use of tables in a research paper

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Note:  This page reflects the latest version of the APA Publication Manual (i.e., APA 7), which released in October 2019. The equivalent resources for the older APA 6 style  can be found at this page  as well as at this page (our old resources covered the material on this page on two separate pages).

The purpose of tables and figures in documents is to enhance your readers' understanding of the information in the document; usually, large amounts of information can be communicated more efficiently in tables or figures. Tables are any graphic that uses a row and column structure to organize information, whereas figures include any illustration or image other than a table.

General guidelines

Visual material such as tables and figures can be used quickly and efficiently to present a large amount of information to an audience, but visuals must be used to assist communication, not to use up space, or disguise marginally significant results behind a screen of complicated statistics. Ask yourself this question first: Is the table or figure necessary? For example, it is better to present simple descriptive statistics in the text, not in a table.

Relation of Tables or Figures and Text

Because tables and figures supplement the text, refer in the text to all tables and figures used and explain what the reader should look for when using the table or figure. Focus only on the important point the reader should draw from them, and leave the details for the reader to examine on their own.

Documentation

If you are using figures, tables and/or data from other sources, be sure to gather all the information you will need to properly document your sources.

Integrity and Independence

Each table and figure must be intelligible without reference to the text, so be sure to include an explanation of every abbreviation (except the standard statistical symbols and abbreviations).

Organization, Consistency, and Coherence

Number all tables sequentially as you refer to them in the text (Table 1, Table 2, etc.), likewise for figures (Figure 1, Figure 2, etc.). Abbreviations, terminology, and probability level values must be consistent across tables and figures in the same article. Likewise, formats, titles, and headings must be consistent. Do not repeat the same data in different tables.

Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. In order for quantitative data to be presented clearly and efficiently, it must be arranged logically, e.g. data to be compared must be presented next to one another (before/after, young/old, male/female, etc.), and statistical information (means, standard deviations, N values) must be presented in separate parts of the table. If possible, use canonical forms (such as ANOVA, regression, or correlation) to communicate your data effectively.

A generic example of a table with multiple notes formatted in APA 7 style.

Elements of Tables

Number all tables with Arabic numerals sequentially. Do not use suffix letters (e.g. Table 3a, 3b, 3c); instead, combine the related tables. If the manuscript includes an appendix with tables, identify them with capital letters and Arabic numerals (e.g. Table A1, Table B2).

Like the title of the paper itself, each table must have a clear and concise title. Titles should be written in italicized title case below the table number, with a blank line between the number and the title. When appropriate, you may use the title to explain an abbreviation parenthetically.

Comparison of Median Income of Adopted Children (AC) v. Foster Children (FC)

Keep headings clear and brief. The heading should not be much wider than the widest entry in the column. Use of standard abbreviations can aid in achieving that goal. There are several types of headings:

• Stub headings describe the lefthand column, or stub column , which usually lists major independent variables.
• Column headings describe entries below them, applying to just one column.
• Column spanners are headings that describe entries below them, applying to two or more columns which each have their own column heading. Column spanners are often stacked on top of column headings and together are called decked heads .
• Table Spanners cover the entire width of the table, allowing for more divisions or combining tables with identical column headings. They are the only type of heading that may be plural.

All columns must have headings, written in sentence case and using singular language (Item rather than Items) unless referring to a group (Men, Women). Each column’s items should be parallel (i.e., every item in a column labeled “%” should be a percentage and does not require the % symbol, since it’s already indicated in the heading). Subsections within the stub column can be shown by indenting headings rather than creating new columns:

Chemical Bonds

     Ionic

     Covalent

     Metallic

The body is the main part of the table, which includes all the reported information organized in cells (intersections of rows and columns). Entries should be center aligned unless left aligning them would make them easier to read (longer entries, usually). Word entries in the body should use sentence case. Leave cells blank if the element is not applicable or if data were not obtained; use a dash in cells and a general note if it is necessary to explain why cells are blank.   In reporting the data, consistency is key: Numerals should be expressed to a consistent number of decimal places that is determined by the precision of measurement. Never change the unit of measurement or the number of decimal places in the same column.

There are three types of notes for tables: general, specific, and probability notes. All of them must be placed below the table in that order.

General  notes explain, qualify or provide information about the table as a whole. Put explanations of abbreviations, symbols, etc. here.

Example:  Note . The racial categories used by the US Census (African-American, Asian American, Latinos/-as, Native-American, and Pacific Islander) have been collapsed into the category “non-White.” E = excludes respondents who self-identified as “White” and at least one other “non-White” race.

Specific  notes explain, qualify or provide information about a particular column, row, or individual entry. To indicate specific notes, use superscript lowercase letters (e.g.  a ,  b ,  c ), and order the superscripts from left to right, top to bottom. Each table’s first footnote must be the superscript  a .

a  n = 823.  b  One participant in this group was diagnosed with schizophrenia during the survey.

Probability  notes provide the reader with the results of the tests for statistical significance. Asterisks indicate the values for which the null hypothesis is rejected, with the probability ( p value) specified in the probability note. Such notes are required only when relevant to the data in the table. Consistently use the same number of asterisks for a given alpha level throughout your paper.

* p < .05. ** p < .01. *** p < .001

If you need to distinguish between two-tailed and one-tailed tests in the same table, use asterisks for two-tailed p values and an alternate symbol (such as daggers) for one-tailed p values.

* p < .05, two-tailed. ** p < .01, two-tailed. † p <.05, one-tailed. †† p < .01, one-tailed.

Borders 

Tables should only include borders and lines that are needed for clarity (i.e., between elements of a decked head, above column spanners, separating total rows, etc.). Do not use vertical borders, and do not use borders around each cell. Spacing and strict alignment is typically enough to clarify relationships between elements.

Example of a table in the text of an APA 7 paper. Note the lack of vertical borders.

Tables from Other Sources

If using tables from an external source, copy the structure of the original exactly, and cite the source in accordance with  APA style .

Table Checklist

(Taken from the  Publication Manual of the American Psychological Association , 7th ed., Section 7.20)

• Is the table necessary?
• Does it belong in the print and electronic versions of the article, or can it go in an online supplemental file?
• Are all comparable tables presented consistently?
• Are all tables numbered with Arabic numerals in the order they are mentioned in the text? Is the table number bold and left-aligned?
• Are all tables referred to in the text?
• Is the title brief but explanatory? Is it presented in italicized title case and left-aligned?
• Does every column have a column heading? Are column headings centered?
• Are all abbreviations; special use of italics, parentheses, and dashes; and special symbols explained?
• Are the notes organized according to the convention of general, specific, probability?
• Are table borders correctly used (top and bottom of table, beneath column headings, above table spanners)?
• Does the table use correct line spacing (double for the table number, title, and notes; single, one and a half, or double for the body)?
• Are entries in the left column left-aligned beneath the centered stub heading? Are all other column headings and cell entries centered?
• Are confidence intervals reported for all major point estimates?
• Are all probability level values correctly identified, and are asterisks attached to the appropriate table entries? Is a probability level assigned the same number of asterisks in all the tables in the same document?
• If the table or its data are from another source, is the source properly cited? Is permission necessary to reproduce the table?

Figures include all graphical displays of information that are not tables. Common types include graphs, charts, drawings, maps, plots, and photos. Just like tables, figures should supplement the text and should be both understandable on their own and referenced fully in the text. This section details elements of formatting writers must use when including a figure in an APA document, gives an example of a figure formatted in APA style, and includes a checklist for formatting figures.

Preparing Figures

In preparing figures, communication and readability must be the ultimate criteria. Avoid the temptation to use the special effects available in most advanced software packages. While three-dimensional effects, shading, and layered text may look interesting to the author, overuse, inconsistent use, and misuse may distort the data, and distract or even annoy readers. Design properly done is inconspicuous, almost invisible, because it supports communication. Design improperly, or amateurishly, done draws the reader’s attention from the data, and makes him or her question the author’s credibility. Line drawings are usually a good option for readability and simplicity; for photographs, high contrast between background and focal point is important, as well as cropping out extraneous detail to help the reader focus on the important aspects of the photo.

Parts of a Figure

All figures that are part of the main text require a number using Arabic numerals (Figure 1, Figure 2, etc.). Numbers are assigned based on the order in which figures appear in the text and are bolded and left aligned.

Under the number, write the title of the figure in italicized title case. The title should be brief, clear, and explanatory, and both the title and number should be double spaced.

The image of the figure is the body, and it is positioned underneath the number and title. The image should be legible in both size and resolution; fonts should be sans serif, consistently sized, and between 8-14 pt. Title case should be used for axis labels and other headings; descriptions within figures should be in sentence case. Shading and color should be limited for clarity; use patterns along with color and check contrast between colors with free online checkers to ensure all users (people with color vision deficiencies or readers printing in grayscale, for instance) can access the content. Gridlines and 3-D effects should be avoided unless they are necessary for clarity or essential content information.

Legends, or keys, explain symbols, styles, patterns, shading, or colors in the image. Words in the legend should be in title case; legends should go within or underneath the image rather than to the side. Not all figures will require a legend.

Notes clarify the content of the figure; like tables, notes can be general, specific, or probability. General notes explain units of measurement, symbols, and abbreviations, or provide citation information. Specific notes identify specific elements using superscripts; probability notes explain statistical significance of certain values.

A generic example of a figure formatted in APA 7 style.

Figure Checklist 

(Taken from the  Publication Manual of the American Psychological Association , 7 th ed., Section 7.35)

• Is the figure necessary?
• Does the figure belong in the print and electronic versions of the article, or is it supplemental?
• Is the figure simple, clean, and free of extraneous detail?
• Is the figure title descriptive of the content of the figure? Is it written in italic title case and left aligned?
• Are all elements of the figure clearly labeled?
• Are the magnitude, scale, and direction of grid elements clearly labeled?
• Are parallel figures or equally important figures prepared according to the same scale?
• Are the figures numbered consecutively with Arabic numerals? Is the figure number bold and left aligned?
• Has the figure been formatted properly? Is the font sans serif in the image portion of the figure and between sizes 8 and 14?
• Are all abbreviations and special symbols explained?
• If the figure has a legend, does it appear within or below the image? Are the legend’s words written in title case?
• Are the figure notes in general, specific, and probability order? Are they double-spaced, left aligned, and in the same font as the paper?
• Are all figures mentioned in the text?
• Has written permission for print and electronic reuse been obtained? Is proper credit given in the figure caption?
• Have all substantive modifications to photographic images been disclosed?
• Are the figures being submitted in a file format acceptable to the publisher?
• Have the files been produced at a sufficiently high resolution to allow for accurate reproduction?

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

Tables in Research Paper – Types, Creating Guide and Examples

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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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• Manuscript Preparation

How to Use Tables and Figures effectively in Research Papers

• 3 minute read
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Table of Contents

Data is the most important component of any research. It needs to be presented effectively in a paper to ensure that readers understand the key message in the paper. Figures and tables act as concise tools for clear presentation . Tables display information arranged in rows and columns in a grid-like format, while figures convey information visually, and take the form of a graph, diagram, chart, or image. Be it to compare the rise and fall of GDPs among countries over the years or to understand how COVID-19 has impacted incomes all over the world, tables and figures are imperative to convey vital findings accurately.

So, what are some of the best practices to follow when creating meaningful and attractive tables and figures? Here are some tips on how best to present tables and figures in a research paper.

Guidelines for including tables and figures meaningfully in a paper:

• Self-explanatory display items: Sometimes, readers, reviewers and journal editors directly go to the tables and figures before reading the entire text. So, the tables need to be well organized and self-explanatory.
• Avoidance of repetition: Tables and figures add clarity to the research. They complement the research text and draw attention to key points. They can be used to highlight the main points of the paper, but values should not be repeated as it defeats the very purpose of these elements.
• Consistency: There should be consistency in the values and figures in the tables and figures and the main text of the research paper.
• Informative titles: Titles should be concise and describe the purpose and content of the table. It should draw the reader’s attention towards the key findings of the research. Column heads, axis labels, figure labels, etc., should also be appropriately labelled.
• Adherence to journal guidelines: It is important to follow the instructions given in the target journal regarding the preparation and presentation of figures and tables, style of numbering, titles, image resolution, file formats, etc.

