in encoding and comparing structural variants

A fundamental hurdle in achieving reproducibility with structural variant callers lies in the inherent ambiguity of encoding genomic variants, stemming from biological complexities rather than technical limitations. While indels (insertions and deletions) can be left-aligned or normalized to a standard representation to facilitate comparability, complex alterations such as large deletions, insertions, duplications, inversions, and translocations present unique characteristics that complicate their consistent encoding and comparison across different replicates and bioinformatics tools. For instance, what one tool interprets as a single large deletion might be seen as multiple smaller deletions by another, due to differences in read alignments. Translocations further exemplify these difficulties, especially when they involve subtle additional changes, such as small insertions at the junction points, which might be detected by some tools but overlooked by others. These complexities significantly challenge assessing the genomic reproducibility of structural variant callers.

Moreover, the detection and characterization of SVs are intricately linked to the performance of read alignment processes. Inaccuracies or variability in aligning sequencing reads to the reference genome can have profound downstream effects on the identification and interpretation of SVs.

Best practices to improve genomic reproducibility

We have compiled a set of recommended standards and guidelines aimed at promoting genomic reproducibility (Table 2 ). These recommendations are based on the expectation that bioinformatics tools already adhere to existing dependency and workflow management standards, enabling their identical execution in different settings [ 38 ]. Dependency management systems like conda, along with shared computing environments and containers such as Docker and Apptainer (formerly Singularity) [ 39 , 40 , 41 ] play a crucial role in ensuring consistent software environments.

We suggest the following best practices for the development and application of bioinformatics tools to ensure genomic reproducibility. First, tools should be documented sufficiently, including detailed explanations of all parameters, their default settings, usage examples, and guidelines. This documentation assists users in selecting appropriate parameter values, which is essential for reproducibility. Furthermore, tool developers should clarify the relationship between parameter selection and reproducibility in the documentation to facilitate accurate and consistent results.

The second essential requirement involves incorporating functionality that allows users to specify random seeds. By implementing this feature, developers provide users control over the random results generated by non-deterministic algorithms. This control is vital for ensuring that the same set of input data consistently produces the same output, enabling methods to assess reproducibility. This consistency is the cornerstone for enabling reliable methods to assess genomic reproducibility systematically. By setting seeds, researchers can replicate runs of bioinformatics tools under the same conditions, thereby validating the reliability of the results and facilitating a transparent evaluation of genomic reproducibility.

Another recommendation pertains to the performance assessment of the bioinformatics tool. It is essential to conduct controlled experiments using synthetic replicates, technical replicates, or a combination of both. The result obtained from these experiments, along with any observed discrepancies or variations, should be thoroughly reported. This comprehensive reporting enables researchers to evaluate the performance and reliability of the tool accurately.

Bioinformatics tool developers can enhance reproducibility by providing result visualization from replicates. However, effectively handling visualization and communicating results poses challenges due to the extensive scale and complexity of the genomic data involved. These challenges can be overcome by employing suitable visualization techniques and dimensionality reduction methods. Through careful analysis of patterns of discrepancies from the visualizations, researchers can gain valuable insights into the reliability and consistency of the results produced by the tool.

Given the vast array of bioinformatics tools and methods available, comprehensible benchmarking becomes increasingly important [ 40 , 42 ]. Benchmarking can not only be used to assess performance against a ground truth, but also to assess reproducibility even in the absence of a ground truth. Reproducibility benchmarking studies are designed to evaluate the consistency of tools when used across synthetic and technical replicates. This dual approach thoroughly illuminates the reliability of tools by rigorously evaluating their performance across diverse scenarios—including variations in parameter settings and random outputs generated by different seed values. Such detailed evaluations are pivotal for pinpointing and mitigating the inherent uncertainties in parameter selection and the inherent randomness of algorithms, thereby ensuring that tools can reliably reproduce results under similar conditions.

