research analysis table

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Common Assignments: Literature Review Matrix

Literature review matrix.

As you read and evaluate your literature there are several different ways to organize your research. Courtesy of Dr. Gary Burkholder in the School of Psychology, these sample matrices are one option to help organize your articles. These documents allow you to compile details about your sources, such as the foundational theories, methodologies, and conclusions; begin to note similarities among the authors; and retrieve citation information for easy insertion within a document.

You can review the sample matrixes to see a completed form or download the blank matrix for your own use.

• Literature Review Matrix 1 This PDF file provides a sample literature review matrix.
• Literature Review Matrix 2 This PDF file provides a sample literature review matrix.
• Literature Review Matrix Template (Word)
• Literature Review Matrix Template (Excel)

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Literature Review Basics

• What is a Literature Review?
• Synthesizing Research
• Using Research & Synthesis Tables
• Additional Resources

About the Research and Synthesis Tables

Research Tables and Synthesis Tables are useful tools for organizing and analyzing your research as you assemble your literature review. They represent two different parts of the review process: assembling relevant information and synthesizing it. Use a Research table to compile the main info you need about the items you find in your research -- it's a great thing to have on hand as you take notes on what you read! Then, once you've assembled your research, use the Synthesis table to start charting the similarities/differences and major themes among your collected items.

We've included an Excel file with templates for you to use below; the examples pictured on this page are snapshots from that file.

• Research and Synthesis Table Templates This Excel workbook includes simple templates for creating research tables and synthesis tables. Feel free to download and use!

Using the Research Table

This is an example of a  research table,  in which you provide a basic description of the most important features of the studies, articles, and other items you discover in your research. The table identifies each item according to its author/date of publication, its purpose or thesis, what type of work it is (systematic review, clinical trial, etc.), the level of evidence it represents (which tells you a lot about its impact on the field of study), and its major findings. Your job, when you assemble this information, is to develop a snapshot of what the research shows about the topic of your research question and assess its value (both for the purpose of your work and for general knowledge in the field).

Think of your work on the research table as the foundational step for your analysis of the literature, in which you assemble the information you'll be analyzing and lay the groundwork for thinking about what it means and how it can be used.

Using the Synthesis Table

This is an example of a  synthesis table  or  synthesis matrix , in which you organize and analyze your research by listing each source and indicating whether a given finding or result occurred in a particular study or article ( each row lists an individual source, and each finding has its own column, in which X = yes, blank = no). You can also add or alter the columns to look for shared study populations, sort by level of evidence or source type, etc. The key here is to use the table to provide a simple representation of what the research has found (or not found, as the case may be). Think of a synthesis table as a tool for making comparisons, identifying trends, and locating gaps in the literature.

How do I know which findings to use, or how many to include?  Your research question tells you which findings are of interest in your research, so work from your research question to decide what needs to go in each Finding header, and how many findings are necessary. The number is up to you; again, you can alter this table by adding or deleting columns to match what you're actually looking for in your analysis. You should also, of course, be guided by what's actually present in the material your research turns up!

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Research Analysis Table

What is research analysis [ edit | edit source ].

After gathering important research data, a family historian or genealogist needs to think about the significant findings.  Placing the data on a research log, such as the Strategic Research Log , is the first step.   After that, for complex problems, it is useful to transfer (copy and paste) just the most important elements (including sources and links) to a place for careful study and pondering.  This is, perhaps, the most important part of the research process, for here one determines the relationships of elements, choosing what appears to be true and casting out false theories.

The following table is an example of how to arrange data for critical analysis. Note that the columns from left to right suggest steps in the thought process:

This Research Analysis Table has been very beneficial to researchers. A sample of it is shown below.

To download, click File:Research Analysis Table.doc .  Click on the link that appears and then click SAVE to keep the files on your own computer:

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Home » Tables in Research Paper – Types, Creating Guide and Examples

Tables in Research Paper – Types, Creating Guide and Examples

Table of Contents

Tables in Research Paper

Definition:

In Research Papers , Tables are a way of presenting data and information in a structured format. Tables can be used to summarize large amounts of data or to highlight important findings. They are often used in scientific or technical papers to display experimental results, statistical analyses, or other quantitative information.

Importance of Tables in Research Paper

Tables are an important component of a research paper as they provide a clear and concise presentation of data, statistics, and other information that support the research findings . Here are some reasons why tables are important in a research paper:

• Visual Representation : Tables provide a visual representation of data that is easy to understand and interpret. They help readers to quickly grasp the main points of the research findings and draw their own conclusions.
• Organize Data : Tables help to organize large amounts of data in a systematic and structured manner. This makes it easier for readers to identify patterns and trends in the data.
• Clarity and Accuracy : Tables allow researchers to present data in a clear and accurate manner. They can include precise numbers, percentages, and other information that may be difficult to convey in written form.
• Comparison: Tables allow for easy comparison between different data sets or groups. This makes it easier to identify similarities and differences, and to draw meaningful conclusions from the data.
• Efficiency: Tables allow for a more efficient use of space in the research paper. They can convey a large amount of information in a compact and concise format, which saves space and makes the research paper more readable.

Types of Tables in Research Paper

Most common Types of Tables in Research Paper are as follows:

• Descriptive tables : These tables provide a summary of the data collected in the study. They are usually used to present basic descriptive statistics such as means, medians, standard deviations, and frequencies.
• Comparative tables : These tables are used to compare the results of different groups or variables. They may be used to show the differences between two or more groups or to compare the results of different variables.
• Correlation tables: These tables are used to show the relationships between variables. They may show the correlation coefficients between variables, or they may show the results of regression analyses.
• Longitudinal tables : These tables are used to show changes in variables over time. They may show the results of repeated measures analyses or longitudinal regression analyses.
• Qualitative tables: These tables are used to summarize qualitative data such as interview transcripts or open-ended survey responses. They may present themes or categories that emerged from the data.

How to Create Tables in Research Paper

Here are the steps to create tables in a research paper:

• Plan your table: Determine the purpose of the table and the type of information you want to include. Consider the layout and format that will best convey your information.
• Choose a table format : Decide on the type of table you want to create. Common table formats include basic tables, summary tables, comparison tables, and correlation tables.
• Choose a software program : Use a spreadsheet program like Microsoft Excel or Google Sheets to create your table. These programs allow you to easily enter and manipulate data, format the table, and export it for use in your research paper.
• Input data: Enter your data into the spreadsheet program. Make sure to label each row and column clearly.
• Format the table : Apply formatting options such as font, font size, font color, cell borders, and shading to make your table more visually appealing and easier to read.
• Insert the table into your paper: Copy and paste the table into your research paper. Make sure to place the table in the appropriate location and refer to it in the text of your paper.
• Label the table: Give the table a descriptive title that clearly and accurately summarizes the contents of the table. Also, include a number and a caption that explains the table in more detail.
• Check for accuracy: Review the table for accuracy and make any necessary changes before submitting your research paper.

Examples of Tables in Research Paper

Examples of Tables in the Research Paper are as follows:

Table 1: Demographic Characteristics of Study Participants

This table shows the demographic characteristics of 200 participants in a research study. The table includes information about age, gender, and education level. The mean age of the participants was 35.2 years with a standard deviation of 8.6 years, and the age range was between 21 and 57 years. The table also shows that 46% of the participants were male and 54% were female. In terms of education, 10% of the participants had less than a high school education, 30% were high school graduates, 35% had some college education, and 25% had a bachelor’s degree or higher.

Table 2: Summary of Key Findings

This table summarizes the key findings of a study comparing three different groups on a particular variable. The table shows the mean score, standard deviation, t-value, and p-value for each group. The asterisk next to the t-value for Group 1 indicates that the difference between Group 1 and the other groups was statistically significant at p < 0.01, while the differences between Group 2 and Group 3 were not statistically significant.

Purpose of Tables in Research Paper

The primary purposes of including tables in a research paper are:

• To present data: Tables are an effective way to present large amounts of data in a clear and organized manner. Researchers can use tables to present numerical data, survey results, or other types of data that are difficult to represent in text.
• To summarize data: Tables can be used to summarize large amounts of data into a concise and easy-to-read format. Researchers can use tables to summarize the key findings of their research, such as descriptive statistics or the results of regression analyses.
• To compare data : Tables can be used to compare data across different variables or groups. Researchers can use tables to compare the characteristics of different study populations or to compare the results of different studies on the same topic.
• To enhance the readability of the paper: Tables can help to break up long sections of text and make the paper more visually appealing. By presenting data in a table, researchers can help readers to quickly identify the most important information and understand the key findings of the study.

Advantages of Tables in Research Paper

Some of the advantages of using tables in research papers include:

• Clarity : Tables can present data in a way that is easy to read and understand. They can help readers to quickly and easily identify patterns, trends, and relationships in the data.
• Efficiency: Tables can save space and reduce the need for lengthy explanations or descriptions of the data in the main body of the paper. This can make the paper more concise and easier to read.
• Organization: Tables can help to organize large amounts of data in a logical and meaningful way. This can help to reduce confusion and make it easier for readers to navigate the data.
• Comparison : Tables can be useful for comparing data across different groups, variables, or time periods. This can help to highlight similarities, differences, and changes over time.
• Visualization : Tables can also be used to visually represent data, making it easier for readers to see patterns and trends. This can be particularly useful when the data is complex or difficult to understand.

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• Volume 24, Issue 2
• Five tips for developing useful literature summary tables for writing review articles
• Article Text
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• http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
• http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
• 1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
• 2 Swat College of Nursing , Pakistan
• 3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
• 4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
• Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

https://doi.org/10.1136/ebnurs-2021-103417

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

• Aromataris E ,
• Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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How to structure your Table for Systematic Review and Meta-analysis

How to search keywords in Google scholar for your Research

Systematic review article andMeta-analysis: Main steps for Successful writing

According to the, a systematic review is “a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question.” It entails systematically identifying, selecting, synthesising, and evaluating primary research studies to produce a high-quality summary of a subject while addressing a pre-specified research question. A meta-analysis is a step forward from a systematic review in that it employs mathematical and statistical methods to summarise the results of studies included in the systematic review (1) .

