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What is a thesis | A Complete Guide with Examples

Table of Contents

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.

What is a thesis?

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.

What is a 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.

Different types of thesis statements

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:

Argumentative (or Persuasive) thesis statement

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.

Analytical thesis statement

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.

Expository (or Descriptive) thesis statement

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.

Cause and effect thesis statement

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.

Compare and contrast thesis statement

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.

What is the difference between a thesis and a thesis statement?

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.

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

15 components of 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.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

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.

Acknowledgments

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.

Table of contents

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.

List of figures and tables

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.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

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.

Methodology

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:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

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.

Results (or 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.

Practical implications (Recommendation) section

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.

References (or Bibliography)

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.

Glossary (optional)

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.

Thesis examples

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

Checklist for your thesis evaluation

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

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

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.

Preparing your thesis defense

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?

Dissertation vs. Thesis

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.

Wrapping up

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!

Frequently Asked Questions

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.

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How to Write a Thesis: A Guide for Master’s Students

By Dr. David James Kritz   |  09/29/2023

Let’s face it. Researching and writing a quality thesis can be daunting for many reasons, including:

• A lack of knowledge on where to begin the assignment process
• What key arguments and questions to ask in relation to the thesis statement
• How to get to the data and subject matter
• How to cope with writer's block, a professor's expectations, and time constraints

According to Dictionary.com , a thesis is “a proposition stated or put forward for consideration, especially one to be discussed and proved or to be maintained against objections.”

Therefore, avoiding a weak thesis statement is vital when writing an applicable paper. Thesis statement examples are pivotal in understanding this position.

Currently, master’s students who select the thesis capstone within American Public University's School of Security and Global Studies intelligence master's degree program must choose a relevant subject.

Typically, these students must write a thesis statement that consists of at least one compelling sentence and at least 50 pages of content, then turn it in within 16 weeks.

I have taught graduate students, primarily from the U.S. Intelligence Community, how to conduct research for over eight years.

Based on my experience as an educator, I have 10 tips for creating good thesis statements. These tips, combined with some apt thesis statement examples, can elucidate the process.

Tip #1 for Effective Thesis Statements: Select an Appropriate Topic and Research Question

First, it is necessary to use a lengthy thinking process before developing a good thesis statement, whether it’s an expository thesis statement or an argumentative one. This process begins with many questions related to how to write a thesis statement, such as:

• What would be an interesting topic?
• What would be an original and interesting research question?
• What will be the main claim, key arguments, and central idea of the thesis statement?
• What is an appropriate research design?
• How will I get to the data to address my central research question?

Regardless of the thesis statement or topic, all research begins with a research question.

Without the right question, the analysis, literature review, and implications might miss their mark. This question should be unique, intriguing, and beyond a mere “yes” or “no” answer.

For instance, rather than asking, “Will Country X pursue nuclear proliferation?”, it's better to pose open-ended questions like, “How does…?” or “To what extent…?” Such an approach ensures nuanced and substantive answers.

Additionally, supplementary key questions should support the main research question's depth and intent.

Tip #2: Begin Work on the Thesis Statement and Break Up the Thesis into Manageable Sections

After selecting an appropriate topic and developing a central research question for the thesis statement, it is then necessary to apply the research and writing skills you have learned throughout your degree program.

It might be necessary to refine the thesis statement after some preliminary research; after all, you want a strong thesis statement rather than a weak thesis statement.

It is also essential to break up the thesis paper into manageable sections during the writing process. This strategy will help you to overcome the most common types of mental hurdle of creating a thesis paper that can be 50 or more pages in length.

For writing a thesis statement, this way of thinking is helpful before you begin writing. Instead of attempting to write every single sentence of a thesis statement in one long stretch, you can work on one section at a time, turn it in for review and work on the next section of the thesis statement while awaiting feedback.

Tip #3: Pay Attention to Your Professor's Feedback about Your Assignment

When I give my essay assignment to my students with advice on how to write a thesis, I also explain the importance of a strong thesis statement.

I advise them to avoid becoming emotionally attached to the thesis. That emotional attachment can lead to a battle of wills and wits with the capstone course's professor over the thesis statement examples they present.

When it comes to implementing feedback, revisions to the thesis paper often need to occur. Faculty members are there to help guide you and assist you in the production of a good-quality, argumentative thesis statement that will provide new insights for the reader.

Just go with the feedback you receive from your instructor as you write a sentence, or more, and move on to complete your thesis paper more efficiently.

Tip #4: Complete an Abstract

The abstract of a thesis is vital, so it must be carefully crafted. The abstract may be the only section of a published, scholarly paper or article that someone may take the time to read, based on their time constraints and interest.

Ideally, the abstract should be 250 words or less and must contain the main point of the paper. I advise students drafting an abstract for scholarly journal editors to ensure that the abstract has these elements:

An introductory sentence

A “hook” (why the reader should care about the thesis statement or its topic and to motivate the reader to look at your paper)

The central research question to show the main point of your paper

The research design – how you collected evidence to support your arguments

The results and implications, such as the negative and positive aspects of your main topic and the broader context of your research

Tip #5: Write the Literature Review

When crafting a literature review, incorporate multiple peer-reviewed articles from academic sources like ProQuest and EBSCOHost. Opt for articles frequently cited in other works to enhance your paper's credibility.

The review examines arguments in thesis statements and their counterarguments from scholarly works. For clear discussions, organize your review thematically, showing topic synthesis and your position. This reduces confusion.

For example, if 40 articles discuss open-source intelligence and seven focus on social media, that could be a central theme.

