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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to present Tables and Figures: An Ultimate Guide

by Peter Ebasone | Jul 10, 2020 | Writing

Introduction

Tables and figures are a great way to communicate your data in an easy and visually appealing way. In this article, you will learn how to present tables and figures that clearly communicate your message, based on recommendations by most Journals and Universities. This article focuses on the practical issues and is suitable both for writing a manuscript for publication and for a thesis/dissertation.

Every Table or Figure MUST be a stand-alone as a sequel to the preceding text. The reader must be able to understand it without further reference. It should be clear, labelled, abbreviations defined, and have a caption and legends which describe it clearly with the aim of making it as simple as possible.

Tables and figures are numbered chronologically in the order in which they appear in the text (for example, Figure 1 is referenced in the text before Figure 2 and so forth). The order of numbering for figures is independent of that for tables (e.g. Table 1., Table 2. AND Figure 1., Figure 2.).

Every table and figure included in your work MUST be referred to in a preceding text, preferably using parenthesis. For example: “We found no association between smoking and coronary artery disease (Table 1).”, “As shown in Table 1,…” or  “Results are shown in Table 1.”

In the text, the word Figure can be abbreviated as “Fig.”, but “Table” should not be abbreviated.

Place each Table or Figure as close as possible to the sentence where you first referred to it in the text. This can be after the paragraph, on the next page or before the next heading.

Reference the author(s) or authority if the table or figure is from or adapted from someone else’s work. Include the words “Adapted from” or “Adapted with permission from” followed by the author’s name and a citation or link at the end of the caption.

The structure of an effective Table

Typically, a table is made up of a caption or title, column titles and a table body (which contains quantitative or qualitative data), it may also include footnotes (Table 1).

1. Caption (Title):

The Caption, also called Legend should clearly inform the reader of what the Table is all about and how it’s organized. Tables are read from top to bottom, so titles are placed above the body of the table and are left-aligned.

2. Column titles:

Column titles are short, descriptive and include the units of analysis.

3. Table body:

Information on tables is usually presented in columns (from top to bottom). When using numerical data with decimals, try to make the decimal points line up whenever possible.

4. Footnote:

You can use them to further explain the data presented in the table. You can use symbols or abbreviations in the table and provide their full meaning in the footnotes.

The structure of an effective Figure

Figures may be graphs, diagrams, photos, drawings, or maps. Pick the simplest Figure which can convey your message most effectively. They should not repeat the information found in the Tables. Like Tables, Figures should be centred horizontally on the page.

1. Figure captions

The Figure caption should be descriptive, concise and comprehensive. Unlike Tables, Figures are read from bottom to up, so captions are placed below the figure and are left-justified (Figure 1).

2. The Body of the Figure or Image:

The reader must be able to read details without straining the eyes, so the Figure should be large enough and have a good image resolution. When using graphs, include axes labels, scale and units.

Formatting of Tables and Figures:

• Generally, Table gridlines should be invisible and only horizontal lines separating the caption, column titles and footnotes from each other should be left visible. Some journals may have a custom design for all Tables and Figures. When you submit your manuscript, these journals can modify it to fit their design in the published version.
• When you have multiple Tables and Figures, be consistent with the design.
• Keep Tables simple, preferably in black and white. Avoid using shouting colours, if you must use colours select a gentle colour like blue. Pick complementary colours that visually match. The bottom line, stick to black and white whenever you can.
• Use the same font for the entire table.
• Keep tables and Figures centred-justified.

The video below shows how to format a table in Microsoft Word.

Tables should be :

• Centred horizontally on the page.
• Numbered chronologically as they appear in the text.
• Referenced in the order they appear in the text.
• Labelled with the table number and descriptive title above the table.
• Labelled with column and/or row labels that describe the data, including units of measurement.
• Set apart from the text itself; the text does not flow around the table.

Figures should be:

• Centred on the page.
• Titled (beneath the figure) with the figure number (“Figure” can be spelt out [“Figure 1.”] or abbreviated [“Fig. 1.”] as long as you are consistent).
• Numbered in the order in which they appear in the text.
• Referenced in the order in which they appear in the text
• Set apart from the text; text should not flow around figures.

Bibliography

• Almost Everything You Wanted to Know About Making Tables and Figures

http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWtablefigs.html

• Figures and Charts by The University of North Carolina at Chapel Hill

https://writingcenter.unc.edu/tips-and-tools/figures-and-charts/

• Figures, Tables and References

http://www.bris.ac.uk/Depts/DeafStudiesTeaching/dissert/Figures,%20Tables%20&%20References.htm

Dr Ebasone is a physician and PhD Candidate at the University of Cape Town. He is the Director of Research Operations at CRENC. He is charged with coordinating the International Epidemiology Databases to Evaluate AIDS (IeDEA) in Cameroon.

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16 comments.

This is a very nice guide. Dr Ebasone explains the presentation of data in very simple and clear terms. Following this can definitely make your data easy to visualise and understand

Thank you Doctor.

Thanks a lot for the great and straight to the point lecture doctor Ebasone! This will go a long way to contribute in my career development.

Thanks Dr ebadone for the clear orientations

You are welcome. Hope this can help you improve your tables and figures in your dissertation.

Thanks Dr for this explicit guide.

You are welcome Sonia.

Straight to the point and gives you most,if not all of what’s needed at the same time ….huge fan

Well-done doc Ebasone. I think it’s an extremely helpful piece.

Concise, precise, easy to read, just Excellent!!!

Thanks Doctor. Straight forward. Great job!

Très instructif Dr , Merci

Merci docteur

Just on point. Thank you Dr.

Kudos Dr. Vanes, Always get chills when I discover African, especially Cameroonians contributing the the world of research.

Very explicit. Thanks Doctor!

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• Manuscript Preparation

How to Use Tables and Figures effectively in Research Papers

• 3 minute read
• 41.1K views

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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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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Figures and tables

Figures and tables (display items) are often the quickest way to  communicate large amounts of complex information that would be complicated to explain in text.

Many readers will only look at your display items without reading the main text of your manuscript. Therefore, ensure your display items can stand alone from the text and communicate clearly your most significant results.

Display items are also important for  attracting readers  to your work. Well designed and attractive display items will hold the interest of readers, compel them to take time to understand a figure and can even entice them to read your full manuscript.

Finally, high-quality display items give your work a  professional appearance . Readers will assume that a professional-looking manuscript contains good quality science. Thus readers may be more likely to trust your results and your interpretation of those results.

When deciding which of your results to present as display items consider the following questions:

• Are there any data that readers might rather see as a display item rather than text?
• Do your figures supplement the text and not just repeat what you have already stated?
• Have you put data into a table that could easily be explained in the text such as simple statistics or p values?

Tables are a concise and effective way to present large amounts of data. You should design them carefully so that you clearly communicate your results to busy researchers.

The following is an example of a well-designed table:

• Clear and concise legend/caption
• Data divided into categories for clarity
• Sufficient spacing between columns and rows
• Units are provided
• Font type and size are legible

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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 .

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Presenting Research Data Effectively Through Tables and Figures

Presenting research data and key findings in an organized, visually attractive, and meaningful manner is a key part of a good research paper. This is particularly important in instances where complex data and information, which cannot be easily communicated through text alone, need to be presented engagingly. The best way to do this is through the use of tables and figures. They help to organize and summarize large amounts of data and present it in an easy-to-understand way.  

Tables are used to present numerical data, while figures are used to display non-numerical data, such as graphs, charts, and diagrams. There are different types of tables and figures, and choosing the appropriate format is essential to present the data effectively. This article provides some insights on how to present research data and findings using tables and figures.  

How to present research data in tables?

When complex data and statistical findings are too unwieldy or difficult to present either in text form or as figures, they can be presented through tables. Tables are best used where exact numerical values need to be analyzed and shared. It also aids in the comparison and contrast of various features or values among the different units. This allows swift and easy identification of patterns in the datasets. While presenting tables in a research paper, it is essential to incorporate certain core elements to ensure that readers are able to draw inferences and conclusions easily and quickly.  

• Title of the table :  The title should be concise and clear and communicate the purpose of the table. Tables must be referenced in the text through table numbers. Both the table number and the title are ideally mentioned just above the table. 
• Body of the table:  A crucial element in preparing the body of a table is to ensure uniformity in terms of units of measurement and the accurate use of decimal places. It is also important to format the table and ensure equal spacing between rows and columns.  
• Keep it simple and accurate:  It is important to ensure that only relevant information is presented in the table. One needs to be cautious not to populate tables with unnecessary information or design elements. Using plain fonts, in italics or bold, and the use of color or border styles help make the table visually appealing. Rows and columns must be labeled clearly and accurately to ensure that there is no ambiguity in analyzing the data presented. 

How to present research data in figures?

Figures are a powerful tool for visually presenting research data and key study findings. Figures are usually used to communicate trends or relationships and general patterns emerging from datasets. They are also used to present research data and complex information in a simpler form. Figures can take various forms like graphs, pie charts, scatter plots, line diagrams, drawings, maps, and photos. Early career researchers need to know how best to present figures in their research papers. The following are some core elements that should be incorporated.  

