This article explains how to interpret the information shown in heatmaps, visuals that display information from matrix questions. Matrix questions collect ordinal data, meaning that the responses have an inherent order to them - for example a scale from "Strongly disagree" to "Strongly agree."

In the leftmost part of a heatmap, you'll find the individual questions that have been asked. These questions correspond to different prompts asked within matrix questions and may be clustered under distinct headings.

On the heatmap, within cells, the larger numbers indicate aggregated values for a specific question prompt and segment of data. These values may represent average scores or percentages (more on this here).

The numbers in parentheses represent response counts, or how many individual submissions are included in each given average or percentage.

The color that displays is automatically generated based on the dataset, with higher score distributions corresponding to darker shades.

Hover over a cell to see a detailed breakdown of the submissions included in the cell's average or percentage score. This summary includes the identifier of the data segment (i.e. "All," "September 2024," etc.), the rubric used by the question, how many and what percentage of responses fall in each rubric category, and the response count.

Click on any given cell to "Drill in" for more detail. The drill-in view shows a table of individual responses for each data segment.

Heatmap data can be grouped by time, staff attributes, or other parameters. When data are grouped in a heatmap, each grouping appears in a separate column with a header delineating the segment of data. In the example below, data are grouped by Month.

Depending on how a heatmap is configured, the large number in each heatmap cell may represent an average score or a percentage of responses. To see exactly which scale is used and how the number is calculated, hover your mouse on any cell.

KickUp recommends this view for heatmaps because it's more statistically sound and it makes it easier to quickly understand how much of a data segment is clustered in the upper range of the rubric.

The Top % view displays the percentage of responses in each cell that fall into the highest category or categories of a given scale. The logic differs depending on the number of points in the scale:

For example, consider a matrix question that uses a 5-point agreement scale with options of "Strongly disagree," "Disagree," "Neutral," "Agree," and "Strongly agree." In the Top % view, the percentage shown in each cell represents the proportion of respondents who responded "Agree" or "Strongly agree."

With the average view, each option on the matrix question rubric is translated to a corresponding numerical response value (e.g. 1, 2, 3, 4, 5) and the number shown is the average of all of the response values.

For example, if a heatmap displays data using a 5-point agreement scale where:

If a cell includes four data submissions whose values are "Strongly disagree," "Neutral," "Neutral," and "Strongly agree," the average shown would be 3.0. This is based on the numerical average of the submissions: (1 + 3 + 3 + 5) / 4 = 3.0​