Introduction

In some aspects, systematic reviews vary from conventional narrative reviews. Narrative reviews are mostly descriptive, do not require a systematic search of the literature, and concentrate on a subset of studies in a field selected based on availability or author preference. As a result, although narrative reviews are informative, they often include an element of selection bias. As the name implies, systematic reviews usually include a thorough and comprehensive plan and search strategy derived a priori to minimise bias by finding, evaluating, and synthesising all related studies on a given subject. A meta-analysis aspect is often used in systematic reviews, which entails using statistical techniques to synthesise data from several studies into a single quantitative estimation or summary effect size. It is a well-known and well-respected multinational non-profit organisation that promotes, funds, and disseminates systematic reviews and meta-analyses on the effectiveness of healthcare interventions (2) .

Need of systemic review and meta-analysis:

There are several reasons for performing a systematic review and meta-analysis:

• It may assist in resolving discrepancies in results published by individual studies that may include bias or errors.
• It may help identify areas in a field where there is a lack of evidence and areas where further research should be conducted.
• It allows the combination of findings from different studies, highlighting new findings relevant to practice or policy.
• It may be able to reduce the need for additional trials.
• Writing a systematic review and meta-analysis will help identify a researcher’s field of interest since they are published in high-impact journals and receive many citations (3) .

Phases to planning a systematic review and meta-analysis

The succeeding components to a successful systematic review and meta-analysis writing are:

• Formulate the Review Question

The first stage involves describing the review topic, formulating hypotheses, and developing a title for the review. It’s usually best to keep titles as short and descriptive as possible by following this formula: Intervention for those with a disease (e.g., Dialectical behaviour therapy for adolescent females with a borderline personality disorder). Since reviews published in other outlets do not need to be listed as such, they should state in the title that they are a systematic review and meta-analysis.

• Define inclusion and exclusion criteria

The PICO (or PICOC) acronym stands for population, intervention, comparison, outcomes (and context). It can help ensure that all main components are decided upon before beginning the study. Authors must, for example, choose their population age range, circumstances, results, and type(s) of interventions and control groups a priori. It’s also crucial to determine what types of experiments to include and exclude (e.g., RCTs only, RCTs and quasi-experimental designs, qualitative research), the minimum number of participants in each group, published and unpublished studies, and language restrictions.

• Develop a search strategy and locate studies

This is where a reference librarian can be particularly beneficial in assisting with the creation and execution of electronic searches. To recognise all applicable trials in a given region, it is essential to create a detailed list of key terms (i.e., “MeSH” terms) related to each component of PICOC. The secret to creating an effective search strategy is to strike a balance between sensitivity and precision.

• Selection of studies

After retrieving and reviewing a detailed list of abstracts, any studies that tend to satisfy inclusion requirements will be collected and thoroughly reviewed. To ensure inter-raterreliability, this procedure is usually carried out by at least two reviewers. It is suggested that authors maintain a list of all checked research, including reasons for inclusion or exclusion. It might be possible to hire study authors to collect missing data for data pooling (e.g., means, standard deviations). It’s also possible that translations will be needed.

• Extract data

To organise the information extracted from each reviewed study (e.g., authors, publication year, number of participants, age range, study design, results, included/excluded), building and using a basic data extraction type or chart can be beneficial. Data extraction by at least two reviewers is necessary to ensure inter-rater reliability and prevent data entry errors.

Table: 1 outline for systemic review and meta-analysis

• Assess study quality

In recent years, there has been a push to improve the consistency of each RCT included in systematic reviews. Double-blinding, which is acceptable for clinical trials but not for psychological or non-pharmacological treatments, significantly impacts this metric. Other more detailed guidelines and criteria, such as the Consolidated Standards of Reporting Trials (CONSORT), as well as articles with recommendations for improving quality in RCTs and meta-analyses for psychological intervention, are available (4) .

• Analyse and Interpret results

The Review Manager (RevMan) software, endorsed by the Cochrane Collaboration, is one example of a statistical programme that can measure effect sizes for meta-analysis . The effect sizes are given, along with a 95 percent confidence interval (CI) range, and are presented in both quantitative and graphical form (e.g., forest plots). Each trial is visually represented as a horizontal diamond shape in forest plots. The middle represents the effect size (e.g., SMD) and the endpoints representing both ends of the CI.

• Disseminate findings

Since the Cochrane Collaboration’s reviews are published in the online Cochrane Database of Systematic Reviews, they are often lengthy and comprehensive. As a result, it is possible and encouraged to publish abbreviated versions of the review in other applicable scholarly journals; indeed, engaging in a review update or joining a well-established review team may be a beneficial way to get involved in the systematic review process .

Future scope

The systematic review’s findings should be discussed in terms of the strength of evidence and shortcomings of the initial research used for the review. It’s also necessary to discuss the review’s weaknesses, the results’ applicability (generalizability), and the findings’ implications for patient care, public health, and future clinical  research (5) .

The steps of a systematic review/meta-analysis include developing a research question and validating it, forming criteria, searching databases, importing all results to a library and exporting to an excel sheet, protocol writing and registration, title and abstract screening, full-text screening, manual searching, extracting data and assessing its quality, data checking, and conducting statistics. The PRISMA or Meta-analysis must be used to write up the systematic study and meta-analysis. This is a reporting checklist for systematic literature reviews and meta-analyses that specifies what information should be included in each portion of a high-quality systematic review (6) .

• Alonso Debreczeni, Felicia, and Phoebe E. Bailey. “A systematic review and meta-analysis of subjective age and the association with cognition, subjective well-being, and depression.”  The Journals of Gerontology: Series B  76.3 (2021): 471-482.
• Vasconcellos, Diego, et al. “Self-determination theory applied to physical education: A systematic review and meta-analysis.”  Journal of Educational Psychology  112.7 (2020): 1444.
• Geary, William L., et al. “Predator responses to fire: A global systematic review and meta‐analysis.”  Journal of Animal Ecology  89.4 (2020): 955-971.
• Donald, James N., et al. “Mindfulness and its association with varied types of motivation: A systematic review and meta-analysis using self-determination theory.”  Personality and Social Psychology Bulletin  46.7 (2020): 1121-1138.
• Madigan, Sheri, et al. “Associations between screen use and child language skills: A systematic review and meta-analysis.”  JAMA paediatrics  174.7 (2020): 665-675.
• McArthur, Genevieve M., et al. “Self-concept in poor readers: a systematic review and meta-analysis.”  PeerJ  8 (2020): e8772.

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How to Use Tables & Graphs in a Research Paper

It might not seem very relevant to the story and outcome of your study, but how you visually present your experimental or statistical results can play an important role during the review and publication process of your article. A presentation that is in line with the overall logical flow of your story helps you guide the reader effectively from your introduction to your conclusion. 

If your results (and the way you organize and present them) don’t follow the story you outlined in the beginning, then you might confuse the reader and they might end up doubting the validity of your research, which can increase the chance of your manuscript being rejected at an early stage. This article illustrates the options you have when organizing and writing your results and will help you make the best choice for presenting your study data in a research paper.

Why does data visualization matter?

Your data and the results of your analysis are the core of your study. Of course, you need to put your findings and what you think your findings mean into words in the text of your article. But you also need to present the same information visually, in the results section of your manuscript, so that the reader can follow and verify that they agree with your observations and conclusions. 

The way you visualize your data can either help the reader to comprehend quickly and identify the patterns you describe and the predictions you make, or it can leave them wondering what you are trying to say or whether your claims are supported by evidence. Different types of data therefore need to be presented in different ways, and whatever way you choose needs to be in line with your story. 

Another thing to keep in mind is that many journals have specific rules or limitations (e.g., how many tables and graphs you are allowed to include, what kind of data needs to go on what kind of graph) and specific instructions on how to generate and format data tables and graphs (e.g., maximum number of subpanels, length and detail level of tables). In the following, we will go into the main points that you need to consider when organizing your data and writing your result section .

Table of Contents:

Types of data , when to use data tables .

• When to Use Data Graphs 

Common Types of Graphs in Research Papers 

Journal guidelines: what to consider before submission.

Depending on the aim of your research and the methods and procedures you use, your data can be quantitative or qualitative. Quantitative data, whether objective (e.g., size measurements) or subjective (e.g., rating one’s own happiness on a scale), is what is usually collected in experimental research. Quantitative data are expressed in numbers and analyzed with the most common statistical methods. Qualitative data, on the other hand, can consist of case studies or historical documents, or it can be collected through surveys and interviews. Qualitative data are expressed in words and needs to be categorized and interpreted to yield meaningful outcomes. 

Quantitative data example: Height differences between two groups of participants Qualitative data example: Subjective feedback on the food quality in the work cafeteria

Depending on what kind of data you have collected and what story you want to tell with it, you have to find the best way of organizing and visualizing your results.

When you want to show the reader in detail how your independent and dependent variables interact, then a table (with data arranged in columns and rows) is your best choice. In a table, readers can look up exact values, compare those values between pairs or groups of related measurements (e.g., growth rates or outcomes of a medical procedure over several years), look at ranges and intervals, and select specific factors to search for patterns. 

Tables are not restrained to a specific type of data or measurement. Since tables really need to be read, they activate the verbal system. This requires focus and some time (depending on how much data you are presenting), but it gives the reader the freedom to explore the data according to their own interest. Depending on your audience, this might be exactly what your readers want. If you explain and discuss all the variables that your table lists in detail in your manuscript text, then you definitely need to give the reader the chance to look at the details for themselves and follow your arguments. If your analysis only consists of simple t-tests to assess differences between two groups, you can report these results in the text (in this case: mean, standard deviation, t-statistic, and p-value), and do not necessarily need to include a table that simply states the same numbers again. If you did extensive analyses but focus on only part of that data (and clearly explain why, so that the reader does not think you forgot to talk about the rest), then a graph that illustrates and emphasizes the specific result or relationship that you consider the main point of your story might be a better choice.

