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The practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
Standard-library analysis for within-subject designs, with sphericity handled properly. Map a subject identifier, the condition or timepoint each measurement was taken under, and the measured value in long format, and get the repeated-measures ANOVA that removes the stable subject-to-subject differences from the error term: the F test with partial and generalized eta-squared, Mauchly's test of sphericity with the Greenhouse-Geisser and Huynh-Feldt corrected p-values, pairwise post-hoc comparisons corrected for multiplicity, condition means with confidence intervals, and a per-subject profile grid. An optional between-subjects group turns it into a mixed design with a group-by-condition interaction.
Interactive bar visualization
Interactive heatmap visualization
Interactive table visualization
Interactive table visualization
Interactive table visualization
Interactive table visualization
Plain-English reading of the results and what to do next.
Test whether a measurement taken on the same people at three or more timepoints actually changed
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MCP Analytics. Repeated-Measures ANOVA. Free tool. https://mcpanalytics.ai/free/standard_repeated_measures. Accessed [date you used it].
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