Now that we know how to go about including tables and figures in the manuscript, let’s take a look at what makes tables and figures stand out and create impact.

How to present data in a table?

For effective and concise presentation of data in a table, make sure to:

• Combine repetitive tables: If the tables have similar content, they should be organized into one.
• Divide the data: If there are large amounts of information, the data should be divided into categories for more clarity and better presentation. It is necessary to clearly demarcate the categories into well-structured columns and sub-columns.
• Keep only relevant data: The tables should not look cluttered. Ensure enough spacing.

Example of table presentation in a research paper

For comprehensible and engaging presentation of figures:

• Ensure clarity: All the parts of the figure should be clear. Ensure the use of a standard font, legible labels, and sharp images.
• Use appropriate legends: They make figures effective and draw attention towards the key message.
• Make it precise: There should be correct use of scale bars in images and maps, appropriate units wherever required, and adequate labels and legends.

It is important to get tables and figures correct and precise for your research paper to convey your findings accurately and clearly. If you are confused about how to suitably present your data through tables and figures, do not worry. Elsevier Author Services are well-equipped to guide you through every step to ensure that your manuscript is of top-notch quality.

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Home → Academic Writing → Tips On Effective Use Of Tables And Figures In Research Papers

Tips On Effective Use Of Tables And Figures In Research Papers

• December 24, 2022

Several studies, journal guidelines, and discourses on scientific writing affirm the critical role that tables, figures, and graphs (or display items) play in enhancing the quality of manuscripts. Scientific tables and graphs can be utilized to represent sizeable numerical or statistical data in a time- and space-effective manner. Readers are often drawn towards tables and figures, because they perceive it as easy-reading, as compared to reading a verbose account of the same content. They rightly assume that these display items will provide them with a larger amount of information in a shorter time span. 

At the manuscript screening stage, these display items offer reviewers and journal editors a quick overview of the study findings, and once the paper is published, they do the same for readers (some of whom look only at these display items and not at the rest of the manuscript). However, tables and figures only add value to the format of a research report, if they are brief yet sufficiently informative.

These visual elements help authors present detailed results and complex relationships, patterns, and trends clearly and concisely; reduce the length of the manuscript and enhance readers’ understanding of the study results. Therefore, these tools are integral to the format of a research paper because, if clear and well-organized, they speed up the comprehension and interpretation of the study’s findings. 

But while well-presented tables and figures in research papers can efficiently capture and present information, poorly crafted tables and figures can confuse readers and impair the effectiveness of a paper.  To help authors get the balance right, this article presents some essential guidelines to the effective use of tables and figures in research papers. 

Planning your paper: When to use tables and figures in scientific papers

Producing effective tables and figures requires careful planning that begins at the manuscript writing stage itself. Here’s how to go about it:

• First, check out what your target journal has to say on the issue. Some journals limit the number of tables and figures and also have specific guidelines on the design aspects of these display items.
• Next, decide whether to use tables and figures or text to put across key information.(Refer to Table 1 below for help on making this decision.)
• After you’ve decided to use a display item, choose the display item that best fits your purpose based on what you wish readers to focus on and what you want to present (Refer to Table 1 below for more information).
• Finally, follow the best-practice guidelines outlined in section 3 and review the examples presented in section 4 of this paper to ensure that your tables and figures are well-designed.

Table 1: How to choose between tables, figures, and text to present data

Best practices for presentation of tables and figures in scientific papers

General guidelines:

• Ensure that display items are self-explanatory : Some readers (and certainly reviewers and journal editors) turn their attention to the tables and figures before they read the entire text, so these display items should be self-contained.
• Refer, but don’t repeat : Use the text to draw the reader’s attention to the significance and key points of the table/figure, but don’t repeat details. So for example, you could highlight your main finding (e.g., “We found that the treatment was effective in only 24% of the cases, as shown in Figure 1”), but don’t repeat exact values (e.g., “As Table 2 shows, 32% of the subjects chose Option 1, 12% chose Option 2, 10% chose Option 3, and 46% chose Option 4”). This defeats the very purpose (efficiency and clarity) of having a table or figure. 
• Be consistent : Ensure consistency between values or details in a table (e.g., abbreviations, group names, treatment names) and those in the text. 
• Give clear, informative titles : Table and figure titles should not be vague but should concisely describe the purpose or contents of the table/figure and should ideally draw the reader’s attention to what you want him/her to notice (e.g., Advantages and disadvantages of using sleep therapy with patients suffering from schizophrenia). Also ensure that column heads, axis labels, figure labels, etc., are clearly and appropriately labelled.
• Adhere to journal guidelines : Check what your target journal has to say about issues like the number of tables and figures, the style of numbering, titles, image resolution, file formats, etc., and follow these instructions carefully. 

Guidelines for tables:

• Combine repetitive tables : Tables and figures that present repetitive information will impair communication rather than enhance it. Examine the titles of all your tables and figures and check if they talk about the same or similar things. If they do, rethink the presentation and combine or delete the tables/graphs.
• Divide the data : When presenting large amounts of information, divide the data into clear and appropriate categories and present them in columns titled accurately and descriptively. 
• Watch the extent of data in your tables : If the data you have to present is extensive and would make the tables too cluttered or long, consider making the tables a part of the Appendix or supplemental material.
• De-clutter your table : Ensure that there is sufficient spacing between columns and rows and that the layout does not make the table look too messy or crowded.  

Guidelines for figures:

• Ensure image clarity : Make sure that all the parts of the figure are clear:18 Use standard font; check that labels are legible against the figure background; and ensure that images are sharp.
• Use legends to explain the key message : Figure legends are pivotal to the effectiveness of a figure. Use them to draw attention to the central message as well as to explain abbreviations and symbols.
• Label all important parts : Label the key sections and parts of schematic diagrams and photographs, and all axes, curves, and data sets in graphs and data plots.
• Give specifics : Include scale bars in images and maps; specify units wherever quantities are listed; include legends in maps and schematics; and specify latitudes and longitudes on maps. This section presents one example each of a well-prepared table and a well-designed figure.

Table 2: The table below is taken from a dietary study on chick-rearing macaroni penguins and is an example of an effective table for the following reasons:

• The title clearly describes what the table is about.
• The column heads are descriptive and clearly indicate the nature of the data presented. The data is divided into categories for clarity.
• It is self-contained and can be understood quite well even without reference to the entire paper.
• Superscript letters and notes are used to offer additional, clarifying information.
• Sufficient spacing is present between columns and rows; the layout is clean, and the font is legible.

Examples of an effective figure (graph)

The figure below from a paper on the efficacy of oyster reefs as natural breakwaters, scores on several counts:

• The informative title that immediately tells the reader what to expect in the graph.
• The axes are labeled clearly.
• The key clearly identifies what each element in the graph stands for.
• A figure legend at the bottom draws the reader’s attention to the graph’s key points.
• A note at the bottom acknowledges the source.
• The graph is 2-dimensional, with no clutter.    

Figures and tables, or display items, are powerful communication tools—they give your manuscript a professional feel, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as most journals editors and reviewers will glance at these display items before they begin a full reading of your paper, their importance cannot be overemphasized. 

Keep striving, researchers! ✨

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Your Guide to Creating Effective Tables and Figures in Research Papers

Research papers are full of data and other information that needs to be effectively illustrated and organized. Without a clear presentation of a study's data, the information will not reach the intended audience and could easily be misunderstood. Clarity of thought and purpose is essential for any kind of research. Using tables and figures to present findings and other data in a research paper can be effective ways to communicate that information to the chosen audience.

When manuscripts are screened, tables and figures can give reviewers and publication editors a quick overview of the findings and key information. After the research paper is published or accepted as a final dissertation, tables and figures will offer the same opportunity for other interested readers. While some readers may not read the entire paper, the tables and figures have the chance to still get the most important parts of your research across to those readers.

However, tables and figures are only valuable within a research paper if they are succinct and informative. Just about any audience—from scientists to the general public—should be able to identify key pieces of information in well-placed and well-organized tables. Figures can help to illustrate ideas and data visually. It is important to remember that tables and figures should not simply be repetitions of data presented in the text. They are not a vehicle for superfluous or repetitious information. Stay focused, stay organized, and you will be able to use tables and figures effectively in your research papers. The following key rules for using tables and figures in research papers will help you do just that.

Check style guides and journal requirements

The first step in deciding how you want to use tables and figures in your research paper is to review the requirements outlined by your chosen style guide or the submission requirements for the journal or publication you will be submitting to. For example, JMIR Publications states that for readability purposes, we encourage authors to include no more than 5 tables and no more than 8 figures per article. They continue to outline that tables should not go beyond the 1-inch margin of a portrait-orientation 8.5"x11" page using 12pt font or they may not be able to be included in your main manuscript because of our PDF sizing.

Consider the reviewers that will be examining your research paper for consistency, clarity, and applicability to a specific publication. If your chosen publication usually has shorter articles with supplemental information provided elsewhere, then you will want to keep the number of tables and figures to a minimum.

According to the Purdue Online Writing Lab (Purdue OWL), the American Psychological Association (APA) states that Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. You can avoid unnecessary tables by reviewing the data and deciding if it is simple enough to be included in the text. There is a balance, and the APA guideline above gives a good standard cutoff point for text versus table. Finally, when deciding if you should include a table or a figure, ask yourself is it necessary. Are you including it because you think you should or because you think it will look more professional, or are you including it because it is necessary to articulate the data? Only include tables or figures if they are necessary to articulate the data.

Table formatting

Creating tables is not as difficult as it once was. Most word processing programs have functions that allow you to simply select how many rows and columns you want, and then it builds the structure for you. Whether you create a table in LaTeX , Microsoft Word , Microsoft Excel , or Google Sheets , there are some key features that you will want to include. Tables generally include a legend, title, column titles, and the body of the table.

When deciding what the title of the table should be, think about how you would describe the table's contents in one sentence. There isn't a set length for table titles, and it varies depending on the discipline of the research, but it does need to be specific and clear what the table is presenting. Think of this as a concise topic sentence of the table.

Column titles should be designed in such a way that they simplify the contents of the table. Readers will generally skim the column titles first before getting into the data to prepare their minds for what they are about to see. While the text introducing the table will give a brief overview of what data is being presented, the column titles break that information down into easier-to-understand parts. The Purdue OWL gives a good example of what a table format could look like:

When deciding what your column titles should be, consider the width of the column itself when the data is entered. The heading should be as close to the length of the data as possible. This can be accomplished using standard abbreviations. When using symbols for the data, such as the percentage "%" symbol, place the symbol in the heading, and then you will not use the symbol in each entry, because it is already indicated in the column title.

For the body of the table, consistency is key. Use the same number of decimal places for numbers, keep the alignment the same throughout the table data, and maintain the same unit of measurement throughout each column. When information is changed within the same column, the reader can become confused, and your data may be considered inaccurate.

Figures in research papers

Figures can be of many different graphical types, including bar graphs, scatterplots, maps, photos, and more. Compared to tables, figures have a lot more variation and personalization. Depending on the discipline, figures take different forms. Sometimes a photograph is the best choice if you're illustrating spatial relationships or data hiding techniques in images. Sometimes a map is best to illustrate locations that have specific characteristics in an economic study. Carefully consider your reader's perspective and what detail you want them to see.

As with tables, your figures should be numbered sequentially and follow the same guidelines for titles and labels. Depending on your chosen style guide, keep the figure or figure placeholder as close to the text introducing it as possible. Similar to the figure title, any captions should be succinct and clear, and they should be placed directly under the figure.

