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Thesis and Dissertation Appendicies – What to Include

• By DiscoverPhDs
• August 12, 2020

An appendix is a section at the end of a dissertation that contains supplementary information. An appendix may contain figures, tables, raw data, and other additional information that supports the arguments of your dissertation but do not belong in the main body.

It can be either a long appendix or split into several smaller appendices. Each appendix should have its own title and identification letters, and the numbering for any tables or figures in them should be reset at the beginning of each new appendix.

Purpose of an Appendix

When writing the main body of your dissertation, it is important to keep it short and concise in order to convey your arguments effectively.

Given the amount of research you would have done, you will probably have a lot of additional information that you would like to share with your audience.

This is where appendices come in. Any information that doesn’t support your main arguments or isn’t directly relevant to the topic of your dissertation should be placed in an appendix.

This will help you organise your paper, as only information that adds weight to your arguments will be included; it will also help improve your flow by minimising unnecessary interruptions.

Note, however, that your main body must be detailed enough that it can be understood without your appendices. If a reader has to flip between pages to make sense of what they are reading, they are unlikely to understand it.

For this reason, appendices should only be used for supporting background material and not for any content that doesn’t fit into your word count, such as the second half of your literature review .

What to Include in a Dissertation Appendix

A dissertation appendix can be used for the following supplementary information:

Research Results

There are various ways in which research results can be presented, such as in tables or diagrams.

Although all of your results will be useful to some extent, you won’t be able to include them all in the main body of your dissertation. Consequently, only those that are crucial to answering your research question should be included.

Your other less significant findings should be placed in your appendix, including raw data, proof of control measures, and other supplemental material.

Details of Questionnaires and Interviews

You can choose to include the details of any surveys and interviews you have conducted. This can include:

• An interview transcript,
• A copy of any survey questions,
• Questionnaire results.

Although the results of your surveys, questionnaires or interviews should be presented and discussed in your main text, it is useful to include their full form in the appendix of a dissertation to give credibility to your study.

Tables, Figures and Illustrations

If your dissertation contains a large number of tables, figures and illustrative material, it may be helpful to insert the less important ones in your appendix. For example, if you have four related datasets, you could present all the data and trend lines (made identifiable by different colours) on a single chart with a further breakdown for each dataset in your appendix.

Letters and Correspondence

If you have letters or correspondence, either between yourself and other researchers or places where you sought permission to reuse copyrighted material, they should be included here. This will help ensure that your dissertation doesn’t become suspected of plagiarism.

List of Abbreviations

Most researchers will provide a list of abbreviations at the beginning of their dissertation, but if not, it would be wise to add them as an appendix.

This is because not all of your readers will have the same background as you and therefore may have difficulty understanding the abbreviations and technical terms you use.

Note: Some researchers refer to this as a ‘glossary’, especially if it is provided as an appendix section. For all intended purposes, this is the same as a list of abbreviations.

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How to Format a Dissertation Appendix

In regards to format, you can include one lengthy appendix or structure it into several smaller appendices.

Although the choice is yours, it is usually better to opt for several different appendices as it allows you to organise your supplementary information into different categories based on what they are.

The following guidelines should be observed when preparing your dissertation appendices section:

• Each appendix should start on a new page and be given a unique title and identifying letter, such as “Appendix A – Raw Data”. This allows you to more easily refer to appendix headings in the text of your main body should you need to.
• Each appendix should have its own page numbering system, comprising the appendix identification letter and the corresponding page number. The appendix identification letter should be reset for each appendix, but the page number should remain continuous. For example, if ‘Appendix A’ has three pages and ‘Appendix B’ two pages, the page numbers should be A-1, A-2, A-3, B-4, B-5.
• The numbering of tables and figures should be reset at the beginning of each new appendix. For example, if ‘Appendix A’ contains two tables and ‘Appendix B’ one table, the table number within Appendix B should be ‘Table 1’ and not ‘Table 3’.
• If you have multiple appendices instead of a single longer one, insert a ‘List of Appendices’ in the same way as your contents page.
• Use the same formatting (font size, font type, spacing, margins, etc.) as the rest of your report.

Example of Appendices

Below is an example of what a thesis or dissertation appendix could look like.

Referring to an Appendix In-Text

You must refer to each appendix in the main body of your dissertation at least once to justify its inclusion; otherwise, the question arises as to whether they are really needed.

You can refer to an appendix in one of three ways:

1. Refer to a specific figure or table within a sentence, for example: “As shown in Table 2 of Appendix A, there is little correlation between X and Y”.

2. Refer to a specific figure or table in parentheses, for example: “The results (refer to Table 2 of Appendix A) show that there is little correlation between X and Y”.

3. Refer to an entire appendix, for example: “The output data can be found in Appendix A”.

Appendices vs Appendixes

Both terms are correct, so it is up to you which one you prefer. However, it is worth noting that ‘appendices’ are used more frequently in the science and research community, so we recommend using the former in academic writing if you have no preferences.

Where Does an Appendix Go?

For a dissertation, your appendices should be inserted after your reference list.

Some people like to put their appendices in a standalone document to separate it from the rest of their report, but we only recommend this at the request of your dissertation supervisor, as this isn’t common practice.

Note : Your university may have its own requirements or formatting suggestions for writing your dissertation or thesis appendix. As such, make sure you check with your supervisor or department before you work on your appendices. This will especially be the case for any students working on a thesis.

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Data and your thesis

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What is research data?

Research data are the evidence that underpins the answer to your research question and can support the findings or outputs of your research. Research data takes many different forms. They may include for example, statistics, digital images, sound recordings, films, transcripts of interviews, survey data, artworks, published texts or manuscripts, or fieldwork observations. The term 'data' is more familiar to researchers in Science, Technology, Engineering and Mathematics (STEM), but any outputs from research could be considered data. For example, Humanities, Arts and Social Sciences (HASS) researchers might create data in the form of presentations, spreadsheets, documents, images, works of art, or musical scores. The Research Data Management Team in the University Library aim to help you plan, create, organise, share, and look after your research materials, whatever form they take. For more information about the Research data Management Team, visit their website .

Data Management Plans

Research Data Management is a complex issue, but if done correctly from the start, could save you a lot of time and hassle when you are writing up your thesis. We advise all students to consider data management as early as possible and create a Data Management Plan (DMP). The Research Data Management Team offer help in creating your DMP and can offer advice and training on how to do this. There are some departments that have joined a pilot project to include Data Management Plans in the registration reviews of PhD students. As part of the pilot, students are asked to complete a brief Data Management Plan (DMP) and supervisors and assessors ensure that the student has thought about all the issues and their responses are reasonable. If your department is taking part in the pilot or would like to, see the Data Management Plans for Pilot for Cambridge PhD Students page. The Research Data Management Team will provide support for any students, supervisors or assessors that are in need.

Submitting your digital thesis and depositing your data

If you have created data that is connected to your thesis and the data is in a format separate to the thesis file itself, we recommend that you deposit it in the data repository and make it open access to improve discoverability. We will accept data that either does not contain third party copyright, or contains third party copyright that has been cleared and is data of the following types:

•     computer code written by the researcher
•     software written by the researcher
•     statistical data
•     raw data from experiments

If you have created a research output which is not one of those listed above, please contact us on the [email protected] address and we will advise whether you should deposit this with your thesis, or separately in the data repository. If you are ready to deposit your data in the data repository, please do so via symplectic elements. More information on how to deposit can be found on the Research Data Management pages . If you wish to cite your data in your thesis, we can arranged for placeholder DOIs to be created in the data repository before your thesis is submitted. For further information, please email:  [email protected]  

Third party copyright in your data

For an explanation of what is third party copyright, please see the OSC third party copyright page . If your data is based on, or contains third party copyright you will need to obtain clearance to make your data open access in the data repository. It is possible to apply a 12 month embargo to datasets while clearance is obtained if you need extra time to do this. However, if it is not possible to clear the third party copyrighted material, it is not possible to deposit your data in the data repository. In these cases, it might be preferable to deposit your data with your thesis instead, under controlled access, but this can be complicated if you wish to deposit the thesis itself under a different access level. Please email [email protected] with any queries and we can advise on the best solution.

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How to collect data for your thesis

Collecting theoretical data

Search for theses on your topic, use content-sharing platforms, collecting empirical data, qualitative vs. quantitative data, frequently asked questions about gathering data for your thesis, related articles.

After choosing a topic for your thesis , you’ll need to start gathering data. In this article, we focus on how to effectively collect theoretical and empirical data.

