Derivation of Regression-Based Normative Data
Summary
Bootstraps a confidence interval for a percentile rank
Bootstraps confidence intervals for a normative table
Check assumptions for a fitted Stage 1 model
Check the fit of the mean structure of a regression model
Check the coding of a variable
Plot densities
Explore data
Fit fractional polynomials
Conduct the General Linear Test (GLT) procedure
Intra class correlation
Explore data
Plot the bootstrap distribution and the percentile bootstrap CI
Evaluate the fit of the mean structure of a fitted Stage 1 model.
Plot means and CIs for test scores.
Graphical depiction of the ICC.
Explore data
Check the model assumptions for a fitted Stage 1 model graphically.
Plot the results for a Stage.2.NormScore object.
Plot the results of Tukey's Honest Significance Difference test.
Explore data
Sandwich estimators for standard errors
Stage 1 of the regression-based normative analysis
Make an automatic scoring sheet
Convert a raw score to a percentile rank
Derive a normative table
Conducts Tukey's Honest Significance Difference test
Write a normative table from R to a .txt/.csv/.xlsx file
Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.