Rather than just listing articles, create broader themes and keep synthesizing. When crafting the thesis, evaluate each paragraph's relevance to the main research question. I advise students to assess the “So what?” factor. If a paragraph isn't pertinent, it might be best to remove it.

Tip #6: Develop a Theoretical Framework within Your Thesis Statement

Theories in theses are often mishandled, reflecting a student’s unclear grasp. Academic theory goes beyond mere "I have a theory" statements and leans on robust, time-tested frameworks.

For instance, a strategic intelligence studies thesis statement might employ national security theory or national defense theory. This theory should align with the thesis's central question.

For example, if probing how Country X uses social media for misinformation, a student might be directed to the communication theory, which aligns well with the study's main topic and question.

Tip #7: Select a Research Design

Before conducting research, students must devise a strategy to address their central question. The research design is their roadmap for data collection. This encompasses methodology, methods, and data gathering instruments like surveys or interviews. Research on humans requires IRB approval, which I advise against due to time constraints in a 16-week paper cycle. Additionally, it's vital to distinguish between “methodology” and “methods,” terms often mistakenly used interchangeably.

Methodology involves the justification of the how and why a research method was selected to address the central research question , according to Indeed. The three primary methodologies include:

• Qualitative methodology
• Quantitative methodology
• Mixed methods

“Mixed methods” involves a researcher’s use of at least one research method from a qualitative methodology and another research method from a quantitative methodology, then explaining how those methods will be integrated into a study.

But if two methods from the same methodology are used in a study, that is referred to as a multi-method approach. An example of a multi-method approach would be using a comparative case study as the first qualitative research method and process tracing as the second research method.

Research methods are linked to either qualitative or quantitative methodologies. They focus on “what” a researcher selected to interpret data.

Research method types include:

• Archival records
• Alternative futures
• Case studies
• Comparative case studies
• Content analysis
• Correlational research
• Descriptive research
• Ethnography
• Experimental research
• Phenomenology
• Process tracing

Tip #8: Write about Research Findings and Data

After gathering data for a thesis, analyzing its significance is crucial, with methods including coding. While qualitative methodology doesn't aim to prove anything, unlike the quantitative approach which tests hypotheses, it can discuss correlations, causation, and delve into theoretical implications in data.

Some may view qualitative research as subjective, but selecting variables in quantitative research has its subjectivity too. Ultimately, it's essential to adhere closely to the scientific method, rather than relying on opinions or claims without concrete evidence.

Tip #9: Consider How Bias Will Affect Your Thesis Statement

When writing thesis statements, it is necessary to consider how bias will affect your writing and your reader. Being 100% objective is an admirable goal, but it is impossible to avoid biases as we are human beings.

All of us have biases, including latent ones. At best, we can mitigate biases, such as using coding software, but never holistically remove bias. As researchers, we just need to be aware of biases and develop strategies to mitigate them.

Tip #10: Be Aware of the Limitations of a Study

The study's limitations section is a pivotal part of a thesis. It highlights the research's shortcomings and indicates what might be done differently.

For instance, a student may mention a 16-week time constraint or contemplate a different research design or question.

This section not only helps students recognize how to enhance their research but also guides future scholars. They can learn from prior omissions or envision alternative research avenues.

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While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

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Thesis and Dissertation: Getting Started

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The resources in this section are designed to provide guidance for the first steps of the thesis or dissertation writing process. They offer tools to support the planning and managing of your project, including writing out your weekly schedule, outlining your goals, and organzing the various working elements of your project.

Weekly Goals Sheet (a.k.a. Life Map) [Word Doc]

This editable handout provides a place for you to fill in available time blocks on a weekly chart that will help you visualize the amount of time you have available to write. By using this chart, you will be able to work your writing goals into your schedule and put these goals into perspective with your day-to-day plans and responsibilities each week. This handout also contains a formula to help you determine the minimum number of pages you would need to write per day in order to complete your writing on time.

Setting a Production Schedule (Word Doc)

This editable handout can help you make sense of the various steps involved in the production of your thesis or dissertation and determine how long each step might take. A large part of this process involves (1) seeking out the most accurate and up-to-date information regarding specific document formatting requirements, (2) understanding research protocol limitations, (3) making note of deadlines, and (4) understanding your personal writing habits.

Creating a Roadmap (PDF)

Part of organizing your writing involves having a clear sense of how the different working parts relate to one another. Creating a roadmap for your dissertation early on can help you determine what the final document will include and how all the pieces are connected. This resource offers guidance on several approaches to creating a roadmap, including creating lists, maps, nut-shells, visuals, and different methods for outlining. It is important to remember that you can create more than one roadmap (or more than one type of roadmap) depending on how the different approaches discussed here meet your needs.

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HOW TO WRITE YOUR MASTER THESIS: THE EASY HANDBOOK

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How to structure a thesis

A typical thesis structure

1. abstract, 2. introduction, 3. literature review, 6. discussion, 7. conclusion, 8. reference list, frequently asked questions about structuring a thesis, related articles.

Starting a thesis can be daunting. There are so many questions in the beginning:

• How do you actually start your thesis?
• How do you structure it?
• What information should the individual chapters contain?

Each educational program has different demands on your thesis structure, which is why asking directly for the requirements of your program should be a first step. However, there is not much flexibility when it comes to structuring your thesis.

Abstract : a brief overview of your entire thesis.

Literature review : an evaluation of previous research on your topic that includes a discussion of gaps in the research and how your work may fill them.

Methods : outlines the methodology that you are using in your research.

Thesis : a large paper, or multi-chapter work, based on a topic relating to your field of study.