• Title:  Every figure must have a title that is clear and concise and must summarize the main point of the data being presented. It should be placed just below the figure. The numbering of the figures should be sequential and must correspond to the reference provided in the text. 
• Type of figure:  The type of figure to be used is usually dictated by the kind of information to be conveyed. Researchers need to decide which type of figure will enable readers to understand the information being shared easily. For example, scatter plots can be used to show relationships between two variables, pie charts can be used to illustrate relative proportions, and graphs can be used for the quantitative relationship between variables.  
• Use of Images:  When using figures, care should be taken to ensure that images are of a high resolution – sharp and clear. 
• Labeling:  Ensuring that all parts of the figures and the axes are labeled accurately is crucial if readers are to glean important details quickly. Use standard font sizes and styles. Experts also suggest the inclusion of scale bars in maps. 

Tips for Effectively Presenting Research Data through Tables and Figures

When presenting research data through tables or figures, it’s important to ensure that it is adding value to the text and not merely repeating values. This means taking care of certain vital aspects to ensure that the presentation is uniform, clear, and easy to read. Here are some tips to help you achieve that:

• Make sure that tables or figures add value to the text
• Ensure uniformity in numbering of tables, figures, and values both in the text and in the visual presentation
• Cite the source if tables and figures are used from a different source
• Use appropriate scales when creating tables and figures
• Use logarithmic scales if the data covers a wide range
• Use linear scales if the data is relatively small
• Check publication or style guide instructions of the target journal regarding the presentation of research data and findings, image resolution, presentation style, formatting, and so on
• Remember, tables and figures are only tools to convey information – using too many of them can overwhelm readers

In summary, presenting research data through tables and figures can be an effective way to convey information. However, it’s important to follow these tips to ensure that the presentation is clear and easy to read. By taking care of these vital aspects, researchers can effectively communicate their findings to their intended audience.

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How to present tables and figures in your paper: an analysis

Referring to tables and figures is something you do in every paper. but what phrases can you use and what common errors should you avoid in this post, we show you the most frequent word combinations with the subjects 'table' and 'figure' across a data set of published papers..

Our analysis

This analysis is a follow-up on our recent post about cross-references to tables and figures. In that post, we analyzed sentence starts (for example, 'As seen in table/figure X, …'). In today’s post, we look at subject-verb-object combinations, which might occur anywhere in the sentence.

From a data set of 300 million sentences from published papers, we extracted the most frequent subject-verb-object combinations that had 'figure' or 'table' as the subject. An example of such a  combination is 'figure + show + results'.

The combinations were extracted from their original sentences and lemmatized. This means that the phrases 'Figure 2 shows the results of…' and '... the test result shown in the above figure…' are both counted as 'figure + show + result'. The final data therefore reveal what words are most often combined with the subjects 'table(s)' and figure(s)', irrespective of the form or sentence structure they were used in. For the visualizations, we extracted the 100 most frequent combinations for each subject.

The charts below show the verbs and objects most often used with 'table' and 'figure'. Most striking is that the verb 'show' is used much more often than other verbs. It is also used with a wide range of objects: tables and figures can show almost anything; from a comparison to a distribution.

We also see clear differences between tables and figures. The verbs 'illustrate', 'depict', and 'demonstrate' are more common when referring to figures, while 'list', 'provide', and 'report' are used more for tables.

Other verbs are used commonly with both subjects, but show different usage. For example, tables 'present' values, data, summaries, parameters, statistics, and numbers, while figures 'present' curves, distributions, relationships, images, and plots. This also reflects the general difference in terms of objects, which is as expected: tables mostly give numerical information (value, parameter, data) and figures are primarily visual (curve, distribution, pattern).

Common errors when referring to tables and figure

While the above word combinations may be helpful, there’s more to presenting objects. We looked at the language edits that Writefull most often makes in sentences about tables and figures, and see authors make five common mistakes. Try to avoid these in your writing!

1. As shown by Table 1 > As shown in Table 1

While it is perfectly OK to write a table or figure ‘shows’ or ‘presents’ something, in the passive voice, it may sound a bit funny to write ‘as shown by’ or ‘as presented by’; it makes the object sound almost animate. Using ‘in’ is a safer choice.

2. table/figure 1 > shown in Table/Figure 1

Don’t forget to capitalize the words ‘table’ and ‘figure’ if you are referring to one or more in particular (Figure 2a, Tables 6 and 7). When you mention tables or figures in general (‘the tables in this section show’, ‘we created five figures’), you do not capitalize the words.

3. Table/Figure 3 and 4 > Tables/Figures 3 and 4

Authors often use the singular form where they refer to multiple tables or figures. Remember to use plural in this case.

4. Figure portrays > Figures shows / illustrates / depicts

The word ‘portrays’ is used for (art)works that describe or show something in a certain way, and generally does not work for figures in research papers. Neutral words like ‘show’ work better; or, if you wish to emphasise the visuals of a figure, you can use ‘depict’.

5. The next / previous / above / below table/figure > Table/Figure 1

Authors often use adjectives like above or below to refer to tables or figures in their paper. To avoid ambiguity, it’s best to refer to an object by its number, especially now that articles are mostly read digitally and objects may not always be presented within the text itself.

About the author

Hilde van Zeeland is Chief Applied Linguist at Writefull .

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

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

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

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

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

Demographics Tables

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

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

Participant Demographics

Tables to Illustrate Initial Codes

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

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

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

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

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

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

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

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

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

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

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

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

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

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

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

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

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• 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.

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How To Present Research Data?

Tong seng fah.

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

Aznida Firzah Abdul Aziz

Introduction.

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

SOME GENERAL RULES

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

Consider these two lines:

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

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

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

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

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

The above can be rewritten as:

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

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

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

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

*WC, waist circumference (in cm)

†SBP, systolic blood pressure (in mmHg)

‡DBP, diastolic blood pressure (in mmHg)

£LDL-cholesterol (in mmol/L)

*Odds ratio (95% confidence interval)

†p=0.04

‡p=0.01

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

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

Some general guides to graphic presentation are:

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

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

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

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

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

Contributor Information

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

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

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Article Contents

Introduction, describing the distribution of values, descriptive statistics in text, descriptive statistics in tables, describing loss of participants in a study, comparing baseline characteristics in rcts, conclusions, acknowledgements, conflicts of interest.

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Describing the participants in a study

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R. M. Pickering, Describing the participants in a study, Age and Ageing , Volume 46, Issue 4, July 2017, Pages 576–581, https://doi.org/10.1093/ageing/afx054

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This paper reviews the use of descriptive statistics to describe the participants included in a study. It discusses the practicalities of incorporating statistics in papers for publication in Age and Aging , concisely and in ways that are easy for readers to understand and interpret.

Most papers reporting analysis of clinical data will at some point use statistics to describe the socio-demographic characteristics and medical history of the study participants. An important reason for doing this is to give the reader some idea of the extent to which study findings can be generalised to their own local situation. The production of descriptive statistics is a straightforward matter, most statistical packages producing all the statistics one could possibly desire, and a choice has to be made over which ones to present. These then have to be included in a paper in a manner that is easy for readers to assimilate. There may be constraints on the amount of space available, and it is in any case a good idea to make statistical display as concise as possible. This article reviews the statistics that might be used to describe a sample of older people, and gives tips on how best to do this in a paper for publication in Age and Aging . It builds on a previously published paper [ 1 ].

The values observed in a group of subjects, when measurements of a quantitative characteristic are made, are called the distribution of values. Graphical displays can be used to show the detail of the distribution in a variety of ways, but they take up a considerable amount of space. A precis of two key features of the distribution, its centre and its spread, is usually presented using descriptive statistics. The centre of a distribution can be described by its mean or median, and the spread by its standard deviation (SD), range, or inter-quartile range (IQR). Definitions and properties of these statistics are given in statistical textbooks [ 2 ].

Figure 1 a shows an idealised symmetric distribution for a quantitative variable. The mean might be used here to describe where the centre of the distribution lies and the SD to give an idea of how spread out values are around the centre. SDs are particularly appropriate where a symmetric distribution approximately follows the bell-shaped pattern shown in Figure 1 a which is called the normal distribution. For such a distribution the large majority, 95%, of values observed in a sample will fall between the values two SDs above and below the mean, called the normal range. Presentation of the mean and SD invites the reader to calculate the normal range and think of it as covering most of the distribution of values. Another reason for presenting the SD is that it is required in calculations of sample size for approximately normally distributed outcomes, and can be used by readers in planning future studies. A graphical display of approximately normally distributed real data (age at admission amongst 373 study participants) is shown in Figure 1 c: with relatively small sample size a smooth distribution such as that shown in Figure 1 a cannot be achieved. The mean (82.9) and SD (6.8) of the age distribution lead to the normal range 69.3–96.5 years, which can be seen in Figure 1 c to cover most of the ages in the sample: 14 subjects fall below 69.3 and 7 fall above 96.5, so that the range actually covers 352 (94.4%) of the 373 participants, close to the anticipated 95%. For familiar measurements, such as age, there is additional value in presenting the range, the minimum and maximum values attained. Knowing that the study included people aged between 65 and 101 years is immediately meaningful, whereas the value of the SD is more difficult to interpret.

Idealised and real data distributions. (a) Symmetrical distribution. (b) Skewed distribution. (c) Dotplot (each dot representing one value) of an approximate symmetrical distribution indicating the normal range: age in years at admission ( n = 373). (d) Dotplot (each dot representing one value) of a skewed distribution with outliers emphasised and indicating mean and median: hours in A&E ( n = 348).

