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.
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.
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.
Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:
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.
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.
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.
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.
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.
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:
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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This is very simplified and direct. Very helpful to understand the research methodology section of a dissertation
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Writing a thesis or dissertation is hard work. You’ve devoted countless hours to your research, and you want your results to be taken seriously. But how does your professor or evaluating committee know that they can trust your results? You convince them by justifying your research methods.
In simple terms, your methods are the tools you use to obtain your data, and the justification (which is also called the methodology ) is the analysis of those tools. In your justification, your goal is to demonstrate that your research is both rigorously conducted and replicable so your audience recognizes that your results are legitimate.
The formatting and structure of your justification will depend on your field of study and your institution’s requirements, but below, we’ve provided questions to ask yourself as you outline your justification.
Does your study rely on quantitative data, qualitative data, or both? Certain types of data work better for certain studies. How did you choose to gather that data? Evaluate your approach to collecting data in light of your research question. Did you consider any alternative approaches? If so, why did you decide not to use them? Highlight the pros and cons of various possible methods if necessary. Research results aren’t valid unless the data are valid, so you have to convince your reader that they are.
Collecting your data was only the first part of your study. Once you had them, how did you use them? Do your results involve cross-referencing? If so, how was this accomplished? Which statistical analyses did you run, and why did you choose them? Are they common in your field? How did you make sure your data were statistically significant ? Is your effect size small, medium, or large? Numbers don’t always lend themselves to an obvious outcome. Here, you want to provide a clear link between the Methods and Results sections of your paper.
Most fields have standard approaches to the research they use, but these approaches don’t work for every project. Did you use methods that other fields normally use, or did you need to come up with a different way of obtaining your data? Your reader will look at unconventional approaches with a more critical eye. Acknowledge the limitations of your method, but explain why the strengths of the method outweigh those limitations.
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You can strengthen your justification by referencing existing research in your field. Citing these references can demonstrate that you’ve followed established practices for your type of research. Or you can discuss how you decided on your approach by evaluating other studies. Highlight the use of established techniques, tools, and measurements in your study. If you used an unconventional approach, justify it by providing evidence of a gap in the existing literature.
● When you’re writing your justification, write for your audience. Your purpose here is to provide more than a technical list of details and procedures. This section should focus more on the why and less on the how .
● Consider your methodology as you’re conducting your research. Take thorough notes as you work to make sure you capture all the necessary details correctly. Eliminating any possible confusion or ambiguity will go a long way toward helping your justification.
Your goal in writing your justification is to explain not only the decisions you made but also the reasoning behind those decisions. It should be overwhelmingly clear to your audience that your study used the best possible methods to answer your research question. Properly justifying your methods will let your audience know that your research was effective and its results are valid.
Want more writing tips? Check out Proofed’s Writing Tips and Academic Writing Tips blogs. And once you’ve written your thesis or dissertation, consider sending it to us. Our editors will be happy to check your grammar, spelling, and punctuation to make sure your document is the best it can be. Check out our services for free .
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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.
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.
Having a good research methodology in place has the following advantages: 3
Types of research methodology.
There are three types of research methodology based on the type of research and the data required. 1
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.
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:
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
Quantitative research 6
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:
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:
Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:
Here are some important factors to consider when choosing a research methodology: 8
How to write a research methodology .
A research methodology should include the following components: 3,9
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Q1. What are the key components of research methodology?
A1. A good research methodology has the following key components:
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
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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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:
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:
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:
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? | Surveys, tests, existing databases. |
Qualitative | Qualitative research is a process of collecting and analyzing both words and textual data. | Observations, interviews, focus groups. |
Mixed-method | A mixed-method approach combines both of the above approaches. | 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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Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
It should include:
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How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.
Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .
It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.
You can start by introducing your overall approach to your research. You have two options here.
What research problem or question did you investigate?
And what type of data did you need to achieve this aim?
Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?
Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .
In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.
Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.
Surveys Describe where, when, and how the survey was conducted.
Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.
Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.
The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.
The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.
Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.
In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.
Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)
Interviews or focus groups Describe where, when, and how the interviews were conducted.
Participant observation Describe where, when, and how you conducted the observation or ethnography .
Existing data Explain how you selected case study materials for your analysis.
In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.
Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.
Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.
Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.
Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.
Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.
In quantitative research , your analysis will be based on numbers. In your methods section, you can include:
In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).
Specific methods might include:
Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.
Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.
In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .
Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.
The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .
Your methodology can be strengthened by referencing existing research in your field. This can help you to:
Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.
Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.
Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).
In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
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McCombes, S. (2022, October 10). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved 5 August 2024, from https://www.scribbr.co.uk/thesis-dissertation/methodology/
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The methodology chapter precisely outlines the research method(s) employed in your dissertation and considers any relevant decisions you made, and challenges faced, when conducting your research. Getting this right is crucial because it lays the foundation for what’s to come: your results and discussion.
Please note: this guide is not specific to any one discipline. The methodology can vary depending on the nature of the research and the expectations of the school or department. Please adapt the following advice to meet the demands of your dissertation and the expectations of your school or department. Consult your supervisor for further guidance; you can also check out our Writing Across Subjects guide .
As part of the Writing the Dissertation series, this guide covers the most common conventions found in a methodology chapter, giving you the necessary knowledge, tips and guidance needed to impress your markers! The sections are organised as follows:
The methodology of a dissertation is like constructing a house of cards. Having strong and stable foundations for your research relies on your ability to make informed and rational choices about the design of your study. Everything from this point on – your results and discussion – rests on these decisions, like the bottom layer of a house of cards.
The methodology is where you explicitly state, in relevant detail, how you conducted your study in direct response to your research question(s) and/or hypotheses. You should work through the linear process of devising your study to implementing it, covering the important choices you made and any potential obstacles you faced along the way.