Using the wrong kind of figure is a common mistake that can affect a reader's experience with your research paper. Carefully consider what type of figure will best describe your point. For example, if you are describing levels of decomposition of different kinds of paper at a certain point in time, then a scatter plot would not be the appropriate depiction of that data; a bar graph would allow you to accurately show decomposition levels of each kind of paper at time "t." The Writing Center of the University of North Carolina at Chapel Hill has a good example of a bar graph offering easy-to-understand information:

If you have taken a figure from another source, such as from a presentation available online, then you will need to make sure to always cite the source. If you've modified the figure in any way, then you will need to say that you adapted the figure from that source. Plagiarism can still happen with figures – and even tables – so be sure to include a citation if needed.

Using the tips above, you can take your research data and give your reader or reviewer a clear perspective on your findings. As The Writing Center recommends, Consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument. If you can summarize the data in a couple of sentences, then don't try and expand that information into an unnecessary table or figure. Trying to use a table or figure in such cases only lengthens the paper and can make the tables and figures meaningless instead of informative.

Carefully choose your table and figure style so that they will serve as quick and clear references for your reader to see patterns, relationships, and trends you have discovered in your research. For additional assistance with formatting and requirements, be sure to review your publication or style guide's instructions to ensure success in the review and submission process.

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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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How to clearly articulate results and construct tables and figures in a scientific paper?

The writing of the results section of a scientific paper is very important for the readers for clearly understanding of the study. This review summarizes the rules for writing the results section of a scientific paper and describes the use of tables and figures.

Introduction

Medical articles consist of review articles, case reports, and letters to the editor which are prepared with the intention of publishing in journals related to the medical discipline of the author. For an academician to be able to progress in carreer, and make his/her activities known in the academic environment, require preparation of the protocol of his/her academic research article, and acquiring sufficient information, and experience related to the composition of this article. In this review article, the information related to the writing of the ‘Results’ section, and use of tables, and figures will be presented to the attention of the readers.

Writing the ‘Results’ section

The ‘Results’ section is perhaps the most important part of a research article. In fact the authors will share the results of their research/study with their readers. Renown British biologist Thomas Henry Huxley (1825–1895) indicated his feelings as “The great tragedy of science: the slaying of a beautiful hypothesis by an ugly fact.” which emphasizes the importance of accurately, and impressively written results.

In essence results provide a response for the question” What is found in the research performed?”. Therefore, it is the most vital part of the article. As a priority, while drafting the ‘Results’ section of a manuscript one should not firstly write down methods in the ‘Material and Method’ section. The first sentence should give information about the number of patients who met the inclusion criteria, and thus enrolled in the study. [ 1 ] Besides information about the number of patients excluded from the study, and the reasons for exclusion is very important in that they will enlighten the readers, and reviewers who critically evaluate the manuscript, and also reflect the seriousness of the study. On the other hand, the results obtained should be recorded in chronological order, and without any comments. [ 2 ] In this section use of simple present tense is more appropriate. The findings should be expressed in brief, lucid, and explicable words. The writing style should not be boring for the reader. During writing process of a research article, a generally ill-conceived point is that positive, and significant findings are more important, attractive, and valuable, while negative, and insignificant findings are worthless, and less attractive. A scientific research is not performed to confirm a hypothesis, rather to test it. Not only positive, and significant results are worth writing, on the other hand negative or statistically insignificant result which support fallacy of a widely accepted opinion might be valuable. Therefore, all findings obtained during research should be inclıuded in the ‘Results’ section. [ 1 ]

While writing the ‘Results’ section, the sequence of results, tabulated data, and information which will be illustrated as figures should be definitively indicated. In indicating insignificant changes, do not use expressions as “decreased” or “increased”, these words should be reserved for significant changes. If results related to more than one parameter would be reported, it is appropriate to write the results under the subheading of its related parameter so as to facilitate reading, and comprehension of information. [ 2 ] Only data, and information concerning the study in question should be included in the ‘Results’ section. Results not mentioned in this section should not be included in the ‘Discussion’ and ‘Summary’ sections. Since the results obtained by the authors are cited in the ‘Results’ section, any reference should not be indicated in this section. [ 3 ]

In the ‘Results’ section, numerical expressions should be written in technically appropriate terms. The number of digits (1, 2 or 3 digits) to be written after a comma (in Turkish) or a point (in especially American English) should be determined The number of digits written after the punctuation marks should not be changed all throughout the text. Data should be expressed as mean/median ± standard deviation. Data as age, and scale scores should be indicated together with ranges of values. Absolute numerical value corresponding to a percentage must be also indicated. P values calculated in statistical analysis should be expressed in their absolute values. While writing p values of statistically significant data, instead of p<0.05 the actual level of significance should be recorded. If p value is smaller than 0.001, then it can be written as p <0.01. [ 2 ] While writing the ‘Results’ section, significant data which should be recalled by the readers must be indicated in the main text. It will be appropriate to indicate other demographic numerical details in tables or figures.

As an example elucidating the abovementioned topics a research paper written by the authors of this review article, and published in the Turkish Journal of Urology in the year 2007 (Türk Üroloji Dergisi 2007;33:18–23) is presented below:

“A total of 9 (56.2%) female, and 7 (43.8%) male patients with were included in this study. Mean age of all the patients was 44.3±13.8 (17–65) years, and mean dimensions of the adrenal mass was 4.5±3.4 (1–14) cm. Mean ages of the male, and female patients were 44.1 (30–65), and 42.4 (17–64) years, while mean diameters of adrenal masses were 3.2 (1–5), and 4.5 (1–14) cm (p age =0.963, p mass size =0.206). Surgical procedures were realized using transperitoneal approach through Chevron incision in 1 (6.2%), and retroperitoneal approach using flank incision with removal of the 11. rib in 15 (93.7%) patients. Right (n=6; 37.5%), and left (n=2; 12.5%) adrenalectomies were performed. Two (12.5%) patients underwent bilateral adrenalectomy in the same session because of clinical Cushing’s syndrome persisted despite transsphenoidal hipophysectomy. Mean operative time, and length of the hospital stay were 135 (65–190) min, and 3 (2–6) days, respectively. While resecting 11. rib during retroperitoneal adrenalectomy performed in 1 patient, pleura was perforated for nearly 1.5 cm. The perforated region was drained, and closed intraoperatively with 4/0 polyglyctan sutures. The patient did not develop postoperative pneumothorax. In none of the patients postoperative complications as pneumothorax, bleeding, prolonged drainage were seen. Results of histopathological analysis of the specimens retrieved at the end of the operation were summarized in Table 1 .” Table 1. Histopathological examination results of the patients Histopathological diagnosis Men n (%) Women n (%) Total n (%) Adrenal cortical adenoma 5 (31.3) 6 (37.6) 11 (68.8) Pheochromocytoma 1 (6.2) 1 (6.2) 2 (12.6) Ganglioneuroma 1 (6.2) - 1 (6.2) Myelolipoma - 1 (6.2) 1 (6.2) Adrenal carcinoma - 1 (6.2) 1 (6.2) Total 7 (43.7) 9 (56.2) 16 (100) Open in a separate window

Use of tables, and figures

To prevent the audience from getting bored while reading a scientific article, some of the data should be expressed in a visual format in graphics, and figures rather than crowded numerical values in the text. Peer-reviewers frequently look at tables, and figures. High quality tables, and figures increase the chance of acceptance of the manuscript for publication.

Number of tables in the manuscript should not exceed the number recommended by the editorial board of the journal. Data in the main text, and tables should not be repeated many times. Tables should be comprehensible, and a reader should be able to express an opinion about the results just at looking at the tables without reading the main text. Data included in tables should comply with those mentioned in the main text, and percentages in rows, and columns should be summed up accurately. Unit of each variable should be absolutely defined. Sampling size of each group should be absolutely indicated. Values should be expressed as values±standard error, range or 95% confidence interval. Tables should include precise p values, and level of significance as assessed with statistical analysis should be indicated in footnotes. [ 2 ] Use of abbreviations in tables should be avoided, if abbreviations are required they should be defined explicitly in the footnotes or legends of the tables. As a general rule, rows should be arranged as double-spaced Besides do not use pattern coloring for cells of rows, and columns. Values included in tables should be correctly approximated. [ 1 , 2 ]

As an example elucidating the abovementioned topics a research paper written by the authors of this review article, and published in the Turkish Journal of Urology in the year 2007 (Türk Üroloji Dergisi 2007;33:18–23).is shown in Table 1 .

Most of the readers priorly prefer to look at figures, and graphs rather than reading lots of pages. Selection of appropriate types of graphs for demonstration of data is a critical decision which requires artist’s meticulousness. As is the case with tables, graphs, and figures should also disploay information not provided in the text. Bar, line, and pie graphs, scatter plots, and histograms are some examples of graphs. In graphs, independent variables should be represented on the horizontal, and dependent variables on the vertical axis. Number of subjects in every subgroup should be indicated The labels on each axis should be easily understandable. [ 2 ] The label of the Y axis should be written vertically from bottom to top. The fundamental point in writing explanatory notes for graphs, and figures is to help the readers understand the contents of them without referring to the main text. Meanings of abbreviations, and acronyms used in the graphs, and figures should be provided in explanatory notes. In the explanatory notes striking data should be emphasized. Statistical tests used, levels of significance, sampling size, stains used for analyses, and magnification rate should be written in order to facilitate comprehension of the study procedures. [ 1 , 2 ]

Flow diagram can be utilized in the ‘Results’ section. This diagram facilitates comprehension of the results obtained at certain steps of monitorization during the research process. Flow diagram can be used either in the ‘Results’ or ‘Material and Method’ section. [ 2 , 3 ]

Histopathological analyses, surgical technique or radiological images which are considered to be more useful for the comprehension of the text by the readers can be visually displayed. Important findings should be marked on photos, and their definitions should be provided clearly in the explanatory legends. [ 1 ]

As an example elucidating the abovementioned issues, graphics, and flow diagram in the ‘Results’ section of a research paper written by the authors of this review article, and published in the World Journal of Urology in the year 2010 (World J Urol 2010;28:17–22.) are shown in Figures 1 , and ​ and2 2 .

a The mean SHIM scores of the groups before and after treatment. SHIM sexual health inventory for male. b The mean IPSS scores of the groups before and after treatment. IPSS international prostate symptom score

Flowchart showing patients’ progress during the study. SHIM sexual health inventory for male, IIEF international index of erectile function, IPSS international prostate symptom score, QoL quality of life, Q max maximum urinary flow rate. PRV post voiding residual urine volume

In conclusion, in line with the motto of the famous German physicist Albert Einstein (1879–1955). ‘If you are out to describe the truth, leave elegance to the tailor .’ results obtained in a scientific research article should be expressed accurately, and with a masterstroke of a tailor in compliance with certain rules which will ensure acceptability of the scientific manuscript by the editorial board of the journal, and also facilitate its intelligibility by the readers.

Research Voyage

Research Tips and Infromation

Best Practices for Designing and Formatting Tables in Research Papers

Introduction

Advantages of using tables in research papers, simple table, complex table, comparison table, statistical table, guidelines for effective use of tables in research papers, i. conditional formatting, ii. data bars, iii. highlighting cells:, how to fit my table by splitting it into multiple pages, can i give citations within the table , what is creative commons license, whether tables are also part of the plagiarism check, how many tables should be there in a research paper of 10 pages, can i put tables at the end of research paper, can i put table in single column in a two column research paper format.

Tables are a crucial aspect of research papers, providing a visual representation of data and results. They are used to effectively and concisely convey information to the reader. The purpose of using tables in research papers is to organize and present data in a manner that is easy to understand and interpret.

A table is a way of arranging data in rows and columns, allowing the reader to quickly identify patterns and trends within the data. It can be used to compare different results or to present large amounts of information in a clear and organized manner.