The abstract is the overview of your thesis and generally very short. This section should highlight the main contents of your thesis “at a glance” so that someone who is curious about your work can get the gist quickly. Take a look at our guide on how to write an abstract for more info.

Tip: Consider writing your abstract last, after you’ve written everything else.

The introduction to your thesis gives an overview of its basics or main points. It should answer the following questions:

• Why is the topic being studied?
• How is the topic being studied?
• What is being studied?

In answering the first question, you should know what your personal interest in this topic is and why it is relevant. Why does it matter?

To answer the "how", you should briefly explain how you are going to reach your research goal. Some prefer to answer that question in the methods chapter, but you can give a quick overview here.

And finally, you should explain "what" you are studying. You can also give background information here.

You should rewrite the introduction one last time when the writing is done to make sure it connects with your conclusion. Learn more about how to write a good thesis introduction in our thesis introduction guide .

A literature review is often part of the introduction, but it can be a separate section. It is an evaluation of previous research on the topic showing that there are gaps that your research will attempt to fill. A few tips for your literature review:

• Use a wide array of sources
• Show both sides of the coin
• Make sure to cover the classics in your field
• Present everything in a clear and structured manner

For more insights on lit reviews, take a look at our guide on how to write a literature review .

The methodology chapter outlines which methods you choose to gather data, how the data is analyzed and justifies why you chose that methodology . It shows how your choice of design and research methods is suited to answering your research question.

Make sure to also explain what the pitfalls of your approach are and how you have tried to mitigate them. Discussing where your study might come up short can give you more credibility, since it shows the reader that you are aware of its limitations.

Tip: Use graphs and tables, where appropriate, to visualize your results.

The results chapter outlines what you found out in relation to your research questions or hypotheses. It generally contains the facts of your research and does not include a lot of analysis, because that happens mostly in the discussion chapter.

Clearly visualize your results, using tables and graphs, especially when summarizing, and be consistent in your way of reporting. This means sticking to one format to help the reader evaluate and compare the data.

The discussion chapter includes your own analysis and interpretation of the data you gathered , comments on your results and explains what they mean. This is your opportunity to show that you have understood your findings and their significance.

Point out the limitations of your study, provide explanations for unexpected results, and note any questions that remain unanswered.

This is probably your most important chapter. This is where you highlight that your research objectives have been achieved. You can also reiterate any limitations to your study and make suggestions for future research.

Remember to check if you have really answered all your research questions and hypotheses in this chapter. Your thesis should be tied up nicely in the conclusion and show clearly what you did, what results you got, and what you learned. Discover how to write a good conclusion in our thesis conclusion guide .

At the end of your thesis, you’ll have to compile a list of references for everything you’ve cited above. Ideally, you should keep track of everything from the beginning. Otherwise, this could be a mammoth and pretty laborious task to do.

Consider using a reference manager like Paperpile to format and organize your citations. Paperpile allows you to organize and save your citations for later use and cite them in thousands of citation styles directly in Google Docs, Microsoft Word, or LaTeX:

🔲 Introduction

🔲 Literature review

🔲 Discussion

🔲 Conclusion

🔲 Reference list

The basic elements of a thesis are: Abstract, Introduction, Literature Review, Methods, Results, Discussion, Conclusion, and Reference List.

It's recommended to start a thesis by writing the literature review first. This way you learn more about the sources, before jumping to the discussion or any other element.

It's recommended to write the abstract of a thesis last, once everything else is done. This way you will be able to provide a complete overview of your work.

Usually, the discussion is the longest part of a thesis. In this part you are supposed to point out the limitations of your study, provide explanations for unexpected results, and note any questions that remain unanswered.

The order of the basic elements of a thesis are: 1. Abstract, 2. Introduction, 3. Literature Review, 4. Methods, 5. Results, 6. Discussion, 7. Conclusion, and 8. Reference List.

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Is it possible not to have a hypothesis in your thesis? [closed]

I have been working on a research report in which forecasting is done on the basis of data, followed by an interpretation of the forecasted results.

Is it possible to have that kind of research without hypothesizing any statement?

If this question is off-topic kindly recommend a suitable community.

• research-process
• methodology

• Would you please provide context for your thesis. For example, what is your area of study and what subfield are you in? –  Richard Erickson Commented Jun 14, 2017 at 17:50
• It would be answerable, if you could please add some more details as suggsted by @RichardErickson. –  Coder Commented Jun 14, 2017 at 18:02
• 3 This question is not about academia but about statistics. It might be on-topic on crossvalidated.se –  henning no longer feeds AI Commented Jun 15, 2017 at 5:58
• 2 @henning I made it about statistics, but the question as it stands is about research protocol. On crossvalidated Ibn e Ashiq can ask how to do the statistics. –  Joris Meys Commented Jun 15, 2017 at 8:52

As a statistician, I'm inclined to say "no, you can't" as a short answer. Reason for this is simple: even in complete random datasets on average 5% of the correlations will be significant when tested in a model. So if you rely on only the data to make any kind of interpretation on association of variables, you're bound to publish false positives. This has been discussed for decades, eg this rather strong opinion of Ioannidis (2005) :

http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124

Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias.

If you don't formulate a hypothesis and still interprete the data, you're -probably unintentionally- reporting selectively. You select from the analysis those results that tell a story, and in doing so, you're likely to report something that isn't a solid association or relation.

That said, you don't always have to formulate a specific hypothesis. For example, if you compare multiple methods on efficiency, you don't have to hypothesize beforehand which one is going to be the best. But the statistical test you use for comparison, will imply a "null hypothesis" that there is no real difference between all methods. Also this is "formulating a hypothesis" merely by the choice of analysis tools.