Some disciplines refer to this chapter as the research methods , whilst others call it the methodology . The two are often used interchangeably, but they are slightly different:
This guide focuses on the methodology, as opposed to the methods, although the content and guidance can be tailored to a methods chapter. Every dissertation is different and every methodology has its own nuances, so ensure you adapt the content here to your research and always consult your supervisor for more detailed guidance.
Your markers are looking for your understanding of the complex process behind original (see definition) research. They are assessing your ability to...
But what does it mean to engage in 'original' research? Originality doesn’t strictly mean you should be inventing something entirely new. Originality comes in many forms, from updating the application of a theory, to adapting a previous experiment for new purposes – it’s about making a worthwhile contribution.
The methodology chapter should outline the research process undertaken, from selecting the method to articulating the tool or approach adopted to analyse your results. Because you are outlining this process, it's important that you structure your methodology in a linear way, showing how certain decisions have impacted on subsequent choices.
Scroll to continue reading, or click a link below to jump immediately to that section:
To ensure you write your methodology in a linear way, it can be useful to think of the methodology in terms of layers, as shown in the figure below.
Figure: 'Research onion' from Saunders et al. (2007).
You don't need to precisely follow these exact layers as some won't be relevant to your research. However, the layered 'out to in' structure developed by Saunders et al. (2007) is appropriate for any methodology chapter because it guides your reader through the process in a linear fashion, demonstrating how certain decisions impacted on others. For example, you need to state whether your research is qualitative, quantitative or mixed before articulating your precise research method. Likewise, you need to explain how you collected your data before you inform the reader of how you subsequently analysed that data.
Using this linear approach from 'outer' layer to 'inner' layer, the next sections will take you through the most common layers used to structure a methodology chapter.
Like any chapter, you should open your methodology with an introduction. It's good to start by briefly restating the research problem, or gap, that you're addressing, along with your research question(s) and/or hypotheses. Following this, it's common to provide a very condensed statement that outlines the most important elements of your research design. Here's a short example:
This study adopted qualitative research through a series of semi-structured interviews with seven experienced industry professionals.
Like any other introduction, you can then provide a brief statement outlining what the chapter is about and how it's structured (e.g., an essay map ).
Restating the research problem (or gap) and your research question(s) and/or hypotheses creates a natural transition from your previous review of the literature - which helped you to identify the gap or problem - to how you are now going to address such a problem. Your markers are also going to assess the relevance and suitability of your method and methodological choices against your research question(s), so it's good to 'frame' the entire chapter around the research question(s) by bringing them to the fore.
A research philosophy is an underlying belief that shapes the way research is conducted. For this reason, as featured in the 'research onion' above, the philosophy should be the outermost layer - the first methodological issue you deal with following the introduction and research outline - because every subsequent choice, from the method employed to the way you analyse data, is directly influenced by your philosophical stance.
You can say something about other philosophies, but it's best to directly relate this to your research and the philosophy you have selected - why the other philosophy isn't appropriate for you to adopt, for instance. Otherwise, explain to your reader the philosophy you have selected (using secondary literature), its underlying principles, and why this philosophy, therefore, is particularly relevant to your research.
The research philosophy is sometimes featured in a methodology chapter, but not always. It depends on the conventions within your school or discipline , so only include this if it's expected.
The reason for outlining the research philosophy is to show your understanding of the role that your chosen philosophy plays in shaping the design and approach of your research study. The philosophy you adopt also indicates your worldview (in the context of this research), which is an important way of highlighting the role you, the researcher, play in shaping new knowledge.
This is where you state whether you're doing qualitative, quantitative or mixed-methods research before outlining the exact instrument or strategy (see definition) adopted for research (interviews, case study, etc.). It's also important that you explain why you have chosen that particular method and strategy. You can also explain why you're not adopting an alternate form of research, or why you haven't used a particular instrument, but keep this brief and use it to reinforce why you have chosen your method and strategy.
Your research method, more than anything else, is going to directly influence how effectively you answer your research question(s). For that reason, it's crucial that you emphasise the suitability of your chosen method and instrument for the purposes of your research.
The data collection part of your methodology explain the process of how you accessed and collected your data. Using an interview as a qualitative example, this might include the criteria for selecting participants, how you recruited the participants and how and where you conducted the interviews. There is often some overlap with data collection and research method, so don't worry about this. Just make sure you get the essential information across to your reader.
The details of how you accessed and collected your data are important for replicability purposes - the ability for someone to adopt the same approach and repeat the study. It's also important to include this information for reliability and consistency purposes (see validity and reliability on the next tab of this guide for more).
After describing how you collected the data, you need to identify your chosen method of data analysis. Inevitably, this will vary depending on whether your research is qualitative or quantitative (see note below).
Qualitative research tends to be narrative-based where forms of ‘coding’ are employed to categorise and group the data into meaningful themes and patterns (Bui, 2014). Quantitative deals with numerical data meaning some form of statistical approach is taken to measure the results against the research question(s).
Tell your reader which data analysis software (such as SPSS or Atlast.ti) or method you’ve used and why, using relevant literature. Again, you can mention other data analysis tools that you haven’t used, but keep this brief and relate it to your discussion of your chosen approach. This isn’t to be confused with the results and discussion chapters where you actually state and then analyse your results. This is simply a discussion of the approach taken, how you applied this approach to your data and why you opted for this method of data analysis.
Detail of how you analysed your data helps to contextualise your results and discussion chapters. This is also a validity issue (see next tab of guide), as you need to ensure that your chosen method for data analysis helps you to answer your research question(s) and/or respond to your hypotheses. To use an example from Bui (2014: 155), 'if one of the research questions asks whether the participants changed their behaviour before and after the study, then one of the procedures for data analysis needs to be a comparison of the pre- and postdata'.
Validity simply refers to whether the research method(s) and instrument(s) applied are directly suited to meet the purposes of your research – whether they help you to answer your research question(s), or allow you to formulate a response to your hypotheses.