When a distribution is skewed (Figure 1 b) just one or two extreme values, ‘outliers’, in one of the tails of the distribution (to the right in Figure 1 b) pull the mean away from the obvious central value. An alternative statistic describing central location is the median, defined as the point with 50% of the sample falling above it and 50% below. Figure 1 d shows the distribution of real data (hours in A&E amongst 348 study participants) following a skewed distribution. A few excessively long A&E stays pull the mean to the higher value of 4.9 h compared to the median of 4.4 h: the effect would be greater with a higher proportion of subjects having long stays. The median is often recommended as the preferred statistic to describe the centre of a skewed distribution, but the mean can be helpful. If the attribute being described takes only a limited number of values, the medians of two groups can take the same value in spite of substantial differences in the tails. In these circumstances, the mean can be sensitive to an overall shift in distribution while the median is not. When a comparison of cost based on length of stay is to be made, presenting means of the skewed distributions facilitates calculation of cost savings per subject by applying unit cost to the difference in means. Figure 1 b suggests that the value with highest frequency might be a useful descriptor of the centre of a distribution. In practice, this can prove awkward: depending on the precision of measurement there may be no value occurring more than once.

It is clear from Figure 1 b that no single number can adequately describe the spread of a skewed distribution because spread is greater in one direction than the other. The range (from 1.7 to 40.3 h in A&E in our skewed example) could be used. Another possibility is the IQR (from 3.5 to 5.4 h in A&E) covering the central 50% of the distribution. The SD may be presented even though a distribution is skewed, and could be useful to readers for approximate power calculations, but the normal range derived from the mean and SD will be misleading. With mean(SD) = 4.9(3.2), the lower limit of the normal range of hours in A&E is the impossible negative value of –1.5 h, while the upper limit of 11.3 h lies well below the extreme values exhibited in Figure 1 d.

Descriptive statistics may be presented in text, for example [ 3 ]:

Participants’ ages ranged from 50 to 87 years ( M  = 66.1, SD = 7.8) with 56% identified as female, 64% married or partnered, 23% reported being retired or not working, 55% had post-secondary and higher education, and <20% reported living alone. Over 60% of the participants identified as NZ European. The mean of net personal annual income was $34,615. The participants reported the diagnosis of an average of 2.63 (±2.07) chronic health conditions, with 50% reported having three or more chronic health conditions.

There are perhaps too many attributes (age, gender, marital status, employment status, educational level, living arrangements, nationality, personal income and number of chronic conditions) being described in the excerpt above: it would be easier to assimilate this information from a table.

Characteristics of subjects at admission and their operations before (1998/99) and after (2000/01) implementation of a care pathway [ 4 ]. Figures are number (% of non-missing values) unless otherwise stated

The distributions of the two quantitative variables in Table 1 are described by mean (SD) and range. The statistics being presented should be stated in the context of the table, here in the left hand column, and could differ across variables. If the same statistics are presented for all the variables in a table they can be indicated in the column headings or title. From the mean (SD) and range in each phase, we can see that the age distribution is reasonably symmetrical because the mean falls close to the centre of the range, and the mean ± 2 SD approach the limits of the range. The distribution of hours in A&E is skewed to the right but has been summarised with the same statistics. We can see that the distribution is skewed because the mean is much closer to the minimum than the maximum, and, if the normal range is calculated, the upper limit does not approach the high values in either phase. For these reasons, the normal range should not be interpreted as covering 95% of values. These conclusions from descriptive statistics alone can be verified in Figure 1 c and d.

A choice arises when describing the distribution of an ordinal variable indicating ordered response categories, such as ambulation score in Table 1 . If the variable takes many distinct values, it can be treated as a quantitative variable and described in terms of centre and spread: ordinal variables often extend from the minimum to maximum possible values and in this case stating the range is not helpful. The meaning of the extremes should be stated in the context of the table to aid interpretation of results. Ordinal variables taking only a few distinct values are better treated as categorical variables and number (%) presented for each category. With only five categories the latter approach was adopted for ambulation score. Display as a categorical variable can be facilitated by combining infrequently occurring adjacent values.

In the original study, 3,182 of 5,719 admissions were screened and 2,286 were eligible. Six hundred and ten patients were not available on the hospital units when the RA [Research Assistant] arrived to complete the CAM [Confusion Assessment Method]; 1,582 patients assented to complete the CAM and 94 patients did not assent; the CAM was not completed for 728 patients because an informant was not available to confirm an acute change and fluctuation in mental status prior to admission or enrolment. The CAM was completed for 854 patients; 375 had delirium; 278 were enroled. Of the 278 enroled patients, 172 were discharged before the follow-up assessment, 73 were still hospitalised, 8 withdrew from the study and 27 died. Of the 172 discharged patients, delirium recovery status was determined for 152, 16 withdrew from the study after discharge and 4 died.

The authors start with the 5,719 admissions and report the numbers lost at successive stages, to arrive at the analysis sample of 152. It may be easier to assimilate the detail of the process from tabular or graphical presentation. The CONSORT guidelines [ 6 ] concerning the reporting of Randomised Controlled Trials (RCTs) recommend that progress of participants through a trial be presented as a flow chart, and an example is shown in Figure 2 . These charts are unequivocally helpful and are now presented in studies other than RCTs.

Recruitment and attrition rates in an RCT of WiiActive exercises in community dwelling older adults [ 7 ].

In addition to loss of participants at each time point as shown in a flow chart, information on specific variables may be missing even though a participant was available at the study point in question. Taking Table 1 as an example, there were 395 and 373 admissions during the 1998/99 and 2000/01 phases, respectively, as stated in the column headings, but the number of participants providing information varies considerably across the characteristics in the table. The reader should be able to establish how many cases contribute to each result, and to this end wherever the number available is lower than the total for the phase, it is stated below the descriptive statistics. For example, ambulation score was only available for 390 of the 395 participants in the 1998/99 phase. The percentages presented for ambulation score were calculated amongst cases where information was available, and this was done for all percentages in the table as indicated in the title. Alternatively, missing values in a categorical variable may be treated as a category in their own right. Where there is a large amount of missing information, this may be the best way of handling the situation with percentages calculated from the total sample size as denominator. Stating the numbers available allows the reader to check this point. Only participants whose operation was delayed by more than 48 h, gave a ‘reason why operation was delayed’ in the table, and from the stated numbers the reader can see that a reason was not given for all delayed cases.

In reports of RCTs, a table describing baseline characteristics in each trial arm demonstrates whether or not randomisation was successful in producing similar groups, as well as addressing the generalisability issue. If there are differences at baseline, comparison of outcome may be confounded. Statistical tests of significance should not be used to decide whether any differences need to be taken into account [ 8 , 9 ]. If the allocation was properly randomised, we know that any differences at baseline must be due to chance. The question facing the researcher is whether or not the magnitude of a difference at baseline is sufficient to confound comparison of outcome, and this depends on the strength of the relationship between the potential confounder and the outcome, as well the baseline difference. A statistical test for baseline differences does not address this question; furthermore, there may be insufficient numbers available to detect quite large baseline differences. Statistics describing baseline characteristics are used to judge whether any differences are large enough to be important. If they are, additional analyses of outcome controlled for characteristics that differ at baseline may be performed. On the other hand, in non-randomised studies, groups are likely to differ, and statistical significance tests can be used to evaluate the evidence that the selection process of patients to each intervention results in different groups. In this situation a primary analysis controlled for many predictors of outcome would probably have been planned, and should be carried out irrespective of any differences, or lack of them, between study groups.

Describing the main features of the distribution of important characteristics of the participants included in a study is the first step in most papers reporting statistical analysis. It is important in establishing the generalisability of research findings, and in the context of comparative studies, flags the need for controlled analysis. Usually space constraints limit the presentation of many descriptive statistics, and in any case, too many statistics can confuse rather than enhance insight. The attrition of subjects during a study should also be described, so that study subjects can be related to the patient base from which they were drawn.

Descriptive statistics are used to describe the participants in a study so that readers can assess the generalisability of study findings to their own clinical practice.

They need to be appropriate to the variable or participant characteristic they aim to describe, and presented in a fashion that is easy for readers to understand.

When many patient characteristics are being described, the detail of the statistics used and number of participants contributing to analysis are best incorporated in tabular presentation.

The author would like to thank Dr Helen Roberts for kindly granting permission to use data from the care pathway study [ 4 ] to produce Figure 1 c and d.

None declared.

Pickering RM . Describing the subjects in a study . Palliat Med 2001 ; 15 : 69 – 75 .

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Yeung P , Breheny M . Using the capability approach to understand the determinants of subjective well-being among community-dwelling older people in New Zealand . Age Aging 2016 ; 45 : 292 – 8 .

Roberts HC , Pickering RM , Onslow E et al.  . The effectiveness of implementing a care pathway for femoral neck fracture in older people: a prospective controlled before and after study . Age Aging 2004 ; 33 : 178 – 84 .

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Schulz KF , Altman DG , Moher D , for the CONSORT Group . CONSORT 2010 statement: updated guidelines for reporting parallel-group randomised trials . BMJ 2010 ; 340 : 698 – 702 .