Empirical data : unique research that may be quantitative, qualitative, or mixed.

Theoretical data : secondary, scholarly sources like books and journal articles that provide theoretical context for your research.

Thesis : the culminating, multi-chapter project for a bachelor’s, master’s, or doctoral degree.

Qualitative data : info that cannot be measured, like observations and interviews .

Quantitative data : info that can be measured and written with numbers.

At this point in your academic life, you are already acquainted with the ways of finding potential references. Some obvious sources of theoretical material are:

• edited volumes
• conference proceedings
• online databases like Google Scholar , ERIC , or Scopus

You can also take a look at the top list of academic search engines .

Looking at other theses on your topic can help you see what approaches have been taken and what aspects other writers have focused on. Pay close attention to the list of references and follow the bread-crumbs back to the original theories and specialized authors.

Another method for gathering theoretical data is to read through content-sharing platforms. Many people share their papers and writings on these sites. You can either hunt sources, get some inspiration for your own work or even learn new angles of your topic. 

Some popular content sharing sites are:

With these sites, you have to check the credibility of the sources. You can usually rely on the content, but we recommend double-checking just to be sure. Take a look at our guide on what are credible sources?

The more you know, the better. The guide, " How to undertake a literature search and review for dissertations and final year projects ," will give you all the tools needed for finding literature .

In order to successfully collect empirical data, you have to choose first what type of data you want as an outcome. There are essentially two options, qualitative or quantitative data. Many people mistake one term with the other, so it’s important to understand the differences between qualitative and quantitative research .

Boiled down, qualitative data means words and quantitative means numbers. Both types are considered primary sources . Whichever one adapts best to your research will define the type of methodology to carry out, so choose wisely.

In the end, having in mind what type of outcome you intend and how much time you count on will lead you to choose the best type of empirical data for your research. For a detailed description of each methodology type mentioned above, read more about collecting data .

Once you gather enough theoretical and empirical data, you will need to start writing. But before the actual writing part, you have to structure your thesis to avoid getting lost in the sea of information. Take a look at our guide on how to structure your thesis for some tips and tricks.

The key to knowing what type of data you should collect for your thesis is knowing in advance the type of outcome you intend to have, and the amount of time you count with.

Some obvious sources of theoretical material are journals, libraries and online databases like Google Scholar , ERIC or Scopus , or take a look at the top list of academic search engines . You can also search for theses on your topic or read content sharing platforms, like Medium , Issuu , or Slideshare .

To gather empirical data, you have to choose first what type of data you want. There are two options, qualitative or quantitative data. You can gather data through observations, interviews, focus groups, or with surveys, tests, and existing databases.

Qualitative data means words, information that cannot be measured. It may involve multimedia material or non-textual data. This type of data claims to be detailed, nuanced and contextual.

Quantitative data means numbers, information that can be measured and written with numbers. This type of data claims to be credible, scientific and exact.

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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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• Research Paper Appendix | Example & Templates

Research Paper Appendix | Example & Templates

Published on August 4, 2022 by Tegan George and Kirsten Dingemanse. Revised on July 18, 2023.

An appendix is a supplementary document that facilitates your reader’s understanding of your research but is not essential to your core argument. Appendices are a useful tool for providing additional information or clarification in a research paper , dissertation , or thesis without making your final product too long.

Appendices help you provide more background information and nuance about your thesis or dissertation topic without disrupting your text with too many tables and figures or other distracting elements.

We’ve prepared some examples and templates for you, for inclusions such as research protocols, survey questions, and interview transcripts. All are worthy additions to an appendix. You can download these in the format of your choice below.

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Table of contents

What is an appendix in a research paper, what to include in an appendix, how to format an appendix, how to refer to an appendix, where to put your appendices, other components to consider, appendix checklist, other interesting articles, frequently asked questions about appendices.

In the main body of your research paper, it’s important to provide clear and concise information that supports your argument and conclusions . However, after doing all that research, you’ll often find that you have a lot of other interesting information that you want to share with your reader.

While including it all in the body would make your paper too long and unwieldy, this is exactly what an appendix is for.

As a rule of thumb, any detailed information that is not immediately needed to make your point can go in an appendix. This helps to keep your main text focused but still allows you to include the information you want to include somewhere in your paper.

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An appendix can be used for different types of information, such as:

• Supplementary results : Research findings  are often presented in different ways, but they don’t all need to go in your paper. The results most relevant to your research question should always appear in the main text, while less significant results (such as detailed descriptions of your sample or supplemental analyses that do not help answer your main question), can be put in an appendix.
• Statistical analyses : If you conducted statistical tests using software like Stata or R, you may also want to include the outputs of your analysis in an appendix.
• Further information on surveys or interviews : Written materials or transcripts related to things such as surveys and interviews can also be placed in an appendix.

You can opt to have one long appendix, but separating components (like interview transcripts, supplementary results, or surveys ) into different appendices makes the information simpler to navigate.

Here are a few tips to keep in mind:

• Always start each appendix on a new page.
• Assign it both a number (or letter) and a clear title, such as “Appendix A. Interview transcripts.” This makes it easier for your reader to find the appendix, as well as for you to refer back to it in your main text.
• Number and title the individual elements within each appendix (e.g., “Transcripts”) to make it clear what you are referring to. Restart the numbering in each appendix at 1.

It is important that you refer to each of your appendices at least once in the main body of your paper. This can be done by mentioning the appendix and its number or letter, either in parentheses or within the main part of a sentence. It’s also possible to refer to a particular component of an appendix.

Appendix B presents the correspondence exchanged with the fitness boutique. Example 2. Referring to an appendix component These results (see Appendix 2, Table 1) show that …

It is common to capitalize “Appendix” when referring to a specific appendix, but it is not mandatory. The key is just to make sure that you are consistent throughout your entire paper, similarly to consistency in  capitalizing headings and titles in academic writing .

However, note that lowercase should always be used if you are referring to appendices in general. For instance, “The appendices to this paper include additional information about both the survey and the interviews .”

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The AI-powered Citation Checker helps you avoid common mistakes such as:

• Missing commas and periods
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The simplest option is to add your appendices after the main body of your text, after you finish citing your sources in the citation style of your choice. If this is what you choose to do, simply continue with the next page number. Another option is to put the appendices in a separate document that is delivered with your dissertation.

Remember that any appendices should be listed in your paper’s table of contents .

There are a few other supplementary components related to appendices that you may want to consider. These include:

• List of abbreviations : If you use a lot of abbreviations or field-specific symbols in your dissertation, it can be helpful to create a list of abbreviations .
• Glossary : If you utilize many specialized or technical terms, it can also be helpful to create a glossary .
• Tables, figures and other graphics : You may find you have too many tables, figures, and other graphics (such as charts and illustrations) to include in the main body of your dissertation. If this is the case, consider adding a figure and table list .

Checklist: Appendix

All appendices contain information that is relevant, but not essential, to the main text.

Each appendix starts on a new page.

I have given each appendix a number and clear title.

I have assigned any specific sub-components (e.g., tables and figures) their own numbers and titles.

My appendices are easy to follow and clearly formatted.

I have referred to each appendix at least once in the main text.

Your appendices look great! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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Yes, if relevant you can and should include APA in-text citations in your appendices . Use author-date citations as you do in the main text.

Any sources cited in your appendices should appear in your reference list . Do not create a separate reference list for your appendices.

An appendix contains information that supplements the reader’s understanding of your research but is not essential to it. For example:

• Interview transcripts
• Questionnaires
• Detailed descriptions of equipment

Something is only worth including as an appendix if you refer to information from it at some point in the text (e.g. quoting from an interview transcript). If you don’t, it should probably be removed.

When you include more than one appendix in an APA Style paper , they should be labeled “Appendix A,” “Appendix B,” and so on.

When you only include a single appendix, it is simply called “Appendix” and referred to as such in the main text.

Appendices in an APA Style paper appear right at the end, after the reference list and after your tables and figures if you’ve also included these at the end.

You may have seen both “appendices” or “appendixes” as pluralizations of “ appendix .” Either spelling can be used, but “appendices” is more common (including in APA Style ). Consistency is key here: make sure you use the same spelling throughout your paper.

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

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Patterns for Presenting Information

Discussing Raw Data

At some point in your college experience (and certainly if you are a graduate student), you will do original research. Perhaps you will dig through an archive of old manuscripts, or you will conduct a survey, or you will evaluate sets of data aggregated by a government agency. 