Validity can be separated into two forms: internal and external. The difference between the two is defined by what exists inside the study (internal) and what exists outside the study (external).
Reliability refers to the consistency with which you designed and implemented your research instrument(s). The idea behind this is to ensure that someone else could replicate your study and, by applying the instrument in the exact same way, would achieve the same results. This is crucial to quantitative and scientific based research, but isn’t strictly the case with qualitative research given the subjective nature of the data.
With qualitative data, it’s important to emphasise that data was collected in a consistent way to avoid any distortions. For example, let’s say you’ve circulated a questionnaire to participants. You would want to ensure that every participant receives the exact same questionnaire with precisely the same questions and wording, unless different questionnaires are required for different members of the sample for the purposes of the research.
Any research involving human participants needs to consider ethical factors. In response, you need to show your markers that you have implemented the necessary measures to cover the relevant ethical issues. These are some of the factors that are typically included:
These are only a few examples of the ethical factors you need to write about in your methodology. Depending on the nature of your research, ethical considerations might form a significant part of your methodology chapter, or may only constitute a few sentences. Either way, it’s imperative that you show your markers that you’ve considered the relevant ethical implications of your research.
Don’t make the mistake of ignoring the limitations of your study (see the next tab, 'What to Avoid', for more on this) – it’s a common part of research and should be confronted. Limitations of research can be diverse, but tend to be logistical issues relating to time, scope and access . Whilst accepting that your study has certain limitations, the key is to put a positive spin on it, like the example below:
Despite having a limited sample size compared to other similar studies, the number of participants is enough to provide sufficient data, whilst the in-depth nature of the interviews facilitates detailed responses from participants.
This portion of the guide will cover some common missteps you should try to avoid in writing your methodology.
It might seem instinctive to hide any flaws or limitations with your research to protect yourself from criticism. However, you need to highlight any problems you encountered during the research phase, or any limitations with your approach. Your markers are expecting you to engage with these limitations and highlight the kind of impact they may have had on your research.
Just be careful that you don’t overstress these limitations. Doing so could undermine the reliability and validity of your results, and your credibility as a researcher.
Don’t mistake your methodology chapter as a detailed review of methods employed in other studies. This level of detail should, where relevant, be incorporated in the literature review chapter, instead (see our Writing the Literature Review guide ). Any reference to methodological choices made by other researchers should come into your methodology chapter, but only in support of the decisions you made.
It’s important to be thorough in a methodology chapter. However, don’t include unnecessary levels of detail. You should provide enough detail that allows other researchers to replicate or adapt your study, but don’t bore your reader with obvious or extraneous detail.
Any materials or content that you think is worth including, but not essential in the chapter, could be included in an appendix (see definition). These don’t count towards your word count (unless otherwise stated), and they can provide further detail and context for your reader. For instance, it’s quite common to include a copy of a questionnaire in an appendix, or a list of interview questions.
A: The past tense. The study has already been conducted and the methodological decisions have been implemented, meaning the chapter should be written in the past tense. For example...
Data was collected over the course of four weeks.
I informed participants of their right to withdraw at any time.
The surveys included ten questions about job satisfaction and ten questions about familial life (see Appendix).
A: Yes, where relevant. Unlike the literature review, the methodology is driven by what you did rather than what other people have done. However, you should still draw on secondary sources, when necessary, to support your methodological decisions.
A: Yes, although it might not form a chapter, as such. Including some detail on how you approached the research phase is always a crucial part of a dissertation, whether primary or secondary. However, depending on the nature of your research, you may not have to provide the same level of detail as you would with a primary-based study.
For example, if you’re analysing two particular pieces of literature, then you probably need to clarify how you approached the analysis process, how you use the texts (whether you focus on particular passages, for example) and perhaps why these texts are scrutinised, as opposed to others from the relevant literary canon.
In such cases, the methodology may not be a chapter, but might constitute a small part of the introduction. Consult your supervisor for further guidance.
A: It’s important to be consistent , so you should use whatever you’ve been using throughout your dissertation. Third-person is more commonly accepted, but certain disciplines are happy with the use of first-person. Just remember that the first-person pronoun can be a distracting, but powerful device, so use it sparingly. Consult your supervisor for further guidance.
It’s important to remember that all research is different and, as such, the methodology chapter is likely to be very different from dissertation to dissertation. Whilst this guide has covered the most common and essential layers featured in a methodology, your methodology might be very different in terms of what you focus on, the depth of focus and the wording used.
What’s important to remember, however, is that every methodology chapter needs to be structured in a linear, layered way that guides the reader through the methodological process in sequential order. Through this, your marker can see how certain decisions have impacted on others, showing your understanding of the research process.
Here’s a final checklist for writing your methodology. Remember that not all of these points will be relevant for your methodology, so make sure you cover whatever’s appropriate for your dissertation. The asterisk (*) indicates any content that might not be relevant for your dissertation. You can download a copy of the checklist to save and edit via the Word document, below.
Aspect of Methodology Chapter | Yes/Unsure/No |
---|---|
I have structured my methodology in a that guides the reader through the research process in sequential order. | |
I have ensured that my chosen method and methodological decisions | |
I have engaged with the of my study. | |
I have addressed any relevant | |
I have only included that allows my study to be | |
I have briefly explained certain methodological were made. |
What is a methodology.
The methodology is perhaps the most challenging and laborious part of the dissertation . Essentially, the methodology helps in understanding the broad, philosophical approach behind the methods of research you chose to employ in your study. The research methodology elaborates on the ‘how’ part of your research.
This means that your methodology chapter should clearly state whether you chose to use quantitative or qualitative data collection techniques or a mix of both.
Your research methodology should explain the following:
You will be required to provide justifications as to why you preferred a certain method over the others. If you are trying to figure out exactly how to write methodology or the structure of a methodology for a dissertation, this article will point you in the right direction.
Students must be sure of why they chose a certain research method over another. “I figured out” or “In my opinion” statements will not be an acceptable justification. So, you will need to come up with concrete academic reasons for your selection of research methods.