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• 4 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
• 5 School of Politics and Economic Administration, Guizhou Minzu University, Guiyang, China

Digital economy is being closely integrated with agricultural development and tapping into its unique potential to alleviate agriculture’s carbon emissions To explore the mechanism of how digital economy reduce the agricultural carbon emissions, this paper constructs a systematic evaluation method with extend STIRPAT model and panel data drawn from 29 provinces (or municipalities and autonomous regions) in the Chinese mainland from 2013–2020. The results show that the development of the rural digital economy has a significant negative influence on agricultural CEs, and this result is still valid given robustness tests. Second, the alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. Third, the influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect. This paper not only provide new empirical evidence for understanding nexus between digital economy and agricultural carbon reduction, but also give constructive policy implication to improve agricultural green development.

1 Introduction

Alleviating carbon emission is receiving more and more attention globally ( Ma S. et al., 2022 ). To maintain harmonious coexistence between humans and natures and realize the United Nations’ Sustainable Development Goals, Chinese central government pledged the global stakeholders that the Chinese people will try their best to have CEs peak before 2030 and achieve carbon neutrality before 2060, which demonstrates a strong determination to solve the problem of climate change. Activities of agricultural sector not only release CO 2 but also hold carbon sequestration function, and the CEs and sequestration function make agricultural production activities have function of maintaining the carbon balance in the atmospheric. However, agricultural CEs have obvious spatial heterogeneity ( Charkovska et al., 2019 ). Faced with issues such as global economic instability, rising energy demand, frequent adverse weather conditions, and expanding food demand ( Fahad et al., 2022 ), the Chinese government should attach importance to cutting agricultural CEs. China is a large and longstanding agricultural country with widespread and extensive agricultural production activities. In the traditional agricultural production mode, the overuse of pesticides and chemical fertilizers, land ploughing and irrigation, as well as the problems of low production efficiency and unreasonable resource allocation in the agricultural production process, will directly or indirectly lead to more agricultural CEs and their higher intensity, thereby seriously restricting the development of low-carbon and high-quality agriculture. The 14th Five-Year Plan for National Agricultural Green Development emphasizes building an agricultural industry system with characteristic of green, low-carbon, and circular, while the 2023 Government Work Report further emphasizes the need to continuously improve the ecological environment and achieve low-carbon and sustainable development.

The digital economy plays an important role in promoting the full and balanced development between urban and rural areas, and its development has driven the economic development of agricultural and rural areas ( Zhao et al., 2023 ). In China, the digital transformation of agriculture sector has shown initial results. According to the Information Center of the Ministry of Agriculture and Rural Affairs, the informatization level of national agricultural production in 2020 was 22.5% and the national level of agricultural product quality and safety traceability informatization was 22.1%. In 2021, the online retail sales agricultural production nationwide has reached 2.05 trillion Yuan, with growth rate of 11.3% compared to the level of the previous year. The construction of digital rural areas has been promoted extensively, with 117 digital rural pilot projects established nationwide, nine agricultural IoT demonstration provinces delineated, and 100 digital agriculture pilot projects established. Alongside these tremendous achievements, the digital economy has a positive impact on carbon emissions from agricultural production ( Zhao et al., 2023 ). Thus, the problem is how to realize the coordinated relationship between them. Would the rural digital economy development bring fresh momentum to reducing agricultural CEs? Meanwhile, how can the rural digital economy empower the reduction of agricultural CEs? Exploring these issues has important practical value for the development of the rural digital economy and improving the reduction of agricultural CEs while also contributing to policy enlightenment in terms of achieving the great mission of China’s “Carbon Peak and Carbon Neutrality”.

The main contribution of this paper comparing to the existing literature are as following. First, we use the extend STIRPAT model to explore the influence mechanism of agricultural digital economy on the agricultural carbon emission. Second, the agricultural green production efficiency is used as a proxy for agricultural green technology change, which not only considering the quantity of the agricultural green development, but also capture the quality of agricultural green development. Third, this paper use three dimensions to measure the agricultural digital economy. Digital infrastructure in rural areas, digitalization of agriculture, and rural digital finance).

The rest of this paper is organized as follows. Section 2 is the literature review. The theory base and research hypnosis are showed in Section 3 . Section 4 describes the models and data used in this paper. Section 5 analysis the estimation results. Section 6 gave the conclusion and presents the policy implications.

2 Literature review

So far, the relevant studies relating to CEs focus on the challenges faced by China in realizing its CEs reduction strategy and corresponding countermeasures. Hu (2021) , OuYang (2021) and others have analyzed the severe challenges faced by China in realizing the goals of dual carbon strategy in terms of international and domestic perspectives, respectively. Liu et al. (2021) and others have analyzed the problems that exist in China in the context of carbon neutrality from on the viewpoint of energy structure, and have put forward countermeasures such as energy conservation and efficiency improvement, while accelerating the transformation and further promotion of energy structures. Adopting another approach, some scholars have conducted empirical analysis on the CEs reduction effect of the carbon trading pilot policy implemented by the Chinese government through the synthetic control method ( Li et al., 2021 ; Yang et al., 2021 ), and have argued that China’s carbon trading pilot policy has played a significant role in the reduction of CEs, but there are problems such as insufficient market driving force for low-carbon innovation, poor pilot policy incentives, and regional heterogeneity. At the same time, Chen et al. (2016) have emphasized that increasing CEs reduces green total factor productivity (GTFP) based on studying the relationship between CEs and GTFP and economic development, and Wang et al. (2019) have also reached the same conclusion in relation to GTFP in agriculture economy development.

In addition, many researchers have devoted attention to agricultural CEs and carried out relevant research on the characteristics and calculation of agricultural CEs, agricultural CEs reduction policies, and influencing factors. Jin and other authors (2021) have explored the structural characteristics of China’s agricultural CEs, and drawn the conclusion that agricultural CEs in China have a phased upward trend alongside regional and provincial heterogeneity. In terms of policy research, Zhang et al. (2001) compared different environmental and economic instruments and argued that the environmental tax system has been more advantageous; Zheng et al. (2011) elaborated on a number of low-carbon special plans and proposed relevant recommendations, such as the establishment of a Chinese low-carbon agricultural model. Based on evolutionary game theory, Fan et al. (2011) suggested that government support and intervention can guide agricultural source farmers to choose CEs reduction strategies. In terms of influencing factors, the empirical studies of Xu et al. (2022a) and Xu et al. (2022b) have suggested that agricultural mechanization and the rural finance service have significant preventative effects on agricultural CEs. Furthermore, He et al. (2020) have discussed the status and role of green production efficiency in agriculture in various provinces.

The digital economy, a new engine of high-quality economic growth, has also attracted extensive attention and discussion in the academic community in recent years. On the one hand, there is research on the definition of the digital economy. Li et al. (2021a) characterize the digital economy on macro, meso- and micro-levels, asserting it includes four levels, namely, broad, middle, narrow and narrowest, and explored the mechanism and evolution process involved in data becoming a production factor ( Li et al., 2021b ). On the other hand, researches about digital economy are mainly about the comprehensive effect of digital economy, and they have put forward the argument that the digital economy can reduce environmental pollution ( Deng, 2022 ), while driving high-quality urban development and promoting a specific economic pattern, which aim to coordinate development between regions ( Zhao et al., 2020 ).

Especially since the strategy “Carbon Peak and carbon neutrality” was put forward, the relationship between the digital economy and carbon emission has become an important topic, and academia has also carried out extensive research ( Yu et al., 2022 ). While researchers hold different conclusion on the nexus between digital economy and carbon emissions. Most studies show that the digital economy has improved the environmental situation, and provided impetus for emission reduction, Wang (2022) point out the digital economy is helpful to reducing the carbon emissions. Zhang (2022a) find that the digital economy plays a significant role in carbon emission reduction. They all conduct their research based on China’s urban data. However, some studies hold that the digital economy has a heterogeneous influence on CEs. Some scholars ( Salahuddin et al., 2015 ; Avom et al., 2020 ) believe that, as the core foundation of the digital economy, the development of digital technology will lead to a large amount of power consumption and energy consumption, thereby increasing carbon emissions.

Furthermore, there are many researches focusing on the development of the digital economy in rural areas. According to theoretical analysis, the existing literature mainly pays attention to the mechanisms or development paths of the rural digital economy. Wang et al. (2021) , Yin and others (2020) and others have explored the significance, practice mode and mechanism of the digital economy development in agriculture production and rural regions, and believe that it should be promoted by, respectively, accelerating the construction of rural digital infrastructure, promoting agricultural digitalization, and developing rural e-commerce. Some researches on digital inclusive finance (DIF) have argued that DIF can push the regional convergence of green economic growth while less developed regions experience a more significant convergence effect ( Wang et al., 2022 ).

Many studies have also been carried out focusing on the influence of digital economy on CEs, mainly adopting the empirical analysis method with panel data based on province- or city-level contexts in China, and have found that digital economy growth can significantly alleviate the intensity of CEs ( Xu et al., 2022 ; Guo et al., 2023 ), however, there exist certain regional differences ( Miao et al., 2022 ; Xie, 2022 ).