When to Use Data Graphs

Graphs are a visual display of information and show the overall shape of your results rather than the details. If used correctly, a visual representation helps your (or your reader’s) brain to quickly understand large amounts of data and spot patterns, trends, and exceptions or outliers. Graphs also make it easier to illustrate relationships between entire data sets. This is why, when you analyze your results, you usually don’t just look at the numbers and the statistical values of your tests, but also at histograms, box plots, and distribution plots, to quickly get an overview of what is going on in your data.

Line graphs

When you want to illustrate a change over a continuous range or time, a line graph is your best choice. Changes in different groups or samples over the same range or time can be shown by lines of different colors or with different symbols.

Example: Let’s collapse across the different food types and look at the growth of our four fish species over time.

You should use a bar graph when your data is not continuous but divided into categories that are not necessarily connected, such as different samples, methods, or setups. In our example, the different fish types or the different types of food are such non-continuous categories.

Example: Let’s collapse across the food types again and also across time, and only compare the overall weight increase of our four fish types at the end of the feeding period.

Scatter plots

Scatter plots can be used to illustrate the relationship between two variables — but note that both have to be continuous. The following example displays “fish length” as an additional variable–none of the variables in our table above (fish type, fish food, time) are continuous, and they can therefore not be used for this kind of graph. 

As you see, these example graphs all contain less data than the table above, but they lead the reader to exactly the key point of your results or the finding you want to emphasize. If you let your readers search for these observations in a big table full of details that are not necessarily relevant to the claims you want to make, you can create unnecessary confusion. Most journals allow you to provide bigger datasets as supplementary information, and some even require you to upload all your raw data at submission. When you write up your manuscript, however, matching the data presentation to the storyline is more important than throwing everything you have at the reader. 

Don’t forget that every graph needs to have clear x and y axis labels , a title that summarizes what is shown above the figure, and a descriptive legend/caption below. Since your caption needs to stand alone and the reader needs to be able to understand it without looking at the text, you need to explain what you measured/tested and spell out all labels and abbreviations you use in any of your graphs once more in the caption (even if you think the reader “should” remember everything by now, make it easy for them and guide them through your results once more). Have a look at this article if you need help on how to write strong and effective figure legends .

Even if you have thought about the data you have, the story you want to tell, and how to guide the reader most effectively through your results, you need to check whether the journal you plan to submit to has specific guidelines and limitations when it comes to tables and graphs. Some journals allow you to submit any tables and graphs initially (as long as tables are editable (for example in Word format, not an image) and graphs of high enough resolution. 

Some others, however, have very specific instructions even at the submission stage, and almost all journals will ask you to follow their formatting guidelines once your manuscript is accepted. The closer your figures are already to those guidelines, the faster your article can be published. This PLOS One Figure Preparation Checklist is a good example of how extensive these instructions can be – don’t wait until the last minute to realize that you have to completely reorganize your results because your target journal does not accept tables above a certain length or graphs with more than 4 panels per figure. 

Some things you should always pay attention to (and look at already published articles in the same journal if you are unsure or if the author instructions seem confusing) are the following:

• How many tables and graphs are you allowed to include?
• What file formats are you allowed to submit?
• Are there specific rules on resolution/dimension/file size?
• Should your figure files be uploaded separately or placed into the text?
• If figures are uploaded separately, do the files have to be named in a specific way?
• Are there rules on what fonts to use or to avoid and how to label subpanels?
• Are you allowed to use color? If not, make sure your data sets are distinguishable.

If you are dealing with digital image data, then it might also be a good idea to familiarize yourself with the difference between “adjusting” for clarity and visibility and image manipulation, which constitutes scientific misconduct .  And to fully prepare your research paper for publication before submitting it, be sure to receive proofreading services , including journal manuscript editing and research paper editing , from Wordvice’s professional academic editors .

Effective Use of Tables and Figures in Research Papers

Research papers are often based on copious amounts of data that can be summarized and easily read through tables and graphs. When writing a research paper , it is important for data to be presented to the reader in a visually appealing way. The data in figures and tables, however, should not be a repetition of the data found in the text. There are many ways of presenting data in tables and figures, governed by a few simple rules. An APA research paper and MLA research paper both require tables and figures, but the rules around them are different. When writing a research paper, the importance of tables and figures cannot be underestimated. How do you know if you need a table or figure? The rule of thumb is that if you cannot present your data in one or two sentences, then you need a table .

Using Tables

Tables are easily created using programs such as Excel. Tables and figures in scientific papers are wonderful ways of presenting data. Effective data presentation in research papers requires understanding your reader and the elements that comprise a table. Tables have several elements, including the legend, column titles, and body. As with academic writing, it is also just as important to structure tables so that readers can easily understand them. Tables that are disorganized or otherwise confusing will make the reader lose interest in your work.

• Title: Tables should have a clear, descriptive title, which functions as the “topic sentence” of the table. The titles can be lengthy or short, depending on the discipline.
• Column Titles: The goal of these title headings is to simplify the table. The reader’s attention moves from the title to the column title sequentially. A good set of column titles will allow the reader to quickly grasp what the table is about.
• Table Body: This is the main area of the table where numerical or textual data is located. Construct your table so that elements read from up to down, and not across.

Related: Done organizing your research data effectively in tables? Check out this post on tips for citing tables in your manuscript now!

The placement of figures and tables should be at the center of the page. It should be properly referenced and ordered in the number that it appears in the text. In addition, tables should be set apart from the text. Text wrapping should not be used. Sometimes, tables and figures are presented after the references in selected journals.

Using Figures

Figures can take many forms, such as bar graphs, frequency histograms, scatterplots, drawings, maps, etc. When using figures in a research paper, always think of your reader. What is the easiest figure for your reader to understand? How can you present the data in the simplest and most effective way? For instance, a photograph may be the best choice if you want your reader to understand spatial relationships.

• Figure Captions: Figures should be numbered and have descriptive titles or captions. The captions should be succinct enough to understand at the first glance. Captions are placed under the figure and are left justified.
• Image: Choose an image that is simple and easily understandable. Consider the size, resolution, and the image’s overall visual attractiveness.
• Additional Information: Illustrations in manuscripts are numbered separately from tables. Include any information that the reader needs to understand your figure, such as legends.

Common Errors in Research Papers

Effective data presentation in research papers requires understanding the common errors that make data presentation ineffective. These common mistakes include using the wrong type of figure for the data. For instance, using a scatterplot instead of a bar graph for showing levels of hydration is a mistake. Another common mistake is that some authors tend to italicize the table number. Remember, only the table title should be italicized .  Another common mistake is failing to attribute the table. If the table/figure is from another source, simply put “ Note. Adapted from…” underneath the table. This should help avoid any issues with plagiarism.

Using tables and figures in research papers is essential for the paper’s readability. The reader is given a chance to understand data through visual content. When writing a research paper, these elements should be considered as part of good research writing. APA research papers, MLA research papers, and other manuscripts require visual content if the data is too complex or voluminous. The importance of tables and graphs is underscored by the main purpose of writing, and that is to be understood.

Frequently Asked Questions

"Consider the following points when creating figures for research papers: Determine purpose: Clarify the message or information to be conveyed. Choose figure type: Select the appropriate type for data representation. Prepare and organize data: Collect and arrange accurate and relevant data. Select software: Use suitable software for figure creation and editing. Design figure: Focus on clarity, labeling, and visual elements. Create the figure: Plot data or generate the figure using the chosen software. Label and annotate: Clearly identify and explain all elements in the figure. Review and revise: Verify accuracy, coherence, and alignment with the paper. Format and export: Adjust format to meet publication guidelines and export as suitable file."

"To create tables for a research paper, follow these steps: 1) Determine the purpose and information to be conveyed. 2) Plan the layout, including rows, columns, and headings. 3) Use spreadsheet software like Excel to design and format the table. 4) Input accurate data into cells, aligning it logically. 5) Include column and row headers for context. 6) Format the table for readability using consistent styles. 7) Add a descriptive title and caption to summarize and provide context. 8) Number and reference the table in the paper. 9) Review and revise for accuracy and clarity before finalizing."

"Including figures in a research paper enhances clarity and visual appeal. Follow these steps: Determine the need for figures based on data trends or to explain complex processes. Choose the right type of figure, such as graphs, charts, or images, to convey your message effectively. Create or obtain the figure, properly citing the source if needed. Number and caption each figure, providing concise and informative descriptions. Place figures logically in the paper and reference them in the text. Format and label figures clearly for better understanding. Provide detailed figure captions to aid comprehension. Cite the source for non-original figures or images. Review and revise figures for accuracy and consistency."

"Research papers use various types of tables to present data: Descriptive tables: Summarize main data characteristics, often presenting demographic information. Frequency tables: Display distribution of categorical variables, showing counts or percentages in different categories. Cross-tabulation tables: Explore relationships between categorical variables by presenting joint frequencies or percentages. Summary statistics tables: Present key statistics (mean, standard deviation, etc.) for numerical variables. Comparative tables: Compare different groups or conditions, displaying key statistics side by side. Correlation or regression tables: Display results of statistical analyses, such as coefficients and p-values. Longitudinal or time-series tables: Show data collected over multiple time points with columns for periods and rows for variables/subjects. Data matrix tables: Present raw data or matrices, common in experimental psychology or biology. Label tables clearly, include titles, and use footnotes or captions for explanations."

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Presentation of Quantitative Research Findings

• First Online: 30 August 2023

Cite this chapter

• Jan Koetsenruijter 3 &
• Michel Wensing 3  

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Valid and clear presentation of research findings is an important aspect of health services research. This chapter presents recommendations and examples for the presentation of quantitative findings, focusing on tables and graphs. The recommendations in this field are largely experience-based. Tables and graphs should be tailored to the needs of the target audience, which partly reflects conventional formats. In many cases, simple formats of tables and graphs with precise information are recommended. Misleading presentation formats must be avoided, and uncertainty of findings should be clearly conveyed in the presentation. Research showed that the latter does not reduce trust in the presented data.

