How to Analyze Click Test Metrics in Stand-Alone Studies

In an earlier article, we reviewed when and why to use click testing. Click testing involves presenting images to participants and tracking where they click based on tasks participants are asked to complete. It’s typically administered using a tool like the MUiQ? platform.

As we cover in our short course, click testing tends to be used in the design and release phases of product development and generates mostly quantitative data.?

Our earlier analyses also showed how click testing provides a reasonable approximation for how people would click on a live website or live product pages (especially when the live web page doesn’t contain dynamic elements).?Click testing can be thought of as a specialized type of usability testing (defined by the ISO specification of usability). Therefore, essential click testing metrics, like usability metrics, can be classified as:

??? ?Effectiveness (e.g., location, success rate)

??? ?Efficiency (e.g., completion time, number of clicks)

??? ?Perception/satisfaction (e.g., SEQ, confidence, preference).?

These metrics provide quantitative support for more visually interpreted representations such as click maps and heat maps. Software like the MUiQ platform makes it relatively straightforward to see where people click or don’t click on images.?

Beyond just eyeballing the data, how do you correctly analyze click testing metrics? ?The answer depends on the study setup and research questions. In this article, we’ll walk through the steps to analyze the data you’ve collected for common research questions addressed with click testing.?

Read the full article on MeasuringU's Blog

Summary

The key points from this discussion of analysis of click test metrics in stand-alone studies are:

• Click testing is a specialized type of usability testing that can produce numerous metrics that can be classified as measures of effectiveness, efficiency, and perception/satisfaction.
• Start with your research question (e.g., “How many people have a successful first click?”) and operationalize it into a percentage or mean.
• Compute a confidence interval around the percentage or mean. The confidence interval provides the best estimate of what the percentage or mean would be if you could somehow measure all your users.
• For percentages, compute the adjusted-Wald binomial confidence intervals.
• For means, use the t-confidence interval (for time data, use a log transformation).
• An advantage of using the MUiQ platform for click testing is the automatic generation of appropriate confidence intervals for many research questions.

Read the full article on MeasuringU's Blog

Click Testing in MUiQ

MUiQ supports Click Testing as an integrated task type.

Click?Test Setup - Upload images of your UI and define success/fail hotspots to determine task success. Set optional min/max required number of clicks and task timeouts.

Click Test Participant UI - Participant experience for the integrated?click?test includes a task description box at the top of the screen and optional?click?marks to confirm where they clicked.

Click Test Results Dashboards - Review the results as?click- and heatmaps along with data filters and detailed data exports.

Advanced Features - Set up your click test to end on the first click, set a min/max number of clicks, set a task timeout timer, and more.

Reach out today to learn more about how your team can use MUiQ!