A few researches have focused on the correlation between digital economy growth and agricultural CEs in China or foreign countries, and these literature mainly concentrate on the introduction of information and communications technology (ICT) into the field of smart agriculture, the promotion of sustainable agriculture, and the reduction of chemical use on the basis of embedding artificial intelligence ( Patrício and Rieder, 2018 ), sensors ( Basnet and Bang, 2018 ), robotics, and remote sensing technologies ( Huang et al., 2018 ) into agricultural modernization processes. ICT, as a main focus of advanced technology trends, can promote comprehensive productivity efficiency, total factor efficiency (TFP) and agricultural sustainability ( Dlodlo and Kalezhi, 2015 ). The prevalence of ICT not only promotes agricultural productivity and TFP, but also improves the progress of sustainable agricultural development. Ma S. Z. et al. (2022) focus on the nexus between the development of the agricultural digital economy and agricultural CEs; their conclusions emphasize that digital economy development inhibits agricultural CEs. In addition, advances in agricultural technologies, the optimization of agricultural industrial structure, and improvements in rural education all significantly inhibit the agricultural CEs in the research area. Adding to the influence factors outlined above, Zhang J. et al. (2022) emphasize that the development of DIF has significantly reduced agricultural CEs. Unlike other countries or regions, China’s agricultural digital economy mostly stresses the digital transformation of rural industrial models ( Wu, 2021 ), agricultural industries ( Zhao MJ. et al., 2022 ; Zhao YL. et al., 2022 ) and the effectiveness of the digital economy ( Xie, 2020 ). These studies all pay attention to the innovative developments in digital agriculture ( Wang et al., 2020 ). Through the systematic review of the literature outlined above, three main shortcomings can be found in the existing research: First, although many researchers have devoted attention to the correlation between the digital economy and CEs, more of them have studied this on urban level, and rarely extended this correlation to the rural development context, hence there is a lack of research that directly and empirically tests the correlations between the rural digital economy and agricultural CEs. Second, when analyzing heterogeneity, most existing studies only conduct sub-sample studies by region, and consider to a lesser extent the role of R&D in leading the high-quality development of the digital economy. Third, the path or mechanisms of the digital economy in rural areas in relation to the reduction of agricultural CEs is unclear, hence this requires further research. Considering the three points mentioned above, this article measures the intensity and amount of agricultural CEs, the progress in agricultural green technology and the development level of rural digital economy at a provincial level in China and tests empirically the nexus between rural digital economy and agricultural CEs. Meanwhile, this study not only examines the regional heterogeneity of the rural digital economy on agricultural CEs, it also analyzes the heterogeneity of this in relation to the science and technology investment level.

3 The mechanism and research hypotheses

The digital economy is an advanced economic mode with data as the important production factor and its development depends on the ability to obtain data. The establishment of a digital infrastructure not only realizes the utilization and transmission of data information, but also improves the efficiency of data circulation, thereby accelerating the process of digital infrastructure construction, the latter having become an indispensable foundation for the promotion of the growth of the digital economy. China has ascribed importance to the construction of digital infrastructure, and since 2018, the Politburo of the Central Committee has repeatedly stressed the need to accelerate the roll out and promotion of new digital infrastructure and its construction. At the same time, the construction of digital infrastructure is an important prerequisite for the integration of the digital and rural economies; whether it is agricultural informatization, agricultural product trading e-commerce, or the rural digital finance development, the prerequisite is it must be a complete rural digital infrastructure construction.

The reports of the China Academy of Information and Communications Technology believe that the definition of the digital economy can be divided into industrial digitization and digital industrialization, whereby industrial digitalization means the output and efficiency improvement brought about by the introduction of ICT into traditional industries. With the empowerment of digital technology, an environmental monitoring system for agricultural pre-production and production can be established, while new formats such as rural e-commerce goods can be formed after production, thereby realizing the transformation of traditional agriculture into a scientifically based and efficient modern model.

The integration of the digital and rural economies has improved the practice model of digital financial services in China’s “San Nong” field. The development of the digital economy has spawned updated financial models while the innovative development of digital finance has continuously added new momentum to the digital economy. The integration of ICT and traditional finance provides the possibility of opening up the farmers’ “last mile”. Furthermore, digital finance enables rural areas to address difficulties in accessing affordable financing at a low cost, fully leveraging the inclusive and the sharing advantages of digital finance, thereby contributing to the rural revitalization strategy while promoting the in-depth and comprehensive growth of the digital economy in today’s China.

Based on these insights, this article mainly explores the effect and mechanism of the rural digital economy growth level (explained from three aspects: rural digital infrastructure construction, agricultural digitalization, and development of the rural digital finance development) on agricultural CEs while also examining the intermediary effect of green technologies progress, which was measured by the agricultural green technological efficiency (see Figure 1 ).

Figure 1 . Model of the impact of the rural digital economy on agricultural CEs.

3.1 Digital infrastructure in rural areas

Digital infrastructure as a foundation for the development of the digital economy plays an important role in realizing agricultural digitalization and rural digital finance. It contributes to promoting the deep development of the digital economy while limiting the digital economy’s CEs. The agricultural CEs reduction effect of rural digital infrastructure construction is mainly manifested in the following two aspects: First, rural digital infrastructure construction can guide residents in rural areas to form green environmental protection concepts. The development of ICT enables rural residents to accelerate their access to the online environment, understand news and public opinion related to environmental pollution, and develop green and environmental protection concepts, thereby promoting the formation of informal environmental regulations on the Internet ( Xu, 2014 ) while helping to alleviate agricultural CEs and reshaping patterns of rural environmental governance. Second, the establishment of perfect rural digital infrastructures can reduce the limitations of geographical space, promote information interconnections and sharing, and help achieve a rational allocation of resources, thereby reducing the energy consumption caused by spatial and time factors in production and life, improving energy efficiency while unleashing CEs reduction effects.

3.2 Digitalization of agriculture

In terms of agricultural production management, the technology of big data analysis can promote the establishment of large-scale and standardized agricultural production bases, realize scientific analysis and reasonable predictions of crop sowing, output and demand, while reducing the imbalance between supply and demand and the waste of resources caused by insufficient and asymmetric information. In addition, through modern information processing technologies such as remote sensing satellites, real-time data collection, monitoring and analysis of agricultural production can be realized, and a scientific environmental monitoring system can be established so as to improve the allocation efficiency of production factor, grasp changes in the ecological environment, accurately measure CEs and trace them in time, thereby promoting effective governance and green development.

Digital technology can also continuously enrich the marketing methods of agricultural products, forming new sales models, i.e., rural e-commerce and live streaming. The continuous popularization of the rural Internet has connected farmers to online consumption cyberspace, realized “point-to-point” transactions, and reduced resource waste and CEs caused by the problems of information asymmetry and high transaction costs in traditional agricultural sales models. In terms of logistics and distribution, low-carbon logistics has become an important future development direction. The Vision 2035 Plan points out that green and low-carbon development should be promoted in the transportation industry while low-carbon freight logistics should also be realized. Aim to achieve development of the low-carbon logistics, relying on digital technology, the logistics and distribution industry is gradually replacing traditional fuel vehicles with clean energy electric vehicles, and accelerating the application of drones in rural areas for logistics distribution to reduce CEs. Regarding the latter, Jingdong drones have been used in some rural areas of Suqian City, Jiangsu Province, and this has already achieved normalized delivery ( Lin et al., 2020 ). Relying on artificial intelligence technology can also promote the intelligence of agricultural product logistics systems, while the establishment of rural smart logistics information platform can optimize distribution routes, achieve resource intensification, continuously save costs, improve efficiency, and deepen the digital economy’s Carbon reduction effect.

3.3 Rural digital finance

The development of rural digital finance has promoted the establishment of rural environmental protection service platforms. Participation in environmental governance and other activities has effectively increased farmers’ enthusiasm for engaging in environmental protection and has helped to improve their sense of social responsibility ( Meng et al., 2022 ; Dong et al., 2023 ). Taking the “Ant Forest” in Alipay’s personal carbon account platform as an example, users collect online energy and plant virtual trees to achieve real afforestation projects in reality, which attracts lots of subscribers to participate in environmental protection actions. In addition, it not only provides a sense of gain for the masses, but also promotes agricultural green development and reduces CEs. Furthermore, the rural environmental protection service platform built by relying on the digital finance development can also analyze the information of platform users through big data technology while rationally allocating resources, thereby reducing agricultural CEs. For example, Alipay’s “garbage sorting and recycling platform” is specially set up for problems such as the low recycling rate of domestic waste, supporting door-to-door collection of waste items so that the resource recycling rate is improved. Digital finance promotes green growth and green technological significantly ( Wu et al., 2022 ; Razzaq and Yang, 2023 ). Mobile payment and online financial services can continuously reduce farmers’ dependence on financial institutions, not only reducing the transaction costs of paper money but also promoting the rational layout of financial business outlets, lowering resource consumption, while uniting both economic and environmental benefits.

In addition, digital finance can effectively compensate for the neglect of traditional finance in rural areas. In the traditional financial environment, farmers have difficulty in financing and own single source of funds, which is not conducive to introduce new agricultural technologies and form the extensive production methods, resulting in more agricultural CEs, hence more serious agricultural pollution problems. The promotion and application of digital finance has broadened the channels of farmers’ capital sources, assisted them to introduce efficient and low-carbon new agricultural technologies, and formed a green agricultural business model, thereby continuously reducing agricultural CEs’ intensity and promoting green agricultural development. Besides, digital finance can also alleviate the misallocation of financial resources and provide more career options for rural residents.