In each instance, you will have to write about the raw data you have researched. Your discussion could be as short as a note about the data in a table, or it could be as long as an entire report. Regardless of length, the general-to-specific pattern usually works best when discussing raw data. 

• READER ENGAGEMENT

To understand the value of the general-to-specific pattern, remember, the purpose of writing about your data is to give it meaning. A common mistake students make is failing to interpret their data. Instead, they merely describe it. The order in which the general-to-specific pattern works encourages you to provide a general interpretation followed by the specific support suggested by your data.

The general-to-specific pattern also engages your readers. Remember that raw data is, almost by nature, puzzling. It invites interpretation and discourse . Your job as the writer is twofold.

1.     Help the reader understand the raw data. Your readers may be as puzzled as you were the first time you saw the raw data. Help them understand the data and why you have interpreted the data in a certain way. 

2.     Invite the reader to engage your interpretation. When you offer an interpretation, paired with raw data, your readers can examine your interpretation and form their own. This process of sharing and inviting interpretation is foundational to the discourse communities in which you will be taking part.

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Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Nature of Research

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

Writing Handbooks

Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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• USC Libraries
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Organizing Your Social Sciences Research Paper

• 7. The Results
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
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• Choosing a Title
• Making an Outline
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• The C.A.R.S. Model
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• The Research Problem/Question
• Theoretical Framework
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• Common Grammar Mistakes
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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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Qualitative Data Coding 101

How to code qualitative data, the smart way (with examples).

By: Jenna Crosley (PhD) | Reviewed by:Dr Eunice Rautenbach | December 2020

As we’ve discussed previously , qualitative research makes use of non-numerical data – for example, words, phrases or even images and video. To analyse this kind of data, the first dragon you’ll need to slay is  qualitative data coding  (or just “coding” if you want to sound cool). But what exactly is coding and how do you do it? 

Overview: Qualitative Data Coding

In this post, we’ll explain qualitative data coding in simple terms. Specifically, we’ll dig into:

• What exactly qualitative data coding is
• What different types of coding exist
• How to code qualitative data (the process)
• Moving from coding to qualitative analysis
• Tips and tricks for quality data coding

What is qualitative data coding?

Let’s start by understanding what a code is. At the simplest level,  a code is a label that describes the content  of a piece of text. For example, in the sentence:

“Pigeons attacked me and stole my sandwich.”

You could use “pigeons” as a code. This code simply describes that the sentence involves pigeons.

So, building onto this,  qualitative data coding is the process of creating and assigning codes to categorise data extracts.   You’ll then use these codes later down the road to derive themes and patterns for your qualitative analysis (for example, thematic analysis ). Coding and analysis can take place simultaneously, but it’s important to note that coding does not necessarily involve identifying themes (depending on which textbook you’re reading, of course). Instead, it generally refers to the process of  labelling and grouping similar types of data  to make generating themes and analysing the data more manageable. 

Makes sense? Great. But why should you bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your  data is valid . In other words, it helps ensure that your  analysis is undertaken systematically  and that other researchers can review it (in the world of research, we call this transparency). In other words, good coding is the foundation of high-quality analysis.

What are the different types of coding?

Now that we’ve got a plain-language definition of coding on the table, the next step is to understand what types of coding exist. Let’s start with the two main approaches,  deductive  and  inductive   coding.

Deductive coding 101

With deductive coding, we make use of pre-established codes, which are developed before you interact with the present data. This usually involves drawing up a set of  codes based on a research question or previous research . You could also use a code set from the codebook of a previous study.

For example, if you were studying the eating habits of college students, you might have a research question along the lines of 

“What foods do college students eat the most?”

As a result of this research question, you might develop a code set that includes codes such as “sushi”, “pizza”, and “burgers”.  

Deductive coding allows you to approach your analysis with a very tightly focused lens and quickly identify relevant data . Of course, the downside is that you could miss out on some very valuable insights as a result of this tight, predetermined focus. 

Inductive coding 101 

But what about inductive coding? As we touched on earlier, this type of coding involves jumping right into the data and then developing the codes  based on what you find  within the data. 

For example, if you were to analyse a set of open-ended interviews , you wouldn’t necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it may go on to include other animals too, and so you’d add these codes as you progress with your analysis. Simply put, with inductive coding, you “go with the flow” of the data.

Inductive coding is great when you’re researching something that isn’t yet well understood because the coding derived from the data helps you explore the subject. Therefore, this type of coding is usually used when researchers want to investigate new ideas or concepts , or when they want to create new theories. 

A little bit of both… hybrid coding approaches

If you’ve got a set of codes you’ve derived from a research topic, literature review or a previous study (i.e. a deductive approach), but you still don’t have a rich enough set to capture the depth of your qualitative data, you can  combine deductive and inductive  methods – this is called a  hybrid  coding approach. 

To adopt a hybrid approach, you’ll begin your analysis with a set of a priori codes (deductive) and then add new codes (inductive) as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it’s pretty common to see this in research.

Need a helping hand?

How to code qualitative data

Now that we’ve looked at the main approaches to coding, the next question you’re probably asking is “how do I actually do it?”. Let’s take a look at the  coding process , step by step.

Both inductive and deductive methods of coding typically occur in two stages:  initial coding  and  line by line coding . 

In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you’re using an inductive approach, this is also where you’ll develop an initial set of codes. Then, in the second stage (line by line coding), you’ll delve deeper into the data and (re)organise it according to (potentially new) codes. 

Step 1 – Initial coding

The first step of the coding process is to identify  the essence  of the text and code it accordingly. While there are various qualitative analysis software packages available, you can just as easily code textual data using Microsoft Word’s “comments” feature. 

Let’s take a look at a practical example of coding. Assume you had the following interview data from two interviewees:

What pets do you have?

I have an alpaca and three dogs.

Only one alpaca? They can die of loneliness if they don’t have a friend.

I didn’t know that! I’ll just have to get five more. 

I have twenty-three bunnies. I initially only had two, I’m not sure what happened. 

In the initial stage of coding, you could assign the code of “pets” or “animals”. These are just initial,  fairly broad codes  that you can (and will) develop and refine later. In the initial stage, broad, rough codes are fine – they’re just a starting point which you will build onto in the second stage. 

How to decide which codes to use

But how exactly do you decide what codes to use when there are many ways to read and interpret any given sentence? Well, there are a few different approaches you can adopt. The  main approaches  to initial coding include:

• In vivo coding 

Process coding

• Open coding

Descriptive coding

Structural coding.

• Value coding

Let’s take a look at each of these:

In vivo coding

When you use in vivo coding, you make use of a  participants’ own words , rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you’ll avoid trying to infer meaning, rather staying as close to the original phrases and words as possible. 

In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In these cases, it’s often difficult to accurately infer meaning due to linguistic or cultural differences. 

For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn’t the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown, so it must be out of sight (or behind us). They know what happened in the past, so their perspective is that it’s positioned in front of them, where they can “see” it. 

In a scenario like this one, it’s not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture’s perception of time. Therefore, in vivo coding is particularly useful, as it avoids interpretation errors.

Next up, there’s process coding, which makes use of  action-based codes . Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds (words ending in “-ing”) – for example, running, jumping or singing.

Process coding is useful as it allows you to code parts of data that aren’t necessarily spoken, but that are still imperative to understanding the meaning of the texts. 

An example here would be if a participant were to say something like, “I have no idea where she is”. A sentence like this can be interpreted in many different ways depending on the context and movements of the participant. The participant could shrug their shoulders, which would indicate that they genuinely don’t know where the girl is; however, they could also wink, showing that they do actually know where the girl is. 

Simply put, process coding is useful as it allows you to, in a concise manner, identify the main occurrences in a set of data and provide a dynamic account of events. For example, you may have action codes such as, “describing a panda”, “singing a song about bananas”, or “arguing with a relative”.

Descriptive coding aims to summarise extracts by using a  single word or noun  that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows the researcher to quickly refer to the content. 

Descriptive coding is very useful when dealing with data that appear in forms other than traditional text – i.e. video clips, sound recordings or images. For example, a descriptive code could be “food” when coding a video clip that involves a group of people discussing what they ate throughout the day, or “cooking” when coding an image showing the steps of a recipe. 

Structural coding involves labelling and describing  specific structural attributes  of the data. Generally, it includes coding according to answers to the questions of “ who ”, “ what ”, “ where ”, and “ how ”, rather than the actual topics expressed in the data. This type of coding is useful when you want to access segments of data quickly, and it can help tremendously when you’re dealing with large data sets. 