The importance of using tables in research papers cannot be overstated. Not only do they improve the overall clarity and organization of the paper, but they also make it easier for the reader to understand and interpret the results.

In this article, we will explore the advantages of using tables in research papers, the different types of tables commonly used, and how to effectively utilize tables in your research paper.

As a researcher or academic, you may have started out presenting data and information in your research papers in a simple format, such as just listing the data as plain text. However, as you progressed in your work, you soon realized the importance of presenting the information in a clear and organized manner.

You may have experienced the difficulties of presenting complex data in a simple format, and struggled with making the information easy for readers to understand. That’s when you discovered the power of tables.

Tables allow you to present complex data in a simple and easy-to-read format, helping your readers understand the information quickly and accurately. They also help to save space and make it easier to compare different data sets.

However, you soon learned that simply presenting the data in a table was not enough. You realized the importance of differentiating the rows in your tables to make the information stand out and easier to understand. You explored different ways to do this, such as using different background colors, shading, bold or italic text, different font sizes or styles, and different alignments.

Through your experience, you learned that tables play a crucial role in research papers, and that differentiating the rows in your tables can greatly improve the clarity and organization of your information. You continued to refine your table-making skills, ultimately resulting in the ability to present your data and information in the best possible way.

There are several benefits to using tables in research papers, including:

• Clarity and Organization of Data: Tables help to visually organize data and results, making it easier for the reader to understand and interpret the information. This can be particularly useful when presenting complex or detailed data sets.
• Easy Comparison of Results: Tables allow for quick and easy comparison of results, making it simpler for the reader to identify trends and patterns in the data. This is especially useful when presenting multiple results or comparing results from different experiments or studies.
• Ability to present large amounts of information: Tables are an effective way to present large amounts of data in a concise and organized manner. They help to break down complex information into manageable chunks, making it easier for the reader to comprehend.
• Improved Visual Appeal: Tables can improve the visual appeal of a research paper, breaking up long sections of text and making it easier for the reader to follow the information being presented. They can also help to clarify and emphasize key results or findings.

By utilizing tables in your research paper, you can effectively communicate your results and make it easier for the reader to understand the information you are presenting. The use of tables can also improve the overall clarity and organization of the paper, making it a valuable tool for any researcher.

Types of Tables Commonly Used in Research Papers

There are several types of tables commonly used in research papers, including:

A simple table presents data in a basic format, with columns and rows to organize the information. This type of table is useful for presenting simple data sets, such as small amounts of numerical or categorical data.

A complex table is used to present more complex data sets, such as large amounts of numerical data or data with multiple categories. This type of table may also include subheadings, footnotes, or other additional information to help the reader understand the data being presented.

A comparison table is used to compare data or results from multiple sources, experiments, or studies. This type of table allows the reader to quickly identify similarities and differences between the data being presented.

A statistical table presents numerical data and statistical results, such as means, standard deviations, and p-values. This type of table is useful for presenting results from statistical analyses and can be used to effectively communicate the significance of the results.

When using tables in research papers, it is important to follow certain guidelines to effectively communicate the information being presented. Some of these guidelines include:

• Clearly label the table and provide a brief description: Label the table with a clear and descriptive title, and provide a brief description of the information being presented. This will help the reader understand the purpose of the table and what information they can expect to find.
• Choose the right type of table: Choose the right type of table for the data being presented, as outlined in the previous section. This will help to effectively communicate the results and make it easier for the reader to understand the information.
• Keep tables simple and concise: Keep tables simple and concise, using only the necessary information to effectively communicate the results. Avoid using overly complex or cluttered tables, as this can make it more difficult for the reader to understand the information being presented.
• Use appropriate formatting: Use appropriate formatting to effectively communicate the information being presented. For example, use bold or italic text to highlight important information, and align the columns and rows in a way that makes the information easy to read.
• Provide clear and concise captions: Provide clear and concise captions for each table, explaining the purpose and results of the data being presented. This will help the reader understand the information being presented and will also provide context for the results.

By following these guidelines, researchers can effectively utilize tables in their research papers to communicate their results in a clear and organized manner. The use of tables can improve the overall clarity and organization of the paper, making it easier for the reader to understand the information being presented.

Highlighting Key Information in Tables Through Row Differentiation

When presenting data in a table, it is important to make sure that the information is organized and easy to understand. One effective way to do this is by differentiating the rows in the table. Here are several ways to achieve this, including using different background colors, shading or borders, text formatting, alignment, row spacing, and highlighting cells with specific values. These methods can help to group similar data, highlight important data points, and make the table easier to read and understand. Whether you are presenting data in a research paper, a business report, or any other type of document, utilizing these techniques can enhance the clarity and impact of your data presentation.

• Background color: Different background colors can be used to distinguish between rows and highlight specific groups of data.
• Shading or borders: Using shading or borders can help to separate rows in a table and distinguish between different groups of data.
• Text formatting: Bold or italic text can be used to highlight specific rows and make them stand out from the rest of the data. Different font sizes or styles can also be used to differentiate between rows.
• Alignment: Different alignments, such as centre or right alignments, can be used to differentiate between rows and distinguish between different types of data.
• Row spacing: Increasing the row spacing can help to separate the rows and make the table easier to read.
• Differentiating rows based on values: This is a feature in spreadsheet programs like Microsoft Excel and Google Sheets that allows you to apply conditional formatting rules to cells based on their values.

These are just a few examples of ways to differentiate rows in a table. The best approach will depend on the type of data being presented and the purpose of the table. The goal should be to make it easy for the reader to understand the information being presented and distinguish between different rows. As points 1-5 are most familiar and well known, I will elaborate point 6 in the following section.

Differentiate Rows in a Table Based on the Values of the Table.

There are several ways to do this:

This is a feature in spreadsheet programs like Microsoft Excel and Google Sheets that allows you to apply conditional formatting rules to cells based on their values. For example, you could apply a certain color to cells with a certain value range or highlight cells with specific values.

This is another feature in spreadsheet programs that allows you to add a bar to cells based on their values. This can help you visualize the relative magnitude of values within a table.

You can also manually highlight cells with specific values to draw attention to them. This can be done using the built-in highlighting tools in spreadsheet programs or by manually adding borders or shading to the cells.

How to fit Big Table in a Research Paper?

Tables are a crucial component of research papers as they help to present data in a clear and organized manner. However, sometimes the amount of data you need to present can result in a table that is too big to fit on one page. In such cases, fitting the table into a research paper can become a challenge. But with a few adjustments and strategies, you can effectively fit a big table into your research paper and ensure that the information is presented in a clear and readable manner. In this article, we’ll discuss a few methods for fitting a large table into a research paper.

• Reduce the font size : Reducing the font size can help fit more data into the same amount of space, but it may make the table more difficult to read.
• Split the table into multiple smaller tables: Splitting the large table into smaller tables that focus on different aspects of the data can make it easier to read and understand.
• Use landscape orientation: Changing the orientation of the page to landscape can provide more space for the table.
• Use a smaller font for numerical values: If the data in the table consists mainly of numerical values, you can use a smaller font for the values and a larger font for the headings.
• Use abbreviations or symbols: Replacing lengthy text with abbreviations or symbols can reduce the size of the table while still conveying the necessary information.
• Use a table that scrolls horizontally: Some word processors and typesetting programs allow you to create tables that can be scrolled horizontally, allowing you to fit more data into the same amount of space.
• Omit non-essential information: If the table contains data that is not critical to your research, consider omitting it to reduce the size of the table.

The best option of all is to split a table and show it across multiple pages when the table contains more items row-wise. in a research paper. The exact method for doing so depends on the word processing software or typesetting system you are using.

For example, in Microsoft Word , you can split a table across multiple pages by selecting the row below which you want to split the table, and then going to “Layout” > “Breaks” > “Next Page” to insert a page break. The upper part of the table will be on one page and the lower part will start on the next page.

In LaTeX , you can split a table across multiple pages using the long table package. The long table package allows you to create tables that span multiple pages, with header and footer rows that repeat on each page.

Regardless of the method used, it is important to ensure that the split table is still readable and the data is easy to understand, even when split across multiple pages.

When splitting a table across multiple pages in a research paper, it is important to ensure that the headings are also repeated on each page to make the table readable and easy to understand.

In Microsoft Word, you can repeat the headings by selecting the first row of the table (which contains the headings) and then right-clicking and selecting “Table Properties.” In the “Row” tab, you can check the “Repeat as header row at the top of each page” option. This will cause the headings to be repeated at the top of each page on which the table is split.

In LaTeX, you can repeat the headings by using the long table package as described in my previous answer. The long table package provides options for defining the header and footer rows that are repeated on each page of the table.

Regardless of the method used, it is important to ensure that the headings are clearly visible and easily distinguishable from the rest of the table. This helps readers understand the data contained in the table and follow its structure, even when split across multiple pages.

When splitting a table across multiple pages in a research paper, it is important to ensure that the headings are also repeated on each page to make the table readable and easy to understand. In Microsoft Word, you can repeat the headings by selecting the first row of the table (which contains the headings) and then right-clicking and selecting “Table Properties.” In the “Row” tab, you can check the “Repeat as header row at the top of each page” option. This will cause the headings to be repeated at the top of each page on which the table is split.

In LaTeX, you can repeat the headings by using the longtable package. The longtable package provides options for defining the header and footer rows that are repeated on each page of the table. Regardless of the method used, it is important to ensure that the headings are clearly visible and easily distinguishable from the rest of the table. This helps readers understand the data contained in the table and follow its structure, even when split across multiple pages.

How to Refer Tables in a Research Paper?

In a research paper, tables are usually referred to in the text by their number, such as Table 1, Table 2, etc. To refer to a specific element within a table, such as a specific row or column, you can specify the table number followed by the row and column number, e.g. “Table 1, Row 2, Column 3”. When referring to a table, it is important to ensure that the reference is clear and accurate and that the table is properly cited if the information is taken from another source.

Yes, you can give references or citations within a table in a research paper. The exact method of citing within a table depends on the referencing style you are using, but common methods include adding a superscript number or symbol in the cell of the table and then listing the corresponding reference in a footnote or in a reference list at the end of the paper. It is important to be consistent and clear in your referencing within tables to avoid confusion and to give credit where it is due.

here is an example of referencing within a table:

In this example, the reference column lists the number of sources where the information for each row was obtained. This information can then be referenced in the text of the research paper. For example, you could write “The sales and expenses for the North region in 2010 and 2011 are shown in Table 1 and are cited in references [1] and [2].”

Copyrights, Permissions and Plagiarism Check for Tables

Tables, like other types of data and images, can be subject to copyright protection. It depends on the specific circumstances surrounding the creation and use of the table. If the table is original and creative, it may be eligible for copyright protection as a literary work. On the other hand, if the table simply presents factual information in a straightforward manner, it may not be eligible for copyright protection. It’s important to consider the legal implications before using a table in a research paper or other publication. In general, it’s advisable to obtain permission from the copyright holder or to use tables that are in the public domain or licensed under a Creative Commons license.

To determine whether a table is under copyright protection, you can consider the following factors:

• Originality: If the table is original and creative, it may be eligible for copyright protection.
• Factual information: If the table simply presents factual information in a straightforward manner, it may not be eligible for copyright protection.
• Attribution: If the table was created by someone else, you should check for any attribution or copyright information. This information may be found in the table itself, in the source material from which the table was created, or in a separate copyright notice.
• Public domain: Tables that are in the public domain are not under copyright protection. You can use these tables without permission.
• Creative Commons license: Some tables may be licensed under a Creative Commons license, which allows you to use the table with certain conditions.

It’s important to check the specific circumstances surrounding the creation and use of the table to determine whether it’s under copyright protection. If in doubt, it’s advisable to obtain permission from the copyright holder or to use tables that are in the public domain or licensed under a Creative Commons license.