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Koetsenruijter, J., Wensing, M. (2023). Presentation of Quantitative Research Findings. In: Wensing, M., Ullrich, C. (eds) Foundations of Health Services Research. Springer, Cham. https://doi.org/10.1007/978-3-031-29998-8_5

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Sample Tables

These sample tables illustrate how to set up tables in APA Style . When possible, use a canonical, or standard, format for a table rather than inventing your own format. The use of standard formats helps readers know where to look for information.

There are many ways to make a table, and the samples shown on this page represent only some of the possibilities. The samples show the following options:

• The sample factor analysis table shows how to include a copyright attribution in a table note when you have reprinted or adapted a copyrighted table from a scholarly work such as a journal article (the format of the copyright attribution will vary depending on the source of the table).
• The sample regression table shows how to include confidence intervals in separate columns; it is also possible to place confidence intervals in square brackets in a single column (an example of this is provided in the Publication Manual ).
• The sample qualitative table and the sample mixed methods table demonstrate how to use left alignment within the table body to improve readability when the table contains lots of text.

Use these links to go directly to the sample tables:

Sample demographic characteristics table

Sample results of several t tests table, sample correlation table, sample analysis of variance (anova) table, sample factor analysis table, sample regression table, sample qualitative table with variable descriptions, sample mixed methods table.

These sample tables are also available as a downloadable Word file (DOCX, 37KB) . For more sample tables, see the Publication Manual (7th ed.) as well as published articles in your field.

Sample tables are covered in the seventh edition APA Style manuals in the Publication Manual Section 7.21 and the Concise Guide Section 7.21

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• Student Paper Setup Guide (PDF, 3MB)

Sociodemographic Characteristics of Participants at Baseline

Note. N = 150 ( n = 50 for each condition). Participants were on average 39.5 years old ( SD = 10.1), and participant age did not differ by condition.

a Reflects the number and percentage of participants answering “yes” to this question.

Results of Curve-Fitting Analysis Examining the Time Course of Fixations to the Target

Note. For each subject, the logistic function was fit to target fixations separately. The maximum asymptote is the asymptotic degree of looking at the end of the time course of fixations. The crossover point is the point in time the function crosses the midway point between peak and baseline. The slope represents the rate of change in the function measured at the crossover. Mean parameter values for each of the analyses are shown for the 9-year-olds ( n = 24) and 16-year-olds ( n = 18), as well as the results of t tests (assuming unequal variance) comparing the parameter estimates between the two ages.

Descriptive Statistics and Correlations for Study Variables

Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals

*** p < .001.

Results From a Factor Analysis of the Parental Care and Tenderness (PCAT) Questionnaire

Note. N = 307. The extraction method was principal axis factoring with an oblique (Promax with Kaiser Normalization) rotation. Factor loadings above .30 are in bold. Reverse-scored items are denoted with an (R). Adapted from “Individual Differences in Activation of the Parental Care Motivational System: Assessment, Prediction, and Implications,” by E. E. Buckels, A. T. Beall, M. K. Hofer, E. Y. Lin, Z. Zhou, and M. Schaller, 2015, Journal of Personality and Social Psychology , 108 (3), p. 501 ( https://doi.org/10.1037/pspp0000023 ). Copyright 2015 by the American Psychological Association.

Moderator Analysis: Types of Measurement and Study Year

Note . Number of studies = 120, number of effects = 782, total N = 52,578. CI = confidence interval; LL = lower limit; UL = upper limit.

Master Narrative Voices: Struggle and Success and Emancipation

Integrated Results Matrix for the Effect of Topic Familiarity on Reliance on Author Expertise

Note . We integrated quantitative data (whether students selected a card about nuclear power or about climate change) and qualitative data (interviews with students) to provide a more comprehensive description of students’ card selections between the two topics.

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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How to Read a Research Table

The tables in this section present the research findings that drive many recommendations and standards of practice related to breast cancer.

Research tables are useful for presenting data. They show a lot of information in a simple format, but they can be hard to understand if you don’t work with them every day.

Here, we describe some basic concepts that may help you read and understand research tables. The sample table below gives examples.

The numbered table items are described below. You will see many of these items in all of the tables.

Sample table – Alcohol and breast cancer risk

Selection criteria

Studies vary in how well they help answer scientific questions. When reviewing the research on a topic, it’s important to recognize “good” studies. Good studies are well-designed.

Most scientific reviews set standards for the studies they include. These standards are called “selection criteria” and are listed for each table in this section. These selection criteria help make sure well-designed studies are included in the table.

Types of studies

The types of studies (for example, randomized controlled trial, prospective cohort, case-control) included in each table are listed in the selection criteria.

Learn about the strengths and weaknesses of different types of research studies .

Selection criteria for most tables include the minimum number of cases of breast cancer or participants for the studies in the table.

Large studies have more statistical power than small studies. This means the results from large studies are less likely to be due to chance than results from small studies.

The power of large numbers

You can see the power of large numbers if you think about flipping a coin. Say you are trying to figure out whether a coin is fixed so that it lands on “heads” more than “tails.” A fair coin would land on heads half the time. So, you want to test whether the coin lands on heads more than half of the time.

If you flip the coin twice and get 2 heads, you don’t have a lot of evidence. It wouldn’t be surprising to flip a fair coin and get 2 heads in a row. With 2 coin flips, you can’t be sure whether you have a fair coin or not. Even 3 or 4 heads in a row wouldn’t be surprising for a fair coin.

If, however, you flipped the coin 20 times and got mostly heads, you would start to think the coin might be fixed.

With an increasing number of observations, you have more evidence on which to base your conclusions. So, you have more confidence in your conclusions. It’s a similar idea in research.

Example of study size in breast cancer research

Say you’re interested in finding out whether or not alcohol use increases the risk of breast cancer.

If there are only a few cases of breast cancer among the alcohol drinkers and the non-drinkers, you won’t have much confidence drawing conclusions.

If, however, there are hundreds of breast cancer cases, it’s easier to draw firm conclusions about a link between alcohol and breast cancer. With more evidence, you have more confidence in your findings.

The importance of study design and study quality

Study design (the type of research study) and study quality are also important. For example, a small, well-designed study may be better than a large, poorly-designed study. However, when all else is equal, a larger number of people in a study means the study is better able to answer research questions.

Learn about different types of research studies .

The studies

The first column (from the left) lists either the name of the study or the name of the first author of the published article.

Below each table, there’s a reference list so you can find the original published articles.

Sometimes, a table will report the results of only one analysis. This can occur for a few reasons. Either there’s only one study that meets the selection criteria or there’s a report that combines data from many studies into one large analysis.

Study population

The second column describes the people in each study.

• For randomized controlled trials, the study population is the total number of people who were randomized at the start of the study to either the treatment (or intervention) group or the control group.
• For prospective cohort studies, the study population is the number of people at the start of the study (baseline cohort).
• For case-control studies, the study population is the number of cases and the number of controls.

In some tables, more details on the people in the study are included. 

Length of follow-up

Randomized controlled trials and prospective cohort studies follow people forward in time to see who will have the outcome of interest (such as breast cancer).

For these studies, one column shows the length of follow-up time. This is the number or months or years people in the study were followed.

Because case-control studies don’t follow people forward in time, there are no data on follow-up time for these studies.

Tables that focus on cumulative risk may also show the length of follow-up. These tables give the length of time, or age range, used to compute cumulative risk (for example, the cumulative risk of breast cancer up to age 70).

Learn more about cumulative risk . 

Other information

Some tables have columns with other information on the study population or the topic being studied. For example, the table Exercise and Breast Cancer Risk has a column with the comparisons of exercise used in the studies.

This extra information gives more details about the studies and shows how the studies are similar to (and different from) each other.

Studies on the same topic can differ in important ways. They may define “high” and “low” levels of a risk factor differently. Studies may look at outcomes among women of different ages or menopausal status.

These differences are important to keep in mind when you review the findings in a table. They may help explain differences in study findings. 

Understanding the numbers

All of the information in the tables is important, but the main purpose of the tables is to present the numbers that show the risk, survival or other measures for each topic. These numbers are shown in the remaining columns of the tables.

The headings of the columns tell you what the numbers represent. For example:

• What is the outcome of interest? Is it breast cancer? Is it 5-year survival? Is it breast cancer recurrence?
• Are groups being compared to each other? If so, what groups are being compared?

Relative risks

Most often, findings are reported as relative risks. A relative risk shows how much higher, how much lower or whether there’s no difference in risk for people with a certain risk factor compared to the risk in people without the factor.

A relative risk compares 2 absolute risks.

• The numerator (the top number in a fraction) is the absolute risk among people with the risk factor.
• The denominator (the bottom number) is the absolute risk among those without the risk factor.

The absolute risk of those with the factor divided by the absolute risk of those without the factor gives the relative risk. 

The confidence interval around a relative risk helps show whether or not the relative risk is statistically significant (whether or not the finding is likely due to chance).

Learn more about confidence intervals .

Example of relative risk

Say a study shows women who don’t exercise (inactive women) have a 25 percent increase in breast cancer risk compared to women who do exercise (active women).

This statistic is a relative risk (the relative risk is 1.25). It means the inactive women were 25 percent more likely to develop breast cancer than women who exercised.

Learn more about relative risk .

Confidence intervals

A 95 percent confidence interval (95% CI) around a risk measure means there’s a 95 percent chance the “true” measure falls within the interval.

Because there’s random error in studies, and study populations are only samples of much larger populations, a single study doesn’t give the “one” correct answer. There’s always a range of likely answers. A single study gives a “best estimate” along with a 95 % CI of a likely range.

Most scientific studies report risk measures, such as relative risks, odds ratios and averages, with 95% CI.

Confidence intervals and statistical significance

For relative risks and odds ratios, a 95% CI that includes the number 1.0 means there’s no link between an exposure (such as a risk factor or a treatment) and an outcome (such as breast cancer or survival).

When this happens, the results are not statistically significant. This means any link between the exposure and outcome is likely due to chance.

If a 95% CI does not include 1.0, the results are statistically significant. This means there’s likely a true link between an exposure and an outcome.

Examples of confidence intervals

A few examples from the sample table above may help explain statistical significance.

