getNormScoreSE

model

<p>either '1' for the standard error senso Oosterhuis et al. (2016) or '2' for
the RMSE (default)</p>

type

<p>Calculates the standard error (SE) or root mean square error (RMSE) of the norm scores
In case of large datasets, both results should be almost identical</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) or root mean square error (RMSE) of the norm scores
In case of large datasets, both results should be almost identical — getNormScoreSE

Calculates the standard error (SE) or root mean square error (RMSE) of the norm scores
In case of large datasets, both results should be almost identical

getNormScoreSE: Calculates the standard error (SE) or root mean square error (RMSE) of the norm scores In case of large datasets, both results should be almost identical

Description

Usage

Arguments

Value

References