For example, if you were coding a collection of theses or dissertations (which would be quite a large data set), structural coding could be useful as you could code according to different sections within each of these documents – i.e. according to the standard  dissertation structure . What-centric labels such as “hypothesis”, “literature review”, and “methodology” would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. 

Structural coding is also useful for data from open-ended surveys. This data may initially be difficult to code as they lack the set structure of other forms of data (such as an interview with a strict set of questions to be answered). In this case, it would useful to code sections of data that answer certain questions such as “who?”, “what?”, “where?” and “how?”.

Let’s take a look at a practical example. If we were to send out a survey asking people about their dogs, we may end up with a (highly condensed) response such as the following: 

Bella is my best friend. When I’m at home I like to sit on the floor with her and roll her ball across the carpet for her to fetch and bring back to me. I love my dog.

In this set, we could code  Bella  as “who”,  dog  as “what”,  home  and  floor  as “where”, and  roll her ball  as “how”. 

Values coding

Finally, values coding involves coding that relates to the  participant’s worldviews . Typically, this type of coding focuses on excerpts that reflect the values, attitudes, and beliefs of the participants. Values coding is therefore very useful for research exploring cultural values and intrapersonal and experiences and actions.   

To recap, the aim of initial coding is to understand and  familiarise yourself with your data , to  develop an initial code set  (if you’re taking an inductive approach) and to take the first shot at  coding your data . The coding approaches above allow you to arrange your data so that it’s easier to navigate during the next stage, line by line coding (we’ll get to this soon). 

While these approaches can all be used individually, it’s important to remember that it’s possible, and potentially beneficial, to  combine them . For example, when conducting initial coding with interviews, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to, and between, conversation topics easily. 

Step 2 – Line by line coding

Once you’ve got an overall idea of our data, are comfortable navigating it and have applied some initial codes, you can move on to line by line coding. Line by line coding is pretty much exactly what it sounds like – reviewing your data, line by line,  digging deeper  and assigning additional codes to each line. 

With line-by-line coding, the objective is to pay close attention to your data to  add detail  to your codes. For example, if you have a discussion of beverages and you previously just coded this as “beverages”, you could now go deeper and code more specifically, such as “coffee”, “tea”, and “orange juice”. The aim here is to scratch below the surface. This is the time to get detailed and specific so as to capture as much richness from the data as possible. 

In the line-by-line coding process, it’s useful to  code everything  in your data, even if you don’t think you’re going to use it (you may just end up needing it!). As you go through this process, your coding will become more thorough and detailed, and you’ll have a much better understanding of your data as a result of this, which will be incredibly valuable in the analysis phase.

Moving from coding to analysis

Once you’ve completed your initial coding and line by line coding, the next step is to  start your analysis . Of course, the coding process itself will get you in “analysis mode” and you’ll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding.  

When it comes to qualitative data analysis, there are  many different types of analyses  (we discuss some of the  most popular ones here ) and the type of analysis you adopt will depend heavily on your research aims, objectives and questions . Therefore, we’re not going to go down that rabbit hole here, but we’ll cover the important first steps that build the bridge from qualitative data coding to qualitative analysis.

When starting to think about your analysis, it’s useful to  ask yourself  the following questions to get the wheels turning:

• What actions are shown in the data? 
• What are the aims of these interactions and excerpts? What are the participants potentially trying to achieve?
• How do participants interpret what is happening, and how do they speak about it? What does their language reveal?
• What are the assumptions made by the participants? 
• What are the participants doing? What is going on? 
• Why do I want to learn about this? What am I trying to find out? 
• Why did I include this particular excerpt? What does it represent and how?

Code categorisation

Categorisation is simply the process of reviewing everything you’ve coded and then  creating code categories  that can be used to guide your future analysis. In other words, it’s about creating categories for your code set. Let’s take a look at a practical example.

If you were discussing different types of animals, your initial codes may be “dogs”, “llamas”, and “lions”. In the process of categorisation, you could label (categorise) these three animals as “mammals”, whereas you could categorise “flies”, “crickets”, and “beetles” as “insects”. By creating these code categories, you will be making your data more organised, as well as enriching it so that you can see new connections between different groups of codes. 

Theme identification

From the coding and categorisation processes, you’ll naturally start noticing themes. Therefore, the logical next step is to  identify and clearly articulate the themes  in your data set. When you determine themes, you’ll take what you’ve learned from the coding and categorisation and group it all together to develop themes. This is the part of the coding process where you’ll try to draw meaning from your data, and start to  produce a narrative . The nature of this narrative depends on your research aims and objectives, as well as your research questions (sounds familiar?) and the  qualitative data analysis method  you’ve chosen, so keep these factors front of mind as you scan for themes. 

Tips & tricks for quality coding

Before we wrap up, let’s quickly look at some general advice, tips and suggestions to ensure your qualitative data coding is top-notch.

• Before you begin coding,  plan out the steps  you will take and the coding approach and technique(s) you will follow to avoid inconsistencies. 
• When adopting deductive coding, it’s useful to  use a codebook  from the start of the coding process. This will keep your work organised and will ensure that you don’t forget any of your codes. 
• Whether you’re adopting an inductive or deductive approach,  keep track of the meanings  of your codes and remember to revisit these as you go along.
• Avoid using synonyms  for codes that are similar, if not the same. This will allow you to have a more uniform and accurate coded dataset and will also help you to not get overwhelmed by your data.
• While coding, make sure that you  remind yourself of your aims  and coding method. This will help you to  avoid  directional drift , which happens when coding is not kept consistent. 
• If you are working in a team, make sure that everyone has  been trained and understands  how codes need to be assigned. 

Psst… there’s more (for free)

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

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31 Comments

I appreciated the valuable information provided to accomplish the various stages of the inductive and inductive coding process. However, I would have been extremely satisfied to be appraised of the SPECIFIC STEPS to follow for: 1. Deductive coding related to the phenomenon and its features to generate the codes, categories, and themes. 2. Inductive coding related to using (a) Initial (b) Axial, and (c) Thematic procedures using transcribe data from the research questions

Thank you so much for this. Very clear and simplified discussion about qualitative data coding.

This is what I want and the way I wanted it. Thank you very much.

All of the information’s are valuable and helpful. Thank for you giving helpful information’s. Can do some article about alternative methods for continue researches during the pandemics. It is more beneficial for those struggling to continue their researchers.

Thank you for your information on coding qualitative data, this is a very important point to be known, really thank you very much.

Very useful article. Clear, articulate and easy to understand. Thanks

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hello, great article! well written and easy to understand. Can you provide some of the sources in this article used for further reading purposes?

You guys are doing a great job out there . I will not realize how many students you help through your articles and post on a daily basis. I have benefited a lot from your work. this is remarkable.

Wonderful one thank you so much.

Hello, I am doing qualitative research, please assist with example of coding format.

This is an invaluable website! Thank you so very much!

Well explained and easy to follow the presentation. A big thumbs up to you. Greatly appreciate the effort 👏👏👏👏

Thank you for this clear article with examples

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D0 you have primary references that was used when creating this? If so, can you share them?

Being a complete novice to the field of qualitative data analysis, your indepth analysis of the process of thematic analysis has given me better insight. Thank you so much.

Excellent summary

Thank you so much for your precise and very helpful information about coding in qualitative data.

Thanks a lot to this helpful information. You cleared the fog in my brain.

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I still don’t understand the coding and categorizing of qualitative research, please give an example on my research base on the state of government education infrastructure environment in PNG

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Just at the right time when I needed to distinguish between inductive and

deductive data analysis of my Focus group discussion results very helpful

Very useful across disciplines and at all levels. Thanks…

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Search tip: Finding data for my thesis or research project

Ever wondered if somebody has created a dataset that you can use for your thesis or project? Or if data is already available from a similar experiment? You can save time and resources if you find data that you can (re)use.

What is data?

With all the interesting research being done, not only are papers being published, but also datasets. Research data consists of anything you collect, observe, create, and analyze for your research. This can vary from models, scripts, specimens, field notebooks, audio files, videos, questionnaire responses, interview transcripts to sequencing data.

Where to find data?

Nowadays publishers and funders require researchers to deposit a paper's data in a permanent data repository with open access. Once deposited, the data can be found by using literature, data repositories, or indexes of datasets.

Re3data.org

A good source to find a data repository with data in your field is re3data.org . On the homepage you can type your topic in the search field. If using multiple words, place them between double quotation marks (“..”) for a phrase search. The results will show data repositories with data on your topic. Using the toolbar, you can select ‘Browse’ and ‘Browse by Subject’, where you can browse from general to more specific subjects. For additional information on data searching and data repositories, check the e-learning module ‘Finding research data’ .