The EPIC study found a relative risk of breast cancer of 1.07, with a 95% CI of 0.96 to 1.19. In the table, you will see 1.07 (0.96-1.19).

Women in the EPIC study who drank 1-2 drinks per day had a 7 percent higher risk of breast cancer than women who did not drink alcohol. The 95% CI of 0.96 to 1.19 includes 1.0. This means these results are not statistically significant and the increased risk of breast cancer is likely due to chance.

The Million Women’s Study found a relative risk of breast cancer of 1.13 with a 95% CI of 1.10 to 1.16. This is shown as 1.13 (1.10-1.16) in the table.

Women in the Million Women’s Study who drank 1-2 drinks per day had a 13 percent higher risk of breast cancer than women who did not drink alcohol. In this case, the 95% CI of 1.10 to 1.16 does not include 1.0. So, these results are statistically significant and suggest there’s likely a true link between alcohol and breast cancer.

For any topic, it’s important to look at the findings as a whole. In the sample table above, most studies show a statistically significant increase in risk among women who drink alcohol compared to women who don’t drink alcohol. Thus, the findings as a whole suggest alcohol increases the risk of breast cancer.

Summary relative risks

Summary relative risks from meta-analyses.

A meta-analysis takes relative risks reported in different studies and “averages” them to come up with a single, summary measure. Findings from a meta-analysis can give stronger conclusions than findings from a single study.

Summary relative risks from pooled analyses

A pooled analysis uses data from multiple studies to give a summary measure. It combines the data from each person in each of the studies into one large data set and analyses the data as if it were one big study. A pooled analysis is almost always better than a meta-analysis.

In a meta-analysis, researchers combine the results from different studies. In a pooled analysis, researchers combine the individual data from the different studies. This usually gives more statistical power than a meta-analyses. More statistical power means it’s more likely the results are not simply due to chance.

Cumulative risk

Sometimes, study findings are presented as a cumulative risk (risk up to a certain age). This risk is often shown as a percentage.

A cumulative risk may show the risk of breast cancer for a certain group of people up to a certain age. Say the cumulative risk up to age 70 for women with a risk factor is 20 percent. This means by age 70, 20 percent of the women (or 1 in 5) with the risk factor will get breast cancer.

Lifetime risk is a cumulative risk. It shows the risk of getting breast cancer during your lifetime (or up to a certain age). Women in the U.S. have a 13 percent lifetime risk of getting breast cancer. This means 1 in 8 women in the U.S. will get breast cancer during their lifetime.

Learn more about lifetime risk .

Sensitivity and specificity

Some tables show study findings on the sensitivity and specificity of screening tests. These measures describe the quality of a breast cancer screening test.

• Sensitivity  shows how well the screening test shows who truly has breast cancer. A sensitivity of 90 percent means 90 percent of people tested who truly have breast cancer are correctly identified as having cancer.
• Specificity  shows how well the screening test shows who truly does not have breast cancer. A specificity of 90 percent means 90 percent of the people who do not have breast cancer are correctly identified as not having cancer.

The goals of any screening test are:

• To correctly identify everyone who has a certain disease (100 percent sensitivity)
• To correctly identify everyone who does not have the disease (100 percent specificity)

A perfect test would correctly identify everyone with no mistakes. There would be no:

• False negatives (when people who have the disease are missed by the test)
• False positives (when healthy people are incorrectly shown to have the disease)

No screening test has perfect (100 percent) sensitivity and perfect (100 percent) specificity. There’s always a trade-off between the two. When a test gains sensitivity, it loses some specificity.

Learn more about sensitivity and specificity .

Finding studies

You may want more detail about a study than is given in the summary table. To help you find this information, the references for all the studies in a table are listed below the table.

Each reference includes the:

• Authors of the study article
• Title of the article
• Year the article was published
• Title and specific issue of the medical journal where the article appeared

PubMed , the National Library of Medicine’s search engine, is a good source for finding summaries of science and medical journal articles (called abstracts).

For some abstracts, PubMed also has links to the full text articles. Most medical journals have websites and offer their articles either for free or for a fee.

If you live near a university with a medical school or public health school, you may be able to go to the school’s medical library to get a copy of an article. Local public libraries may not carry medical journals, but they may be able to find a copy of an article from another source.

More information on research studies

If you’re interested in learning more about health research, a basic epidemiology textbook may be a good place to start. The National Cancer Institute also has information on epidemiology studies and randomized controlled trials.

Updated 07/25/22

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

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This paper is in the following e-collection/theme issue:

Published on 9.7.2024 in Vol 26 (2024)

Determining an Appropriate Sample Size for Qualitative Interviews to Achieve True and Near Code Saturation: Secondary Analysis of Data

Authors of this article:

Original Paper

• Claudia M Squire, MS   ; 
• Kristen C Giombi, PhD   ; 
• Douglas J Rupert, MPH   ; 
• Jacqueline Amoozegar, MSPH   ; 
• Peyton Williams, MPH  

RTI International, Research Triangle Park, NC, United States

Corresponding Author:

Claudia M Squire, MS

RTI International

3040 East Cornwallis Road

Research Triangle Park, NC, 27709-2194

United States

Phone: 1 9195416613

Email: [email protected]

Background: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals’ perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews.

Objective: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation.

Methods: The analyses for this study were based on data from 5 Food and Drug Administration–funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached.

Results: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, “in general”), uncertainty or confusion (eg, “don’t know”), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached.

Conclusions: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

Introduction

In-depth interviews are commonly used to collect qualitative data for a wide variety of research purposes across many subject matter areas. These types of interviews are an ideal approach for examining individuals’ perceptions and behaviors at a level of depth, complexity, and richness that would be challenging to achieve with quantitative data collection methods. Typically, trained interviewers conduct interviews using a guide designed to address the study’s key research aims by asking a series of questions and probes ordered by topic. These interview guides can range from highly structured to completely unstructured (eg, loosely organized conversations). Following the completion of data collection, interview notes and transcripts generated from audio recordings of the interviews are analyzed to assess for patterns in responses among the interviewees or subsets of the participants [ 1 , 2 ].

During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was not possible, and researchers continue to use web-based data collection methods post the COVID-19 emergency, citing advantages such as accessing marginalized populations, achieving greater geographic diversity, being able to offer a more flexible schedule, and saving on travel expenses [ 3 ]. Potential concerns about web-based data collection, such as the inability to build rapport and data richness, have been largely unfounded [ 3 , 4 ].

While we do not expect web-based data collection to supplant in-person research, it continues to show signs of growth. To date, much of the research on qualitative methods has focused on in-person data collection. Consequently, it will be important to conduct research to determine if previous widely accepted findings hold true for web-based data collection.

Researchers typically make a priori decisions about the number of interviews to conduct with the aim of balancing the need for sufficient data with resource limitations and respondent burden. The concept of saturation is frequently used to justify the study’s rigor with respect to the selected sample size. To provide empirically based recommendations on adequate minimum sample sizes, researchers have conducted studies to assess when saturation occurs. However, multiple types of saturation exist—such as theoretical, thematic, code, and meaning—and within each type of saturation, the definitions and measurement approaches used by investigators vary substantially, as does the level of detail researchers report in publications about their methods for achieving and assessing saturation [ 5 ].

This study aimed to examine the number of interviews needed to obtain code saturation for 5 recently conducted studies funded by the Food and Drug Administration [ 6 ] involving web-based interviews. Specifically, how many web-based interviews are needed to obtain true code saturation (ie, the use of 100% of all codes applied in the study) and how many web-based interviews are needed to achieve near code saturation (ie, the use of 90% of all codes applied in the study)?

Literature Review

Multiple authors have defined saturation as the point during data collection and analysis, at which no new additional data are found that reveal a new conceptual category [ 7 - 13 ] or theme related to the research question—an indicator that further data collection is redundant [ 11 ]. Additionally, Coenen et al [ 14 ] specified that no new second-level themes are revealed in 2 consecutive focus groups or interviews.

Other authors have distinguished between various types of saturation. One of the most common types of saturation mentioned in the literature is theoretical saturation, which emerges from grounded theory and occurs when the concepts of a theory are fully reflected in the data and no new insights, themes, or issues are identified from the data [ 5 , 11 , 12 , 15 - 18 ]. Hennink et al [ 17 ] expanded this definition, adding that all relevant conceptual categories should have been identified, thus emphasizing the importance of sample adequacy over sample size. Guest et al [ 15 ] operationalized the concept of theoretical saturation as the point in data collection and analysis when new information produces little or no change to the codebook, and van Rijnsoever [ 19 ] operationalized it as being when all the codes have been observed once in the sample.

Some authors have defined theoretical saturation, thematic saturation, and data saturation as the same concept [ 16 , 18 ], whereas others have defined these terms differently [ 12 , 20 ]. For example, some authors have defined thematic saturation as the point where no new codes or themes are emerging from the data [ 12 , 21 ]. For thematic saturation to be achieved, data should be collected until nothing new is generated [ 20 , 22 ]. Data saturation has been defined as the level to which new data are repetitive of the data that have been collected [ 12 , 23 , 24 ].

Furthermore, Hennink et al [ 17 ] distinguished between code saturation and meaning saturation. Code saturation is based on primary or parent codes and relates to the quantity of the data (“hearing it all”). Meaning saturation is based on sub or child codes and relates to the quality or richness of the data (“understanding it all”). Constantinou et al [ 7 ] made the point that it is the categorization of the raw data, rather than the data, that are saturated.

The literature reflects multiple methods that have been used to determine saturation [ 7 - 10 , 13 - 18 , 21 , 25 ]. Sim et al [ 26 ] discussed the four general approaches that have been used to determine sample size for qualitative research: (1) rules of thumb, based on a combination of methodological considerations and past experience; (2) conceptual models, based on specific characteristics of the proposed study; (3) numerical guidelines derived from the empirical investigation; and (4) statistical approaches, based on the probability of obtaining a sufficient sample size.

