Package: cNORM 3.7.0

cNORM: Continuous Norming

Generates continuous test norms in psychometrics and biometrics, and analyzes model fit. The package offers distribution-free modeling using Taylor polynomials, as well as parametric modeling using the beta-binomial distribution (for bounded accuracy tests), the Conway-Maxwell-Poisson distribution (for speeded tests and count data with over-, equi-, or under-dispersion), and the 'Sinh-Arcsinh' (SHASH) distribution. Originally developed for psychological and educational assessment, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and supports conventional norming. It generates norm tables including confidence intervals and provides methods for addressing representativeness issues through Iterative Proportional Fitting. Based on Lenhard et al. (2016) <doi:10.1177/1073191116656437>, Lenhard et al. (2019) <doi:10.1371/journal.pone.0222279>, Lenhard and Lenhard (2021) <doi:10.1177/0013164420928457>, and Gary et al. (2023) <doi:10.1007/s00181-023-02456-0>.

Authors:Alexandra Lenhard [aut], Wolfgang Lenhard [cre, aut], Sebastian Gary [aut], WPS Publisher [fnd]