Creative Commons is a nonprofit organization that provides a set of standardized licenses for creators to use when making their work available to others. These licenses are designed to help creators maintain control over their work, while also making it possible for others to use, share, and build upon that work in ways that are legal and consistent with the creator’s intentions.

Some common creative commons licenses include Attribution (CC BY), Attribution-ShareAlike (CC BY-SA), and Attribution-NoDerivs (CC BY-ND) licenses. These licenses specify how others are allowed to use a creator’s work, such as by requiring attribution, allowing derivative works, or requiring that any derivative works be shared under the same license.

To obtain a Creative Commons license, one should approach the Creative Commons organization, which provides free, flexible copyright licenses that allow creators to share their work with the public while maintaining control over their rights.

The organization provides a license wizard that allows creators to choose the license that best suits their needs and provides guidance on how to properly use the license. The license can be applied to various types of creative works, including text, images, videos, and music.

Tables are also part of the plagiarism checks in research papers. All sources and information used in a research paper, including tables, should be properly cited to avoid plagiarism. Tables created from original data and analysis are also subject to plagiarism checks, as they are considered original content. It is important to ensure that all information in a research paper, including tables, is properly cited and does not violate copyright or plagiarism laws.

To avoid plagiarism in tables in a research paper, one should follow the following guidelines:

• Always properly cite any sources used to create the table, including any data, calculations, or other information that has been used.
• Use original language and explanations when writing captions and annotations for the table.
• Use a plagiarism detection tool, such as Turnitin, to check the content of the table.
• Make sure to use different sources to create the table, rather than relying on a single source.
• Use proper referencing styles and formatting, such as MLA, APA, or Chicago style, to avoid any accidental plagiarism.
• Avoid copying tables directly from other sources, even if you have cited the source.
• Create the table yourself, using data and calculations that you have obtained independently.
• If you must use a table from another source, make sure to make substantial changes to it so that it is no longer an exact copy.
• Always be mindful of copyright laws and make sure to obtain permission to use any copyrighted materials in your table.
• Regularly review and update your table to ensure that it is in compliance with plagiarism and copyright laws.

I have written an article on The Consequences of Plagiarism: What You Need to Know? . This article will help you to understand the importance of understanding consequences of plagiarism.

In conclusion, tables are an essential tool for presenting data and information in a clear and organized manner in research papers. They are used to present complex data in a simple and easy-to-read format, which helps readers to understand the information quickly and accurately. Tables also help to save space and make it easier to compare different data sets.

Differentiating the rows in a table is an important aspect of table design, as it helps to make the information stand out and makes it easier to understand. There are several ways to differentiate rows in a table, including using different background colors, shading, bold or italic text, different font sizes or styles, and different alignments. The most appropriate method will depend on the data being presented and the purpose of the table.

Frequently Asked Questions

The number of tables in a research paper can vary depending on the specific requirements of the paper and the nature of the research being presented. As a general guideline, there is no strict rule on how many tables should be included in a 10-page research paper, as it can vary greatly depending on the research topic, methodology, and the amount of data being presented. As a rough estimate, a 10-page research paper may include anywhere from 1 to 5 tables, but this can vary significantly based on the factors mentioned above.

Yes, it is common to include tables at the end of a research paper, after the references section. This is typically done to keep the main body of the research paper focused on the narrative and analysis, while providing supplementary information, such as tables or other supporting data, at the end.

Yes, it is possible to include a table in a single column format within a two-column research paper. In a two-column format, the text typically flows in two columns, side by side, across the page. However, if you need to include a table that requires a wider layout or if it is easier to read as a single column, you can insert a table that spans the entire width of the page in a single column format.

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• Citing tables and figures from other sources in APA Style

Citing Tables and Figures in APA Style | Format & Examples

Published on November 6, 2020 by Jack Caulfield . Revised on December 27, 2023.

When you reprint or adapt a table or figure from another source, the source should be acknowledged in an in-text citation and in your reference list . Follow the format for the source type you took the table or figure from.

You also have to include a copyright statement in a note beneath the table or figure. The example below shows how to cite a figure from a journal article .

Table of contents

Citing tables and figures, including a copyright note, examples from different source types, frequently asked questions about apa style citations.

Tables and figures taken from other sources are numbered and presented in the same format as your other tables and figures . Refer to them as Table 1, Figure 3, etc., but include an in-text citation after you mention them to acknowledge the source.

You should also include the source in the reference list. Follow the standard format for the source type you took the table or figure from.

Prevent plagiarism. Run a free check.

As well as a citation and reference, when you reproduce a table or figure in your own work, you also need to acknowledge the source in a note directly below it.

The image below shows an example of a table with a copyright note.

If you’ve reproduced a table or figure exactly, start the note with “From …” If you’ve adapted it in some way for your own purposes (e.g. incorporating part of a table or figure into a new table or figure in your paper), write “Adapted from …”

This is followed by information about the source (title, author, year, publisher, and location), and then copyright information at the end.

Types of copyright and permission

A source will either be under standard copyright, under a Creative Commons license, or in the public domain. You need to state which of these is the case.

Under standard copyright, you sometimes also need permission from the publisher to reprint or adapt materials. If you sought and obtained permission, mention this at the end of the note.

Look for information on copyright and permissions from the publisher. If you’re having trouble finding this information, consult your supervisor for advice.

• From a journal article
• From a website
• From a book

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

If you adapt or reproduce a table or figure from another source, you should include that source in your APA reference list . You should also include copyright information in the note for the table or figure, and include an APA in-text citation when you refer to it.

Tables and figures you created yourself, based on your own data, are not included in the reference list.

In most styles, the title page is used purely to provide information and doesn’t include any images. Ask your supervisor if you are allowed to include an image on the title page before doing so. If you do decide to include one, make sure to check whether you need permission from the creator of the image.

Include a note directly beneath the image acknowledging where it comes from, beginning with the word “ Note .” (italicized and followed by a period). Include a citation and copyright attribution . Don’t title, number, or label the image as a figure , since it doesn’t appear in your main text.

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• Yingyun Wang 3 ,
• Bianca Lee 3 ,
• Margo Orlen   ORCID: orcid.org/0000-0002-9834-6282 5 ,
• Sha Tian 6 ,
• Clint A. Stalnecker   ORCID: orcid.org/0000-0002-0570-4416 7 , 8 ,
• Kristina Drizyte-Miller 7 ,
• Marie Menard 3 ,
• Julien Dilly   ORCID: orcid.org/0000-0002-4006-5285 9 , 10 ,
• Stephen A. Sastra 1 , 2 ,
• Carmine F. Palermo 1 , 2 ,
• Marie C. Hasselluhn   ORCID: orcid.org/0000-0001-9765-4075 1 , 2 ,
• Amanda R. Decker-Farrell 1 , 2 ,
• Stephanie Chang   ORCID: orcid.org/0009-0000-2026-5215 3 ,
• Lingyan Jiang 3 ,
• Xing Wei 3 ,
• Yu C. Yang 3 ,
• Ciara Helland 3 ,
• Haley Courtney 3 ,
• Yevgeniy Gindin 3 ,
• Karl Muonio 3 ,
• Ruiping Zhao 3 ,
• Samantha B. Kemp 5 ,
• Cynthia Clendenin   ORCID: orcid.org/0000-0003-4535-2088 11 ,
• Rina Sor   ORCID: orcid.org/0000-0003-2042-5746 11 ,
• William P. Vostrejs   ORCID: orcid.org/0000-0002-1659-0186 5 ,
• Priya S. Hibshman 4 ,
• Amber M. Amparo   ORCID: orcid.org/0000-0003-3805-746X 7 ,
• Connor Hennessey 9 , 10 ,
• Matthew G. Rees   ORCID: orcid.org/0000-0002-2987-7581 12 ,
• Melissa M. Ronan   ORCID: orcid.org/0000-0003-4269-1404 12 ,
• Jennifer A. Roth   ORCID: orcid.org/0000-0002-5117-5586 12 ,
• Jens Brodbeck 3 ,
• Lorenzo Tomassoni 2 , 13 ,
• Basil Bakir 1 , 2 ,
• Nicholas D. Socci 14 ,
• Laura E. Herring   ORCID: orcid.org/0000-0003-4496-7312 15 ,
• Natalie K. Barker 15 ,
• Junning Wang 9 , 10 ,
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• Brian M. Wolpin   ORCID: orcid.org/0000-0002-0455-1032 9 , 10 ,
• John A. Chabot 16 ,
• Michael D. Kluger 16 ,
• Gulam A. Manji 1 , 2 ,
• Kenneth Y. Tsai   ORCID: orcid.org/0000-0001-5325-212X 17 ,
• Miroslav Sekulic 18 ,
• Stephen M. Lagana 18 ,
• Andrea Califano 1 , 2 , 13 , 19 , 20 , 21 , 22 , 23 ,
• Elsa Quintana 3 ,
• Zhengping Wang 3 ,
• Jacqueline A. M. Smith   ORCID: orcid.org/0000-0001-5028-8725 3 ,
• Matthew Holderfield 3 ,
• David Wildes   ORCID: orcid.org/0009-0009-3855-7270 3 ,
• Scott W. Lowe   ORCID: orcid.org/0000-0002-5284-9650 6 , 24 ,
• Michael A. Badgley 1 , 2 ,
• Andrew J. Aguirre   ORCID: orcid.org/0000-0002-0701-6203 9 , 10 , 12 , 25 ,
• Robert H. Vonderheide   ORCID: orcid.org/0000-0002-7252-954X 5 , 11 , 26 ,
• Ben Z. Stanger   ORCID: orcid.org/0000-0003-0410-4037 5 , 11 ,
• Timour Baslan 27 ,
• Channing J. Der   ORCID: orcid.org/0000-0002-7751-2747 7 , 8 ,
• Mallika Singh 3 &
• Kenneth P. Olive   ORCID: orcid.org/0000-0002-3392-8994 1 , 2  

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

• Pancreatic cancer
• Pharmacodynamics

Broad-spectrum RAS inhibition holds the potential to benefit roughly a quarter of human cancer patients whose tumors are driven by RAS mutations 1,2 . RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS, and NRAS, with affinity for both mutant and wild type (WT) variants (RAS(ON) multi-selective) 3 . As >90% of human pancreatic ductal adenocarcinoma (PDAC) cases are driven by activating mutations in KRAS 4 , we assessed the therapeutic potential of the RAS(ON) multi-selective inhibitor RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumor activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo . Pharmacological analyses revealed divergent responses to RMC-7977 in tumor versus normal tissues. Treated tumors exhibited waves of apoptosis along with sustained proliferative arrest whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumors identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro . Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

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These authors contributed equally: Urszula N. Wasko, Jingjing Jiang

Authors and Affiliations

Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Lorenzo Tomassoni, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Revolution Medicines, Inc., Redwood City, CA, USA

Jingjing Jiang, Yingyun Wang, Bianca Lee, Marie Menard, Stephanie Chang, Lingyan Jiang, Xing Wei, Yu C. Yang, Ciara Helland, Haley Courtney, Yevgeniy Gindin, Karl Muonio, Ruiping Zhao, Jens Brodbeck, Elsa Quintana, Zhengping Wang, Jacqueline A. M. Smith, Matthew Holderfield, David Wildes & Mallika Singh

Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

A. Cole Edwards & Priya S. Hibshman

University of Pennsylvania Perelman School of Medicine, Department of Medicine, Philadelphia, PA, USA

Margo Orlen, Samantha B. Kemp, William P. Vostrejs, Robert H. Vonderheide & Ben Z. Stanger

Cancer Biology & Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Sha Tian & Scott W. Lowe

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker, Kristina Drizyte-Miller, Amber M. Amparo & Channing J. Der

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker & Channing J. Der

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Julien Dilly, Connor Hennessey, Junning Wang, James M. Cleary, Brian M. Wolpin & Andrew J. Aguirre

Harvard Medical School, Boston, MA, USA

University of Pennsylvania Perelman School of Medicine, Abramson Cancer Center, Philadelphia, PA, USA

Cynthia Clendenin, Rina Sor, Robert H. Vonderheide & Ben Z. Stanger

The Broad Institute of Harvard and MIT, Cambridge, MA, USA

Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth & Andrew J. Aguirre

Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

Lorenzo Tomassoni & Andrea Califano

Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Nicholas D. Socci

UNC Michael Hooker Proteomics Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Laura E. Herring & Natalie K. Barker

Department of Surgery, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

John A. Chabot & Michael D. Kluger

Departments of Pathology, Tumor Microenvironment and Metastasis; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

Kenneth Y. Tsai

Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

Miroslav Sekulic & Stephen M. Lagana

Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Andrea Califano

J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA

Chan Zuckerberg Biohub New York, New York, NY, USA

Howard Hughes Medical Institute, Chevy Chase, MD, USA

Scott W. Lowe

Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Andrew J. Aguirre

Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

Robert H. Vonderheide

Department of Biomedical Sciences, School of Veterinary Medicine, The University of Pennsylvania, Philadelphia, PA, USA

Timour Baslan

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Corresponding authors

Correspondence to Mallika Singh or Kenneth P. Olive .