And this is even more important to realize: you might not formulate a hypothesis explicitly, but the nature of the statistical tools you use to come to your interpretation, will imply a set of rather rigid hypotheses and assumptions. You need to be aware of those hypotheses and also of those assumptions.

Because that's something I see far too often: people not explicitly formulating a hypothesis, still interpreting results from a statistical methodology, but failing to realize that their data does not meet the assumptions of that methodology. And that invalidates your entire interpretation.

This problem is even more stringent when forecasting. If you use regression models, you should be aware that predictions outside the boundaries of the original data cannot be interpreted. The uncertainty on those predictions is simply too big. If you use spline methods, you can even get into trouble at the edge of your original data. So definitely in the case of forecasting I would write out both the goal of the research and what you expect the predictions to show, including the scientific reason why. Only in those cases you can use forecasts as some form of evidence for or against the expected relation. If you don't do that, your forecasting model might as well be a fancy random number generator.

So in conclusion: even if your research goal isn't necessarily a defined hypothesis, you still need to formulate the hypotheses you want to test before carrying out the actual statistical tests.

And in all honesty, writing down what you expect to see is always a good idea, even if it's only to order your own thoughts.

• 1 +1 This is a really important point and a core principle of the scientific method, but for some reason many people either ignore it (out of convenience) or do not know about it. –  101010111100 Commented Jun 14, 2017 at 18:38
• There are fields - especially in Engineering - where results do not depend on a statistical analysis so as non-statistician, I would say, yes you can write a thesis without a hypothesis. –  o4tlulz Commented Jun 15, 2017 at 4:04
• 1 @Kevin That's using cross-validation, an often used statistical technique with its own assumptions. If you do that, you have to keep in mind that your training data and testing data have to be completely independent, or any hypothesis testing (yes, also there you test a hypothesis) is invalid. Independence is one of the most important assumptions in about every common statistical technique. –  Joris Meys Commented Jun 15, 2017 at 8:47
• 1 @Ooker I always tell my students to only use methods they know and understand. You can't know everything about statistics. But what you need to know, is all the details of the techniques you use yourself. And in any case every student should have the knowledge of the basic tests used in the majority of papers. Because if you don't understand those, there's no way you can evaluate yourself whether the conclusion in a paper actually makes sense. –  Joris Meys Commented Jun 15, 2017 at 8:51
• 1 @o4tlulz and how do you assure me that it solves the issue? Can you prove that? Can you show me your "solution" isn't just random luck? –  Joris Meys Commented Jun 16, 2017 at 7:45

Not the answer you're looking for? Browse other questions tagged phd research-process thesis methodology .

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How To Write The Methodology Chapter

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical foundations of your research and detail the specific methodological choices you’ve made. In other words, the purpose of this chapter is to explain exactly how you designed your study and, just as importantly, why you made those choices.

Your methodology chapter should comprehensively describe and justify all the methodological decisions involved in your study. For instance, the research approach you took (qualitative, quantitative, or mixed methods), your sampling strategy (who you collected data from), how you gathered your data, and how you analysed it. If that sounds a bit daunting, don’t worry – we’ll walk you through all these methodological aspects in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these.

Now, it’s important to understand that every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. 

Need a helping hand?

How to write up the methodology chapter

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover.

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies. https://www.youtube.com/watch?v=fSmedyVv-Us Video can't be loaded because JavaScript is disabled: Sampling Methods 101: Probability & Non-Probability Sampling Explained Simply (https://www.youtube.com/watch?v=fSmedyVv-Us) Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Methodology Chapter Example

Wrapping up.

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

Learn More About Methodology

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Research Limitations 101: What You Need To Know

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In Vivo Coding 101: Full Explainer With Examples

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Process Coding 101: Full Explainer With Examples

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Qualitative Coding 101: Inductive, Deductive & Hybrid Coding

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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
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• Reading Research Effectively
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• Theoretical Framework
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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How to do your dissertation secondary research in 4 steps

(Last updated: 12 May 2021)

Since 2006, Oxbridge Essays has been the UK’s leading paid essay-writing and dissertation service

We have helped 10,000s of undergraduate, Masters and PhD students to maximise their grades in essays, dissertations, model-exam answers, applications and other materials. If you would like a free chat about your project with one of our UK staff, then please just reach out on one of the methods below.

If you are reading this guide, it's very likely you may be doing secondary research for your dissertation, rather than primary. If this is indeed you, then here's the good news: secondary research is the easiest type of research! Congratulations!

In a nutshell, secondary research is far more simple. So simple, in fact, that we have been able to explain how to do it completely in just 4 steps (see below). If nothing else, secondary research avoids the all-so-tiring efforts usually involved with primary research. Like recruiting your participants, choosing and preparing your measures, and spending days (or months) collecting your data.

That said, you do still need to know how to do secondary research. Which is what you're here for. So, go make a decent-sized mug of your favourite hot beverage (consider a glass of water , too) then come back and get comfy.

Here's what we'll cover in this guide:

The basics: What's secondary research all about?

Understanding secondary research, advantages of secondary research, disadvantages of secondary research, methods and purposes of secondary research, types of secondary data, sources of secondary data, secondary research process in 4 steps, step 1: develop your research question(s), step 2: identify a secondary data set, step 3: evaluate a secondary data set, step 4: prepare and analyse secondary data.

To answer this question, let’s first recall what we mean by primary research . As you probably already know, primary research is when the researcher collects the data himself or herself. The researcher uses so-called “real-time” data, which means that the data is collected during the course of a specific research project and is under the researcher’s direct control.

In contrast, secondary research involves data that has been collected by somebody else previously. This type of data is called “past data” and is usually accessible via past researchers, government records, and various online and offline resources.