For example, Galvin [ 9 ] used a statistical approach based on binomial logic to establish the relationship between identifying a theme in a particular sample and within the larger population; for example, number of chances of detecting a theme if that theme exists within number of the population. Using the probability equation, the researcher can determine the number of interviews needed for a stated level of confidence that all relevant themes held by a certain proportion of the population will occur within the interview sample. This method assumes the researcher knows in advance the emergent themes from the study and at what rate they may occur.

Constantinou et al [ 7 ] used the comparative method for themes saturation, which relies on both a deductive and an inductive approach to generate codes (keywords extracted from the participants’ words) and themes (codes that fall into similar categories). Themes are compared across interviews, and theme saturation is reached when the next interview does not produce any new themes. The sequence of interviews is reordered multiple times to check for order-induced error. When exploring the various methods for determining saturation, researchers reached different conclusions on when saturation was achieved (findings on saturation by other authors are present in Multimedia Appendix 1 ) [ 7 - 10 , 13 - 17 , 21 , 25 , 27 , 28 ].

Most studies assessing saturation focused on in-person data collection or did not specify the data collection method. Given recent increases in web-based data collection, studies assessing saturation for web-based interviews are critical to ensure that recommendations regarding sample size are tailored to the mode of data collection [ 4 ]. While there is evidence to suggest that the content of data coded from in-person as compared with web-based interviews is conceptually similar [ 29 ], this is a relatively new area of exploration. Rapport may be higher with in-person as compared with web-based interviews [ 30 ], which may impact the amount and type of content generated. Additionally, participants in web-based data collection studies are more geographically diverse and may be more likely to be non-White, less educated, and less healthy than participants in in-person data collection studies [ 31 ].

Study Design

This study was based on analyses from data collected for 5 Food and Drug Administration–funded studies conducted using web-based platforms, such as Zoom (Zoom Video Communications) and Adobe Connect (Adobe Systems), and focused on patients with underlying medical conditions or on health care providers who provide primary or specialty care to patients. All platforms used for these interviews offered audio and video components and allowed for the sharing of stimuli on screen. A brief description of each study is provided in Table 1 . Each study’s data had been coded and stored using NVivo software (version 11; QSR International).

Ethical Considerations

This project was determined to not research with human participants by Research Triangle Institute’s institutional review board (STUDY00021985). The original 5 studies that this project is based on were reviewed by Research Triangle Institute’s institutional review board and were determined to be exempt under category 2ii. Participants in these studies were provided information about measures used to protect their privacy and the confidentiality of their data in the study’s consent forms. All participants were provided compensation for their time (the amount and type varied by study).

Data Preparation and Analysis

We established and applied a systematic approach to analyze all 5 study data sets. Our analytic approach was organized into 2 stages—data preparation and data analysis.

Data Preparation

First, because previous interviews sometimes influence moderator probes—for example, the moderator asks a follow-up question based on something they heard in a previous interview—we sorted interviews from each study by interview order. We then extracted code- and interview-specific data from the NVivo databases—including transcript name, code name, number of files coded, number of associated parent and child codes, and number of coding references—and compiled these data in an Excel (Microsoft Corp) file. We then updated the Excel file with important code and interview characteristics, including the order in which interviews were conducted, whether each code was directly (ie, child codes) or indirectly (ie, parent codes) applied to transcripts (in a tiered coding scheme, direct codes are those that have no child codes, whereas indirect codes function as “parents” that have additional codes nested beneath them), and the point at which each code was first applied to an interview. Finally, we created pivot tables within each Excel file to compile the data.

Data Analysis

Once the data were compiled, the data summaries were examined to determine when true saturation and near saturation occurred during data collection. True saturation was defined as 100% of all applied codes being used; near saturation was defined as 90% of all applied codes being used. We calculated saturation separately for each study’s data set, and we calculated saturation separately for all codes (ie, parent and child codes) as compared with direct codes (ie, child codes only). True saturation and near saturation points were identified by calculating the cumulative percentage of new codes for each interview, flagging when 100% and 90% of applied codes had been used.

True and Near Saturation

The number of web-based interviews used across the 5 studies ranged from 30 to 70 ( Table 2 ). True saturation (100% use of all applied codes) was reached in the final or near final interview ( Figure 1 ), suggesting that, even with a large sample size, additional interviews are likely to continue uncovering a small number of new codes or findings.

Across all studies, near saturation (90% use of all applied codes) was reached near—and often before—the midpoint of data collection. In other words, only a small number of new codes or findings were uncovered once the first half of the sample had been interviewed. In terms of absolute numbers, the point at which near saturation was reached occurred between 33% and 60% (n=15-23) of planned interviews ( Table 2 ). Despite the participants being more geographically, and possibly demographically, diverse compared with typical in-person participants, our findings were similar to previous studies on saturation [ 10 , 15 , 17 ].

We examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes (n=8-33, 57%-62%) represented previously established core concepts or themes, such as a trusted source of information, a behavioral intention, or a recommended change to educational material. Codes representing newly identified concepts (n=2-8, 10%-15%), other miscellaneous responses (eg, “in general”; n=6-9, 13%-41%), uncertainty or confusion (eg, “don’t know”; n=0-6, 0%-11%), or categorization for analysis (eg, “correct as compared with incorrect”; n=0-3, 0%-4%) were less commonly applied after near saturation had been reached.

The overwhelming majority of codes applied after near saturation (n=9-41, 73%-82%) had already been established in study codebooks before analysis. Only a small number of codes applied after this point (n=4-20, 18%-27%) were conceptually distinct enough to merit updating the study codebooks by including them. Likewise, most of the codes used after near saturation (n=11-35, 44%-64%) were applied to only a single interview. Far fewer codes were applied to 2 interviews (n=0-13, 0%-27%), 3 interviews (n=0-6, 0%-21%), or 4 or more interviews (n=0-12, 0%-21%).

Study B was an outlier in terms of codes applied after near saturation. This study had fewer codes representing core established concepts (n=8, 28%) and more codes representing newly identified concepts (n=7, 24%) or providing categorization for analysis (n=3, 10%) than other studies. The study also had a much higher proportion of new codes (n=20, 69%) that were added to the study codebook during analysis. These differences may be because the study sampled 2 populations with very different medical conditions (ie, type 2 diabetes as compared with vulvovaginal atrophy), leading to a broader range of applied codes.

In examining the relationship between the number of codes in the codebook for each study, the study with the most codes (study A: 657 codes) required the largest number of interviews to reach both true saturation and near saturation. However, this pattern did not hold true for the remainder of the studies. The study with the next highest number of codes (study C: 362 codes) was third to reach true saturation and last to reach near saturation.

Parent and Child Codes

All 5 study codebooks included both parent (ie, top-level codes) and child codes (ie, subcodes). We examined saturation using two analytic lenses—(1) all codes (parent and child) and (2) parent codes only—to determine if there were differences in when saturation was reached. We found no differences in when true saturation was reached. However, near saturation was reached slightly later (ie, after an additional 3 to 4 interviews) when examining only parent codes ( Figure 2 ).

Differences by Study

In total, 3 of the studies had codebooks that consisted almost entirely of deductive (ie, concept-driven) codes, whereas the codebooks in the remaining 2 studies contained a mix of both deductive and inductive (ie, data-driven) codes. Although the results were largely consistent across the 5 studies, as expected, the studies that relied heavily on deductive coding reached both true saturation and near saturation sooner. This finding suggests that studies using more inductive coding and analytic techniques may require slightly larger sample sizes to reach saturation.

Structure of an Interview Guide

Although all the studies used a semistructured interview guide, the level of structure varied across studies. The 3 studies (ie, studies C, D, and E) that had a more structured interview guide (eg, questions for which participants were asked their preference among discrete choices or the range of likely answers was limited) reached both true saturation and near saturation sooner. In fact, the study with the most structured guide reached near saturation the soonest, although it fell in the middle for true saturation. This finding suggests that studies using a less structured interview guide may need to conduct more interviews to reach an acceptable level of saturation.

Principal Findings

Although true saturation was not reached until the final interview or close to the final interview, near saturation was reached much sooner, ranging from just below to just above the midpoint of data collection, with most of the studies falling just below the midpoint. Although additional interviews conducted after near saturation may result in new information, our findings suggest there may be diminishing returns relative to the resources expended. We have identified several study characteristics that researchers can consider when making decisions on sample size for web-based interviews.

Although our findings were mostly consistent across the 5 studies we examined, near saturation was reached sooner on the studies that consisted of largely deductive codes compared with those that had a greater number of inductive codes. Consequently, researchers should consider their analytic approach when determining sample size. Studies that intend for the coding scheme to be iterative throughout the coding process may want to err on the side of having a slightly higher sample size than if the codebook is expected to consist largely of deductive codes tied to the interview guide.

These studies ranged in length from 30 to 90 minutes, and a majority (n=3) lasted 60 minutes. Although the 90-minute study reached both true saturation and near saturation at the latest point, the shortest interview (at 30 minutes) required the second-highest number of interviews to reach both saturation points. Although the length of the interview may be a minor consideration, the level of structure of the interview guide and the types of codes used seem to be larger drivers.

Our findings point to the need for a slightly higher number of interviews to reach an acceptable level of saturation—categorized by us as near code saturation—than what has been found in other studies. For example, Guest et al [ 15 ] found that 6 interviews were enough to get high-level themes, reaching a plateau at 10 to 12 interviews. Similarly, Young and Casey [ 27 ] found that near code saturation was reached at 6 to 9 interviews.

Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites. Data from our studies included participants from all US Census Bureau regions, which provides support that these findings may be more generalizable than previous studies.

Limitations

Our study had several limitations. First, our analysis was conducted on a sample of 5 studies that had similarities. All the studies were related to the medical field, and our study populations (patients with an identified medical condition and health care providers) were knowledgeable about the topics discussed. Second, all the studies were conducted using semistructured interview guides that leaned toward being more structured (ie, interviewers largely stuck to scripted probes as compared with guides that allow for unscripted follow-up probes and unstructured conversations). Additionally, all the studies used a similar approach to coding by using a mix of both deductive and inductive codes (though to varying extents). Consequently, studies with a less structured approach to both the interview and coding process may yield different results. Finally, all our studies are broadly classified as social science research. The findings for other fields of inquiry, such as economic or medical studies, may differ.

