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Research Table Builder

Choose a table, paste your data or results, and get a formatted table with its caption and notes, ready for your thesis or article. Tables from raw data are calculated for you; project tables are filled from your research details, so nothing is typed twice.

Free, no accountNew

Your project

Optional. Enter what you know about your research to see the tables it needs, and to fill tables built from the project.

Add your project details

One per line.

One variable per line. Put its indicators after a colon, separated by semicolons, such as “wellbeing: life satisfaction; positive mood”.

Hypotheses

Until the tools share a saved project, hypotheses can be drafted from your variables with the Hypothesis Builder's rules.

Research onion choices

A plan from the Sample Size Calculator, using Cochran's formula at 95% confidence.

Table type

Describing the sample and data. For each numeric variable: the number of responses, mean, standard deviation, minimum, maximum, skewness and kurtosis. Paste raw data, one row per participant; the builder calculates the statistics.

Best for: Summarising the study variables before testing hypotheses.

Your data

Example data is fictional, for trying the builder.

Layout: One row per participant and one numeric column per variable, with names in the first row.

Caption and notes

Leave blank to use the suggested title. Use title case for APA.

Added after any note the builder writes, such as the sample size.

Where the table's content comes from, if it isn't your own data, such as “Adapted from …”.

One per line; they are lettered a, b, c in order. Mark cells in your data with ^a, ^b and so on.

Style and layout

Style

The table number in bold, the title in italic title case on the next line, both above the table. Horizontal rules only, no vertical lines. Notes start with “Note.” in italics.

For statistics. Counts are whole numbers, percentages take one decimal and p-values three.

Borders
Orientation
More options for this table
Cell padding
Text alignment
Number alignment
Bold column headings
Shade alternate rows
Repeat headings on each page

Word repeats the heading rows; the PDF also marks the caption “(continued)”.

Your table

Choose a table and add its data to see it here.

Table types

Every table the builder makes: what it shows, when to use it, and what to paste. Formatting follows each style's general guidance; references will be added after academic review.

Front matter

Table of contents

Chapter and section headings with their page numbers, indented by level.

Best for
The contents page of a thesis, dissertation or report.
Content
Type or paste the entries, one per row.
Layout
One heading per row: the heading (numbered like 1.2 to set its level), then its page number.
Example input
Heading,Page
1 Introduction,1
1.1 Background,2
1.2 Research questions,5
2 Literature review,7
2.1 Sleep and screen use,8
3 Method,15

List of tables

Every table's number, title and page, numbered automatically when numbers are left out.

Best for
The list of tables after the contents page.
Content
Type or paste the entries, one per row.
Layout
One table per row: its title, then its page. An optional first column gives its number.
Example input
Title,Page
Demographic Profile of Participants,32
Descriptive Statistics for Study Variables,34
Correlations Among Study Variables,36

List of figures

Every figure's number, title and page, numbered automatically when numbers are left out.

Best for
The list of figures after the list of tables.
Content
Type or paste the entries, one per row.
Layout
One figure per row: its title, then its page. An optional first column gives its number.
Example input
Title,Page
Conceptual Framework,12
Research Onion,18
Screen Time and Sleep Quality,37

Describing the sample and data

Demographic profile

For each characteristic, such as gender or age group, the number and percentage of participants in each category.

Best for
Describing who took part, usually the first table of the results.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant and one column per characteristic, with names in the first row.
Example input
Gender,Age group,Faculty
Female,18–24,Arts
Male,18–24,Science
Female,25–34,Science
Female,18–24,Business
Male,25–34,Arts
Female,18–24,Science
Male,35 or over,Business
Female,25–34,Arts
Male,18–24,Science
Female,18–24,Business

Frequency distribution

How often each value or category occurs, with percentages and cumulative percentages. Many numeric values are grouped into intervals.

Best for
Showing the distribution of one variable in detail.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One column of responses, one per participant, with its name in the first row.
Example input
Hours of sleep
6
7
7
8
6
5
7
8
9
7
6
7

Percentage distribution

The percentage of responses in each category, for one or several items that share the same categories, such as Likert items.

