cNORM

Conventional methods for producing standard scores or percentiles in psychometrics or biometrics are often plagued with 'jumps' or 'gaps' (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016, doi:10.1177/1073191116656437; 2019, doi:10.1371/journal.pone.0222279; 2021 doi:10.1177/0013164420928457) estimates percentile development (e. g. over age) and generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample. It includes procedures for post stratification of norm samples to overcome bias in data collection and to mitigate violations of representativeness. An online demonstration is available via .