Conclusions

Saturation is an important consideration in planning and conducting qualitative research, yet, there is no definitive guidance on how to define and measure saturation, particularly for web-based data collection, which allows for data to be collected from a more geographically diverse sample. Our study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population being studied. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Rather than trying to reach a consensus on the number of interviews needed to achieve saturation in qualitative research overall, we recommend that future research should explore saturation within different types of studies, such as different fields of inquiry, subject matter, and populations being studied. Creating a robust body of knowledge in this area will allow researchers to identify the guidance that best meets the needs of their work.

Acknowledgments

Research Triangle Institute–affiliated authors received support for the development of this manuscript from the RTI Fellow’s program under RTI Fellow, Leila Kahwati, MPH, MD. All studies included in the analyses were funded by the Food and Drug Administration. The authors would like to thank the following Food and Drug Administration staff for their contribution to this research: Kit Aikin, Kevin Betts, Amie O’Donoghue, and Helen Sullivan.

Data Availability

The data sets analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Achieving saturation in interviews: saturation type, methods for achieving saturation, and findings by other authors.

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Edited by A Mavragani; submitted 22.09.23; peer-reviewed by K Kelly, G Guest; comments to author 24.10.23; revised version received 30.01.24; accepted 09.05.24; published 09.07.24.

©Claudia M Squire, Kristen C Giombi, Douglas J Rupert, Jacqueline Amoozegar, Peyton Williams. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.07.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 08 July 2024

Associations of early changes in lung ultrasound aeration scores and mortality in invasively ventilated patients: a post hoc analysis

• Jante S. Sinnige 1 ,
• Daan F. L. Filippini 1 ,
• Laura A. Hagens 1 ,
• Nanon F. L. Heijnen 2 , 3 ,
• Ronny M. Schnabel 2 ,
• Marcus J. Schultz 1 , 4 , 5 , 6 , 8 ,
• Dennis C. J. J. Bergmans 2 , 3 ,
• Lieuwe D. J. Bos 1 , 7 , 8 &
• Marry R. Smit 1  

Respiratory Research volume  25 , Article number:  268 ( 2024 ) Cite this article

Metrics details

Lung ultrasound (LUS) in an emerging technique used in the intensive care unit (ICU). The derivative LUS aeration score has been shown to have associations with mortality in invasively ventilated patients. This study assessed the predictive value of baseline and early changes in LUS aeration scores in critically ill invasively ventilated patients with and without ARDS (Acute Respiratory Distress Syndrome) on 30- and 90-day mortality.

This is a post hoc analysis of a multicenter prospective observational cohort study, which included patients admitted to the ICU with an expected duration of ventilation for at least 24 h. We restricted participation to patients who underwent a 12-region LUS exam at baseline and had the primary endpoint (30-day mortality) available. Logistic regression was used to analyze the primary and secondary endpoints. The analysis was performed for the complete patient cohort and for predefined subgroups (ARDS and no ARDS).

A total of 442 patients were included, of whom 245 had a second LUS exam. The baseline LUS aeration score was not associated with mortality (1.02 (95% CI: 0.99 – 1.06), p  = 0.143). This finding was not different in patients with and in patients without ARDS. Early deterioration of the LUS score was associated with mortality (2.09 (95% CI: 1.01 – 4.3), p  = 0.046) in patients without ARDS, but not in patients with ARDS or in the complete patient cohort.

In this cohort of critically ill invasively ventilated patients, the baseline LUS aeration score was not associated with 30- and 90-day mortality. An early change in the LUS aeration score was associated with mortality, but only in patients without ARDS.

Trial registration

ClinicalTrials.gov, ID NCT04482621.

Acute respiratory distress syndrome (ARDS) is characterized by bilateral pulmonary opacities on imaging, accompanied by hypoxemia within one week of a known clinical insult [ 1 ]. The presence of ARDS in invasively ventilated patients is associated with high mortality and morbidity [ 2 ]. The pulmonary edema, present in ARDS, can be quantified at the bedside by using the chest X-ray based Radiographic Assessment of Lung Edema (RALE) score or by estimating extravascular lung water with a transpulmonary thermodilution method [ 3 , 4 ]. These techniques showed to have predictive value for mortality in ARDS patients [ 5 , 6 , 7 , 8 ]. However, are invasive or require radiation.

Lung ultrasound (LUS) is a non-invasive, easy to learn, bedside technique that does not require radiation. It can accurately quantify the extent of pulmonary edema through the LUS aeration score [ 9 , 10 , 11 ]. The LUS aeration score was identified as a predictor for mortality by several studies in adult patients with COVID-19 [ 12 , 13 , 14 ]. However, the predictive value of the LUS aeration score remains unknown in ARDS patients without COVID-19 or in invasively ventilated patients without ARDS on mortality. Furthermore, the previous studies only assessed the predictive value of LUS aeration scores on admission, while early changes in the extent of pulmonary edema could be of additional predictive value [ 15 ].

In this study, we assessed the association of the baseline LUS aeration score and of early changes in LUS aeration scores with mortality in critically ill invasively ventilated patients with and without ARDS. We hypothesized that a both a higher baseline LUS aeration score and an early increase in LUS aeration score are associated with higher 30 and 90-day mortality in patients with and without ARDS.

This is a post hoc analysis of patients included in the ‘Diagnosis of Acute Respiratory disTress Syndrome’ (DARTS) project. This multicentre prospective observational cohort study recruited patients from March 27, 2019 until February 27, 2021 in two hospitals in the Netherlands; (1) Amsterdam University Medical Center (Amsterdam UMC), location Academic Medical Center (AMC) and (2) Maastricht University Medical Center + (MUMC +). The protocol was approved by the institutional ethics committees of both centers (ref: W18_311 #18.358 and 2019–1137) and patients or legal representatives provided deferred consent for the use of data. The protocol of the DARTS project was previously published [ 16 ].

Adult patients were included in the study if they were admitted to a participating ICU and were expected to be invasively ventilated for at least 24 h. Patients were excluded if they had received invasive ventilation more than 48 h in the last 7 days or were receiving invasive ventilation by a tracheostomy. This post hoc analysis was restricted to patients who received a 12-region LUS exam at inclusion and had data on 30-day mortality available. ARDS was diagnosed by an expert panel according to the Berlin criteria using chest imaging, clinical parameters, and blood gas analysis [ 17 ].

• Lung ultrasound

Patients received a 12-region LUS exam at inclusion and 24 h after inclusion by three dedicated investigators [ 16 , 18 ]. During the LUS exam, patients were positioned in supine position. LUS exams were performed with a linear probe using the clinically available ultrasound device. The use of other probes was allowed when the linear probe did not generate a sufficient image. Patients were scanned at two anterior, two lateral and two posterior locations per hemi thorax [ 16 ]. Each LUS image was scored as ‘0’ when A-lines were present, as ‘1’ when more than two B-lines covered < 50% of the pleura, as ‘2’ when B-lines covered > 50% of the pleura, and as ‘3’ when a consolidation of the lung was present (Fig.  1 ). If a lung region could not be scored or scanned (e.g., subcutaneous emphysema, chest drains, or wounds) the mean LUS aeration score of the same lung region (anterior, lateral, or posterior) was used as a substitute. Patients with more than four missing regions were excluded from this analysis. The LUS aeration score was calculated as the sum of LUS aeration scores in the 12 regions and could range from 0–36.

A-pattern; repeating horizontal A-lines parallel to the pleural line. B1-pattern; three or more vertical B-lines starting from the pleural line and reaching the bottom of the screen cover ≤ 50% of the pleural line (score 1). B2-pattern; B-lines cover ≥ 50% of the pleural line. C-pattern; consolidated lung [ 19 ]

A sensitivity analysis was conducted on the LUS aeration score with only anterolateral regions, as the posterior regions might contain less signal as they commonly present loss of aeration, and the anterolateral regions are easy to reach (LUS darts). The LUS aeration score for the anterolateral fields can range from 0–24. Patients with more than three regions missing were excluded from this sensitivity analysis.

The early changes in LUS aeration score were calculated by subtracting the LUS aeration score at inclusion from the LUS aeration score 24 h after inclusion. A negative score correlates with an improvement of the LUS aeration score as a positive score correlates with a deterioration of the LUS aeration score.

Study endpoints

The primary endpoint of the study was the association between LUS aeration score at baseline and the 30 and 90-day mortality. Additional endpoints were (1) association between early changes and deterioration of the LUS aeration score and 30-day mortality, (2) differences in LUS aeration scores between the predefined subgroups (ARDS and no ARDS), (3) the association between the baseline LUS aeration score and ARDS severity, and (4) the association between the baseline and early changes of the anterolateral LUS aeration score and 30-day mortality. Endpoints were adjusted for age, gender and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score as they are prognostic variables for outcomes in the general ICU population [ 3 , 20 ].

Statistical analysis

The DARTS project sample size was based on an expected sensitivity of 80% for the exhaled breath analyses, with a minimal acceptable lower confidence limit of 65%, requiring at least 52 ARDS patients. Given a predicted ARDS incidence of 10.4%, a total sample size of at least 500 patients was needed to meet the primary endpoint. We did not calculate a sample size or perform a power analysis for this post hoc analysis.

Continuous data was reported as mean with standard deviation (SD) or median with interquartile range (IQR), depending on the distribution of the data. Categorical data was reported as number with percentage. The respective appropriate test was used, either normal distributed (t-test) or non-normal distributed (Kruskal Wallis or Mann–Whitney U test). The statistical distribution of data was controlled by the visual assessment of histograms and Q-Q plots. Logistic regression was used to analyze the primary and secondary endpoints. Independent variables were assessed for multicollinearity using the variance inflation factor. A locally estimated scatterplot smoothing (LOESS) regression was employed to visualize the association between LUS aeration scores and mortality, aiming to assess the feasibility of categorization without relying on arbitrary cut-off values. Data was tested two-sided, a type I error below 5% was considered statistically significant. The analyses were performed using RStudio (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria).