While establishing the reliability of tools through rigorous benchmarking is vital, it's equally essential to acknowledge the potential limitations that may arise, particularly regarding the selection of cell lines for experimentation. One significant challenge is the potential presence of somatic mutations within these cell lines, which can introduce biases in evaluating tool performance. These mutations, occurring during the lifetime of the cell, can inadvertently influence experimental outcomes, leading to skewed results.

To mitigate these challenges and ensure the benchmarking studies themselves are reproducible, it is imperative that they adhere to clear guidelines. These guidelines should cover the documentation of methodologies, parameters, and experimental conditions in detail, facilitating the replication of studies by other researchers [ 40 , 42 ]. Incorporating workflow management systems can further bolster the reproducibility of benchmarking studies by automating and documenting the analytical processes, thereby enhancing the consistency and transparency of genomic research.

In addition to structured benchmarking, community-driven and continuous benchmarking efforts can play important roles in advancing bioinformatics tools. For example, continuous benchmarking, as supported by Omnibenchmark [ 43 ], enables researchers to monitor tool efficacy amidst evolving datasets and computational landscapes, adapting to emerging challenges and driving progress in genomic research. This ongoing process reinforces the foundation of genomic reproducibility, promoting transparency, accountability, and adaptability within the scientific community. Embracing this structured and iterative approach to benchmarking enhances the reliability of bioinformatics tools and fortifies the foundation of genomic reproducibility.

Reproducibility is critical in all fields of science, engineering, and medicine to ensure the reliability and integrity of findings and the safeness of their applications. However, there are various challenges and limitations to achieving reproducibility in practice. The field of genomics faces several hurdles to reproducibility due to rapid advancements in sequencing technologies and data generation. Each new technology introduces unique biases and sources of variation, which need to be carefully considered and addressed during data analysis. Additionally, genomic studies often involve complex bioinformatics pipelines, which are susceptible to errors and require rigorous validation.

Bioinformatics tools have made significant contributions to mitigating some of these challenges and enhancing genomic reproducibility. These tools facilitate the standardization and automation of data processing, analysis and visualizations, minimizing human error, and increasing the reliability of results. However, bioinformatics tools are not without limitations and can even introduce unwanted variations that compromise genomic reproducibility. The use of technical and synthetic replicates presents valuable approaches for evaluating essential aspects of bioinformatics algorithms and their influence on genomic reproducibility.

The use of technical replicates offers advantages, as it captures the diversity across different sequencing runs. In order to correctly assess bioinformatics tools in terms of genomic reproducibility, it is important to acknowledge that despite efforts to control experimental conditions, variations can arise due to factors such as human errors in sample preparations or unknown batch effects. These confounding factors and other experimental parameters such as variations in sequencing platforms can influence genomic results. We recommend the use of technical replicates to capture variations arising from different runs of sequencing and different library preparations.

Additionally, it is vital to understand the extent to which non-deterministic algorithms influence genomic results and to tailor the assessment of genomic reproducibility accordingly. It is important to note that while setting seeds ensures consistent results under the same conditions and facilitates reproducibility, it may also mask underlying variability across different seeds. This type of variability, if substantial, raises critical questions about genomic reproducibility.

Synthetic replicates are a fast and cost-efficient way of generating replicates in genomics. They cannot fully represent real data variation as they capture only some of the differences produced between different sequencing runs. However, they provide a useful and easily accessible way of assessing necessary features of bioinformatics algorithms and the way they impact on genomic reproducibility. When evaluating genomic reproducibility through synthetic replicates, employing shuffling and reverse complementing facilitates meaningful comparisons in read alignment. This approach enables a direct assessment of read alignments present across synthetic replicates, enhancing the effectiveness of the analysis, as the set of reads is consistent across replicates. In contrast, subsampling and bootstrapping challenge such direct comparisons; subsampling involves selecting a portion of the original reads, and bootstrapping changes the read composition by resampling with replacement. Despite this, subsampling offers valuable insights by allowing the evaluation of bioinformatics tools across different subsets of reads, serving as an indirect measure of reproducibility. Bootstrapping provides a way to simulate various sampling scenarios, creating numerous pseudo-replicates. This method enables the exploration of the inherent variability and stability in read alignment and variant detection processes under different sampling conditions. By repeatedly analyzing these varied samples, researchers can better understand how changes in read frequency and composition affect the reproducibility and accuracy of genomic analyses.