So to recap, secondary research involves re-analysing, interpreting, or reviewing past data. The role of the researcher is always to specify how this past data informs his or her current research.

In contrast to primary research, secondary research is easier, particularly because the researcher is less involved with the actual process of collecting the data. Furthermore, secondary research requires less time and less money (i.e., you don’t need to provide your participants with compensation for participating or pay for any other costs of the research).

TABLE 1 outlines the differences between primary and secondary research:

One of the most obvious advantages is that, compared to primary research, secondary research is inexpensive . Primary research usually requires spending a lot of money. For instance, members of the research team should be paid salaries. There are often travel and transportation costs. You may need to pay for office space and equipment, and compensate your participants for taking part. There may be other overhead costs too.

These costs do not exist when doing secondary research. Although researchers may need to purchase secondary data sets, this is always less costly than if the research were to be conducted from scratch.

As an undergraduate or graduate student, your dissertation project won't need to be an expensive endeavour. Thus, it is useful to know that you can further reduce costs, by using freely available secondary data sets.

But this is far from the only consideration.

Most students value another important advantage of secondary research, which is that secondary research saves you time . Primary research usually requires months spent recruiting participants, providing them with questionnaires, interviews, or other measures, cleaning the data set, and analysing the results. With secondary research, you can skip most of these daunting tasks; instead, you merely need to select, prepare, and analyse an existing data set.

Moreover, you probably won’t need a lot of time to obtain your secondary data set, because secondary data is usually easily accessible . In the past, students needed to go to libraries and spend hours trying to find a suitable data set. New technologies make this process much less time-consuming. In most cases, you can find your secondary data through online search engines or by contacting previous researchers via email.

A third important advantage of secondary research is that you can base your project on a large scope of data . If you wanted to obtain a large data set yourself, you would need to dedicate an immense amount of effort. What's more, if you were doing primary research, you would never be able to use longitudinal data in your graduate or undergraduate project, since it would take you years to complete. This is because longitudinal data involves assessing and re-assessing a group of participants over long periods of time.

When using secondary data, however, you have an opportunity to work with immensely large data sets that somebody else has already collected. Thus, you can also deal with longitudinal data, which may allow you to explore trends and changes of phenomena over time.

With secondary research, you are relying not only on a large scope of data, but also on professionally collected data . This is yet another advantage of secondary research. For instance, data that you will use for your secondary research project has been collected by researchers who are likely to have had years of experience in recruiting representative participant samples, designing studies, and using specific measurement tools.

If you had collected this data yourself, your own data set would probably have more flaws, simply because of your lower level of expertise when compared to these professional researchers.

The first such disadvantage is that your secondary data may be, to a greater or lesser extent, inappropriate for your own research purposes. This is simply because you have not collected the data yourself.

When you collect your data personally, you do so with a specific research question in mind. This makes it easy to obtain the relevant information. However, secondary data was always collected for the purposes of fulfilling other researchers’ goals and objectives.

Thus, although secondary data may provide you with a large scope of professionally collected data, this data is unlikely to be fully appropriate to your own research question. There are several reasons for this. For instance, you may be interested in the data of a particular population, in a specific geographic region, and collected during a specific time frame. However, your secondary data may have focused on a slightly different population, may have been collected in a different geographical region, or may have been collected a long time ago.

Apart from being potentially inappropriate for your own research purposes, secondary data could have a different format than you require. For instance, you might have preferred participants’ age to be in the form of a continuous variable (i.e., you want your participants to have indicated their specific age). But the secondary data set may contain a categorical age variable; for example, participants might have indicated an age group they belong to (e.g., 20-29, 30-39, 40-49, etc.). Or another example: A secondary data set may contain too few ethnic categories (e.g., “White” and “Other”), while you would ideally want a wider range of racial categories (e.g., “White”, “Black or African American”, “American Indian”, and “Asian”). Differences such as these mean that secondary data may not be perfectly appropriate for your research.

The above two disadvantages may lead to yet another one: the existing data set may not answer your own research question(s) in an ideal way. As noted above, secondary data was collected with a different research question in mind, and this may limit its application to your own research purpose.

Unfortunately, the list of disadvantages does not end here. An additional weakness of secondary data is that you have a lack of control over the quality of data. All researchers need to establish that their data is reliable and valid. But if the original researchers did not establish the reliability and validity of their data, this may limit its reliability and validity for your research as well. To establish reliability and validity, you are usually advised to critically evaluate how the data was gathered, analysed, and presented.

But here lies the final disadvantage of doing secondary research: original researchers may fail to provide sufficient information on how their research was conducted. You might be faced with a lack of information on recruitment procedures, sample representativeness, data collection methods, employed measurement tools and statistical analyses, and the like. This may require you to take extra steps to obtain such information, if that is possible at all.

TABLE 2 provides a full summary of advantages and disadvantages of secondary research:

At this point, we should ask: “What are the methods of secondary research?” and “When do we use each of these methods?” Here, we can differentiate between three methods of secondary research: using a secondary data set in isolation , combining two secondary data sets, and combining secondary and primary data sets. Let’s outline each of these separately, and also explain when to use each of these methods.

Initially, you can use a secondary data set in isolation – that is, without combining it with other data sets. You dig and find a data set that is useful for your research purposes and then base your entire research on that set of data. You do this when you want to re-assess a data set with a different research question in mind.

Let’s illustrate this with a simple example. Suppose that, in your research, you want to investigate whether pregnant women of different nationalities experience different levels of anxiety during different pregnancy stages. Based on the literature, you have formed an idea that nationality may matter in this relationship between pregnancy and anxiety.