The methodology generally acts as a guideline or plan for exactly how you intend to carry out your research. This is especially true for students who must submit their methodology chapter before carrying out the research.
Your methodology should link back to the literature review and clearly state why you chose certain data collection and analysis methods for your research/dissertation project.
The methodology chapter consists of the following:
For those who are submitting their dissertation as a single paper, their methodology should also touch on any modifications they had to make as their work progressed.
However, it is essential to provide academic justifications for all choices made by the researcher.
The theme of your research methodology chapter should be related to your literature review and research question (s).
You can visit your college or university library to find textbooks and articles that provide information about the commonly employed research methods .
An intensive reading of such books can help you devise your research philosophy and choose the appropriate methods. Any limitations or weaknesses of your chosen research approach should also be explained, as well as the strategies to overcome them.
To research well, you should read well! Read as many research articles (from reputed journals) as you can. Seeing how other researchers use methods in their studies and why will help you justify, in the long run, your own research method(s).
Regardless of the chosen research approach, you will find researchers who either support it or don’t. Use the arguments for and against articulated in the literature to clarify why you decided to choose the selected research design and why the research limitations are irrelevant to your research.
The typical structure of the methodology chapter is as follows:
In research jargon, generalisability is termed external validity . It means how generalisable your research findings are to other contexts, places, times, people, etc. External validity is expected to be significantly high, especially in quantitative studies.
According to USC-Research Guides (2017) , a research design’s primary function is to enable the researcher to answer the research questions through evidence effectively. Generally, this section will shed light on how you collected your data.
The researcher will have to justify their choice of data collection methods, such as the one that was reviewed, the use of data tools (interviews, phone surveys, questionnaires, observation, online surveys , etc.) and the like.
Moreover, data sampling choice should also be clearly explained with a focus on how you chose the ethnicity, group, profession and age of the participants.
It is recommended to prepare these questions at the start of your research. You should develop your research problem and questions. This approach can allow the room to change or modify research questions if your data collection methods do not give the desired results.
It’s a good practice to keep referring to your research questions whilst planning or writing the research design section. This will help your reader recall what the research is about; why you have done what you did. Even though this technique is recommended to be applied at the start of every section within a dissertation, it’s especially beneficial in the methodology section.
In short, you will need to make sure that the data you are going to collect relates to the topic you are exploring. The complexity and length of the research design section will vary depending on your academic subject and the scope of your research, but a well-written research design will have the following characteristics:
This will discuss your chosen philosophy to strengthen your research and the research model. Commonly employed philosophies in academia are
There are several other research philosophies that you could adopt.
The choice of philosophy will depend on many factors, including your academic subject and the type and complexity of the research study. Regardless of which philosophy is used, you will be required to make different assumptions about the world.
Once you have chosen your research philosophy, the next step will describe your research context to answer all the questions, including when, where, why, how and what of your research.
Essentially, as a researcher, you will be required to decide whether you will be using a qualitative method, a quantitative method or a mix of both.
Using both qualitative and quantitative methods leads to the use of a mixed-methods approach. This approach also goes by another seldom-used name: eclectic approach.
The process of data collection is different for each method. Typically, you would want to decide whether you will adopt the positivist approach, defining your hypothesis and testing it against reality.
If this is the case, you will be required to take the quantitative approach, collecting numerical data at a large scale (from 30 or more respondents) and testing your hypotheses with this data.
Collecting data from at least 30 respondents/participants ensures reliable statistical analysis . This is especially true for quantitative studies. If the data contains less than 30 responses, it won’t be enough to carry out reliable statistical analyses on such data.
The other option for you would be to base your research on a qualitative approach, which will point you in a direction where you will be investigating broader areas by identifying people’s emotions and perceptions of a subject.
With a qualitative approach, you will have to collect responses from respondents and look at them in all their richness to develop theories about the field you are exploring.
Finally, you can also use a mix of qualitative and quantitative methods (which is becoming increasingly popular among researchers these days). This method is beneficial if you are interested in putting quantitative data into a real-world context or reflecting different perspectives on a subject.
Research philosophy in the ‘research onion.’
This section will require you to clearly specify how you gathered the data and briefly discuss the tools you used to analyse it. For example, you may choose to conduct surveys and/or interviews as part of the data collection process.
Similarly, if you used software such as Excel or SPSS to process the data , you will have to justify your software choice. In this section of your methodology chapter , you will also have to explain how you arrived at your findings and how reliable they are.
It is important to note that your readers or supervisor would want to see a correlation between your findings and the hypothesis/research questions you based your study on at the very beginning.
Your supervisor or dissertation research assistant can play a key role in helping you write the methodology chapter according to established research standards. So, keep your supervisor in the loop to get their contributions and recommendations throughout the process.
In this section, you should briefly describe the methods you’ve used to analyse the data you’ve collected.
The qualitative method includes analysing language, images, audio, videos, or any textual data (textual analysis). The following types of methods are used in textual analysis .
Discourse analysis:
Discourse analysis is an essential aspect of studying a language and its uses in day-to-day life.
Content analysis:
It is a method of studying and retrieving meaningful information from documents Thematic analysis:
It’s a method of identifying patterns of themes in the collected information, such as face-to-face interviews, texts, and transcripts.
Example: After collecting the data, it was checked thoroughly to find the missing information. The interviews were transcribed, and textual analysis was conducted. The repetitions of the text, types of colours displayed, and the tone of the speakers was measured.
Quantitative data analysis is used for analysing numerical data. Include the following points:
Other important sections of your methodology are:
Always consider how your research will influence other individuals who are beyond the scope of the study. This is especially true for human subjects. As a researcher, you are always expected to make sure that your research and ideas do not harm anyone in any way.Discussion concerning data protection, data handling and data confidentiality will also be included in this brief segment.