While we recommend testing tools across synthetic and technical replicates, significant concerns arise from the inherent uncertainty when using different bioinformatics tools or adjusting their settings, which leads to substantial variability in results [ 44 ]. This variability, and how method choice contributes to it, can be exploited to achieve desired outcomes, which can harm reproducibility [ 45 ] selectively. These considerations extend beyond the scope of our study but remain highly relevant and important in the broader context of genomic analysis.

Precision medicine heavily relies on accurate and reliable genomic information. However, the reliability of genomic results can only be ensured if they are reproducible by bioinformatics tools. As such, it is essential to consider reproducibility as a key evaluation criterion when assessing the quality of these tools. We recommend that both developers and users of bioinformatics tools follow the guidelines in Table 2 to ensure genomic reproducibility. By implementing these guidelines, we can improve the reliability of analyzing genomic data, and facilitate the successful translation of precision medicine to clinical practice.

Availability of data and materials

No datasets or codes were used for data analysis.

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Open access funding provided by Swiss Federal Institute of Technology Zurich SM is supported by the National Science Foundation (NSF) grants 2041984 and 2316223, National Institutes of Health (NIH) grant R01AI173172, and USC Office of Research and Innovation Zumberge Preliminary Studies in Research Award.

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Serghei Mangul and Niko Beerenwinkel contributed equally to this work.

Authors and Affiliations

Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland

Pelin Icer Baykal & Niko Beerenwinkel

SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland

Pelin Icer Baykal, Daniel J. Stekhoven & Niko Beerenwinkel

Małopolska Centre of Biotechnology, Jagiellonian University, 30-387, Gronostajowa 7A, Krakow, Poland

Paweł Piotr Łabaj

Department of Biotechnology, Boku University Vienna, Muthgasse 18, 1190, Vienna, Austria

Cancer Research UK Cambridge Research Institute, Cambridge, CB2 0RE, UK

Florian Markowetz

Department of Oncology, University of Cambridge, Cambridge, CB2 2XZ, UK

Institute for Genome Sciences, University of Maryland School of Medicine, HSFIII, 670 W. Baltimore St, Baltimore, MD, 21201, USA

Lynn M. Schriml

NEXUS Personalized Health Technologies, ETH Zurich, 8952, Zurich, Switzerland

Daniel J. Stekhoven

Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, 1540 Alcazar Street, Los Angeles, CA, 90033, USA

Serghei Mangul

Department of Quantitative and Computational Biology, University of Southern California Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA, 90089, USA

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PIB was responsible for drafting the entire manuscript. NB and SM contributed significantly to the work presented, both having equal contributions as main contributors. DS provided substantial feedback and insights. PPL identified and pointed out critical data that were included in Table 1 of the manuscript. FM and LS made valuable contributions to the manuscript.

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Additional file 1: Table S1: Glossary of definitions. Table containing the definitions of all terms used throughout the manuscript.

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Baykal, P.I., Łabaj, P.P., Markowetz, F. et al. Genomic reproducibility in the bioinformatics era. Genome Biol 25 , 213 (2024). https://doi.org/10.1186/s13059-024-03343-2

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How to manifest: Here are a few beginner tips from coaches.

Though manifestation and the law of attraction are age-old concepts with origins as far back as Hindu scriptures and Buddhism , it has enjoyed a modern-day refresh since the 2006 documentary “The Secret.” Now, manifestation fills social media feeds and coaching philosophies alike.

The hashtag #Manifestation has over 51 billion views on TikTok. Scroll for a bit and you’ll find not every manifestation coach has the same definition. We spoke to three practicing manifesters about what it means to them.

What is manifestation?

As a verb, manifesting is “to make evident or certain by showing or displaying .” In other words, manifesting is the act or practice of bringing something into your life through belief.

Candice Nikeia’s lessons in manifestation arrive at a place of gratitude. For her, manifesting is more than just wishing for physical items — a new car, a house, clothes — it’s acknowledging what you already have.