Supplementary information

Supplementary figure 1.

uncropped Western Blot images with marked areas of interest, and target molecular weight.

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Wasko, U.N., Jiang, J., Dalton, T.C. et al. Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer. Nature (2024). https://doi.org/10.1038/s41586-024-07379-z

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• Open access
• Published: 15 April 2024

Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China

• Xueying Xu 1 ,
• Yichao Li 2 &
• Hong Mi 2  

BMC Health Services Research volume  24 , Article number:  469 ( 2024 ) Cite this article

Metrics details

China has piloted Long-Term Care Insurance (LTCI) to address increasing care demand. However, many cities neglected adjusting LTCI premiums since the pilot, risking the long-term sustainability of LTCI. Therefore, using Zhejiang Province as a case, this study simulated mortality-adjusted long-term care demand and the balance of LTCI funds through dynamic financing mechanism under diverse life expectancy and disability scenarios.

Three-parameter log-quadratic model was used to estimate the mortality from 1990 to 2020. Mortality with predicted interval from 2020 to 2080 was projected by Lee-Carter method extended with rotation. Cohort-component projection model was used to simulate the number of older population with different degrees of disability. Disability data of the older people is sourced from China Health and Retirement Longitudinal Study 2018. The balance of LTCI fund was simulated by dynamic financing actuarial model.

Life expectancy of Zhejiang for male (female) is from 80.46 (84.66) years in 2020 to 89.39 [86.61, 91.74] (91.24 [88.90, 93.25]) years in 2080. The number of long-term care demand with severe disability in Zhejiang demonstrates an increasing trend from 285 [276, 295] thousand in 2023 to 1027 [634, 1657] thousand in 2080 under predicted mean of life expectancy. LTCI fund in Zhejiang will become accumulated surplus from 2024 to 2080 when annual premium growth rate is 5.25% [4.20%, 6.25%] under various disability scenarios, which is much higher than the annual growth of unit cost of long-term care services (2.25%). The accumulated balance of LTCI fund is sensitive with life expectancy.

Conclusions

Dynamic growth of LTCI premium is essential in dealing with current deficit around 2050 and realizing Zhejiang’s LTCI sustainability in the long-run. The importance of dynamic monitoring disability and mortality information is emphasized to respond immediately to the increase of premiums. LTCI should strike a balance between expanding coverage and controlling financing scale. This study provides implications for developing countries to establish or pilot LTCI schemes.

Peer Review reports

The lack of sufficient long-term care (LTC) for older individuals has become a pressing concern in both developed and developing countries with global population aging and increased longevity [ 1 ]. Although healthy life expectancy generally increased over last decades [ 2 ], the episode of disability in older people could have catastrophic impact on their household welfare [ 3 ]. Several developed countries, such as the Netherlands, Germany, and Japan, have established social long-term care insurance (LTCI) to address LTC demands of households with disabled older individuals. This approach proves more efficient in pooling disability risks than private LTCI [ 4 , 5 ]. Nonetheless, many developed countries had to reform their LTCI systems to deal with increasing aging population with LTC demands, often by raising premiums. Even though, these adjustments usually had time lags which affected the long-term sustainability of LTCI schemes. However, establishing social LTCI in developing countries proves more challenging than in developed countries because the lower income of residents restricts the financing capacity of LTCI. In addition, the lack of high-quality death registration and health survey data hinders optimizing LTCI systems design according to changing LTC demands, particularly in developing countries or small areas [ 6 ].

Massive evidence shows that there will be a steady and slow increase in life expectancy [ 7 , 8 , 9 ]. Evidence from developed countries shows that the long-term care needs increasing rapidly because of the increasing life expectancy [ 10 , 11 ]. The trend of the gap between life expectancy and healthy life expectancy is still inconclusive [ 12 ], which also affects the identification of LTC needs [ 13 ]. There is still mixed conclusion of disability and LTC demands trend in the future based on the three different assumptions of health transitions [ 14 , 15 , 16 ]. Whereas, there is less evidence regarding the assessment of LTC needs under different mortality scenarios. Zeng, et al. [ 17 ] calculated long-term care needs under different life expectancy scenarios, but the setting of life expectancy was relatively subjective. Besides, many studies in country-level controlled the impact of underreported mortality on the LTCI system by using modified mortality data [ 18 , 19 ], but few studies in the provincial level took that into consideration.

Most countries such as Germany and the Netherlands adopt a fixed percentage of income model to collect social LTCI premiums from individuals [ 20 ], and a few countries such as Singapore adopt a fixed amount premium model [ 21 ]. The premium of Germany LTCI has been 3.05% of gross income or 3.40% if individuals aged 23 and above without children since 2020 [ 22 ]. The Netherlands also has a tax-funded LTCI with the compulsory contribution of 9.65% of taxable income since 2017 [ 20 ]. In Singapore, fixed amount premium of LTCI is determined by the age of starting contribution and sex. The premium for a 30-year-old male (female) is around 200 (250) Singapore Dollars in 2020 [ 21 ], with an increase of 2% per year from 2020 to 2025 [ 23 ]. Financing parameters from both models should be adjusted regularly to ensure sustainability [ 24 , 25 ]. In China, both models are adopted in different LTCI pilot areas [ 26 ], but the areas that adopt the fixed amount of premium have not increased the premium level since the pilot, which affects long-term sustainability.

OECD countries will face high pressure of LTCI financing because of increasing average public LTC expenditures to 2.3% of GDP in 2040 for the future financing level of LTCI [ 27 ]. Therefore, an adjustment factor is suggested incorporated to simulate LTCI fund to reduce future financing pressure [ 22 ], but a higher short-term financing will bring greater resistance to reforms. Most simulation studies on China’s LTCI, based on fixed percentage of income model, demonstrated that LTCI financing will increase rapidly based on different disability scenarios [ 28 , 29 , 30 , 31 ]. Some studies also simulated LTCI financing based on fixed amount of premium model [ 32 , 33 ], but they did not consider its variation under different mortality scenarios. Only one study modified the mortality in a pilot city by using national mortality data when simulating the dynamic financing burden [ 34 ]. However, it only simulated to 2040 which did not cover plateau period of China’s aging.

China, as a developing country, pioneered social LTCI schemes in 2016. Local governments were granted significant autonomy, resulting in fragmented LTCI structures due to regional disparities in the pilot cities [ 35 ]. Thus it has become crucial to ensure the sustainability of China’s LTCI pilot areas. Zhejiang Province stands as a representative case among these pilot areas and its five cities (Tonglu, Ningbo, Jiaxing, Yiwu and Whenzhou) have piloted LTCI since 2017. Zhejiang has standardized disability assessments, coverage groups, benefit levels, and financing amounts of LTCI in province-level by 2022 [ 36 ]. It faces rapid aging ahead with high life expectancy in China. Notably, Zhejiang, one of the areas with fixed amount of premium of LTCI in China, has never increased its fixed premium since the pilot’s inception [ 36 ]. This lack of financing adjustment coupled with inflationary pressures strains Zhejiang’s LTCI fund. Zhejiang has capacities to facilitate LTCI operations through modified financing mechanism as the demonstration zone for the Initiative of Common Prosperity in China. Therefore, it can serve as a practical model for other developing countries establishing LTCI schemes to evaluate life expectancy and LTC demand parameters and guide its LTCI financing.

In summary, massive studies predict the LTC needs in developed countries and China. However, most of the studies on LTCI financing in China pilots overlook the potential death underreporting in census and uncertainty of mortality in projection period, which may misestimate the future LTC needs and financing pressure. In addition, current studies on the sustainability of China’s LTCI rarely involve the dynamic financing adjustment of fixed amount of premium model, and most studies do not cover the plateau period of China’s aging in the future, which may underestimate the financing level to achieve sustainable LTCI. Therefore, drawing from the Zhejiang Province case in China, this study proposes a dynamic financing mechanism to achieve a balance between sustainability and efficiency in social LTCI schemes, utilizing a simulation model with limited mortality and disability information. Our aim is to offer insights for developing countries to establish or pilot LTCI schemes. Three research questions will be addressed:

What is the long-term trend of life expectancy in Zhejiang from 1990 to 2080?

What extent of LTC demand will be reached among older people in Zhejiang from 2023 to 2080, with aging process?

What level of LTCI dynamic financing standards will achieve an actuarial equilibrium of the LTCI fund in Zhejiang, with rising life expectancy and LTC demand?

Data sources

For demographic data, the age-specific mortality and the population number by gender are from population census of Zhejiang Province in 1990, 2000, 2010 and 2020. The population census, which has been conducted once every 10 years since 1990, is a complete account of the entire population, mortality and fertility by age and sex in each census year and has the province-level representativeness of Zhejiang. Child mortality data is from Chinese Center for Disease Control and Prevention (CDC) in 1990–2013 [ 37 ], and official annual data of Zhejiang reported u p to 2020 [ 38 ]. Chinese CDC sorted and estimated under-5 mortality rates in China before 2013 with county-level and province-level representativeness, including data in Zhejiang. Data on the prevalence rate of disability of the older people is sourced from China Health and Retirement Longitudinal Study (CHARLS) in 2018. CHARLS is a national representative survey which covers a wide range of topics related to the adults aged 45 and above, including demographic information and health status. The national prevalence rate of disability by age and sex from CHARLS is used as a proxy for Zhejiang referring to existing research, due to lack of latest representative disability data in Zhejiang [ 39 ]. Older people are defined as those aged 60 and above based the statistical standards from World Health Organization [ 40 ], whose age groups are covered by CHARLS. The benefit criteria and financing criteria data is from the LTCI official regulations of pilot cities in Zhejiang [ 41 , 42 , 43 , 44 , 45 ]. Healthcare Consumer Price Index (CPI) from 2010 to 2020 in Zhejiang is from National Bureau of Statistics of China, covering the socio-economic indicators at province-level [ 46 ]. The change rate of total fertility of China from 2020 to 2080 is from World Population Prospects 2022 which forecasted fertility in country-level around the world [ 47 ].