Best for
Summarising responses to rating items or several yes/no questions side by side.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant and one column per item, with the item names in the first row.
Example input
I feel prepared for exams,The marking criteria are clear
Agree,Agree
Strongly agree,Neutral
Neutral,Disagree
Agree,Agree
Disagree,Agree
Agree,Strongly agree
Strongly agree,Agree
Neutral,Neutral

Cross-tabulation

Counts for every combination of two categorical variables, with percentages within rows, columns or the whole sample.

Best for
Showing how one categorical variable is distributed across the groups of another.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant: the row variable in the first column, the column variable in the second.
Example input
Faculty,Uses a sleep app
Arts,Yes
Arts,No
Science,Yes
Science,Yes
Business,No
Arts,Yes
Science,No
Business,No
Business,Yes
Science,Yes

Descriptive statistics

For each numeric variable: the number of responses, mean, standard deviation, minimum, maximum, skewness and kurtosis.

Best for
Summarising the study variables before testing hypotheses.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant and one numeric column per variable, with names in the first row.
Example input
Screen time,Sleep quality,Wellbeing
2.5,7.1,3.8
4,6.2,3.1
1.5,8.0,4.2
3,6.9,3.6
5,5.8,2.9
2,7.4,4.0
3.5,6.5,3.3
1,8.3,4.5

Checking the measures

Reliability (Cronbach's alpha)

Cronbach's alpha for each scale, calculated from the item responses, with optional item-total correlations and alpha if each item is deleted.

Best for
Reporting the internal consistency of multi-item scales.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant and one numeric column per item. Items are grouped into scales by name: SQ1, SQ2 and SQ3 form “SQ”, or write “Sleep quality: SQ1”.
Example input
SQ1,SQ2,SQ3,WB1,WB2,WB3
4,4,5,3,3,4
2,3,2,4,4,4
5,4,5,2,3,2
3,3,4,4,5,4
1,2,1,5,4,5
4,5,4,3,3,3
2,2,3,4,4,5
5,5,4,2,2,3

Validity (CR and AVE)

Standardised loadings for each item, with composite reliability and average variance extracted calculated for each construct.

Best for
Reporting convergent validity after a confirmatory factor analysis or PLS-SEM.
Content
Paste results from your statistics software, with their column names.
Layout
One row per item: the construct, the item, then its standardised loading.
Example input
Construct,Item,Loading
Sleep quality,SQ1,0.82
Sleep quality,SQ2,0.79
Sleep quality,SQ3,0.85
Wellbeing,WB1,0.74
Wellbeing,WB2,0.81
Wellbeing,WB3,0.77

Factor analysis

Factor loadings for each item, grouped by the factor each loads on most, with small loadings suppressed and eigenvalues and variance explained below.

Best for
Reporting an exploratory factor analysis, such as a rotated component matrix.
Content
Paste results from your statistics software, with their column names.
Layout
One row per item: the item, then its loading on each factor. Optional rows named “Eigenvalue” and “% of variance” go at the end.
Example input
Item,Factor 1,Factor 2
SQ1,0.81,0.12
SQ2,0.77,0.20
SQ3,0.84,0.09
WB1,0.15,0.72
WB2,0.22,0.80
WB3,0.10,0.75
Eigenvalue,2.41,1.87
% of variance,40.2,31.1

Relationships and tests

Correlation matrix

Means, standard deviations and correlations between every pair of numeric variables, with significance marked.

Best for
Showing how the study variables relate before regression or modelling.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant and one numeric column per variable, with names in the first row.
Example input
Screen time,Sleep quality,Wellbeing
2.5,6.4,3.9
4,6.8,3.0
1.5,7.2,3.6
3,5.9,3.8
5,6.1,2.7
2,7.9,3.4
3.5,5.6,3.9
1,7.0,4.4
4.5,6.5,3.1
2,6.3,4.1
3,7.4,3.2
1.5,6.0,4.0

Regression table

Coefficients for each predictor with the model's R², adjusted R² and F test in the note.

Best for
Reporting a multiple regression in one compact table.
Content
Paste results from your statistics software, with their column names.
Layout
One row per predictor: the predictor, then B, SE, β, t and p. Optional rows named R², Adjusted R² and F (with its two degrees of freedom) give the model fit.
Example input
Predictor,B,SE,β,t,p
(Constant),8.95,0.41,,21.83,0.000
Screen time,-0.52,0.09,-0.48,-5.78,0.000
Caffeine,-0.21,0.08,-0.22,-2.63,0.009
R²,0.34
Adjusted R²,0.33
F,33.45,2,197

Model summary

Fit statistics for one or more regression models, such as R, R², adjusted R², R² change and the F change test.