Citing data

Always remember to cite the dataset's source when it in your thesis or manuscript. The citation should include the creator(s), year, title, repository, and persistent identifier (usually a doi). Currently, there is no standard format for referencing datasets. You can follow your referencing style, journal guidelines/requirements, or guidelines from the repository. Remember to include datasets in the reference list at the end of your text to properly credit the data's creator.

Do you have questions about finding data? Please email us.

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How To Present Research Data?

Tong seng fah.

MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia

Aznida Firzah Abdul Aziz

Introduction.

The result section of an original research paper provides answer to this question “What was found?” The amount of findings generated in a typical research project is often much more than what medical journal can accommodate in one article. So, the first thing the author needs to do is to make a selection of what is worth presenting. Having decided that, he/she will need to convey the message effectively using a mixture of text, tables and graphics. The level of details required depends a great deal on the target audience of the paper. Hence it is important to check the requirement of journal we intend to send the paper to (e.g. the Uniform Requirements for Manuscripts Submitted to Medical Journals 1 ). This article condenses some common general rules on the presentation of research data that we find useful.

SOME GENERAL RULES

• Keep it simple. This golden rule seems obvious but authors who have immersed in their data sometime fail to realise that readers are lost in the mass of data they are a little too keen to present. Present too much information tends to cloud the most pertinent facts that we wish to convey.
• First general, then specific. Start with response rate and description of research participants (these information give the readers an idea of the representativeness of the research data), then the key findings and relevant statistical analyses.
• Data should answer the research questions identified earlier.
• Leave the process of data collection to the methods section. Do not include any discussion. These errors are surprising quite common.
• Always use past tense in describing results.
• Text, tables or graphics? These complement each other in providing clear reporting of research findings. Do not repeat the same information in more than one format. Select the best method to convey the message.

Consider these two lines:

• Mean baseline HbA 1c of 73 diabetic patients before intervention was 8.9% and mean HbA 1c after intervention was 7.8%.
• Mean HbA 1c of 73 of diabetic patients decreased from 8.9% to 7.8% after an intervention.

In line 1, the author presents only the data (i.e. what exactly was found in a study) but the reader is forced to analyse and draw their own conclusion (“mean HbA 1c decreased”) thus making the result more difficult to read. In line 2, the preferred way of writing, the data was presented together with its interpretation.

• Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values of HbA 1c in the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple sentence including its interpretation. For example, the majority of diabetic patients enrolled in the study were male (80%) compare to female (20%).
• Using qualitative words to attract the readers’ attention is not helpful. Such words like “remarkably” decreased, “extremely” different and “obviously” higher are redundant. The exact values in the data will show just how remarkable, how extreme and how obvious the findings are.

“It is clearly evident from Figure 1B that there was significant different (p=0.001) in HbA 1c level at 6, 12 and 18 months after diabetic self-management program between 96 patients in intervention group and 101 patients in control group, but no difference seen from 24 months onwards.” [Too wordy]

Changes of HbA 1c level after diabetic self-management program.

The above can be rewritten as:

“Statistical significant difference was only observed at 6, 12 and 18 months after diabetic self-management program between intervention and control group (Fig 1B)”. [The p values and numbers of patients are already presented in Figure 1B and need not be repeated.]

• Avoid redundant words and information. Do not repeat the result within the text, tables and figures. Well-constructed tables and graphics should be self-explanatory, thus detailed explanation in the text is not required. Only important points and results need to be highlighted in the text.

Tables are useful to highlight precise numerical values; proportions or trends are better illustrated with charts or graphics. Tables summarise large amounts of related data clearly and allow comparison to be made among groups of variables. Generally, well-constructed tables should be self explanatory with four main parts: title, columns, rows and footnotes.

• Title. Keep it brief and relate clearly the content of the table. Words in the title should represent and summarise variables used in the columns and rows rather than repeating the columns and rows’ titles. For example, “Comparing full blood count results among different races” is clearer and simpler than “Comparing haemoglobin, platelet count, and total white cell count among Malays, Chinese and Indians”.

*WC, waist circumference (in cm)

†SBP, systolic blood pressure (in mmHg)

‡DBP, diastolic blood pressure (in mmHg)

£LDL-cholesterol (in mmol/L)

*Odds ratio (95% confidence interval)

†p=0.04

‡p=0.01

• Footnotes. These add clarity to the data presented. They are listed at the bottom of tables. Their use is to define unconventional abbreviation, symbols, statistical analysis and acknowledgement (if the table is adapted from a published table). Generally the font size is smaller in the footnotes and follows a sequence of foot note signs (*, †, ‡, §, ‖, ¶, **, ††, # ). 1 These symbols and abbreviation should be standardised in all tables to avoid confusion and unnecessary long list of footnotes. Proper use of footnotes will reduce the need for multiple columns (e.g. replacing a list of p values) and the width of columns (abbreviating waist circumference to WC as in table 1B )
• Consistent use of units and its decimal places. The data on systolic blood pressure in Table 1B is neater than the similar data in Table 1A .
• Arrange date and timing from left to the right.
• Round off the numbers to fewest decimal places possible to convey meaningful precision. Mean systolic blood pressure of 165.1mmHg (as in Table 1B ) does not add much precision compared to 165mmHg. Furthermore, 0.1mmHg does not add any clinical importance. Hence blood pressure is best to round off to nearest 1mmHg.
• Avoid listing numerous zeros, which made comparison incomprehensible. For example total white cell count is best represented with 11.3 ×10 6 /L rather than 11,300,000/L. This way, we only need to write 11.3 in the cell of the table.
• Avoid too many lines in a table. Often it is sufficient to just have three horizontal lines in a table; one below the title; one dividing the column titles and data; one dividing the data and footnotes. Vertical lines are not necessary. It will only make a table more difficult to read (compare Tables 1A and ​ and1B 1B ).
• Standard deviation can be added to show precision of the data in our table. Placement of standard deviation can be difficult to decide. If we place the standard deviation at the side of our data, it allows clear comparison when we read down ( Table 1B ). On the other hand, if we place the standard deviation below our data, it makes comparison across columns easier. Hence, we should decide what we want the readers to compare.
• It is neater and space-saving if we highlight statistically significant finding with an asterisk (*) or other symbols instead of listing down all the p values ( Table 2 ). It is not necessary to add an extra column to report the detail of student-t test or chi-square values.

Graphics are particularly good for demonstrating a trend in the data that would not be apparent in tables. It provides visual emphasis and avoids lengthy text description. However, presenting numerical data in the form of graphs will lose details of its precise values which tables are able to provide. The authors have to decide the best format of getting the intended message across. Is it for data precision or emphasis on a particular trend and pattern? Likewise, if the data is easily described in text, than text will be the preferred method, as it is more costly to print graphics than text. For example, having a nicely drawn age histogram is take up lots of space but carries little extra information. It is better to summarise it as mean ±SD or median depends on whether the age is normally distributed or skewed. Since graphics should be self-explanatory, all information provided has to be clear. Briefly, a well-constructed graphic should have a title, figure legend and footnotes along with the figure. As with the tables, titles should contain words that describe the data succinctly. Define symbols and lines used in legends clearly.

Some general guides to graphic presentation are:

• Bar charts, either horizontal or column bars, are used to display categorical data. Strictly speaking, bar charts with continuous data should be drawn as histograms or line graphs. Usually, data presented in bar charts are better illustrated in tables unless there are important pattern or trends need to be emphasised.

• Line graphs are most appropriate in tracking changing values between variables over a period of time or when the changing values are continuous data. Independent variables (e.g. time) are usually on the X-axis and dependant variables (for example, HbA 1c ) are usually on the Y-axis. The trend of HbA 1c changes is much more apparent with Figure 1B than Figure 1A , and HbA 1c level at any time after intervention can be accurately read in Figure 1B .
• Pie charts should not be used often as any data in a pie chart is better represented in bar charts (if there are specific data trend to be emphasised) or simple text description (if there are only a few variables). A common error is presenting sex distribution of study subjects in a pie chart. It is simpler by just stating % of male or female in text form.
• Patients’ identity in all illustrations, for example pictures of the patients, x-ray films, and investigation results should remain confidential. Use patient’s initials instead of their real names. Cover or blackout the eyes whenever possible. Obtain consent if pictures are used. Highlight and label areas in the illustration, which need emphasis. Do not let the readers search for details in the illustration, which may result in misinterpretation. Remember, we write to avoid misunderstanding whilst maintaining clarity of data.