Estimation of mortality pattern with three-parameter model life table approach

Model life tables methods are widely used in simulation of mortality for their effectiveness and accessibility to overcome the limited mortality information in developing countries [ 48 , 49 ]. Two-parameters log-quadratic model considering the child and adult mortality overcomes the shortage of Coale-Demeny and UN model life tables, among those model life tables methods [ 50 ]. Three-parameter log-quadratic model is designed on this to calculate the life table considering extra old-age mortality parameter with an adjustment of intercept with real census information [ 51 ]. It is so-called developing countries mortality database (DCMD) model which was adopted in the World Population Prospects 2019 since the three-parameter log-quadratic model life table was initially used in those developing countries without the high-quality mortality data [ 52 ]. The basic function of DCMD model is showed below:

This study used adjusted DCMD model to estimate the mortality in Zhejiang from 1990 to 2020 to make it usable for open population conditions. Child mortality ( \({\,}_{5}{q_0}\) ) is the first parameter of DCMD model, and adult mortality ( \({\,}_{{45}}{q_{15}}\) ) is the second parameter to be compared with estimated adult mortality ( \({\,}_{{45}}{\hat {q}_{15}}\) ) from two-parameter log-quadratic model with adjustment factor \(k\) . Specifically, child mortality by gender in consecutive years is estimated by sex ratio of child mortality in China [ 53 ]. Adult mortality in census years is calculated from census life table directly as the register completeness of adults’ death is higher in China [ 53 ]. Moreover, we averaged old-age mortality estimated from two-parameter log-quadratic model and from survival model for midpoint of old-age mortality between censuses (1995, 2005 and 2015) [ 51 ]. We averaged old-age mortality from two-parameter log-quadratic model and from census life table calculations for census years (1990, 2000, 2010 and 2020). The adjusted DCMD model was constructed on the incorporated old-age mortality. After that, the cubic hermite polynomial interpolation approach (pchip package in R) was adopted to estimate adult and old-age mortality from 1990 to 2020 [ 54 ]. The life table for consecutive years was estimated with DCMD model.

After that, Lee-Carter method extended with rotation (LC_ER) (mortcast package in R) was used to forecast the mortality up to 2080 [ 55 ], which provides critical death parameters to assess the LTCI demands in our case area. Since in low mortality countries, mortality decline is decelerating at younger ages and accelerating at older ages [ 56 ], the static assumption of mortality decline of traditional Lee-Carter model would be anomalous in long-term projection. LC_ER is a time-varying Lee-Carter model considering the changes of mortality decline between different age groups when modeling, which was widely recognized and adopted by World Population Prospects 2022 [ 57 , 58 ]. Therefore, potential LTCI demands change caused by changes in old-age mortality decline in long-term projections could be captured by LC_ER. The predicted mean of life expectancy would be set as the medium life expectancy scenario, and the lower and upper 95% predicted interval would be set as the low and high life expectancy scenarios.

Number of severe disabled older adults

LTCI beneficiaries refer to the severe disabled population according to the rules of LTCI in Zhejiang [ 36 ]. The study used the cohort-component projection (CCP) method to forecast the number of older population of Zhejiang from 2020 to 2080 [ 59 ]. The number of age-specific population by sex from Zhejiang population census 2020 was used as the base population of CCP model. Furthermore, the age-specific prevalence rate of disability from CHARLS 2018 was calculated. After that, the number of severe disabled older adults as the LTCI beneficiaries was calculated by multiplying age-specific older population and prevalence rate of disability. The basic project method is as follows:

\({\,}_{{x+1}}P_{x}^{{t+1}}\) represents the population of single age groups with the age of x to x  + 1 at the t  + 1 time. \(\left[ {{L^t}(x+1)/{L^t}(x)} \right]\) represents the survival ratio of age x to x  + 1 at t time. \(N{I^*}\) represents the net migration numbers in the corresponding age group from the t to t +  1 period, from other regions to Zhejiang.

Our estimated mortality will be used in CCP model. Since the total fertility of Zhejiang is lower than that of China, this study assumed that the total fertility of Zhejiang would start at 1.04 in 2020 based on Zhejiang population census [ 60 ]. Then, the future trend of Zhejiang’s total fertility would follow the United Nations’ estimated change rate of total fertility of China from 2020 to 2080 [ 47 ]. For net migration, The Census Survival Ratio Method was used to estimate the migration pattern based on the census data [ 61 ]. As one of the highest net in-migration provinces since 2010, Zhejiang will face the lower net in-migration intensity and be close to migration equilibrium in 2040 [ 62 ]. Based on this, it is assumed that the net migration rate in Zhejiang will experience a linear decrease and realize migration equilibrium by 2045.

Disability is defined as a difficulty in performing at least one of six Activities in Daily Living (ADL) [ 63 ], including bathing, dressing, eating, getting in/out of bed, using the toilet, and controlling urination and defecation in CHARLS. Then, mild disability is defined as having difficulty in 1–2 items of ADL, moderate disability as having difficulty in 3–4 items of ADL, and severe disability as having difficulty in at least 5 items of ADL [ 64 , 65 ]. Based on the discussion on the Disease Expansion, Disease Compression and Dynamic Equilibrium Theory [ 66 ], three different scenarios in changing disability were calculated [ 16 ]: a 0.8% annual decrease for age-specific prevalence rate of disability as the low disability scenario, the constant age-specific prevalence rate of disability as the middle disability scenario, and a 0.8% annual increase for age-specific prevalence rate of disability as the high disability scenario.

Dynamic financing actuarial model of social LTCI schemes

The study built a macro simulation model to further simulate the expenditure, financing and fund balance of LTCI based on the projection of severe disabled older population ( \(DisOP\) ) aged 60 and above and contribution population ( \(CP\) ) of LTCI aged 20 and above. The macro model is showed below:

In Formula (4), \(LTCE\) means LTC expenditures, \(HbdcCost\) , \(IcCost\) , \(HbdmcCost\) and \(NhcCost\) represent the unit cost of home-based daily living care (HBDC), institutional care, home-based daily living & medical care (HBDMC) and nursing hospital care per person per year, respectively. Among them, HBDC means that beneficiaries only receive formal daily living care services at home but without any medical care. HBDMC means that beneficiaries receive both formal daily living care services and professional medical care services at home. The difference of institutional care and nursing hospital care lies in that the former focuses more on daily living care, while the latter specializes in medical care. From 2023 to 2080, the unit cost of each type of LTC services is given an increase of 2.25% annually based on the average increase of healthcare CPI from 2010 to 2020. \(\alpha \) means the percentage of different types of LTC services utilization. Formula (5) describes the dynamic financing model and current balance of LTCI every year. \(premiu{m_{{t_0}}}\) is the fixed amount of premiums of LTCI in our base period. \(\lambda \) is annual growth of the amount of LTCI premiums. Formula (6) shows the accumulated balance of LTC fund which is determined by the current balance and the accumulated balance in previous period. \(\gamma \) is the interest rate of LTCI fund which represents the time value of the LTCI fund. Taking the inflation rate (2.25%) as a reference in the simulation process, we test the minimum value of \(\lambda \) that ensures a consistently positive accumulated balance in the LTCI fund up to 2080 across various disability scenarios.

Parameters of LTCI schemes in Zhejiang Province, China

The policies of LTCI schemes in five pilot cities in Zhejiang are sorted in Additional Table 1  (see Additional file 1 ) [ 41 ]. The LTCI schemes in Jiaxing City are representative among five pilot cities of LTCI in Zhejiang. Firstly, Jiaxing is the first city covering all employees and urban and rural residents equitably with the same benefits and premium since the adoption of LTCI (in 2017), which has navigated the reform of LTCI in Zhejiang. Secondly, LTCI benefits in Jiaxing are at the middle level among the five pilot cities, which is representative of average level in Zhejiang. The maximum benefits of HBDC in Jiaxing are lower than those in Yiwu and Wenzhou, and equal to those in Tonglu and Ningbo. Besides, the maximum benefits of institutional care are also lower than those in Yiwu, but higher than those in Tonglu and Ningbo. Overall, Jiaxing’s LTCI benefits stay average in Zhejiang. Thirdly, LTCI financing criteria in Jiaxing align with Ningbo and Tonglu (90 Chinese Yuan (CNY)/person/year), reflecting the standards across five cities. Therefore, this study adopted Jiaxing’s LTCI criteria as the parameters of LTCI simulations in Zhejiang. The unit costs of HBDC, institutional care, HBDMC and nursing hospital care are set at 1200 CNY/month, 2100 CNY/month, 1680 CNY/month and 1680 CNY/month in 2024 according to LTCI maximum benefits in Jiaxing (see Additional Table 1 , Additional file 1 ) [ 41 ]. The contributory group of LTCI is the group participating in social health insurance, whether retired or not. The LTCI financing parameter \(premiu{m_{{t_0}}}\) is based on a fixed amount of premiums in Jiaxing, of which the standard is 90 CNY/person/year [ 41 ].

Chinese government proposed a model of elderly care named “9073” model: 90% of older people receive home-based care, 7% receive community care and 3% receive institutional care [ 67 ]. “9073” model represents the prospects of China’s elderly care and is therefore suitable for the long-term simulation in this study [ 29 , 62 ]. Specifically, proportion of HBDC ( \({\alpha _{\text{1}}}\) ), institutional care ( \({\alpha _2}\) ), and combination of HBDMC and nursing hospital care ( \({\alpha _3}\) + \({\alpha _4}\) ) are set at 90%, 3% and 7%, respectively. Disabled older people can choose to receive HBDMC at home or receive nursing hospital care at medical institutions when facing medical care needs. It is free to choose the locations for these two LTC services, and it is quite similar to receiving community care in nature, as it also allows the option of receiving services at home or at community centers. Additionally, the LTCI benefits of these two LTC services in Jiaxing are equal. Therefore, we grouped them together when determining the beneficiaries’ choice of LTC services type ( \({\alpha _3}\) + \({\alpha _4}\) ). We set the interest rate of LTCI fund at 2.5% based on current interest rate of 5-year time deposit in China’s banks [ 68 ]. The sources of each parameter for simulation framework of the study are demonstrated in Additional Fig.  1 (see Additional File 1 ).

The mortality pattern and life expectancy of Zhejiang

The estimated mortality of Zhejiang from 1990 to 2020 is demonstrated in Fig.  1 based on adjusted DCMD model. Overall, the mortality for male and female presents a declining trend. Specially, the child mortality had a continued decline during the estimation period, but the adult mortality and old-age mortality had a slight increase between 1990 and 2000, then with a sharp decline between 2000 and 2020 afterwards.

We further predict the life expectancy at birth with 95% confidential interval under the LC_ER model from 2020 to 2080. The estimated and predicted life expectancy is demonstrated in Fig.  2 . Life expectancy of female had a stable increase from 1990 to 2020. While there was a slight decline of life expectancy of male from 1990 to 2000, then there was a rapid increase until 2020. The model results based on historical information show that life expectancy of both female and male will have an upward trend from 2020 to 2080. Besides, the gender difference in life expectancy will remain relatively stable in the future. In 2020, life expectancy was 80.46 years for male, 84.66 years for female. In 2080, the life expectancy will reach 89.39 [86.61, 91.74] years for male, 91.24 [88.90, 93.25] years for female. Besides, the age-specific rates of mortality decline of Zhejiang from 2021 to 2080 estimated by LC_ER are illustrated in Additional Fig.  2 (see Additional File 1 ).

Mortality pattern of Zhejiang in 1990–2020 based on adjusted DCMD model

Estimated and predicted life expectancy of Zhejiang in 1990–2080

The simulation of long-term care demand and expenditures in Zhejiang

Based on CCP method, the study has projected the number of older people and the number of severely disabled older people with different scenarios of disability in Zhejiang from 2020 to 2080 (shown in Table  1 ). It is illustrated that the population aged 60 and above in Zhejiang will firstly expand to around 2060 and then shrink until 2080. The number of older people with disabilities, especially those with severe disability, reflects the long-term care demand from a demographic perspective. We found that the number of older people with severe disability will continue to increase to 2080 under both medium and high disability scenarios. However, the number of older people with different degrees of disability will increase before 2060, and then decline slightly in the following 20 years under the low disability scenario. We also found that the number of severely and moderately disabled older people will be of little difference before 2050, which means that severe and moderate LTC demand is roughly equal.