Best for
Comparing the steps of a hierarchical regression.
Content
Paste results from your statistics software, with their column names.
Layout
One row per model: its name, then any of R, R², adjusted R², SE, ΔR², F change, df1, df2, p and Durbin–Watson, named in the first row.
Example input
Model,R,R²,Adjusted R²,SE,ΔR²,F change,df1,df2,p
1,0.41,0.17,0.16,1.12,0.17,40.33,1,198,0.000
2,0.58,0.34,0.33,1.00,0.17,50.71,1,197,0.000

Coefficient table

Unstandardised and standardised coefficients with standard errors, t, p, confidence intervals and collinearity statistics when given.

Best for
Reporting regression coefficients in full, as software prints them.
Content
Paste results from your statistics software, with their column names.
Layout
One row per predictor: the predictor, then any of B, SE, β, t, p, the confidence interval's lower and upper bounds, tolerance and VIF, named in the first row. A row with only a name, such as “Step 2”, starts a group.
Example input
Predictor,B,SE,β,t,p,Lower,Upper,VIF
(Constant),8.95,0.41,,21.83,0.000,8.14,9.76,
Screen time,-0.52,0.09,-0.48,-5.78,0.000,-0.70,-0.34,1.12
Caffeine,-0.21,0.08,-0.22,-2.63,0.009,-0.37,-0.05,1.12

ANOVA table

Sums of squares, degrees of freedom, mean squares, F, p and partial eta squared for each source. Missing mean squares, F, p and effect sizes are calculated.

Best for
Reporting an analysis of variance.
Content
Paste results from your statistics software, with their column names.
Layout
One row per source: its name, then SS and df. Name the error row “Within groups”, “Error” or “Residual”; a “Total” row is optional.
Example input
Source,SS,df
Between groups,24.60,2
Within groups,118.20,87
Total,142.80,89

Chi-square table

Counts and percentages of each characteristic across groups, with the chi-square test of association, its degrees of freedom, p and Cramér's V.

Best for
Testing whether categorical characteristics differ between groups.
Content
Paste raw data, one row per participant; the builder calculates the statistics.
Layout
One row per participant: the grouping variable in the first column, then one column per characteristic to test.
Example input
Group,Gender,Uses a sleep app
Intervention,Female,Yes
Control,Male,No
Intervention,Female,Yes
Control,Female,No
Intervention,Male,Yes
Control,Female,Yes
Intervention,Male,No
Control,Male,No
Intervention,Female,Yes
Control,Female,No
Intervention,Female,Yes
Control,Male,Yes

Research design

Hypothesis summary

Each hypothesis from your project, the analysis planned to test it and, once results are entered, whether it was supported.

Best for
Summarising hypothesis tests at the end of the results chapter.
Content
Built from your project details; there is nothing to paste.
Layout
Optional: one row per hypothesis with its label and p-value, such as “H1,0.003”, to fill in the decision.
Example input
Hypothesis,p
H1,0.003
H2,0.214

Variable operationalisation

Each variable with its role, conceptual and operational definitions, indicators, measurement level and scale, from your project.

Best for
The methodology chapter, showing how each concept is measured.
Content
Built from your project details; there is nothing to paste.

Measurement scale

Each construct with its number of items, response scale, measurement level and source, from your project and questionnaire.

Best for
Describing the instruments in the methodology chapter.
Content
Built from your project details; there is nothing to paste.

Questionnaire summary

Each questionnaire section with its questions, question types and the variables it measures.

Best for
Describing the structure of a questionnaire.
Content
Built from your project details; there is nothing to paste.

Sample size summary

The population, sampling technique and every value used to calculate the sample size, from your project.

Best for
Justifying the sample in the methodology chapter.
Content
Built from your project details; there is nothing to paste.

Research timeline

Each activity with the months it runs, marked in a grid of months.

Best for
A proposal's work plan or Gantt chart.
Content
Type or paste the entries, one per row.
Layout
One activity per row: the activity, its first month, then its last month, as numbers from 1.
Example input
Activity,Start,End
Literature review,1,3
Ethics approval,2,3
Data collection,4,6
Data analysis,6,8
Writing up,7,10

Other tables

Reference coding

The studies in a literature review or systematic review, with the details coded for each.