If you wanted to test this relationship by collecting the data yourself, you would need to recruit many pregnant women of different nationalities and assess their anxiety levels throughout their pregnancy. It would take you at least a year to complete this research project.

Instead of undertaking this long endeavour, you thus decide to find a secondary data set – one that investigated (for instance) a range of difficulties experienced by pregnant women in a nationwide sample. The original research question that guided this research could have been: “to what extent do pregnant women experience a range of mental health difficulties, including stress, anxiety, mood disorders, and paranoid thoughts?” The original researchers might have outlined women’s nationality, but weren’t particularly interested in investigating the link between women’s nationality and anxiety at different pregnancy stages. You are, therefore, re-assessing their data set with your own research question in mind.

Your research may, however, require you to combine two secondary data sets . You will use this kind of methodology when you want to investigate the relationship between certain variables in two data sets or when you want to compare findings from two past studies.

To take an example: One of your secondary data sets may focus on a target population’s tendency to smoke cigarettes, while the other data set focuses on the same population’s tendency to drink alcohol. In your own research, you may thus be looking at whether there is a correlation between smoking and drinking among this population.

Here is a second example: Your two secondary data sets may focus on the same outcome variable, such as the degree to which people go to Greece for a summer vacation. However, one data set could have been collected in Britain and the other in Germany. By comparing these two data sets, you can investigate which nation tends to visit Greece more.

Finally, your research project may involve combining primary and secondary data . You may decide to do this when you want to obtain existing information that would inform your primary research.

Let’s use another simple example and say that your research project focuses on American versus British people’s attitudes towards racial discrimination. Let’s say that you were able to find a recent study that investigated Americans’ attitudes of these kind, which were assessed with a certain set of measures. However, your search finds no recent studies on Britons’ attitudes. Let’s also say that you live in London and that it would be difficult for you to assess Americans’ attitudes on the topic, but clearly much more straightforward to conduct primary research on British attitudes.

In this case, you can simply reuse the data from the American study and adopt exactly the same measures with your British participants. Your secondary data is being combined with your primary data. Alternatively, you may combine these types of data when the role of your secondary data is to outline descriptive information that supports your research. For instance, if your project is focusing on attitudes towards McDonald’s food, you may want to support your primary research with secondary data that outlines how many people eat McDonald’s in your country of choice.

TABLE 3 summarises particular methods and purposes of secondary research:

We have already provided above several examples of using quantitative secondary data. This type of data is used when the original study has investigated a population’s tendency to smoke or drink alcohol, the degree to which people from different nationalities go to Greece for their summer vacation, or the degree to which pregnant women experience anxiety.

In all these examples, outcome variables were assessed by questionnaires, and thus the obtained data was numerical.

Quantitative secondary research is much more common than qualitative secondary research. However, this is not to say that you cannot use qualitative secondary data in your research project. This type of secondary data is used when you want the previously-collected information to inform your current research. More specifically, it is used when you want to test the information obtained through qualitative research by implementing a quantitative methodology.

For instance, a past qualitative study might have focused on the reasons why people choose to live on boats. This study might have interviewed some 30 participants and noted the four most important reasons people live on boats: (1) they can lead a transient lifestyle, (2) they have an increased sense of freedom, (3) they feel that they are “world citizens”, and (4) they can more easily visit their family members who live in different locations. In your own research, you can therefore reuse this qualitative data to form a questionnaire, which you then give to a larger population of people who live on boats. This will help you to generalise the previously-obtained qualitative results to a broader population.

Importantly, you can also re-assess a qualitative data set in your research, rather than using it as a basis for your quantitative research. Let’s say that your research focuses on the kind of language that people who live on boats use when describing their transient lifestyles. The original research did not focus on this research question per se – however, you can reuse the information from interviews to “extract” the types of descriptions of a transient lifestyle that were given by participants.

TABLE 4 highlights the two main types of secondary data and their associated purposes:

Internal sources of data are those that are internal to the organisation in question. For instance, if you are doing a research project for an organisation (or research institution) where you are an intern, and you want to reuse some of their past data, you would be using internal data sources.

The benefit of using these sources is that they are easily accessible and there is no associated financial cost of obtaining them.

External sources of data, on the other hand, are those that are external to an organisation or a research institution. This type of data has been collected by “somebody else”, in the literal sense of the term. The benefit of external sources of data is that they provide comprehensive data – however, you may sometimes need more effort (or money) to obtain it.

Let’s now focus on different types of internal and external secondary data sources.

There are several types of internal sources. For instance, if your research focuses on an organisation’s profitability, you might use their sales data . Each organisation keeps a track of its sales records, and thus your data may provide information on sales by geographical area, types of customer, product prices, types of product packaging, time of the year, and the like.

Alternatively, you may use an organisation’s financial data . The purpose of using this data could be to conduct a cost-benefit analysis and understand the economic opportunities or outcomes of hiring more people, buying more vehicles, investing in new products, and so on.

Another type of internal data is transport data . Here, you may focus on outlining the safest and most effective transportation routes or vehicles used by an organisation.

Alternatively, you may rely on marketing data , where your goal would be to assess the benefits and outcomes of different marketing operations and strategies.

Some other ideas would be to use customer data to ascertain the ideal type of customer, or to use safety data to explore the degree to which employees comply with an organisation’s safety regulations.

The list of the types of internal sources of secondary data can be extensive; the most important thing to remember is that this data comes from a particular organisation itself, in which you do your research in an internal manner.