Besides, the results of LTC demand under the high and low life expectancy scenarios are illustrated in Additional Table  2 and Additional Table  3 (see Additional file 1 ). It can be seen that Zhejiang Province will have a higher LTC demand under the scenario of higher life expectancy. The number of older people with severe disability under 95% upper interval of life expectancy in 2080 is 154 thousands higher than that under the predicted mean of life expectancy. And the number of older people with severe disability under 95% lower interval of life expectancy in 2080 is 169 thousands lower than that under the predicted mean of life expectancy. This result demonstrates the importance of mortality level prediction for assessing LTC demand.

Our study further calculated the LTCI expenditure paid by insurance fund every year from 2020 to 2080 to analyze the future long-term care demand in our case area from a financial perspective. The expenditure from LTCI illustrates an upward trend from 2023 to 2080 (see Fig. 3 ), with the higher price of long-term care services and increasing number of severe disabled older people. The LTCI expenditure is still increasing although there will be a slight decline in severe disabled older people under low disability scenario.

Projection of Long-term care insurance expenditure in Zhejiang, 2024–2080. Notes Results are based on the predicted mean of life expectancy

The simulation of LTCI fund under diverse disability and financing scenarios

The accumulated balance of LTCI fund from 2022 to 2080 is simulated on different dynamic financing growth scenarios in order to test how to make LTCI achieve actuarial balance in the long run. The accumulated balance and current balance of LTCI fund in Zhejiang are shown in Figs.  4 and 5 . When we set the annual premium growth rate at 2.25% which is equal to the average increase of healthcare CPI, there will be a deficit of current balance before 2028. As a result, the accumulated balance will become negative in 2032 under medium disability scenario, under high disability scenario in 2030 and under low disability scenario in 2036. This result shows that LTCI fund can only be sustainable within 12 years if the financing level grows at a low pace from 2024.

Accumulated balance of LTCI fund under different financing and disability scenarios. Notes Results are based on the predicted mean of life expectancy

Current balance of LTCI fund under different financing and disability scenarios. Notes Results are based on the predicted mean of life expectancy

The minimum annual premium growth is further tested to achieve the positive accumulated balance of LTCI fund under various scenarios from 2022 to 2080. We found that when the annual premium growth rate equals to 4.20%, LTCI fund will realize the long-term sustainable under low disability scenario, which means that the 4.20% financing growth standard is effective to make LTCI sustainable at a relatively low premium level under low disability scenario; however, it will still face the risk of the shortage of financing with 4.20% annual premium growth under the medium and high disability scenarios after 2039 and 2033.

Furthermore, the accumulate balance of LTCI fund remains at a moderate surplus and will not face a shortage until 2080 under the medium disability scenario when the annual premium growth rate equals 5.25%. Although the current balance of LTC fund will be negative in 2043 to 2058 under 5.25% annual premium growth (see Fig.  5 ), the accumulated surplus before 2042 and continuous interest will still realize the accumulated surplus of LTCI fund (5.83 billion CNY) in 2058. Overall, the annual premium growth rate at 5.25% is the best parameter choice if the age-specific prevalence rate of disability in Zhejiang Province is projected to remain stable. Finally, LTCI will be sustainable under all disability scenarios when the premium increases by 6.25% per year. However, this level will put a heavy payment burden on the residents, and there will be a large amount of fund redundancy if the disability does not continue to increase.

The simulation of LTCI fund under diverse life expectancy and financing scenarios

The impact of different life expectancy trend on the sustainability of LTCI schemes is further discussed. The simulation results of accumulated balance of LTCI fund under predicted mean, 95% upper confidential interval and lower confidential interval of life expectancy scenarios are demonstrated in Fig.  6 . It is learned that the sustainability of the LTCI fund will face a completely different situation in the long-term because of the difference trends in life expectancy even under the same disability level and financing level. Under the 5.25% annual premium growth rate and medium disability scenario, LTCI fund will become accumulated deficit under 95% upper interval of life expectancy after 2045. However, the LTCI fund will always remain in surplus before 2080 with the predicted mean or lower 95% interval of life expectancy. Therefore, the balance of LTCI fund is sensitive to life expectancy. In addition to affecting LTC expenditures when other conditions are the same, life expectancy is also related the total amount of financing by the number of contributors, thereby influencing the sustainability of LTCI fund.

Current and accumulated balance of LTCI under different life expectancy scenarios. Notes Results are based on the 5.25% annual premium growth rate scenario and medium disability scenario

This study shows two novel contributions to the existing literature. The first contribution is that we have found an important but often overlooked point that LTCI financing is sensitive to the variability of life expectancy in the long-term. In 2080, the 95% upper interval of the life expectancy in Zhejiang Province will be 2.01 years for female (2.35 years for male) higher than the predicted mean, and its cumulative impact will make LTCI unsustainable 35 years in advance. This finding shows that the accurate estimation of life expectancy is critical for assessing the sustainability of social insurance schemes like LTCI [ 69 , 70 ], and also reveals the significance of life expectancy analysis in this study, because health factors can be dynamically monitored through the evaluation and reimbursement records within the LTCI system [ 34 , 71 ], but life expectancy estimation will become difficult due to the lack of timely statistical data. Besides, the study also finds that LTCI financing is also sensitive to the variability of prevalence rate of disability in the long-term. Only 4.20% annual growth of premium can make Zhejiang’s LTCI sustainable under a disability compression assumption. However, the 6.25% annual growth of premium is necessary for Zhejiang’s LTCI sustainability under disability expansion assumption. The results are consistent with some existing research with various disability scenarios [ 28 , 72 ]. The overall incidence of disability will face a growing trend with population aging [ 17 ]. Therefore, proposing health promotion and postponing disability actions to reduce the incidence and duration of severe disability among older people will mitigate the pressure of LTCI funding [ 73 ].

The second contribution is that Zhejiang’s LTCI financing needs to grow at a relative high speed annually (5.25% under the medium scenario) to achieve sustainability in the long-term. It should be noticed that the LTCI financing parameters to achieve short-term and long-term fund equilibrium are different, and it is clear that long-term fund balance is a necessary condition to ensure the sustainability of the system [ 22 , 29 ]. If the accumulated surplus of the LTCI fund in Zhejiang Province before 2050 is used as a criterion for determining sustainability, as many studies have done [ 19 , 74 ], our results indicate that Zhejiang LTCI fund is projected to experience an accumulating deficit for over 20 years after 2050. Like Zhejiang, there are also several pilot cities in China that have adopted the fixed amount of premium model without premium adjustment [ 32 ]. LTCI funds in these regions will run the risk of accumulating deficits in the short term [ 43 ]. China and other countries adopting social LTCI need to adjust the scale of premium in a timely and dynamic manner to cope with the long-term LTCI financing pressure since China’s aging plateau will continue after 2060 [ 47 ].

Our simulation results can also be used as a reference for countries and regions that adopt a fixed percentage of income model of LTCI financing although we focus on the fixed amount model of LTCI financing. The study finds that LTCI premium in Zhejiang needs to increase by 5.25% per year to ensure sustainability to 2080 under the assumption of disability with dynamic equilibrium. However, the growth rate may exceed the income growth rate of some countries in the context of declining global economic growth [ 75 ]. Therefore, even those countries based on a fixed percentage of income model need adjust financing parameters dynamically [ 1 ]. In LTCI fund management, China and other countries can learn from Germany’s experience to deal with the long-term impact of population aging, which has established a demographic reserve fund which saves 0.1% of premium every year for payment in the future [ 25 ].

Reasonable coverage and benefits are also important factors to achieve sustainable LTCI. Like developed countries, the LTCI pilot cities in Zhejiang Province cover all urban and rural residents. However, most of the LTCI pilot cities in China only cover urban employees [ 35 ]. Therefore, the analysis of LTCI in Zhejiang Province in this paper provides implications for other LTCI pilot cities in China to expand the coverage and promote the equity of receiving LTC. Besides, it should be noted that this study only considers the older adults with severe disabilities according to the rules when estimating LTC needs in Zhejiang Province [ 36 ]. Whereas, it is not only the families of severely disabled groups that face the burden of long-term care [ 17 ]. Moderately disabled people in some developed countries and pilot cities in China are also covered by LTCI [ 76 , 77 ]. Even considering only severe disability, our simulation results show that only a high premium growth rate can make the system sustainable in the long run. Therefore, LTCI policymakers need to comprehensively weigh residents’ payment pressure and long-term care benefits, and make a balance between expanding coverage and increasing financing with the aim of protecting the most vulnerable groups.

This study has explored and built a long-term care insurance system that can be a reference for China and other developing countries to provide LTC services for the disabled older adults in the future. The strength of this study is that a more accurate life expectancy estimation based on the DCMD model is adopted when estimating dynamic financing of LTCI. However, this paper still has some limitations. Firstly, the paper only considers the activities of daily living when estimating the prevalence rate of disability of older people in Zhejiang Province, but does not consider cognitive function, perception and communication function due to the lack of data. Secondly, this study only considers the expenditure cost of LTC in the simulation analysis, but does not consider the operating cost of the LTCI system. Thirdly, this study only considers the total amount of financing for LTCI, but does not discuss the financing structure including individual contributions, government subsidies, and pooling funds. Finally, this study focuses only on the case in Zhejiang, but does not simulate the LTCI financing standard for actuarial equilibrium in other LTCI pilot areas in China.

In summary, this study estimates and predicts the mortality rate in Zhejiang Province from 1990 to 2080 through the DCMD model and LC model, and further evaluates the increasing LTC need in Zhejiang Province in the future. The LTCI dynamic financing in Zhejiang Province under different disability scenarios and life expectancy scenarios is simulated on the LTCI expenditure forecast results, and it is found that only by maintaining a relatively high level (5.25% under medium scenario) of premium growth can Zhejiang’s LTCI be sustainable in the long run. Our empirical case in Zhejiang offers implications for developing countries and LTCI pilot areas that lack high-quality mortality information to establish and dynamically optimize LTCI financing. Therefore, policy makers are called upon to assess the sustainability of LTCI from a long-term perspective, and regularly monitor changes in residents’ health and life expectancy to ensure that LTCI fund can meet LTCI expenditure and control the financing burden.

Data availability

In this study, all the data sources are publicly available. The data calculated in this study is available upon request to the corresponding author.

Abbreviations

Long-term care

• Long-term care insurance

China Health and Retirement Longitudinal Study

Center for Disease Control and Prevention

Consumer Price Index

Developing Country Mortality Database

Lee-Carter method extended with rotation

Cohort-component projection

Chinese Yuan

Home-based daily living care

Home-based daily living & medical care

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We would like to thank Professor Xiangming Fang from Zhejiang University, Professor Guangdi Chen from Zhejiang University and Chengxu Long from King’s College London for their constructive advice during the research process of the paper. We would also like to appreciate any comments from the 34th REVES meeting.

This work was supported by the Major Project of Zhejiang Provincial Natural Science Foundation of China (LD21G030001).

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Xu, X., Li, Y. & Mi, H. Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China. BMC Health Serv Res 24 , 469 (2024). https://doi.org/10.1186/s12913-024-10875-7

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Published on 11.4.2024 in Vol 26 (2024)

Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

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, 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.

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A collection of guides and examples for the Gemini API.

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Folders and files, repository files navigation, welcome to the gemini api cookbook.

This is a collection of guides and examples for the Gemini API, including quickstart tutorials for writing prompts and using different features of the API, and examples of things you can build.

Get started with the Gemini API

The Gemini API gives you access to Gemini models created by Google DeepMind . Gemini models are built from the ground up to be multimodal, so you can reason seamlessly across text, images, code, and audio. You can use these to develop a range of applications .

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• Go to Google AI Studio .
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Learn about the capabilities of the Gemini API by checking out the quickstarts for safety , embeddings , function calling , audio , and more.

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The Gemini API is a REST API. You can call the API using a command line tool like curl , or by using one of our official SDKs:

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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.

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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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