--- title: "Test and effect size details" output: rmarkdown::html_vignette: toc: true toc_depth: 4 vignette: > %\VignetteIndexEntry{Test and effect size details} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r} #| label = "setup", #| include = FALSE source("setup.R") ``` ## Introduction This article describes the data that `{statsExpressions}` functions expect, and summarizes, for each function, the statistical tests carried out, the effect sizes returned, and the underlying functions used to compute them. Abbreviations used: CI = Confidence Interval ## Data requirements The one-sample, group-comparison, correlation, and centrality functions expect **long (tidy) data** — one row per observation. Contingency-table functions also accept a `counts` column for aggregated frequencies; `meta_analysis()` instead requires `estimate` and `std.error` columns, with one row per study. A few additional requirements are worth noting: - **Within-subjects (repeated measures) designs**: The data must contain exactly *one* observation per subject per condition (a complete, balanced block design). If you have multiple trials per subject-condition cell, aggregate them first (e.g., by taking the mean) before passing the data. You can verify this with `table(data$subject, data$condition)` — every cell should equal `1`. - **`subject.id` argument**: For within-subjects designs, always specify `subject.id` explicitly. If omitted, the function pairs observations by row order within each condition, so any data that is not already sorted identically within every condition level can produce silently incorrect paired tests — even with exactly two conditions and no missing values. - **Missing data**: Missing values are removed internally. For within-subjects designs, any subject who has `NA` in *any* condition, or lacks a row in a condition, is removed entirely by `complete_subjects()`, ensuring a balanced design is maintained. For between-subjects designs, only the rows with `NA` are removed, unless `subject.id` is also supplied, in which case all rows of a subject with an `NA` are removed. ## Summary of functionality ```{r child="../man/rmd-fragments/functionality.Rmd"} ``` ## Summary of tests and effect sizes To learn how an argument is resolved, or to browse the source code, consult the underlying function listed in these tables. For example, for the between-subjects one-way ANOVA, run `?stats::oneway.test` in your R console. ### `centrality_description()` ```{r child="../man/rmd-fragments/centrality_description.Rmd"} ``` ### `oneway_anova()` ```{r child="../man/rmd-fragments/oneway_anova.Rmd"} ``` ### `two_sample_test()` ```{r child="../man/rmd-fragments/two_sample_test.Rmd"} ``` ### `one_sample_test()` ```{r child="../man/rmd-fragments/one_sample_test.Rmd"} ``` ### `corr_test()` ```{r child="../man/rmd-fragments/corr_test.Rmd"} ``` ### `contingency_table()` ```{r child="../man/rmd-fragments/contingency_table.Rmd"} ``` ### `pairwise_comparisons()` ```{r child="../man/rmd-fragments/pairwise_comparisons.Rmd"} ``` ### `pairwise_contingency_table()` ```{r child="../man/rmd-fragments/pairwise_contingency_table.Rmd"} ``` ### `meta_analysis()` ```{r child="../man/rmd-fragments/meta_analysis.Rmd"} ``` ## Effect size interpretation See `{effectsize}`'s interpretation functions to check different rules/conventions to interpret effect sizes: ## References - For parametric and non-parametric effect sizes: - For robust effect sizes: - For Bayesian posterior estimates: ## Suggestions If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: ## Citation ```{r citation, echo = FALSE, comment = ""} citation("statsExpressions") ```