“It's really about learning how to love yourself, thinking of yourself as something that you desire to be, believing in yourself, striving for more than you can ever imagine,” Nikeia says. “Trusting that what's out there for you is out there for you; what's meant for you is meant for you.”

Kathleen Cameron is a wealth manifester . For her, manifestation is all about mindset — the art of “becoming a new version of yourself that you have yet to become in order to create the life that you have yet to experience.”

Ryan Lu has a bit more of a spiritual approach . Her manifestation philosophy is about working with the universe to “meet in the middle.”

“Manifestation is everyone's individual relationship with their higher power, whether that be God, the universe, whatever their religion is, whatever they personally believe,” Lu says.

What are angel numbers? Here's what they could mean in your life.

How to manifest

Manifestation is, at its core, believing you are the person who can achieve the goals you set. Here's a five-step recipe to help get started, according to the coaches:

Be grateful for what you already have
Shift your mindset
Put it all out there: Envision what you desire
Take inspired action

♦ Be grateful for what you already have

This is the base of Nikeia’s practice. You can start by thinking something positive as you wake up and before you go to bed.

She uses the example of manifesting a new job: instead of harvesting the feeling of not having that job, focus on what’s currently in your life.

“Be grateful for the moments you have today because when you do get that job, there's something that's going to make you want to complain,” Nikeia says.

♦ Shift your mindset

"It's not necessarily some magical thing you do, abracadabra, it's not like that," Nikeia says. "It's really a mindset and the words you speak — whether you're conscious of it or not, your words are manifesting into your reality."

When it comes to a mindset change, Cameron walks into situations thinking, “What can I do right now that would be something that a confident version of myself would do?”

In daily practice, that’s self care for Cameron — being in tune with becoming her best self.

“Whatever it is for you, that self care for you puts you in a good energy,” Cameron says. “When you look after and care for yourself, you're so much more powerful.”

♦ Put it all out there: Envision what you desire

Once you’re in the mindset to work toward what you want, the rest of the manifesting begins. The most common quick method is with a pen and paper — putting what you desire into words.

Others practice by using repetition, candle magic or waiting for certain phases of the moon.

Lu’s message to beginners? Don’t complicate it. Do what works for you.

“I am a lazy manifester, I simply do not have the time to be doing all these elaborate rituals,” Lu says. “But hey, if they work for someone, please, by all means, do it. That's how I started, I wrote things down.”

One of her favorite methods is making a to-do list on her phone; an easy way to reference those manifestations throughout the year.

Nikeia recommends beginning with affirmations. This could be as simple as speaking aloud your wishes as if they’ve already come true. You could even say it as if you’re talking to a friend or a family member if talking to yourself feels unnatural.

♦ Take inspired action

Lu shared one of her recent manifestations — hit a million followers on TikTok. While she’s been manifesting it since 2020, she just recently hit that milestone.

Her account is an example of manifestation in real time, she says — it’s not magic, you have to take action alongside your desires. There’s no way to reach a million followers if you never post a video.

“I can control making videos, I can control the quality of the videos, I can control the content,” Lu says. “I cannot control if people are going to like that, I can't control if people are going to follow. So I'm doing what I can do, and I'm trusting the universe with the rest.”

♦ Be patient

But what about when things happen that are out of control? Nikeia calls those “energetic tests.” Part of manifestation is about bringing a positive mindset to negative situations.

“Is this the moment where I say I'm done, I give up? I'm not going to do these manifestations anymore?” she says. “Or is this a test that, okay, I know, this happened to me, but this is happening for me, and not to me, this is happening for my growth.”

There’s also patience — manifesting something once doesn’t mean you’ll wake up with it the next day.

“Life can turn in dramatic ways, but when we find the power in the now and find the realms of just allowing life to unfold at the right time, that is what manifestation is,” Nikeia says.

Tools and tips to help you manifest

Whether you’re looking for signs in repeated numbers (known as angel numbers ), collecting crystals or getting tarot readings on the regular, there are many tools that accompany the world of manifesting.