Papers are often rejected because wrong statistical tests are used or interpreted incorrectly. A simple approach is to consult the statistician early. Bearing in mind that most readers are not statisticians, the reporting of any statistical tests should aim to be understandable by the average audience but sufficiently rigorous to withstand the critique of experts.

• Simple statistic such as mean and standard deviation, median, normality testing is better reported in text. For example, age of group A subjects was normally distributed with mean of 45.4 years old kg (SD=5.6). More complicated statistical tests involving many variables are better illustrated in tables or graphs with their interpretation by text. (See section on Tables).
• We should quote and interpret p value correctly. It is preferable to quote the exact p value, since it is now easily obtained from standard statistical software. This is more so if the p value is statistically not significant, rather just quoting p>0.05 or p=ns. It is not necessary to report the exact p value that is smaller than 0.001 (quoting p<0.001 is sufficient); it is incorrect to report p=0.0000 (as some software apt to report for very small p value).
• We should refrain from reporting such statement: “mean systolic blood pressure for group A (135mmHg, SD=12.5) was higher than group B (130mmHg, SD= 9.8) but did not reach statistical significance (t=4.5, p=0.56).” When p did not show statistical significance (it might be >0.01 or >0.05, depending on which level you would take), it simply means no difference among groups.
• Confidence intervals. It is now preferable to report the 95% confidence intervals (95%CI) together with p value, especially if a hypothesis testing has been performed.

The main core of the result section consists of text, tables and graphics. As a general rule, text provides narration and interpretation of the data presented. Simple data with few categories is better presented in text form. Tables are useful in summarising large amounts of data systemically and graphics should be used to highlight evidence and trends in the data presented. The content of the data presented must match the research questions and objectives of the study in order to give meaning to the data presented. Keep the data and its statistical analyses as simple as possible to give the readers maximal clarity.

Contributor Information

Tong Seng Fah, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

Aznida Firzah Abdul Aziz, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

FURTHER READINGS

*Statistics and Data: Finding Raw Data

• Demographics
• By Disease or Demographic
• Global, National, State & Local
• Healthcare Administration Statistics
• Journalism / Mass Media
• Political Science
• Criminal Justice
• Finding Raw Data
• Collecting New Data
• Analyzing Data
• TEACHING with Data & Statistics
• GSU's Research Data Services (RDS) Department

Need data for a thesis, dissertation, course project, or grant proposal?  The resources highlighted on this page will enable you to find raw data to analyze for your project. 

GSS ~ U.S. Sociological and Attitudinal Trend Data, 1972 - 2021

To identify variables for the entire dataset and/or specific years:

• Browse the entire GSS codebook
• Use the GSS Data Explorer to conduct a variable search ,   explore key trends , or create custom tabulations. Note: a free account is needed to access some features. 
• Search/browse GSS variables with SDA 4.0  (Survey Documentation and Analysis, a tool for accessing codebooks and conducting data analysis online). For help using SDA 4.0, check out these SDA 4.0 video tutorials .

Options for Analyzing GSS Data: 

Use SDA to analyze data online analysis with no need for downloading or special software:

• Use the SDA 4.0 online analysis tool. For help using SDA 4.0, check out these SDA 4.0 video tutorials .
• Check out the "Quick Tables" options, which allow you to produce simple tables using a pre-selected subset of variables. 
• Conduct simple bivariate analyses using the GSS Explorer Tabulations feature. 

Download data for analysis in statistical software (e.g., SPSS, SAS, Stata, R): 

• Download GSS Data directly from the GSS website  (available in SAS, SPSS, or Stata format). Single year and multi-year (panel) data available. 
• Download the entire GSS (available in SPSS or Stata format) via GSS Explorer.
• Create customized data extracts containing just the variables you need (available in Excel, SPSS, SAS, Stata, DDI, and R format) via GSS Explorer.
• Download the entire dataset, pre-constructed subsets, or create customized subsets of the data using SDA 4.0 . For help using SDA 4.0, check out these SDA 4.0 video tutorials .

Education Datasets

• Data & Statistics from the U.S. Department of Education Data and Statistics from the U.S. Department of Education, including K-12, post-secondary education, and student outcome data.
• The Database of Accredited Postsecondary Institutions and Programs (DAPIP) The Database of Accredited Postsecondary Institutions and Programs (DAPIP): Contains data reported to the U.S. Department of Education directly by recognized accrediting agencies and state approval agencies.

ICPSR is research science data on topics from social media, politics, to GIS & more. By providing this data ICPSR seeks to advance and expand social and behavioral research.

• The National Center for Education Statistics (NCES) The National Center for Education Statistics (NCES): Datasets and data-related tools from NCES, the primary federal entity for collecting and analyzing education-related data in the U.S.
• School Attendance Boundary Information System (SABINS) The School Attendance Boundary Information System (SABINS) provides, free of charge, aggregate census data and GIS-compatible boundary files for school attendance areas, or school catchment areas, for selected areas in the United States for the 2009-10, 2010-11 and 2011-12 school years.
• U.S. Department of Education’s Open Data Platform Data.ed.gov is the U.S. Department of Education’s open data catalog.

Religion Datasets

• Association of Religion Data Archives (ARDA) Over 1,000 freely available data files free of charge, as well as a "Measurement Wizard," to help researchers appropriately measure constructs and teaching tools.
• The Berman Jewish DataBank The Berman Jewish DataBank, a project of the Jewish Federations of America provides open access to hundreds of quantitative studies of North American and global Jewry.
• Pew Research Center's Religion Datasets Religion datasets from the Pew Research Center, including U.S. and international data. Most data are available for download in SPSS or excel format.
• PRRI (Public Religion Research Institute) PPRI is a "nonprofit, nonpartisan organization dedicated to conducting independent research at the intersection of religion, culture, and public policy." Data products include trends and reports, interactive maps, and a Data Vault, which can be searched or browsed. Note that data are exported as Word documents.

International Datasets

• DataPortal.asia Data from the Asia Open Data Partnership (AODP) organized by category and data catalog.
• European Union's Open Data Portal The EU's Open Data Portal, the official portal for EU data from 36 countries.
• ICPSR (Inter-university Consortium for Political and Social Research) This link opens in a new window Seeks to advance and expand social and behavioral research by providing training in data access, curation, and methods of analysis for the social science research community as well a a data archive social science research.
• IMF Data Economic data from the International Monetary Fund (IMF), an organization of 190 countries.
• OECD Data The Organisation for Economic Co-operation and Development (OECD) with data visualizations, tables, and raw data available for download.
• Our World in Data Over 3000 data charts and downloadable raw datasets on nearly 300 topics. All All visualizations, data, and code are completely open access.
• UN Data The United Nations Statistics Division (UNSD) of the Department of Economic and Social Affairs (DESA) has launched a new internet-based data service for the global user community. It brings UN statistical databases within easy reach of users through a single entry point from which users can now search and download a variety of statistical resources of the UN System.
• U.S. Census Bureau International Programs Access to raw data, including the International Database (IDB) and the World Population Clock, as well as data tables, tools, and apps.
• World Bank Open Data Freely available global development data from the World Bank.
• World Values Survey The World Values Survey (WVS) is a global network of social scientists studying changing values and their impact on social and political life. The WVS consists of nationally representative surveys conducted in almost 100 countries.

Business & Economic Datasets

• Atlanta Research Data Center (ARDC) Located at the Federal Reserve Bank of Atlanta, the Atlanta Research Data Center (ARDC) seeks to provide qualified researchers in Atlanta, and around the Southeast, with the opportunity to perform statistical analysis on non-public Census microdata.
• Federal Reserve Economic Data (FRED) Federal Reserve Economic Data (FRED) is an online database from the Federal Reserve Bank of St. Louis includes hundreds of thousands of economic datasets from national, international, public, and private sources.
• National Bureau of Economic Research (NBER) Public Use Data Archive Described as an “eclectic mix of public use economic, demographic, and enterprise data” from NBER-affiliated researchers. Over 200 datasets available, including historic data.
• UNCTADstat UNCTADstat from the United Nations Conference on Trade and Development is the UN agency aimed at promoting interests of developing states in world trade. UNCTADstat provides freely available data for download, data visualization and exploration tools, and country profiles.
• U.S. Bureau of Labor Statistics (BLS) Data related to employment and the economy in the U.S., including data tables, downloadable datasets, and calculators (e.g., inflation calculator).
• U.S. Census Data Business and Economy Data Data from the U.S. Census related to business and economy, including data on business dynamics and an economic indicator dashboard.
• U.S. Department of Commerce Bureau of Economic Analysis (BEA) The BEA produces data on the U.S., state, and local economies, including the U.S. gross domestic product (GDP), data on consumer spending, employment, international investments, and inflation.
• Wharton Research Data Services (WRDS) WRDS describes itself as the "leading business intelligence, data analytics, and research platform" and offers a broad collection of data from a variety of global sources. Registration using GSU credentials and institutional approval are required prior to use. Atlanta Campus only.