Study population

A total of 442 (85%) of the 519 patients within the DARTS project had a LUS exam at inclusion and the primary endpoint available (Fig.  2 , Table  1 ). ARDS was present in 152 (34%) of the patients and 171 (39%) patients were deceased by day 30. Patients who were deceased at day 30 were significantly older, had higher lactate levels, and had a higher APACHE II and Sequential Organ Failure Assessment (SOFA) score. Two hundred forty-five patients (55%) had a second LUS exam 24 h after inclusion and could be included in the analyses for the early changes in the LUS aeration score (Additional file 1 ).

CONSORT figure of the patient enrolment in the DARTS consortium with additional exclusion criteria for the secondary analysis of this study. MV = Mechanically ventilated; DARTS = ‘Diagnosis of Acute Respiratory Distress Syndrome’ project [ 16 ]; LUS = Lung Ultrasound

Baseline LUS aeration scores in patients with and without ARDS

The median baseline LUS aeration score was significantly higher in patients with ARDS in comparison to patients without ARDS (13 [IQR 8, 16] vs. 5 [IQR 2, 9], p  < 0.001, Fig.  3 , Additional file 2). Patients with severe ARDS had a significantly higher median baseline LUS aeration scores than patients with mild ARDS (15 [IQR 8, 20] vs. 11 [IQR 5, 13], p  = 0.007). The distribution of LUS scoring in the six regions of the lungs are presented in Fig.  4 , stratified for patients with and without ARDS.

Differences in distributions of the baseline LUS aeration scores in the predefined groups. Individual patients are displayed as single-coloured dots. When a significant difference was found, the p-value was displayed above the figure. ARDS = Acute Respiratory Distress Syndrome; LUS = Lung Ultrasound

Distribution of the LUS patterns in patients with and without ARDS at baseline. The scores of the left and right lung are combined resulting in six regions per group. ARDS = acute respiratory distress syndrome; LUS = lung ultrasound, UTS = unable to score

Association between baseline LUS aeration score and mortality

The baseline LUS aeration scores in patients with and without ARDS were not associated with mortality at day 30 and day 90 in invasively ventilated patients on the ICU (Tables  2  and   3 , Fig.  5 ). The results remained consistent across both univariable and multivariable analyses. Visualization of the individual data points did not result in a cut-off value to dichotomize the baseline LUS aeration score to improve these results (Additional file 3-5). The results remained consistent when only anterolateral regions were analysed (Additional file 6).

Differences in the baseline and early changes (Δ) of the LUS aeration scores in survivors and deceased patients with and without ARDS. ARDS = Acute Respiratory Distress Syndrome; LUS = Lung Ultrasound

Association between early LUS changes and survival

In patients without ARDS ( n  = 75), deterioration of LUS aeration score was associated with mortality (Table  4 ). This relation remained in the multivariable analysis. However, there was no association between mortality and the deterioration of LUS aeration score in patients with ARDS, or in all patients in the multivariable analysis. Furthermore, the early changes in the LUS aeration scores and analysis of anterolateral fields did not have any additional predictive value in across patients and in the predefined subgroups (Fig.  5 , Additional file 7).

In this post hoc analysis of the DARTS project, we did not find an association between the baseline LUS aeration scores and 30- and 90-day mortality in invasively ventilated ICU patients and in the predefined ARDS subgroups. For early changes of the LUS aeration score, we did find that deterioration of the LUS aeration score in patients without ARDS was associated with 30-day mortality. However, this association was not found in ARDS patients nor in the whole cohort.

In the context of patients with ARDS, several studies assessed the predictive value of the LUS aeration score on mortality, but predominately in COVID-19 patients. While some of these studies showed an association between mortality and the LUS aeration score at baseline [ 12 , 14 , 21 ], other studies did not find this association [ 13 , 22 ]. In addition to these contradictory findings, there is considerable variation in the timing of the LUS exam across these studies. Some studies conduct the exam upon admission, while another study performed the LUS exam seven days after admission. The studies using a larger timeframe from admission seem to find a better association between the LUS aeration score and mortality, potentially explaining why we did not find predictive value of the baseline LUS aeration score and early changes in the LUS aeration score in ARDS patients on mortality.

It is noteworthy that within the DARTS project, a similar study assessed the predictive value of the radiography-based RALE score on mortality in patients with and without ARDS [ 5 ]. This study showed that the early changes in the RALE score have predictive value for 30-day mortality in patients with ARDS, but not in patients without ARDS. Discrepancies in the findings between this and our study may arise from the differences in assessment of lung edema between the two imaging modalities. LUS has a tomographic approach, is sensitive to changes in lung aeration and typically scans a subpleural layer of the lung. On the other hand, chest X-ray (CXR) acquires a two-dimensional image of the entire lung, is less sensitive for changes in aeration than LUS and therefore probably requires more edema for the RALE score to increase [ 23 ]. Furthermore, our study cohort is a different patient group because the LUS exams were performed per protocol in the DARTS project, while the CXRs were performed on clinical indication. Studies on the RALE score as a predictive tool on mortality in ventilated ICU patients with ARDS show conflicting results, similar to the LUS aeration score [ 3 , 5 , 6 , 24 , 25 , 26 , 27 ].

A strength of this prospective study is the large sample size containing multiple LUS exams per patient. Furthermore, unlike previous studies that mainly concentrated on the predictive value of LUS aeration score on mortality in COVID-19 patients, only 11% of the patients in this study were tested positive for SARS-CoV-2. This makes the findings of this study more generalizable for the ICU population. Additionally, LUS knows a high inter observer agreement [ 28 ]. Finally, in the current study, ARDS diagnosis was performed by a panel of experts, mitigating the typical challenges associated with substantial inter-observer variability in diagnosing ARDS [ 17 ]. A potential limitation of this study is the relatively short follow-up period of 24 h between the first and second LUS exam. This could have attributed to the absence of differences in the early changes of the LUS aeration score among ARDS patients, as severe pulmonary distress might not resolve or decrease within 24 h. Lastly, the study did not incorporate ventilator-free days as an endpoint, and therefore, the predictive value of LUS for duration of ventilation remains unknown.

This is the first study to highlight the predictive potential of LUS in determining mortality at day 30 in invasively ventilated patients without ARDS. While baseline LUS aeration scores did not demonstrate an association with mortality, such association was found in the early changes analysis with a repeated LUS exam after 24 h. After further validation of these findings, early changes in LUS aeration scores might serve as a potential indicator for predictive enrichment or as an early sign of treatment response in invasively ventilated patients without ARDS. Moving forward, the present findings should be externally validated and additional research on the timing of the LUS exam in invasively ventilated patients is warranted. Furthermore, incorporating subpleural consolidations and pleural abnormalities with the LUS aeration score could potentially improve the predictive value on mortality in ARDS patients.

Conclusions

In conclusion, this study showed that early changes in the LUS aeration score have a predictive value for 30-day mortality in invasively ventilated ICU patients without ARDS. There was no association found between the baseline LUS aeration score and 30- and 90-mortality in patients with and without ARDS.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Intensive Care Unit

Acute Respiratory Distress Syndrome

Radiographic Assessment of Lung Edema

Coronavirus disease 2019

Diagnosis of Acute Respiratory disTress Syndrome

Amsterdam University Medical Center

Academic Medical Center

Maastricht University Medical Center

Acute Physiology and Chronic Health Evaluation II

Standard Deviation

Interquartile range

Locally estimated scatterplot smoothing

Body Mass Index

Sequential Organ Failure Assessment

Positive End-Expiratory Pressure

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Acknowledgements

Not applicable.

The DARTS study received funding by Health Holland (10.2.17.181PPS) via the Dutch Lung Foundation. The funders played no part in the DARTS study design, data collection, data analysis and data interpretation. Furthermore, no specific funding was allocated for this secondary analysis; resources were sourced from institutional and/or departmental channels.

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Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, the Netherlands

Jante S. Sinnige, Daan F. L. Filippini, Laura A. Hagens, Marcus J. Schultz, Lieuwe D. J. Bos & Marry R. Smit

Department of Intensive Care, Maastricht UMC+, Maastricht University, Maastricht, 6229 HX, The Netherlands

Nanon F. L. Heijnen, Ronny M. Schnabel & Dennis C. J. J. Bergmans

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Marcus J. Schultz

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Lieuwe D. J. Bos

Laboratory of Experimental Intensive Care and Anaesthesiology (L.E.I.C.A.), University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands

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Conceptualization and Methodology (present analysis): JS, DF, LB, and MRS; Conceptualization and Methodology (DARTS): LH, NH, RS, MJS, DB, LB, and MRS; Data Collection: LH, NH, and MRS; Writing of Original Draft Preparation, JS, LB, and MRS; Writing, Critical Review and Editing: JS, DF, LH, NH, RS, MJS, DB, LB and MRS. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jante S. Sinnige .

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Ethical approval for the protocol was obtained from the ethics committee of the Amsterdam UMC (ref: W18_311 #18.358) and from the MUMC + (ref: 2019–1137). All included patients or legal representatives provided deferred consent for the use of data.

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Competing interests.

LB declares that he have received a grant from Health Holland (10.2.17.181PPS) via the Dutch Lung Foundation for the submitted work. The grant provider had no role in the study design, data collection, analysis, and interpretation of the results. Outside of the submitted work, LB declares receiving grants from the Longfonds, Innovative Medicine Initiative, Amsterdam UMC, Health Holland, ZonMW, Volition, and Santhera. Furthermore, LB has contributed to advisory boards for Sobi NL, Impentri, Novartis, AstraZeneca, CSL Behring, and has received consulting fees from Scailyte.

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Sinnige, J.S., Filippini, D.F.L., Hagens, L.A. et al. Associations of early changes in lung ultrasound aeration scores and mortality in invasively ventilated patients: a post hoc analysis. Respir Res 25 , 268 (2024). https://doi.org/10.1186/s12931-024-02893-0

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Received : 19 April 2024

Accepted : 26 June 2024

Published : 08 July 2024

DOI : https://doi.org/10.1186/s12931-024-02893-0

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