Lu says while these aren’t necessary, they can help us be “in a state of empowerment.”

“Sometimes we already have the answers, we know what's up,” Lu says. “But it's nice to be reassured, to hear something and be like, this is someone I trust, this method I trust, I'm hearing that things are going to work out, and I can sleep tonight.”

But get far enough down the manifesting rabbit hole on TikTok and you may wind up scrolling through dozens of prediction videos or “signs” from the universe.

“If you believe that you're supposed to hear this message, then you're watching every single video getting 1,000 messages, you’re going to be so confused,” Cameron says. “So this is why you have to trust you, you have to trust what you see, what you believe, your intuition, what you feel.”

Is manifestation real?

There’s no scientific evidence that manifestation automatically makes dreams come true, but some research suggests positive mindsets have equally positive outcomes.

One study found imagining a hypothetical event will land you with a better action plan for how to make the event actually happen. The concept of growth mindset says that individuals who believe their talents can grow tend to achieve more than those who don’t.

Certainly, the thought-to-action aspects of manifestation hold some ground. Some scientists have referenced manifestation’s close alignment with cognitive behavioral therapy , which is modifying unhelpful ways of thinking and behavior to cope with and prevent further psychological problems.

And yet, the “magic” of manifesting that has come into popular culture is not necessarily what many manifestation coaches actually believe.

“It's not about bringing like that one thing or that one amount of money into your life. It's about being a different version of yourself,” Cameron says. “It doesn't come from thin air — it comes from your actions that you take based on the energy that you're in.”

Manifestation, all three coaches said, is something we’re doing all the time. We wish on birthday candles, when we see the time is 11:11 or when we set a particular goal.

“You're always running based on the beliefs you hold,” Cameron says. “Every day you're creating the next day of your life over and over and over again, so people are manifesting but they don't know what or how they're doing it.”

Is manifesting dangerous?

There can be too much of a good thing.

Some research suggests the practice could be harmful for those with anxiety or obsessive-compulsive disorders or who may be actively working toward unlearning harmful “thought equals reality” triggers — what manifestation holds at its core.

“You have to know yourself well enough and your mental health to understand where you are going to take your practice of manifesting,” Lu says. “If it is something that you end up being obsessive over and ends up really impeding your life, please take a step back and find a way that really works for you.”

What is my Zodiac sign? Horoscopes, astrology and what the stars say about you.

Just Curious for more? We've got you covered

USA TODAY is exploring the questions you and others ask every day. From " Is Mercury in retrograde? " to " Are cats nocturnal? " to " Are witches real? " – we're striving to find answers to the most common questions you ask every day. Head to our Just Curious section to see what else we can answer.