Media Datasets

• Dataset Download | Pew Research Center The Pew Research Center for The People & The Press offers free access (with registration) to its data archive. Datasets are currently available dating back to January 1997.
• ProPublica Data Store Provides access to the raw data behind ProPublica reporting. Some datasets are freely available, while others require fees to access.

Criminal Justice & Law Datasets

• The Caselaw Access Project The Caselaw Access Project (CAP) aims to make all published U.S. court decisions freely available. Via the Harvard Law School Library.
• Georgia’s Criminal Justice Coordinating Council (CJCC) Statistical Analysis Center (SAC) The Statistical Analysis Center (SAC), part of Georgia’s Criminal Justice Coordinating Council (CJCC) is responsible for coordinating the CJCC data collection and analysis activities. Data from SAC research activities are available by request. The Statistical Analysis Resources page provides additional external links to state and national crime and criminal justice data.
• National Archive of Criminal Justice Data The National Archive of Criminal Justice Data (NACJD) from ICPSR facilitates research in criminal justice and criminology through the preservation, enhancement, and sharing of computerized data resources.
• Prison Policy Initiative Data. The Prison Policy Initiative is a non-profit, non-partisan organization that uses research to advocate for prison reform. Their national and state data are available to download for free, and they also publish reports, data visualizations, and state profiles.
• Supreme Court Database The Supreme Court Database contains information on all U.S. Supreme Court cases between the 1791 and 2020 terms.
• U.S. Bureau of Justice Statistics (BJS) Data Collections Search data collections from the Bureau of Justice Statistics (BJS), the primary statistical agency of the Department of Justice. The mission of BJS is to “collect, analyze, publish, and disseminate information on crime, criminal offenders, victims of crime, and the operation of justice systems at all levels of government.” Also includes online data analysis and visualization tools, such as the Corrections Statistical Analysis Tool (CSAT).

More Dataset Repositories

• Data Citation Index Discover research data, including data studies, data sets from a wide range of international data repositories and connect them with the scientific literature to track data citation.
• Data Observation Network for Earth (DataONE) DataONE is a collaboration among many partner organizations, and is funded by the US National Science Foundation (NSF) under a Cooperative Agreement.
• Harvard Dataverse The Harvard Dataverse Repository is a free data repository open to researchers across disciplines.
• List of Data Repositories for "Open Data" This is a list of repositories and databases for open data.
• Re3Data.org Registry of Research Data Repositories

ICPSR (Inter-University Consortium for Political and Social Research) - Database of Datasets

Searching for datasets for research in the social and behavioral sciences icpsr is a great place to start.

ACCESSING ICPSR DATA: Check out this page for all the ins and outs of accessing ICPSR dat a and downloading datasets.

SEARCHING TIPS: You can keyword search for datasets, but browsing by topics , series , or thematic data collections is sometimes more efficient. For about 76% of their datasets, they also have variable-level/question-level search capabilities.

HELP RESOURCES: Check out some of ICPSR's YouTube videos for searching tips.

Demographic Datasets

• AmericasBarometer This link opens in a new window The only scientifically rigorous comparative survey of democratic values and behaviors. Covering all independent countries in North, Central, and South America, as well as a significant number of countries in the Caribbean. Note: Please accept the user agreement to access the database.
• American Community Survey (ACS) This initiative of the Census Bureau helps local officials, community leaders and businesses understand the changes taking place in their communities. It is the premier source for detailed information about the American people and workforce.
• Data Sharing for Demographic Research (DSDR) DSDR provides resources to demographic data producers and users, including confidentiality and disclosure review, restricted data contract development and data dissemination, a searchable index of important demography and population study data, and a catalogue of publications using data indexed.
• International Database (IDB) from the U.S. Census Bureau Population estimates and projections for 227 countries and areas.
• IPUMS The MPC is one of the world's leading developers of demographic data resources. We provide population data to thousands of researchers, policymakers, teachers, and students. All MPC data are available free over the internet.
• National Longitudinal Surveys Sponsored by the Bureau of Labor Statistics, the National Longitudinal Surveys (NLS) are a family of surveys dedicated to tracking the labor market and other life experiences of American men and women. The seven NLS cohorts are: (1) National Longitudinal Survey of Youth 1997 (NLSY97); (2) National Longitudinal Survey of Youth 1979 (NLSY79); (3) NLSY79 Child and Young Adult; (4) Older Men; (5) Mature Women; (6) Young Men; and (7) Young Women
• Social Explorer Social Explorer provides access to current and historical census data (back to 1790), demographic information, and interactive demographic maps that can be queried and manipulated; users can also create maps and reports to illustrate, analyze, and understand demography and social change. A free account is needed to access some features.
• UNdata Data compiled by the United Nations (UN) statistical system and other international agencies.
• UN Population Division The UN Population Division of the Department of Economic and Social Affairs hosts data on world population, urbanization and migration, family planning, fertility, and family composition, and other topics.
• UN Statistics Division Demographic and Social Statistics The UN Statistics Division collects, compiles and disseminates official statistics on a wide range of social and demographic topics.
• USA.gov Data and Statistics U.S. demographic, economic and population data from a variety of governmental sources, including the Bureau of Justice, Bureau of Labor, and Internal Revenue Service.
• U.S. Census Data via Data.census.gov Data.census.gov is a data dissemination platform that allows users to search Census data by variables, and access and download data tables and maps.

Health and Science Datasets

Provides U.S. public opinion poll data through a full-text retrieval system organized at the question level

• Polling the Nations This link opens in a new window This critically acclaimed online database of public opinion polls contains full text of more than 700,000 questions and responses from more than 18,000 surveys and 1,700 polling organizations, conducted from 1986 through the present in the United States and more than 100 other countries around the world.
• Behavioral Risk Factor Surveillance System (BRFSS) The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services.
• BioLINCC The mission of BioLINCC is to facilitate access to, and maximize the scientific value of, the Biorepository and Data Repository and promote the availability and use of other NHLBI funded population-based biospecimen and data resources.
• CDC National Center for Health Statistics The National Center for Health Statistics (NCHS) is a rich source of data for researchers, teachers, and students who want to perform data analysis. This page compiles key sources of information found on the NCHS website for those who are interested in analysis of NCHS data as well as documentation and methodology of NCHS data systems.
• CDC Wonder CDC WONDER (Wide-ranging Online Data for Epidemiologic Research) is a platform that makes CDC, reference materials, reports and guidelines on health-related topics freely available online.
• County Health Rankings The annual County Health Rankings measure vital health factors, including high school graduation rates, obesity, smoking, unemployment, access to healthy foods, the quality of air and water, income, and teen births in nearly every county in America.
• CMS Data Public data from the U.S. Centers for Medicare & Medicaid Services (CMS).
• Data at WHO Raw datasets, data dashboards, visualization, and data collection and analysis tools are available on a variety of global health topics, including immunization, SDGs, and COVID 19.
• Demographic and Health Surveys (DHS) Program Data on fertility, family planning, maternal and child health, gender, HIV/AIDS, malaria, and nutrition. The DHS Program is funded by the U.S. Agency for International Development (USAID).
• Framingham Heart Study Data The Framingham Heart Study (FHS) is a longitudinal epidemiologic study that has enrolled ~15,000 participants since 1948. Available data include clinical and biological data, genetics data, omics data, and biomarker data. Note: a proposal must be submitted via the FHS Research Application to access data via the FHS Repository. Costs are associated with data access via FHS Repository. Subsets of the dataset are available for free via the database of Genotypes and Phenotypes (dbGaP) and Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC).
• Health and Medical Care Archive HMCA preserves and disseminates data collected by research projects funded by The Robert Wood Johnson Foundation, the nation's largest philanthropy devoted exclusively to improving the health and health care of all Americans.
• HealthData.gov Health-related data from the U.S. Department of Health and Human Services and state partners.
• HealthyPeople.gov Health People is a national effort to improve the health and well-being of people in the U.S. via 10-year goals and objectives. Data on each indicator are available via the Health People Data Search.
• IPUMS Health Surveys: NHIS U.S. National Health Interview Survey (NHIS) includes data on health, health care access, and health behaviors of the civilian, non-institutionalized U.S. population. Data files date from 1963 to present.
• National Cancer Institute - SEER A program of the National Cancer Institute, Surveillance, Epidemiology, and End Results Program (SEER) is a source of information on cancer incidence and survival in the United States.
• National Health and Nutrition Examination Survey (NHANES) A program of studies designed to assess the health and nutritional status of adults and children in the United States.
• National Health Interview Survey The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics which is part of the Centers for Disease Control and Prevention.
• National Immunization Surveys National immunization surveys are conducted annually and used to obtain national, state, and selected local area estimates of vaccination coverage rates for U.S. children age 19-35 months and for U.S. adolescents age 13-17 years.
• National Institute for Cardiovascular Outcomes Research (NICOR) NICOR (National Institute for Cardiovascular Outcomes Research) contains databases about cardiovascular patients collected by hospitals across the UK. An application process and fees are required to use data.
• National Institute for Occupational Safety and Health (NIOSH) Data and Statistics Gateway Provides centralized access to NIOSH public-use research datasets.
• Open Payments Data CMS Open Payments is a national program that collects data on financial relationships between drug and medical device companies and physicians, physician assistants, advanced practice nurses and teaching hospitals.
• Pregnancy Risk Assessment Monitoring System (PRAMS) A CDC survey of maternal attitudes and experiences before, during, and shortly after pregnancy.
• WHO Global Health Observatory Data Repository The Global Health Observatory is WHO's gateway to health-related statistics for more than 1000 indicators for its 194 Member States.
• WISQARS (Web-based Injury Statistics Query and Reporting System) CDC's interactive, online database that provides fatal and nonfatal injury, violent death, and cost of injury data from a variety of trusted sources.
• Youth Risk Behavior Surveillance System (YRBSS) The YRBSS includes national, state, territorial, tribal government, and local school-based surveys of representative samples of 9th through 12th grade students.