The list of external secondary data sources can be just as extensive. One example is the data obtained through government sources . These can include social surveys, health data, agricultural statistics, energy expenditure statistics, population censuses, import/export data, production statistics, and the like. Government agencies tend to conduct a lot of research, therefore covering almost any kind of topic you can think of.

Another external source of secondary data are national and international institutions , including banks, trade unions, universities, health organisations, etc. As with government, such institutions dedicate a lot of effort to conducting up-to-date research, so you simply need to find an organisation that has collected the data on your own topic of interest.

Alternatively, you may obtain your secondary data from trade, business, and professional associations . These usually have data sets on business-related topics and are likely to be willing to provide you with secondary data if they understand the importance of your research. If your research is built on past academic studies, you may also rely on scientific journals as an external data source.

Once you have specified what kind of secondary data you need, you can contact the authors of the original study.

As a final example of a secondary data source, you can rely on data from commercial research organisations. These usually focus their research on media statistics and consumer information, which may be relevant if, for example, your research is within media studies or you are investigating consumer behaviour.

TABLE 5 summarises the two sources of secondary data and associated examples:

At this point, you should have a clearer understanding of secondary research in general terms.

Now it may be useful to focus on the actual process of doing secondary research. This next section is organised to introduce you to each step of this process, so that you can rely on this guide while planning your study. At the end of this blog post, in Table 6 , you will find a summary of all the steps of doing secondary research.

For an undergraduate thesis, you are often provided with a specific research question by your supervisor. But for most other types of research, and especially if you are doing your graduate thesis, you need to arrive at a research question yourself.

The first step here is to specify the general research area in which your research will fall. For example, you may be interested in the topic of anxiety during pregnancy, or tourism in Greece, or transient lifestyles. Since we have used these examples previously, it may be useful to rely on them again to illustrate our discussion.

Once you have identified your general topic, your next step consists of reading through existing papers to see whether there is a gap in the literature that your research can fill. At this point, you may discover that previous research has not investigated national differences in the experiences of anxiety during pregnancy, or national differences in a tendency to go to Greece for a summer vacation, or that there is no literature generalising the findings on people’s choice to live on boats.

Having found your topic of interest and identified a gap in the literature, you need to specify your research question. In our three examples, research questions would be specified in the following manner: (1) “Do women of different nationalities experience different levels of anxiety during different stages of pregnancy?”, (2) “Are there any differences in an interest in Greek tourism between Germans and Britons?”, and (3) “Why do people choose to live on boats?”.

It is at this point, after reviewing the literature and specifying your research questions, that you may decide to rely on secondary data. You will do this if you discover that there is past data that would be perfectly reusable in your own research, therefore helping you to answer your research question more thoroughly (and easily).

But how do you discover if there is past data that could be useful for your research? You do this through reviewing the literature on your topic of interest. During this process, you will identify other researchers, organisations, agencies, or research centres that have explored your research topic.

Somewhere there, you may discover a useful secondary data set. You then need to contact the original authors and ask for a permission to use their data. (Note, however, that this happens only if you are relying on external sources of secondary data. If you are doing your research internally (i.e., within a particular organisation), you don’t need to search through the literature for a secondary data set – you can just reuse some past data that was collected within the organisation itself.)

In any case, you need to ensure that a secondary data set is a good fit for your own research question. Once you have established that it is, you need to specify the reasons why you have decided to rely on secondary data.

For instance, your choice to rely on secondary data in the above examples might be as follows: (1) A recent study has focused on a range of mental difficulties experienced by women in a multinational sample and this data can be reused; (2) There is existing data on Germans’ and Britons’ interest in Greek tourism and these data sets can be compared; and (3) There is existing qualitative research on the reasons for choosing to live on boats, and this data can be relied upon to conduct a further quantitative investigation.

Because such disadvantages of secondary data can limit the effectiveness of your research, it is crucial that you evaluate a secondary data set. To ease this process, we outline here a reflective approach that will allow you to evaluate secondary data in a stepwise fashion.

Step 3(a): What was the aim of the original study?

During this step, you also need to pay close attention to any differences in research purposes and research questions between the original study and your own investigation. As we have discussed previously, you will often discover that the original study had a different research question in mind, and it is important for you to specify this difference.

Let’s put this step of identifying the aim of the original study in practice, by referring to our three research examples. The aim of the first research example was to investigate mental difficulties (e.g., stress, anxiety, mood disorders, and paranoid thoughts) in a multinational sample of pregnant women.

How does this aim differ from your research aim? Well, you are seeking to reuse this data set to investigate national differences in anxiety experienced by women during different pregnancy stages. When it comes to the second research example, you are basing your research on two secondary data sets – one that aimed to investigate Germans’ interest in Greek tourism and the other that aimed to investigate Britons’ interest in Greek tourism.

While these two studies focused on particular national populations, the aim of your research is to compare Germans’ and Britons’ tendency to visit Greece for summer vacation. Finally, in our third example, the original research was a qualitative investigation into the reasons for living on boats. Your research question is different, because, although you are seeking to do the same investigation, you wish to do so by using a quantitative methodology.

Importantly, in all three examples, you conclude that secondary data may in fact answer your research question. If you conclude otherwise, it may be wise to find a different secondary data set or to opt for primary research.

Step 3(b): Who has collected the data?

Let’s say that, in our example of research on pregnancy, data was collected by the UK government; that in our example of research on Greek tourism, the data was collected by a travel agency; and that in our example of research on the reasons for choosing to live on boats, the data was collected by researchers from a UK university.

Let’s also say that you have checked the background of these organisations and researchers, and that you have concluded that they all have a sufficiently professional background, except for the travel agency. Given that this agency’s research did not lead to a publication (for instance), and given that not much can be found about the authors of the research, you conclude that the professionalism of this data source remains unclear.