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Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
What Is Research Methodology? Types, Process, Examples In Research
Research methodology is a crucial framework that guides the entire research process. It involves choosing between various qualitative and quantitative approaches, each tailored to specific research questions and objectives. Your chosen methodology shapes how data is gathered, analysed, and interpreted, ultimately influencing the reliability and ...
What Is Research Methodology? (Why It's Important and Types)
A research methodology gives research legitimacy and provides scientifically sound findings. It also provides a detailed plan that helps to keep researchers on track, making the process smooth, effective and manageable. A researcher's methodology allows the reader to understand the approach and methods used to reach conclusions.
What is research methodology? [Update 2024]
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 ...
Research Methodology Guide: Writing Tips, Types, & Examples
1. Qualitative research methodology. Qualitative research methodology is aimed at understanding concepts, thoughts, or experiences. This approach is descriptive and is often utilized to gather in-depth insights into people's attitudes, behaviors, or cultures. Qualitative research methodology involves methods like interviews, focus groups, and ...
The Ultimate Guide To Research Methodology
Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.
Your Step-by-Step Guide to Writing a Good Research Methodology
Provide the rationality behind your chosen approach. Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome. 3. Explain your mechanism.
What are research methodologies?
A research methodology is different from a research method because research methods are the tools you use to gather your data (Dawson, 2019). You must consider several issues when it comes to selecting the most appropriate methodology for your topic. Issues might include research limitations and ethical dilemmas that might impact the quality of ...
Research Methodology: An Introduction
2.1 Research Methodology. Method can be described as a set of tools and techniques for finding something out, or for reducing levels of uncertainty. According to Saunders (2012) method is the technique and procedures used to obtain and analyse research data, including for example questionnaires, observation, interviews, and statistical and non-statistical techniques [].
Research
Definition: Research refers to the process of investigating a particular topic or question in order to discover new information, ... Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or ...
Methodology
In its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal, like acquiring knowledge or verifying knowledge claims. This normally involves various steps, like choosing a sample ...
A tutorial on methodological studies: the what, when, how and why
In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts). In the past 10 years, there has been an increase in the use of terms related to ...
Research Methods: What are research methods?
What are research methods. Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic. There are different types of research methods which use different tools for data collection.
Research Methods
Research Methods. Definition: Research Methods refer to the techniques, procedures, and processes used by researchers to collect, analyze, and interpret data in order to answer research questions or test hypotheses.The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.
Research Guide: Research Methods
Research methodology can be understood as a way to systemically solve or answer the research problem. Thus essentially, it can be understood as the process of studying how research is done in a scientific manner. Through the methodology, we study the various steps that are generally adopted by a researcher in studying his/her research problem ...
What are research methods?
Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project. The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data. The following boxes below contain a ...
PDF Research Methodology: An Introduction Meaning Of Research
research methods. Since the object of research, particularly the applied research, it to arrive at a solution for a given problem, the available data and the unknown aspects of the problem have to be related to each other to make a solution possible. Keeping this in view, research methods can be put into the following three groups: 1.
Methodology Definition & Meaning
methodology: [noun] a body of methods, rules, and postulates employed by a discipline : a particular procedure or set of procedures.
Title page setup
For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center ...
Research Design
Definition: Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. ... It is an important planning phase that outlines the research methodology, data collection methods, and ...
The Root Mean Square Intersect Method to Identify Vortex Cores
The proposed root mean square (RMS) Intersect Method can be applied directly to any data set, such as DPIV data, DNS, and CFD data, to detect the presence of vortices in a laminar or turbulent ...
Journal of Medical Internet Research
The outcomes were analyzed using a generalized estimating equation method by intention-to-treat analysis. Results: A total of 92 participants (46 in each group) were recruited in this study. Of these, 86 completed the course and follow-up evaluations with a mean age of 62.38 (SD 12.91) years.
Genomic reproducibility in the bioinformatics era
In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific ...
What does manifestation mean? Here's how you can do it in a few steps
The most common quick method is with a pen and paper — putting what you desire into words. Others practice by using repetition, candle magic or waiting for certain phases of the moon.

What is Research Methodology? Definition, Types, and Examples

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Research Methods | Definitions, Types, Examples

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Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Qualitative analysis methods

Quantitative analysis methods

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What Is Research Methodology? Types, Process, Examples In Research Design

Types Of Research Methodology

Qualitative Research Methodology

Quantitative Research Methodology

Mixed Research Methodology

Factors To Consider When Deciding On Research Methodology

Research Question Compatibility

Data Requirements

Resources and Constraints

Skill Set and Expertise

Ethical and Practical Considerations

What Is Research Methodology? Answered

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What is research methodology?

The basics of research methodology

Checklist: Dissertation Proposal

Examples: Edited Papers

Defining what is research methodology

Types of research methodology

2. Quantitative research methodology

3. Mixed-Methods research methodology

Research methodology examples and application

Importance of methodology in research papers

How to structure the research methods chapter

Step 1: Explain your research methodology

Step 2: Explain the methods you used to test your hypothesis

Step 3: Explain how you analyzed the results

Step 4: Defend your choice of methodology

Step 5: Discuss the obstacles you encountered and how you overcame them

Tips to write an effective methodology chapter

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The Ultimate Guide To Research Methodology

What Is Research Methodology?

Facilitating Rigorous Inquiry

Ensuring Reproducibility And Reliability

Guiding Decision-Making Processes