Even though there is no established rule to include ethical considerations and limitations within the methodology section, it’s generally recommended to include it in this section, as it makes more sense than including it, say, after the discussions section or within the conclusion.
This is mainly because limitations almost always occur in the methodology stage of research. And ethical considerations need to be taken while sampling, an important aspect of the research methodology.
Here are some examples of ethical issues that you should be mindful of
All such issues should be categorically addressed and a justification provided for your chosen research methodology by highlighting the study’s benefits.
Is your research study and findings reliable for other researchers in your field of work? To establish yourself as a reliable researcher, your study should be both authentic and reliable.
Reliability means the extent to which your research can yield similar results if it was replicated in another setting, at a different time, or under different circumstances. If replication occurs and different findings come to light, your (original) research would be deemed unreliable.
Good dissertation writers will always acknowledge the limitations of their research study. Limitations in data sampling can decrease your results’ reliability.
A classic example of research limitation is collecting responses from people of a certain age group when you could have targeted a more representative cross-section of the population.Be humble and admit to your own study’s limitations. Doing so makes your referees, editors, supervisors, readers and anyone else involved in the research enterprise aware that you were also aware of the things that limited your study.
Limitations are NOT the same as implications. Sometimes, the two can be confused. Limitations lead to implications, that is, due to a certain factor being absent in the study (limitation) for instance, future research could be carried out in a setting where that factor is present (implication).
At this point, you might have a basic understanding of how to craft a well-written, organised, accurate methodology section for your dissertation. An example might help bring all the aforementioned points home. Here is a dissertation methodology example in pdf to better understand how to write methodology for a dissertation.
Sample Dissertation Methodology
If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.
A scientific or lab-based study.
A methodology section for a scientific study will need to elaborate on reproducibility and meticulousness more than anything else. If your methods have obvious flaws, the readers are not going to be impressed. Therefore, it is important to ensure that your chosen research methodology is vigorous in nature.
Any information related to the procedure, setup and equipment should be clearly stated so other researchers in your field of study can work with the same method in the future if needed.
Variables that are likely to falsify your data must be taken into the equation to avoid ambiguities. It is recommended to present a comprehensive strategy to deal with these variables when gathering and analysing the data and drawing conclusions.
Statistical models employed as part of your scientific study will have to be justified, and so your methodology should include details of those statistical models.
Another scholar in the future might use any aspect of your methodology as the starting point for their research. For example, they might base their research on your methodology but analyse the data using other statistical models. Hence, this is something you should be mindful of.
Like scientific or lab-based research, a behavioural and social sciences methodology needs to be built along the same lines. The chosen methodology should demonstrate reproducibility and firmness so other scholars can use your whole dissertation methodology or a part of it based on their research needs.
But there are additional issues that the researcher must take into consideration when working with human subjects. As a starting point, you will need to decide whether your analysis will be based on qualitative data, quantitative data or mixed-method of research, where qualitative data is used to provide contextual background to quantitative data or the other way around.
Here are some questions for you to consider:
While you will be required to demonstrate that you have taken care of the above questions, it is equally important to make sure that you address your research study’s ethical issues side-by-side.
Of course, the first step in that regard will be to obtain formal approval for your research design from the ethics bodies (such as IRBs – institutional review boards), but still, there will be many more issues that could trigger a sense of grief and discomfort among some of the readers.
The rigour and dependability of the methods of research employed remain undisputed and unquestionable for humanities and arts-based dissertations as well. However, the way you convince your readers of your dissertation’s thoroughness is slightly different.
Unlike social science dissertation or a scientific study, the methodology of dissertations in arts and humanities subjects needs to be directly linked to the literature review regardless of how innovative your dissertation’s topic might be.
For example, you could demonstrate the relationship between A and B to discover a new theoretical background or use existing theories in a new framework.
The methodology section of humanities and arts-based dissertations is less complex, so there might be no need to justify it in detail. Students can achieve a seamless transition from the literature review to the analysis section.
However, like with every other type of research methodology, it is important to provide a detailed justification of your chosen methodology and relate it to the research problem.
Failing to do so could leave some readers unconvinced of your theoretical foundations’ suitability, which could potentially jeopardise your whole research.
Make sure that you are paying attention to and giving enough information about the social and historical background of the theoretical frameworks your research methodology is based on. This is especially important if there is an essential difference of opinion between your research and the research done on the subject in the past.
A justification of why opposing schools of thought disagree and why you still went ahead to use aspects of these schools of thought in your methodology should be clearly presented for the readers to understand how they would support your readings.
Some degree programs in the arts allow students to undertake a portfolio of artworks or creative writing rather than produce an extended dissertation research project.However, in practice, your creative research will be required to be submitted along with a comprehensive evaluative paper, including background information and an explanation that hypothesises your innovative exercise.
While this might seem like an easy thing to do, critical evaluation of someone’s work is highly complex and notorious in nature. This further reinforces the argument of developing a rigorous methodology and adhering to it.
As a scholar, you will be expected to showcase the ability to critically analyse your methodology and show that you are capable of critically evaluating your own creative work.Such an approach will help you justify your method of creating the work, which will give the readers the impression that your research is grounded in theory.
All chapters of a dissertation paper are interconnected. This means that there will undoubtedly be some information that would overlap between the different chapters of the dissertation .
For example, some of the text material may seem appropriate to both the literature review and methodology sections; you might even end up moving information from pillar to post between different chapters as you edit and improve your dissertation .
However, make sure that you are not making the following a part of your dissertation methodology, even though it may seem appropriate to fit them in there:
It might seem relevant to include details of the models your dissertation methodology is based on. However, a detailed review of models and precedents used by other scholars and theorists will better fit in the literature review chapter, which you can link back to. This will help the readers understand why you decided to go in favour of or against a certain tactic.
There is absolutely no need to provide extensive details of things like lab equipment and experiment procedures. Having such information in the methodology chapter would discourage some readers who might not be interested in your equipment, setup, lab environment, etc.