Step 3(c): Which measures were employed?

Original authors should have documented all their sample characteristics, measures, procedures, and protocols. This information can be obtained either in their final research report or through contacting the authors directly.

It is important for you to know what type of data was collected, which measures were used, and whether such measures were reliable and valid (if they were quantitative measures). You also need to make a clear outline of the type of data collected – and especially the data relevant for your research.

Let’s say that, in our first example, researchers have (among other assessed variables) used a demographic measure to note women’s nationalities and have used the State Anxiety Inventory to assess women’s anxiety levels during different pregnancy stages, both of which you conclude are valid and reliable tools. In our second example, the authors might have crafted their own measure to assess interest in Greek tourism, but there may be no established validity and reliability for this measure. And in our third example, the authors have employed semi-structured interviews, which cover the most important reasons for wanting to live on boats.

Step 3(d): When was the data collected?

Ideally, you want your secondary data to have been collected within the last five years. For the sake of our examples, let’s say that all three original studies were conducted within this time-range.

Step 3(e): What methodology was used to collect the data?

We have already noted that you need to evaluate the reliability and validity of employed measures. In addition to this, you need to evaluate how the sample was obtained, whether the sample was large enough, if the sample was representative of the population, if there were any missing responses on employed measures, whether confounders were controlled for, and whether the employed statistical analyses were appropriate. Any drawbacks in the original methodology may limit your own research as well.

For the sake of our examples, let’s say that the study on mental difficulties in pregnant women recruited a representative sample of pregnant women (i.e., they had different nationalities, different economic backgrounds, different education levels, etc.) in maternity wards of seven hospitals; that the sample was large enough (N = 945); that the number of missing values was low; that many confounders were controlled for (e.g., education level, age, presence of partnership, etc.); and that statistical analyses were appropriate (e.g., regression analyses were used).

Let’s further say that our second research example had slightly less sufficient methodology. Although the number of participants in the two samples was high enough (N1 = 453; N2 = 488), the number of missing values was low, and statistical analyses were appropriate (descriptive statistics), the authors failed to report how they recruited their participants and whether they controlled for any confounders.

Let’s say that these authors also failed to provide you with more information via email. Finally, let’s assume that our third research example also had sufficient methodology, with a sufficiently large sample size for a qualitative investigation (N = 30), high sample representativeness (participants with different backgrounds, coming from different boat communities), and sufficient analyses (thematic analysis).

Note that, since this was a qualitative investigation, there is no need to evaluate the number of missing values and the use of confounders.

Step 3(f): Making a final evaluation

We would conclude that the secondary data from our first research example has a high quality. Data was recently collected by professionals, the employed measures were both reliable and valid, and the methodology was more than sufficient. We can be confident that our new research question can be sufficiently answered with the existing data. Thus, the data set for our first example is ideal.

The two secondary data sets from our second research example seem, however, less than ideal. Although we can answer our research questions on the basis of these recent data sets, the data was collected by an unprofessional source, the reliability and validity of the employed measure is uncertain, and the employed methodology has a few notable drawbacks.

Finally, the data from our third example seems sufficient both for answering our research question and in terms of the specific evaluations (data was collected recently by a professional source, semi-structured interviews were well made, and the employed methodology was sufficient).

The final question to ask is: “what can be done if our evaluation reveals the lack of appropriateness of secondary data?”. The answer, unfortunately, is “nothing”. In this instance, you can only note the drawbacks of the original data set, present its limitations, and conclude that your own research may not be sufficiently well grounded.

Your first sub-step here (if you are doing quantitative research) is to outline all variables of interest that you will use in your study. In our first example, you could have at least five variables of interest: (1) women’s nationality, (2) anxiety levels at the beginning of pregnancy, (3) anxiety levels at three months of pregnancy, (4) anxiety levels at six months of pregnancy, and (5) anxiety levels at nine months of pregnancy. In our second example, you will have two variables of interest: (1) participants’ nationality, and (2) the degree of interest in going to Greece for a summer vacation. Once your variables of interest are identified, you need to transfer this data into a new SPSS or Excel file. Remember simply to copy this data into the new file – it is vital that you do not alter it!

Once this is done, you should address missing data (identify and label them) and recode variables if necessary (e.g., giving a value of 1 to German participants and a value of 2 to British participants). You may also need to reverse-score some items, so that higher scores on all items indicate a higher degree of what is being assessed.

Most of the time, you will also need to create new variables – that is, to compute final scores. For instance, in our example of research on anxiety during pregnancy, your data will consist of scores on each item of the State Anxiety Inventory, completed at various times during pregnancy. You will need to calculate final anxiety scores for each time the measure was completed.

Your final step consists of analysing the data. You will always need to decide on the most suitable analysis technique for your secondary data set. In our first research example, you would rely on MANOVA (to see if women of different nationalities experience different stress levels at the beginning, at three months, at six months, and at nine months of pregnancy); and in our second example, you would use an independent samples t-test (to see if interest in Greek tourism differs between Germans and Britons).

The process of preparing and analysing a secondary data set is slightly different if your secondary data is qualitative. In our example on the reasons for living on boats, you would first need to outline all reasons for living on boats, as recognised by the original qualitative research. Then you would need to craft a questionnaire that assesses these reasons in a broader population.

Finally, you would need to analyse the data by employing statistical analyses.

Note that this example combines qualitative and quantitative data. But what if you are reusing qualitative data, as in our previous example of re-coding the interviews from our study to discover the language used when describing transient lifestyles? Here, you would simply need to recode the interviews and conduct a thematic analysis.

In summary…

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Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

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. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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