3.4 The progress of agricultural green technology

Generally speaking, a valuable way to achieve high-quality agricultural development is via green agricultural technological change ( Deng et al., 2022 ).

In the existing agricultural economics research, more studies focus on green technological change or environmental technological change using different methods to assess agricultural green technology’s efficiency or that of environmentally friendly technology’s efficiency. According to the existing study on agricultural green technology change (AGTC) of China, the improvement of China’s agricultural productivity is overestimated due to ignoring the influence of environmental factors. Considering the regional heterogeneity of environmental conditions, agricultural technological change in rural China shows an increase trend, while there is a descending trend in the eastern, western, and central regions respectively. The northeast region has experienced an obvious decline in levels of technological change, while technological change without environmental constraints has exhibited a descending trend from eastern to western China ( Jiang et al., 2022 ). He et al. (2021) have identified some important factors affecting agricultural green innovation efficiency, such as the level of agricultural technologies’ diffusion, absorption, implementation, and informatization, the amounts of agricultural extension workers, the average schooling of households, and levels of agricultural mechanization.

To estimate the green efficiency of agricultural production, Korhonen and Luptacik (2004) developed and extended the DEA considering environmental aspects. Existing literature usually through two ways to calculate the green efficiency, one is choosing the environmental factors as the inputs, the other is taking the environmental factors, especially the bad environmental results as bad outputs. The SBM-DEA taking account undesirable outputs is a widely used model to deal with economic and ecological issues ( Liu et al., 2022 ). In this paper, we also chose the SBM-DEA model to estimate the agricultural green production efficiency, taking the carbon emission as the bad output in the DEA model.

In view of the above analysis regarding how the rural digital economy influences agricultural CEs, this article puts forward two research hypothesizes.

Hypothesis 1:. The rural digital economy may reduce the level and intensity of agricultural CEs significantly.

Hypothesis 2:. The rural digital economy may reduce CEs through green technological innovation efficiencies.

4 Research design

4.1 constructing the modelling.

The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model initially proposed by Dietz and Rosa (1994) explores the factors influencing atmospheric emissions, such as socioeconomic, demographic, and technological issues. In the existing literature, the STIRPAT model mainly has been introduced to explore the causes of CEs in different industries, countries or cross-government economic organizations. These researches have concluded that certain factors such as rising population and affluence levels, the growth of urbanization, the structure of economic development and energy consumption as well as the energy mix and related technological issues are all responsible for increasing emissions. The STIRPAT model is in introduced in our study and is extended from a base IPAT model, which was initially proposed by Ehrlich and Holdren (1971) . The advantage of this is that it allows for appropriate decomposition of population, technology, and wealth, while also adding other issues when analyzing environmental impact factors. The expression is:

where I i is the influence in observational unit i from population P , affluence A and technology T . μ i is the random error term, α、η、κ and φ are the parameters.

The fixed-effects model can be used to control regional invisible differentiation, so the endogeneity issue generated by invisible or unchanging is addressed ( Liu et al., 2024 ). Because of the advantages of fixed-effects, here we choose the fixed-effects model.

To effectively avoid the heteroscedasticity of the model, this article converts the terms in Equation 1 into their logarithms as follows:

where i indicates province; t indicates time; λ i indicates provincial fixed effects; and ε i t represents random error terms. β is the coefficient that this article focuses on, and it is expected to be negative.

A E i t stands for the agricultural CEs intensity of the i th province (city) in the t year; A D I G i t represents the comprehensive level of rural digital economy growth in the t year of i th province (city), which is the core explanatory variable of this paper. In York et al. (2003) , the STIRPAT model was introduced to interpret the technology term, which can be composed of more than one variable considering the needs of a given study. In the STIRPAT model, the estimated coefficients of core explanatory variables can be clarified as environmental effect elasticities, which means the percentage change of CEs for one percentage change in digital economy growth.

Thus in our paper we choose certain control variables, including urbanization rate ( U R B A N i t ), level of agricultural mechanization ( M E C H i t ), planting structure ( S T R U i t ), agrochemical input intensity ( C H E M i t ), traffic ( T R A N i t ), rural electricity use ( E L E C i t ) to represent the population, affluence and technology of a given rural area.

Digital agriculture is conducive to the green transformation of agricultural industry, meanwhile, the progress of green technologies can decrease the CEs level of agricultural production. Thus, the influence path of digital agricultural economy on CEs can be expressed as the following models, as shown in (3) to (5) .

Here, Eq. 5 is the total effect model, Eq. 4 is the estimated model of the agricultural digital economy on agricultural green production efficiency, and Eq. 3 is the estimated model that considers both the agricultural digital economy and the mediating mechanism. Where, the mediator variable is the variable GTFP, the green agricultural production efficiency. The coefficient ω 1 in the formula (5) reflects the overall effect of the digital economy on the agricultural CEs, the coefficient λ 2 represents the direct effect of digital economy on the agricultural CEs, and the magnitude of the mediating effect can be determined by ω 1 − λ 2 . If the coefficient ω 1 , λ 2 and ζ 1 are all significant, and λ 2 < ω 1 or the significance of λ 2 is lower than ω 1 , it can be inferred that the mediating effect exists.

4.2 Variable selection

1. Variable to be explained: Agricultural carbon intensity (AE). In this study, agricultural CEs intensity is chosen to measure the level of agricultural CEs in provinces. Agricultural CEs intensity is expressed by the ratio of total agricultural CEs to agricultural added value. The total amounts of agricultural CEs of each province were calculated from six dimensions: agricultural fertilizer, pesticide, farm PE film, agricultural diesel, tilling and irrigation ( Li et al., 2011 ).

The CEs estimation formula is:

where variable E is the total CEs generated by agriculture production. E i stands for the CEs amount of various carbon sources, T i is the amount of i th carbon source, and δ i is the CEs coefficient of i th carbon source. The CEs coefficients of different carbon sources are listed as follows: 0.896 kg kg -1 for agricultural fertilizers, 4.934 kg kg -1 for pesticides, 5.180 kg kg -1 for agricultural film, 0.593 kg kg -1 for agricultural diesel, and 312.600 kg km -2 for ploughing. Agricultural irrigation is 25 kg hm -2 ( Dubey and Lal, 2009 ). After calculating the total agricultural CEs of each province, divide by the agricultural added value of each province to get the agricultural CEs intensity of each province (kg/10,000 yuan). The average values of total agricultural CEs and agricultural CEs’ intensity from 2013–2020 in each province (municipality) are shown in Figure 2 . The top five average agricultural CEs are Henan, Shandong, Heilongjiang, Hebei and Anhui, mainly in the major agricultural provinces. Nearly half of whole country have agricultural carbon emissions exceeding five million tons. From the viewpoint of agricultural CEs’ intensity, the top five areas are Gansu, Jilin, Inner Mongolia, Shanxi and Xinjiang, which produce large volumes of CEs per 10,000 yuan of agricultural added value, all exceeding 180kg, on the one hand because they may be dominated by extensive agricultural production methods, while on the other hand it is also related to the less development level of the agricultural digital economy.

2. Core explanatory variable: Rural Digital Economy Development Index (ADIG). Based on the existing research, this paper selects 10 indicators such as rural Internet penetration rate and agricultural meteorological observation stations from the three aspects of rural digital economy infrastructure construction, agricultural digitalization, and rural digital services, and constructs an evaluation index system for the growth level of the digital economy in rural areas, as shown in Table 1 . The Internet penetration rate in rural areas is assessed using the proportion of rural Internet broadband access users to the rural population in an area, while the number of Taobao villages is taken from the Ali Research Institute’s China Taobao Village Research Report , 1 the DIF coverage breadth index is obtained from the digital inclusive financial index data of Peking University ( Guo et al., 2020 ) measured by account coverage status, including the number of Alipay accounts per 10,000 people, the ratio of Alipay card users, and the average amounts of bank cards bound to an Alipay account. Other metric data is available directly. Among these, the average population served by postal outlets is a negative indicator while the others are positive indicators. In this research, the entropy method is introduced to measure 10 indicators of rural digital economy growth at three dimensions in order to get the rural digital economy development index of each province (city).

Figure 2 . Average level of total agricultural CEs amounts and intensity in each province (city), 2013–2020.

Table 1 . Evaluation index system of rural digital economy development.

The growth level of the rural digital economy in every province (city) in 2013 and 2020 are shown in Figure 3 . It is found that there is significant heterogeneity in the growth level of the rural digital economy between different regions and different years.

3. Mediated variables: Green efficiency agricultural development (GE). In the existing literature, the total factor productivity (TFP) calculated by DEA-Malmquist index is always used to measure the technological change, while using the Malmquist index will sacrifice time information. Thus, this paper uses agricultural green technological efficiency with environmental constraints. In the DEA model of this paper, agricultural added value was defined as the good output, agricultural CEs constitute the bad output, meanwhile the sown area of crops, fixed capital investment and the agricultural workers were set as the input variables.

Figure 3 . Comparison of comprehensive scores of rural digital economy development in 29 provinces (municipalities and districts) in China, 2013–2020.

From Figure 4 , it is obvious that the green agricultural technological efficiency of less than half province is more than 1, which means that more than half of provinces have less efficient green agricultural technologies. Thus, for China, there is still more space to improve the green technologies. In this paper, we use GE to stand for green technological efficiency.