Your aim as the author of the document will be to retain the readers’ interest and make the methodology chapter as readable as possible.
While it is important to get all the information relating to how others can reproduce your experiment, it is equally important to ensure your methodology section isn’t unnecessarily long. Again, additional information is better to be placed within the appendices chapter.
The methodology is not the section to provide raw data, even if you are only discussing the data collection process. All such information should be moved to the appendices section.
Even if you feel some finding or numerical data is crucial to be presented within the methodology section, you can, at most, make brief comments about such data. Its discussion, however, is only allowed in the discussions section .
The factors which can determine if your dissertation methodology is ‘great’ depend on many factors, including the level of study you are currently enrolled in.
Undergraduate dissertations are, of course, less complex and less demanding. At most universities in the UK, undergraduate students are required to exhibit the ability to conduct thorough research as they engage for the first time with theoretical and conceptual frameworks in their chosen research area.
As an undergraduate student, you will be expected to showcase the capacity to reproduce what you have learnt from theorists in your academic subject, transform your leanings into a methodology that would help you address the research problem, and test the research hypothesis, as mentioned in the introduction chapter.
A great undergraduate-level dissertation will incorporate different schools of thought and make a valuable contribution to existing knowledge. However, in general, undergraduate-level dissertations’ focus should be to show thorough desk-based and independent research skills.
Postgraduate dissertation papers are much more compound and challenging because they are expected to make a substantial contribution to existing knowledge.
Depending on the academic institute, some postgraduate students are even required to develop a project published by leading academic journals as an approval of their research skills.
It is important to recognise the importance of a postgraduate dissertation towards building your professional career, especially if your work is considered impactful in your area of study and receives citations from multiple scholars, enhancing your reputation in academic communities.
Even if some academics cite your literature review and conclusion in their own work, it is a well-known fact that your methodology framework will result in many more citations regardless of your academic subject.
Other scholars and researchers in your area of study are likely to give much more value to a well-crafted methodology, especially one they can use as the starting point for their own research.
Of course, they can alter, refine and enhance your methodology in one way or another. They can even apply your methodological framework to a new data set or apply it in a completely new situation that is irrelevant to your work.
Finally, postgraduate dissertations are expected to be highly convincing and demonstrate in-depth engagement. They should be reproducible and show rigour, so the findings and conclusions can be regarded as authentic and reliable among scientific and academic communities.
The methodology is the door to success when it comes to dissertation projects. An original methodology that takes into consideration all aspects of research is likely to have an impact on the field of study.
As a postgraduate student, you should ask yourself, Is my dissertation methodology reproducible and transferable? Producing a methodology that others can reproduce in the future is as important as answering research questions .
The methodology chapter can either make or break the grade of your research/dissertation paper. It’s one of the research elements that leave a memorable impression on your readers. So, it would help if you took your time when it comes to choosing the right design and philosophical approach for your research.
Always use authentic academic sources and discuss your plans in detail with your supervisor if you believe your research design or approach has flaws in it.
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Home » Thesis – Structure, Example and Writing Guide
Table of contents.
Definition:
Thesis is a scholarly document that presents a student’s original research and findings on a particular topic or question. It is usually written as a requirement for a graduate degree program and is intended to demonstrate the student’s mastery of the subject matter and their ability to conduct independent research.
The concept of a thesis can be traced back to ancient Greece, where it was used as a way for students to demonstrate their knowledge of a particular subject. However, the modern form of the thesis as a scholarly document used to earn a degree is a relatively recent development.
The origin of the modern thesis can be traced back to medieval universities in Europe. During this time, students were required to present a “disputation” in which they would defend a particular thesis in front of their peers and faculty members. These disputations served as a way to demonstrate the student’s mastery of the subject matter and were often the final requirement for earning a degree.
In the 17th century, the concept of the thesis was formalized further with the creation of the modern research university. Students were now required to complete a research project and present their findings in a written document, which would serve as the basis for their degree.
The modern thesis as we know it today has evolved over time, with different disciplines and institutions adopting their own standards and formats. However, the basic elements of a thesis – original research, a clear research question, a thorough review of the literature, and a well-argued conclusion – remain the same.
The structure of a thesis may vary slightly depending on the specific requirements of the institution, department, or field of study, but generally, it follows a specific format.
Here’s a breakdown of the structure of a thesis:
This is the first page of the thesis that includes the title of the thesis, the name of the author, the name of the institution, the department, the date, and any other relevant information required by the institution.
This is a brief summary of the thesis that provides an overview of the research question, methodology, findings, and conclusions.
This page provides a list of all the chapters and sections in the thesis and their page numbers.
This chapter provides an overview of the research question, the context of the research, and the purpose of the study. The introduction should also outline the methodology and the scope of the research.
This chapter provides a critical analysis of the relevant literature on the research topic. It should demonstrate the gap in the existing knowledge and justify the need for the research.
This chapter provides a detailed description of the research methods used to gather and analyze data. It should explain the research design, the sampling method, data collection techniques, and data analysis procedures.
This chapter presents the findings of the research. It should include tables, graphs, and charts to illustrate the results.
This chapter interprets the results and relates them to the research question. It should explain the significance of the findings and their implications for the research topic.
This chapter summarizes the key findings and the main conclusions of the research. It should also provide recommendations for future research.
This section provides a list of all the sources cited in the thesis. The citation style may vary depending on the requirements of the institution or the field of study.
This section includes any additional material that supports the research, such as raw data, survey questionnaires, or other relevant documents.
Here are some steps to help you write a thesis:
Example of Thesis template for Students:
Title of Thesis
Table of Contents:
Chapter 1: Introduction
Chapter 2: Literature Review
Chapter 3: Research Methodology
Chapter 4: Results
Chapter 5: Discussion
Chapter 6: Conclusion
References:
Appendices:
Note: That’s just a basic template, but it should give you an idea of the structure and content that a typical thesis might include. Be sure to consult with your department or supervisor for any specific formatting requirements they may have. Good luck with your thesis!