Research Data Services @ Georgia State University Library

The Library’s Research Data Services (RDS) Department  supports GSU students, faculty, and staff in using data analysis tools & methods, data visualization, finding data & statistics, data collection, and data cleaning & management.

• Visit this page for more information about our services, 
• Check out our  LIVE WORKSHOPS  and  RECORDINGS  plus our micro-credential  BADGES  and  DATA EVENTS .
• Email to schedule  ONE-ON-ONE HELP  or  CUSTOM SESSIONS  for classes & research teams.
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Data Sets for Quantitative Research: Public Use Datasets

• Public Use Datasets
• Roper Center
• Missouri government grants and grant-writing aids
• Contact Librarian

Finding Datasets on the Internet

There are many research organizations making data available on the web, but still no perfect mechanism for searching the content of all these collections. The links below will take you to data search portals which seem to be among the best available. Note that these portals point to both free and pay sources for data, and to both raw data and processed statistics.

• PEW Research Center
• Open Access Directory (OAD) Data Repositories
• UK Data Archive
• Socioeconomic Applications Data Center
• Council of European Social Science Data Archives (CESSDA)
• NTIS Federal Computer Products Center .*  Includes databases, data files, CD-ROM, etc. available for purchase.  
• Harvard DataVerse
• r3Data.org Registry of Research Data Repositories
• Open Data: European Commission Launches European Data Portal (over 1 million datasets From 36 countries)
• Awesome Public Datasets (on github)*. Includes a mix of free and pay resources.
• SNAP (Stanford Network Analysis Project)
• Statistics, Resources and Big Data on the Internet, 2020 *

 * Resources that are not entirely free are marked with an asterisk.

Transform web information into machine-readable data for analysis

Have you found fantastic numeric information in a less-than-ideal format, such as PDF or HTML?   Here are some software products that may help you transform those formats into numbers that you can read into a spreadsheet or statistical software program.  Some of these are free or offer limited time, free trials:

• Spark OCR : Find tables in images, visually identify rows and columns, and extract data from cells into data frames. Turn scans from financial disclosures, academic papers, lab results and more into usable data. 
• PDFTables : PDF to Excel Converter
• Tabula : Extract tables from PDFs
• table-ocr : For those who know Python
• Abbyy Finereader : Access and modify information locked in paper-based documents and PDF files
• OCR Space : This free service transforms PDFs into plain text files directly in your browser.  Rows and columns are preserved, making it easier to import the file into Excel using the Import Text Wizard .  See further explanation and instructions here:  Table recognition with OCR .  
• Parsehub : Data mining tool for data scientists and journalists
• Webhose : Turn unstructured web content into machine-readable data feeds
• Data Streamer : Index weblogs, mainstream news, and social media
• Outwit : Turn websites into structured data

Feeling intrigued, but unsure how to leverage web-based data for your own research?  Here are some how-to guides:

• Data Journalism: What it is and why should I care?
• How to get data from the Web
• Manipulating data
• Data for journalists: a practical guide for computer-assisted reporting by Brant Houston (2019)
• Scraping for journalists by Paul Bradshaw (2013)
• Data Journalism Heist by Paul Bradshaw (2013)

Selected datasets on the Internet, arranged by topic

These are some of the most significant datasets available on the internet, arranged by topic.  Almost everything here is freely available. The few that do involve fees are marked with asterisks (*). Note that some of the listings below are also available in ICPSR.

Political Science/Public Policy

• American National Election Studies
• Conflict and Peace Data Bank, 1948-1978  available through ICPSR
• Correlates of War
• Cross-National Time-Series Data Archive  (available as a library item on CD-ROM)
• International Country Risk Guide (IRCG) Table 3B: Political Risk Points by Component, 1984-2009  (available through MU Library to current affiliates)
• Polidata Presidential Results By Congressional District 1992-2004  (available through MU Library to current affiliates)
• Record of American Democracy, 1984-1990
• Survey of Income and Program Participation 

Demographics

• IPUMS: Integrated Public Use Microdata Series
• Geocorr  -- Geographic Correspondence Engine
• Missouri Census Data Center UEXPLORE/Dexter  ( explanation )
• National Historical Geographic Information System

Business and Economics

• Consumer Expenditure Surveys microdata
• National Bureau of Economic Research data
• National Longitudinal Surveys from the Bureau of Labor Statistics
• Panel Study of Income Dynamics
• World Bank – Poverty and Equity Data
• International industrial development  (manufacturing, mining, utilities, etc.) data from the United Nations (UNIDO)
• Biologic Specimen and Data Repository Information Coordinating Center (bioLINCC)
• Demographic and Health Surveys (mainly 3rd world countries)
• Global Health Observatory data repository  from the World Heath Organization
• ICPSR Health and Medical Care Archive  
• ICPSR National Addiction and HIV Data Archive Program
• National Center for Health Statistics Public Use Data Files  from the U.S. Centers for Disease Control
• Missouri Information for Community Assessment (MICA) health datasets
• National Longitudinal Study of Adolescent Health
• National Cancer Institute SEER data
• DataONE   Earth and environmental data
• EPA Environmental dataset gateway
• General Social Survey
• National Longitudinal Surveys  (U.S. Bureau of Labor Statistics)
• National Survey of Households and Families
• Pew Internet & Technology
• World Values Survey
• National Center for Education Statistics DataLab
• The National Survey of College Graduates (NSCG)
• NCES Public Elementary & Secondary Schools Universe Survey Data
• The Survey of Doctorate Recipients (SDR)

Miscellaneous

• American Religion Data Archive
• National Household Travel Survey
• Roper Opinion Polls * 

*Resources that are not entirely free are marked with an asterisk

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Data to accompany microseismic analysis, Clearfield County, PA hydraulic fracturing operation (PhD Thesis and Paper)

This passive seismic dataset was analyzed as part of the PhD Thesis by David Rampton, "A Comprehensive Geophysical Analysis to Determine Induced Fracture Distribution from a Hydraulic Fracturing Operation in the Marcellus Shale Formation", March 2014 and will be presented in an upcoming paper. This data was acquired in conjunction with a timelapse crosswell dataset, data also available on Kilthub. The entire dataset is available on NETL's data exchange EDX, but this paper only analyzed four stages. The raw data is included, along with contractor information, final results, and scripts used to generate intermediate and final results.

This research was supported in part by an appointment to the U.S. Department of Energy (DOE) Postgraduate Research Program at the National Energy Technology Laboratory administered by the Oak Ridge Institute for Science and Education.

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