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Study Design and Power Analysis Planner

This browser-based tool helps researchers, students, and HCI practitioners plan quantitative user studies with a-priori power analysis. Enter your independent and dependent variables, estimate effect sizes, and generate guidance for ANOVA, t-tests, and regression including minimum and required sample sizes. The planner runs its core statistical calculations directly in JavaScript in the browser and is intended as a practical starting point for study design, sample-size planning, and analysis preparation.

What This Tool Helps With

Use the planner to prepare browser-based power analyses for common HCI user-study scenarios and document how your final sample-size recommendation was derived.

Supported planning tasks

Study Design and Power Analysis Planner supports one-way and mixed ANOVA planning, independent and paired t-tests, multiple regression, effect-size conversion, required sample-size estimation, design-sequence guidance, and placeholder data formatting for reports or preregistrations.

Study Setup

Describe your independent and dependent variables first. The planner keeps your workflow and reveals the remaining estimation controls when they become relevant.

Add independent variables (IV)*
Please enter a valid IV name.
IV design
With levels
    Add dependent variables (DV)*
    Please enter a valid DV name.

      Estimation Controls

      Tune the assumed effect size, variance and sample size. These controls provide an approximate starting point for planning and should be interpreted in the context of your specific within-, between- or mixed-design setup.

      Estimated mean difference between min. and max. measures (in %):
      20 %
      Estimated pooled standard deviation (in % of the mean difference):
      40 %
      Effect size input:
      Target statistical power:
      80 %
      Correlation among repeated measures:
      0.5
      Estimated initial effect sizes:
      Sample size preview:
      Please wait...
      0

      Results and Guidance

      Use the generated interpretation as a starting point for discussing design choices, required sample sizes and appropriate statistical tests with your supervisor or research team.

      Statistical Power and Effect Sizes
      Instructions for your experimental design
      Instructions for your statistical test(s)

      Example Study Setups

      Load one of the built-in examples to inspect how the planner configures within-subject, mixed-design and regression scenarios.

      Example 2x3 Within-Subject Design Example 2x4 Mixed-Design Example Regression

      Source Code

      Public source repository for the browser-based Study Design and Power Analysis Planner.

      FAQ

      What kinds of studies can I plan with this app?

      You can configure between-subject, within-subject, and mixed experimental designs as well as two-condition t-tests and multiple regression scenarios for quantitative HCI studies.

      What is the difference between Minimum N and Required N?

      Minimum N is the smallest sample size that reaches the target power for the selected effect. Required N is the design-compatible sample size after rounding to the current sequence or balancing constraints.

      Does the planner also suggest statistical tests?

      Yes. The app generates design guidance, explains formulas, and suggests suitable R analysis code for ANOVA, t-tests, regression, and optional MANOVA reporting guidance.