4. Control variables. Due to the complexity of factors influencing the agricultural carbon emission, considering only the impact of the agricultural digital economy on agricultural CEs might lead to bias, and even serious endogeneity issues. Therefore, the following variables are selected to ensure the comprehensiveness and accuracy of empirical analysis. Is complexity and variables: 1) Urbanization rate (URBAN), measured by the proportion of urban population in a region to total population in the same area; 2) The level of agricultural mechanization (MECH), expressed as the total power of agricultural machinery; 3) Planting structure (STRU), expressed as the ratio of the grain sown area to the crop sown area; 4) Agricultural chemical input intensity (CHEM), expressed as the ratio of fertilizer use to the crop sown area; 5) Traffic conditions (TRAN), expressed as the sum of railway operating mileage and highway mileage; 6) Rural electricity consumption (ELEC), expressed in terms of agricultural power generation. The above variables are logarithmic.

Figure 4 . Average green agricultural technological efficiency of 29 provinces, 2013–2020.

Considering the availability of data, the Institute uses all data for 29 provinces (cities) in China from 2013–2020 (excluding Shanghai, Tibet, Taiwan, Hong Kong and Macao), which are derived from the China Statistical Yearbook (2014–2021) 2 and China Rural Statistical Yearbook (2014–2021), the EPS data platform, the Ali Research Institute Report, and the Peking University Digital Inclusive Finance Index (2011–2020). The descriptive results for all variables chosen are shown in Table 2 .

Table 2 . Description of main variables and descriptive statistical analysis.

As shown in Table 2 , except for lnELEC , all other variables have very small fluctuation trends, namely, less than 1.

5 Empirical results and analysis

5.1 estimates of basic regression model.

Firstly, only the core explanatory variable, namely, rural digital economy development composite score (ADIG) is considered, while the mixed-, fixed- and random-effects model is selected, and the F-test is 25.04 and the p -value is 0.0000, and the fixed-effect model should be selected. The Hausmann test shows that χ 2 is 4.77 and the p -value is 0.029, choosing a fixed-effect model. The other control variables were then added, and mixed-, fixed-, and random-effects models were selected, and the F-test was 42.79 and the p -value was 0.0000, and the fixed-effect model should be selected. The Hausmann test showed that χ 2 was 17.29 and the p -value was 0.0156, choosing a fixed-effect model.

Table 3 reports the baseline estimation of the influence effect of the rural digital economy development on the intensity of agricultural CEs. 1) considers only the core explanatory variable, and finds that the rural digital economy growth significantly reduces agricultural CEs intensity at the 1% level. Adding control variables to column 2), it is found that for every 1 unit increase in the growth level of rural digital economy, agricultural CEs intensity decreases by 40.01%, and this negative impact is still significant at the 1% level, thus validating the research hypothesis. For one thing, the development of the rural digital economy accelerates rural residents’ access to the network environment, not only promoting information interconnection and sharing while realizing the rational allocation of resources, but also helps rural residents establish the concept of green consumption and to develop informal network environment regulations, thereby reducing agricultural CEs intensity. And for another, the close combination of digital technology and agriculture helps farmers to, respectively, grasp agricultural production data accurately, improve production efficiency, and effectively reduce agricultural pollution caused by waste of resources. In addition, in an environment marked by the continuous development of rural digital finance, rural residents can broaden financing channels, introduce efficient and low-carbon new agricultural technologies, form a green business model, and promote the transformation of traditional extensive agricultural production methods to intensive ones, thereby realizing the agricultural CEs reduction effect of the rural digital economy.

Table 3 . Baseline regression results.

5.2 Endogeneity test

To alleviate the impact of endogeneity on empirical results, this article also verifies the relationship between agricultural digital economy with a lag of one period and agricultural CEs, the results are in the column 3) in Table 3 . The results of Table 3 have verified the negative impact of agricultural digital economy on agricultural carbon emissions. If the digital economy is an endogenous variable, then the estimation results in this paper are biased. This paper will test the core explanatory variable and each control variable with a lag of one period to overcome the possible reverse causal relationship between contemporaneous variables. The corresponding empirical results are shown in column 4) of Table 3 . The regression results show that the coefficient of the core explanatory variable is −0.4564, with a p -value of 0.047, excluding the possibility that agricultural digital economy is an endogenous variable.

5.3 Robustness test

1. Replace the explanatory variable. In the baseline regression, the logarithmic form of agricultural CEs intensity was used as the explanatory variable. In order to further enhance the robustness of the conclusion, the dependent variable was replaced with the total amounts of agricultural CEs (logarithmic value) for robustness testing, and the results are shown in columns 1) and 2), Table 4 . With the variables to be replaced, the growth of the rural digital economy still has a significant negative impact on agricultural CEs.

2. Exclude part of sampling. Considering substantial heterogeneity in the levels digital economy growth among Chinese provinces, in order to further strength the robustness of the conclusions, the data of two provinces with a digital economy scale of more than 15 trillion yuan and 12 provinces (cities) with a digital economy scale of more than one trillion yuan of 2020 are excluded. The results in column 3) and column 4) of Table 4 show that the development of rural digital economy still has a significant negative impact on agricultural CEs, and this negative impact has become stronger, which may be due to the fact that the digital economy in these provinces is on the rise, with accelerated development speed and greater development potential, so it is easier to reduce agricultural CEs intensity.

Table 4 . Robustness test results.

5.4 Heterogeneity analysis

1. Regional heterogeneity. This study categorizes the samples into four parts: eastern, central, western and northeastern regions for sub-sample regression, and discusses the regional heterogeneous impact of rural digital economy development on agricultural CEs intensity in the four parts. The estimations of regional heterogeneity analysis are shown in Table 5 ; for the eastern and central China, the development of rural digital economy still has a significant negative impact on agricultural CEs intensity and the central China have greater influence than their eastern counterparts while the western China is not significant. Possible explanations are: the eastern region has a good economic development foundation; the digital economy came early; it has a relatively complete rural digital economy infrastructure; and the integration and development of digital technology and agriculture is higher. Meanwhile, the central region is China’s most important agricultural production zone, the central government places greater focus on agricultural input, especially its green agricultural policy and finance support, which may lead to a larger and more significant negative impact on the intensity of agricultural CEs. The development and application of digital technology in the western region started late, that is might the reason why the impact is not significant. But it is not rational to deny its rapid upward phase and the low-carbon development potential of agriculture. The results also show that the coefficient of the rural digital economy development in the northeast region is positive, indicating that the development of the rural digital economy may increase the intensity of agricultural CEs. The development of the digital economy in northeast China is relatively backward, its digital infrastructure is not yet perfect, the coverage of rural digital finance is small, the proportion of secondary industry is large, while the integration of digital technology and agriculture is not complete.

2. Heterogeneity of scientific investment. As the primary productive and innovative force, the increased science and technology investment plays an important supporting role in the reduction of CEs and the growth of the digital economy. On the one hand, advances in science and technology have a direct impact on CEs’ reduction. At present, technological progress is an important driving force for the reduction of CEs and green development, while investment in science and technology helps to promote green technology innovations ( Yang et al., 2019 ; Gu et al., 2022 ), saving production costs, promoting the professional division of labor in various fields, and improving productivity, thereby directly reducing CEs. On the other hand, the progress of science and technology will also promote the progress of digital technologies such as AI and big data, accelerating the development process of industrial digitalization and digital industrialization, thereby promoting the high-quality development of the digital economy, thus further reducing CEs.

Table 5 . Results of regional heterogeneity analysis.

To examine the impact of rural digital economy development on agricultural CEs’ intensity against the background of different scientific and technological inputs, this paper divides 29 provinces (municipalities) into high and low sample groups for heterogeneity analysis based on the average science and technology expenditures in each province (municipality) over 2013–2020, and the results are shown in Table 6 . For the high-tech input group, the development of the rural digital economy still had a significant negative impact on the intensity of agricultural CEs, while the low-tech input group was not significantly negative. This shows that high scientific and technological investment can help promote the green development of agriculture while reducing the intensity of agricultural CEs. The development of the rural digital economy is premised on the completion and improvement of rural digital infrastructure as well as the production, transportation, sales of agricultural products, as well as the supervision, measurement, and traceability of CEs in the whole process of agricultural digitalization, which depends on sound digital infrastructure. High levels of investment in science and technology is conducive to promoting scientific and technological innovation and building a higher quality digital economy infrastructure, thereby providing the realization method and technical guarantee required for the close integration of digital technology and agriculture while promoting the reduction of agricultural CEs. At the same time, the continuous inflow of high-tech labor as a result of government investment in science and technology in the form of subsidies can enhance the level of local innovation, thereby promoting the sustainable and high-quality development of the digital economy and realizing the digital economy’s capacity to reduce CEs. Therefore, local governments should vigorously promote innovation-driven development strategies, increase financial support for science and technology, establish a sound incentive system, and encourage applied research and technological innovation in key fields. In addition, local governments can also increase the weight and proportion of indicators such as scientific and technological investment and their application in the government assessment index system, design a sound talent introduction system, and pay attention to cultivating high-quality talent ( Bian et al., 2020 ), so as to achieve high-quality development and deepen the digital economy’s CEs reduction effects.

