How to Generate a Publication-Ready Table

Some findings are better shown in a table than a chart. When your audience needs to compare exact values across several variables and several groups at once, a well-structured table communicates more precisely and more compactly than any visualisation. The Table function in AnalyZ Solutions generates that output in one step — mixing categorical and continuous variables, computing group comparisons, and producing something ready to place directly in a report.

01What is a table, and when should you use one instead of a chart?

A table organises data into rows and columns so that individual values can be read precisely and compared across multiple dimensions at once. A chart shows shape — a trend, a proportion, a distribution. A table shows substance — the actual numbers, side by side, for every variable and every group.

The choice between them is not a matter of preference but of what the reader needs to do with the information. Use a table when:

• Exact values matter. A chart showing that Group A scored higher than Group B does not tell a reader the actual scores. A table does.
• Multiple variables need to be compared simultaneously. A chart can show one variable clearly. Showing five variables across three groups in a single chart produces visual clutter. A table handles this cleanly.
• The output will be referenced, not just read. Readers who need to extract a specific figure — the dropout rate in the northern district, the average age of female participants — can scan a table row and column. They cannot do that with a chart.
• Mixed variable types are involved. Combining counts and percentages for categorical variables with means and standard deviations for continuous ones is natural in a table. It is awkward in a chart.

Use a chart instead when you want to communicate a single, visual pattern — a trend over time, the dominance of one category, the spread of a distribution. Charts are better for persuasion and narrative; tables are better for reference and precision.

02A worked example

Suppose a programme evaluation survey collects data on 240 participants split across two groups — intervention and control. The following table summarises four variables: gender (categorical), education level (categorical), age (continuous), and satisfaction score (continuous), broken down by group.

03How to do it in AnalyZ Solutions

In AnalyZ Solutions, although you can cross-tabulate variables and generate summary statistics, the Table function allows you to create more complex tables with multiple row and column variables. For this, go to the Descriptive Analysis module and select Table.

1. Select the variable at the top. This is the grouping variable — its distinct values become the column headers. Choose a variable with a small number of categories, typically two to five. Programme arm, gender, location, and round are common choices.
Layout preview. As soon as you select any variables, a layout preview appears below the Generate button. It shows a skeleton of the output — the column variable as header, each row variable with one example sub-row — so you can confirm the structure is correct before generating. The preview is replaced by the real table when you click Generate table. If you change your variable selection after generating, the preview reappears automatically.
2. Select variables on the side. These are the row variables — the variables you want to describe. Select as many as you need. You can mix categorical and continuous variables freely. Use the search box to find variables by name or label.
3. Configure the options. Choose whether to show a Total N column, whether to include p-values, and whether to show counts alongside percentages. For categorical row variables, choose whether percentages are column %, row %, or total %.
How Table differs from Cross-Tabulate and Summary Statistics. Cross-Tabulate works with two variables of the same type and produces a single two-way frequency table. Summary Statistics works with numeric variables only and produces one set of statistics at a time. The Table function combines any number of variables — categorical and continuous together — into a single output, broken down by one grouping variable. That is the combination neither of the other functions can produce.
4. Apply a subsample filter (Optional) Apply appropriate filters to restrict the table to a subset of rows — for example, only a particular region or programme site.
5. Click Generate table. The table appears immediately below the controls.
6. Export when ready. Click Export Word for a formatted document with bold variable name rows, indented sub-category rows, and a footnote explaining the tests used — designed to paste directly into a report. CSV and Excel are also available. Use Add to AnalyZense or Add to Canvas to include the table in a project or data story.

03Common mistakes to watch for

• Choosing a column variable with too many categories. A grouping variable with eight categories produces a very wide, hard-to-read table. Two to five groups is the practical range for most reports.
• A variable appearing in the wrong format. If a numeric variable is showing sub-rows instead of Mean (SD), or a categorical variable is showing Mean (SD) instead of sub-rows, its type is set incorrectly. Adjust it in Data Management or the Column Inspector in Data View and regenerate.
• Including variables with high missingness. Counts in the table reflect only respondents who answered. Note significant non-response in a table footnote so readers are not misled by smaller Ns than expected.
• Interpreting a significant p-value as a large difference. In large samples, even trivial differences produce p-values below 0.05. Always read the actual cell values — the counts, percentages, or means — alongside the p-value column.
• Wrong percentage direction. Column % shows what proportion of each group falls into each category — the right choice when you want to describe each group's composition. Row % answers a different question: what proportion of a category is in each group.