Best for
Summarising reviewed studies in a literature review or an appendix.
Content
Type or paste the entries, one per row.
Layout
One study per row, with the column names in the first row, such as Study, Design, Sample, Findings and Theme.
Example input
Study,Design,Sample,Findings,Theme
Study A (2021),Survey,312 students,Longer screen time before bed linked to poorer sleep,Screen use
Study B (2022),Experiment,48 adults,Blue-light filters had no effect on sleep onset,Interventions
Study C (2023),Longitudinal,1 204 adolescents,Sleep problems predicted later wellbeing,Wellbeing

Appendix table

Any table for an appendix, numbered with the appendix letter, such as Table A1.

Best for
Supplementary results or raw summaries placed in an appendix.
Content
Type or paste the entries, one per row.
Layout
Any table, with the column names in the first row.
Example input
Item,Wording,Source
SQ1,I fall asleep easily,Adapted
SQ2,I wake feeling rested,Adapted
SQ3,I sleep through the night,New

Custom table

Any table you paste, formatted in your chosen style. Numbers are rounded to the decimals you choose.

Best for
Results from other software, or anything the other tables don't cover.
Content
Type or paste the entries, one per row.
Layout
Any table, with the column names in the first row. A row with only its first cell filled starts a group.
Example input
Group,n,M,SD
Intervention,45,6.82,1.10
Control,43,6.21,1.24

What should I do next?

Next in the research journey: statistics.

  • Statistical Test Finder

    Helps you identify statistical tests that may suit your research question and data, and explains why each may fit.

  • Statistical Assumption Checker

    Explains which assumptions to check before each statistical analysis, how to check them, the usual thresholds, and what to do if they fail.

  • Power Analysis

    Plans the sample size a study needs, or the power it will have, for seven common designs, with every assumption stated.

  • How to choose a statistical test

    A practical, step-by-step guide to matching a research question, variables and study design with statistical tests, assumptions and interpretation.

How the builder works

  • Add your project details if you like. The builder suggests the tables your project needs, and fills the hypothesis summary, variable operationalisation, measurement scales, questionnaire summary and sample size tables from them.
  • Choose a table type. For tables summarised from data, paste one row per participant: frequencies, percentages, descriptive statistics, correlations, Cronbach's alpha and chi-square tests are calculated for you.
  • For results from statistics software, such as regression or ANOVA output, paste them with their column names; the builder recognises the names software prints.
  • Write the title and notes, choose a style, and adjust fonts, borders, alignment, decimals and orientation. Captions, numbering and notes follow the style.
  • Copy the table straight into Word, or download it as a Word document, PDF, HTML, Markdown, CSV or an Excel-ready file.

What this tool can't do

  • Tables from raw data are calculated here: frequencies, percentages, descriptive statistics, correlations with their p-values, Cronbach's alpha and chi-square tests. Regression, ANOVA and factor analysis results must come from your statistics software; the builder formats them and fills in only values that follow directly, such as mean squares, F and p.
  • Style presets follow each manual's general guidance for tables. They don't guarantee compliance with a particular edition, publisher or university; check your own guide, and use the custom style to match it.
  • Harvard is a family of institutional guides, not one manual, so its preset follows the most common pattern.
  • p-values use standard approximations to the t, F and chi-square distributions, accurate well beyond the three decimals reported. Spearman's p uses the t approximation, which is less exact for very small samples.
  • Word continues long tables with their header rows repeated but can't add “(continued)” to the caption; the PDF does.
  • PDF export uses the standard Times and Helvetica fonts, with Greek letters from the Symbol font. Characters outside these fonts appear as “?”; Arial, Calibri and Georgia are shown in the nearest standard font.
  • Project tables use what the project details record. Anything missing appears in square brackets for you to fill in; nothing is invented.
  • Everything is calculated in your browser; no data leaves your device.

Awaiting review

  • Style review: each preset against the current APA, IEEE, Chicago and MLA manuals, and common Harvard guides.
  • Statistical review: the calculations, notes and recommendations, by a statistician.
  • Accessibility review: the table markup and exported documents with screen reader users.
  • References: sources for the formatting rules and formulas, after academic review.

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