Table 6 . Analysis results of scientific and technological inputs’ heterogeneity.

5.5 Mediated effect analysis

From above analysis, it is obvious that the digital economy development has ability to decrease the agriculture CEs intensity and amounts. Further to explore the influence mechanism of the digital economy development on the agriculture CEs, the model 3) and model 4) mentioned in Section 4.1 is run using Stata software. To directly and conveniently compare the mediating effects with the estimates of the basic model of digital economy influence on agricultural CEs’ intensity, the baseline regression results in Table 3 were listed again in column 1), Table 7 . The dependent variable in column 2) is the mediator variable agriculture green efficiency (GE), while the explanatory variable focused on in this paper, agricultural digital economy (ADIG), is significantly positive, consistent with expectations. The dependent variable in column 3) is the agricultural CEs intensity (lnAE). After adding the mediating variable GE, the explanatory variable agricultural digital economy (ADIG) remained significantly negative at the 1% level, while the mediating variable agricultural green efficiency (GE) was significantly negative.

Table 7 . Analysis results of mediating effect.

Comparing the results of Table 3 and Table 7 , the coefficient β = 0.4001 with 1% significance, the coefficient λ 2 = -0.3375 is significant at 1% level, besides the coefficient ζ 1 = 0.9143 is significant at 5% level, the mediating effect is β − λ 2 = -0.0626, and the mediating effect of green agricultural technology exists through the empirically analysis. The coefficient −0.4001 show the total effect, and means when the agricultural digital economy increases one unit, the agricultural CEs will decrease 40.01%. The coefficient −0.3375 is the direct effect of agricultural digital economy with one unit increase on the agricultural CEs reduction is 33.75%. The gap between the total and direct effect is the mediating effect.

6 Discussion

6.1 the construction of agricultural digital economy indicators.

Based on the existing researches, this paper mainly focuses on the three aspects of rural digital economy infrastructure, digitalization of agriculture and rural digital services to construct the indicator of agricultural digital economy. This indicator not only consider the hardware and software agricultural digital economy level, but also digital service level. In Zhao et al. (2023) study, the indicators of digitalization level mainly focus on two aspects of digital economy infrastructure and digital economy service level, while they choose the digitization levels to substitute the rural digitalization index. In our study, we use the agricultural digital economy, which is closely related to the development agriculture and rural areas, and can better reflect the digitization level of agriculture.

6.2 The main effect of agricultural digital economy on agricultural carbon emission

In the existing studies, the level of digitalization can significantly reduce the agricultural carbon emission ( Zhao et al., 2023 ), although their research chose the carbon emission intensity of different agricultural sector, cropping and livestock sector respectively. Even in the city level or other sector of China, most studies also hold the same conclusion as our study, such as Wang et al. (2022) , Zhang W. et al. (2022) . And our study also support the carbon emission reduction effect of digital economy.

6.3 The mediating effect of agricultural digital economy on agricultural carbon emission intensity

Through the mediating effect analysis, it is obvious that the agricultural green production technology is an important mechanism for the development of the digital economy’s capacity to alleviate agricultural CEs. The same results are also evident in the research of Rong et al. (2023) . They emphasize that green technology can effectively suppress agricultural CEs directly, which has significantly negative spatial spillover effects on agricultural CEs in both the short and long term. Except for the influence mechanism, Guo et al. (2023) underline that the role of agricultural green technology in reducing agricultural CEs is particularly dominant in the main grain-producing areas. Zhao et al. (2023) emphasis digitalization can reduce China’s carbon intensity by promoting the agricultural technological input. This can support our influence mechanism of agricultural digital economy on the agricultural carbon emission. Except for the agricultural technology inputs, Zhao et al. (2023) also emphasis the role of human capital level and urbanization rate. In our research we use the agricultural green production efficiency as the mediating variable, which both considering the input and output of agricultural technology, and considering the agricultural green transformation.

6.4 Discussion of heterogeneity in the impact of agricultural digital economy on the agricultural carbon emissions

In Zhao et al. (2023) study, the carbon reduction effect is slightly greater in the central and western regions than that in the eastern regions, which is slightly different with our results, one reason is the different research period, the former chose the 2006–2018, while we chose the 2013–2020, considering the fact China’s digital economy has entered a mature period since the year 2013, thus we choose the 2013 is more rational for agricultural digital economy. Other reasons such as the region and province chosen difference also would lead to the less reduction effect of west region.

7 Conclusion and policy implications

This study uses the data of 29 provinces (cities) in China from 2013–2020 in order to measure the intensity of agricultural CEs as well as the development level of rural digital economy in each province. On this basis, the influence of the development of the rural digital economy on agricultural CEs is empirically estimated. The results show that: 1) the development of the rural digital economy could significantly reduce the intensity of agricultural CEs, a conclusion which is still valid after robustness test such as replacing the explanatory variables and removing some samples. The overall environmental effect is 40.01%, which means the agricultural CEs would decrease 40.01% when the agricultural digital economy increase one unit, the direct effect of digital economy on the agricultural CEs reduction is 33.75%; 2) The alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. 3) The influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect, and the mediating effect is −6.26%, which means the agricultural CEs would decrease 6.26% for one unit agricultural digital economy increase, through mediating effect of the agricultural green technology. Based on the above conclusions, this article puts forward the policy recommendations as follows.

Firstly, continuously improve the level of agricultural digital economy. Including build a complete rural digital economy infrastructure, strength the agricultural digitalization and promote the agricultural finance service. Further promote the full coverage of rural Internet, accelerate the construction of rural 5G networks, realize the in-depth application of agricultural Internet, and establish a smart agricultural technology system. Accelerate information interconnection and sharing, build a unified Big Data platform for agricultural and rural development, and provide solid information infrastructure support for the rural digital economy and agricultural digitalization, so as to accelerate the agricultural CEs reduction effect of the rural digital economy. Besides, increase the accessibility and coverage of agricultural finance is crucial for the green transformation of agricultural industry. The agricultural green development balances the agricultural industry growth and the sustainability of the rural environment.

Secondly, focus on achieving the balanced the rural digital economy development in various regions and better effect of agricultural CEs reduction. On the one hand, it is necessary to strengthen the interconnection and information sharing of various regions while deepening cooperation to promote the establishment of data sharing platforms. On the other hand, it is necessary to raise financial investment in the central, western and northeast regions, implement coordinated and sustainable digital economy development policies in accordance with local conditions, strive to eliminate the digital divide between regions, and bring into play the CEs reduction effect of digital economy. Meanwhile, the central China and western China can also take the initiative to expand foreign cooperation, such as introducing information technology to empower agriculture through free trade zone cooperation, thereby giving full scope to local comparative advantages, hence accelerating the digitization transformation of agriculture ( Guo, 2021 ) while realizing the coordinated the digital economy development between regions.

Thirdly, the government should pay attention to agricultural green development, because the agricultural carbon reduction effect of digital economy needs to be achieved through the mediating variable of agricultural green technology change. Considering the peculiarity of agricultural development, there is a need to increase financial support and incentives for science and technology, set up special funds to encourage agricultural green technology R&D and innovation levels, continuously strengthen the scientific and technological research and technology research capacity of low-carbon technologies, while promoting agriculture’s turn to low-carbon and green development.

8 Limitations

This paper has some shortcomings and can be further analyzed. The assessment of agricultural digital economy has consistently constituted an important issue and challenge in related research. Although this paper assesses the agricultural digital economy by establishing a novel evaluation framework, because of the availability and measurability of data, some regions and some indicators cannot be included in the evaluation system in this paper. Thus, there is still space to further improve the evaluation methodology in the future, to enhance the comprehensiveness and scientific rigor of the research. Furthermore, since the agricultural digitalization and CEs are highly influence by the grassroots government, the role of township-level government played in the agricultural green development and agricultural digital economy is very direct and important. While the related data on the grassroots government is relatively incomplete, which would not provide sufficient evidence for our study. If we would get enough data of township level government, we would conduct more comprehensive research in this area.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HZ: Writing–original draft, Conceptualization, Funding acquisition, Investigation, Resources. KG: Conceptualization, Data curation, Formal Analysis, Methodology, Writing–original draft, Resources. ZL: Conceptualization, Funding acquisition, Investigation, Writing–original draft, Data curation, Formal Analysis, Methodology, Validation. ZJ: Data curation, Formal Analysis, Methodology, Project administration, Resources, Visualization, Writing–original draft. JY: Data curation, Formal Analysis, Software, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Guizhou Planning Office of Philosophy and Social Science grant numbers 22GZQN28.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords: agricultural carbon emissions, agricultural green technology efficiency, rural digital economy, rural digital finance, digitalization of agriculture

Citation: Zhang H, Guo K, Liu Z, Ji Z and Yu J (2024) How has the rural digital economy influenced agricultural carbon emissions? Agricultural green technology change as a mediated variable. Front. Environ. Sci. 12:1372500. doi: 10.3389/fenvs.2024.1372500

Received: 18 January 2024; Accepted: 20 March 2024; Published: 08 April 2024.

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Copyright © 2024 Zhang, Guo, Liu, Ji and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jinna Yu, [email protected]

This article is part of the Research Topic

Low-Carbon Economy and Sustainable Development: Driving Force, Synergistic Mechanism, and Implementation Path