Thesis is an important academic document that serves several purposes. Here are some of the applications of thesis:
The purpose of a thesis is to present original research findings in a clear and organized manner. It is a formal document that demonstrates a student’s ability to conduct independent research and contribute to the knowledge in their field of study. The primary purposes of a thesis are:
The timing for writing a thesis depends on the specific requirements of the academic program or institution. In most cases, the opportunity to write a thesis is typically offered at the graduate level, but there may be exceptions.
Generally, students should plan to write their thesis during the final year of their graduate program. This allows sufficient time for conducting research, analyzing data, and writing the thesis. It is important to start planning the thesis early and to identify a research topic and research advisor as soon as possible.
In some cases, students may be able to write a thesis as part of an undergraduate program or as an independent research project outside of an academic program. In such cases, it is important to consult with faculty advisors or mentors to ensure that the research is appropriately designed and executed.
It is important to note that the process of writing a thesis can be time-consuming and requires a significant amount of effort and dedication. It is important to plan accordingly and to allocate sufficient time for conducting research, analyzing data, and writing the thesis.
The characteristics of a thesis vary depending on the specific academic program or institution. However, some general characteristics of a thesis include:
There are several advantages to writing a thesis, including:
There are also some limitations to writing a thesis, including:
Researcher, Academic Writer, Web developer
A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.
However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.
Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.
A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.
Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.
Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.
A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.
As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.
While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.
A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.
Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.
Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.
A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.
Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:
Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.
Example : Reducing plastic use in daily life is essential for environmental health.
Purpose : To break down an idea or issue into its components and evaluate it.
Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.
Purpose : To explain a topic or subject to the reader.
Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.
Purpose : To demonstrate a cause and its resulting effect.
Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.
Purpose : To highlight similarities and differences between two subjects.
Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."
When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.
While both terms are frequently used interchangeably, they have distinct meanings.
A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.
Here’s an in-depth differentiation table of a thesis and a thesis statement.
Aspect | Thesis | Thesis Statement |
Definition | An extensive document presenting the author's research and findings, typically for a degree or professional qualification. | A concise sentence or two in an essay or research paper that outlines the main idea or argument. |
Position | It’s the entire document on its own. | Typically found at the end of the introduction of an essay, research paper, or thesis. |
Components | Introduction, methodology, results, conclusions, and bibliography or references. | Doesn't include any specific components |
Purpose | Provides detailed research, presents findings, and contributes to a field of study. | To guide the reader about the main point or argument of the paper or essay. |
Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure
Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.
Here are the key components or different sections of a thesis structure:
Your thesis begins with the title page. It's not just a formality but the gateway to your research.
Here, you'll prominently display the necessary information about you (the author) and your institutional details.
In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.
This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.
Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.
This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.
A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.
By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.
Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.
It's a visual index, ensuring that readers can quickly locate and reference your graphical data.
Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.
The introduction should captivate your readers, making them eager to delve deeper into your research journey.
Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.
It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.
To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.
In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.
Here's a breakdown of what it should encompass:
Moreover, different research questions necessitate different types of methodologies. For instance:
The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.
This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.
Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.
Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.
Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.
In the discussion section, the raw data transforms into valuable insights.
Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?
Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.
Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.
Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.
When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.
The conclusion provides closure to your research narrative.
It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.
Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.
Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.
Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.
In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .
Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.
To properly cite the sources used in the study, you can rely on online citation generator tools to generate accurate citations!
Here’s more on how you can cite your sources.
Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.
Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.
For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.
In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.
The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.
By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.
Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.
As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.
To further elucidate the concept of a thesis, here are illustrative examples from various fields:
Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.
Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix
Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.
Content and structure
Clarity and coherence
Research quality
Originality and significance
Formatting and presentation
Grammar and language
Feedback and revision
Overall assessment
Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.
After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.
A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.
Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.
The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.
Here’s how you can effectively prepare for your thesis defense .
Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.
One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?
Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.
To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.
Here's a table differentiating between the two.
Aspect | Thesis | Dissertation |
Purpose | Often for a master's degree, showcasing a grasp of existing research | Primarily for a doctoral degree, contributing new knowledge to the field |
Length | 100 pages, focusing on a specific topic or question. | 400-500 pages, involving deep research and comprehensive findings |
Research Depth | Builds upon existing research | Involves original and groundbreaking research |
Advisor's Role | Guides the research process | Acts more as a consultant, allowing the student to take the lead |
Outcome | Demonstrates understanding of the subject | Proves capability to conduct independent and original research |
From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.
As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.
It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.
Good luck with your thesis writing!
A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.
A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.
To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.
The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.
A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.
What’s the difference between method and methodology.
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.
Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.
Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .
Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.
Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.
Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.
A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”
To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.
Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.
While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.
Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.
Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.
In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.
The higher the content validity, the more accurate the measurement of the construct.
If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.
Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.
When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.
For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).
On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.
A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.
Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.
Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.
Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .
This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .
Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.
Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .
Snowball sampling is best used in the following cases:
The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.
Reproducibility and replicability are related terms.
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.
The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).
Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.
A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.
The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.
Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.
On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.
Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.
However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.
In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.
A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .
A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .
An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.
While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.
Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.
Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.
Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.
Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .
When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.
There are two subtypes of construct validity.
Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.
The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.
Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.
You can think of naturalistic observation as “people watching” with a purpose.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.
Overall, your focus group questions should be:
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.
This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.
There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.
A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.
Unstructured interviews are best used when:
The four most common types of interviews are:
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .
In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.
Deductive reasoning is also called deductive logic.
There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.
Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.
In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottom-up reasoning.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Triangulation can help:
But triangulation can also pose problems:
There are four main types of triangulation :
Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.
However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.
Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.
Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.
In general, the peer review process follows the following steps:
Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.
You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.
Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.
Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.
Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.
Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.
Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.
Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.
Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.
For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.
After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.
Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.
These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.
Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.
Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.
In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.
These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .
You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.
You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .
These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.
In multistage sampling , you can use probability or non-probability sampling methods .
For a probability sample, you have to conduct probability sampling at every stage.
You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.
But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .
These are four of the most common mixed methods designs :
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.
In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.
No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.
To find the slope of the line, you’ll need to perform a regression analysis .
Correlation coefficients always range between -1 and 1.
The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.
The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.
These are the assumptions your data must meet if you want to use Pearson’s r :
Quantitative research designs can be divided into two main categories:
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
The priorities of a research design can vary depending on the field, but you usually have to specify:
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
Questionnaires can be self-administered or researcher-administered.
Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.
You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.
Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.
A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.
The third variable and directionality problems are two main reasons why correlation isn’t causation .
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.
Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.
While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .
Controlled experiments establish causality, whereas correlational studies only show associations between variables.
In general, correlational research is high in external validity while experimental research is high in internal validity .
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .
A correlation reflects the strength and/or direction of the association between two or more variables.
Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .
You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.
Systematic error is generally a bigger problem in research.
With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.
Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.
Random and systematic error are two types of measurement error.
Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).
Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).
On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.
Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.
The difference between explanatory and response variables is simple:
In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables .
There are 4 main types of extraneous variables :
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.
A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.
Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .
Advantages:
Disadvantages:
While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .
Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.
The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
To implement random assignment , assign a unique number to every member of your study’s sample .
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.
Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.
Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .
If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.
Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
If something is a mediating variable :
A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
There are three key steps in systematic sampling :
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.
For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.
You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.
Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.
For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.
In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).
Once divided, each subgroup is randomly sampled using another probability sampling method.
Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.
However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.
There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.
The clusters should ideally each be mini-representations of the population as a whole.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,
If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.
Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.
Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .
Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.
A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.
Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .
If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.
However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).
For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.
Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.
The type of data determines what statistical tests you should use to analyze your data.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.
To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).
The process of turning abstract concepts into measurable variables and indicators is called operationalization .
There are various approaches to qualitative data analysis , but they all share five steps in common:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
There are five common approaches to qualitative research :
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
When conducting research, collecting original data has significant advantages:
However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.
In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .
In statistical control , you include potential confounders as variables in your regression .
In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.
Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .
Probability sampling means that every member of the target population has a known chance of being included in the sample.
Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .
Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .
Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.
Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.
A sampling error is the difference between a population parameter and a sample statistic .
A statistic refers to measures about the sample , while a parameter refers to measures about the population .
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.
The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).
The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.
Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .
Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.
Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.
Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.
Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
Longitudinal study | Cross-sectional study |
---|---|
observations | Observations at a in time |
Observes the multiple times | Observes (a “cross-section”) in the population |
Follows in participants over time | Provides of society at a given point |
There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
The research methods you use depend on the type of data you need to answer your research question .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Discrete and continuous variables are two types of quantitative variables :
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:
When designing the experiment, you decide:
Experimental design is essential to the internal and external validity of your experiment.
I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .
External validity is the extent to which your results can be generalized to other contexts.
The validity of your experiment depends on your experimental design .
Reliability and validity are both about how well a method measures something:
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
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.
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.
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Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic.
Learn exactly what a research methodology is with Grad Coach's plain language, easy-to-understand explanation, including examples and videos.
Dissertation Methodology In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.
Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It's an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings.
Learn how to write up a high-quality research methodology chapter for your dissertation or thesis. Step by step instructions + examples.
Learn how to write a clear and effective methodology section for your social sciences research paper. Find tips and examples from USC experts.
Research methodology refers to how your project will be designed, what you will observe or measure, and how you will collect and analyze data. The methods you choose must be appropriate for your field and for the specific research questions you are setting out to answer.
Research methodology is the set of procedures and techniques used to collect, analyze, and interpret data to understand and solve a research problem.
What Does Justifying Your Methods Mean? In simple terms, your methods are the tools you use to obtain your data, and the justification (which is also called the methodology) is the analysis of those tools. In your justification, your goal is to demonstrate that your research is both rigorously conducted and replicable so your audience recognizes that your results are legitimate.
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 ...
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.
Having the right research methodology can be a make-or-break factor for your academic work. What is research methodology, and how can you get ahead?
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
Research methodology is the process or the way you intend to execute your entire research. A research methodology provides a description of the process you will undertake to convert your idea into a study. Read more to find out about types, structure, importance, and tips on research methodology.
This 'Writing the Dissertation' guide focuses on the method or methodology. It demonstrates what is usually included in the methodology chapter and outlines how this chapter connects to others in a typical dissertation.
Summary of Methods Chapter Strategies Most important: Explain each of your methodology choices by linking it to what you want to learn. Show how your methods are the best way to answer your research question - how various methodological choices you made (e.g., decision to do multiple site comparison) provided leverage for understanding the empirical reality.
In this comprehensive guide, you will learn what is a methodology and the step-by-step guide to writing the perfect methodology for your dissertation.
The thesis should follow a specific structure that includes an introduction, literature review, methodology, results, discussion, conclusion, and references. Edit and Proofread: After completing the thesis, you need to edit and proofread it carefully.
What is a thesis? A thesis is an in-depth research study that identifies a specific topic of inquiry and presents a clear argument or perspective about that topic.
In a thesis, dissertation, academic journal article or other formal pieces of research, there are often details of how the researcher approached the study and the methods and techniques they used. If you're designing a research study, then it's helpful to understand what research methodology is and the selection of techniques and tools available to you. In this article, we explore what ...
What's the difference between method and methodology? 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 ...
The methodology in a research paper, thesis paper or dissertation is the section in which you describe the actions you took to investigate and research a problem and your rationale for the specific processes and techniques you use within your research to identify, collect and analyze information that helps you understand the problem.
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.
A thesis is typically a deep investigation of a certain topic, frequently with a case study or concentrated analysis, that reflects the student's academic experience at the master's level.