getNormScoreSE

model

<p>Calculates the standard error (SE) of the norm scores</p>

Conventional methods for producing standard scores 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>) 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. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.

Wolfgang Lenhard

cNORM

Continuous Norming

Alexandra Lenhard

Sebastian Gary

getNormScoreSE function

Calculates the standard error (SE) of the norm scores — getNormScoreSE

Calculates the standard error (SE) of the norm scores

getNormScoreSE: Calculates the standard error (SE) of the norm scores

Description

Usage

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References