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

A comprehensive toolkit for generating continuous test norms in
psychometrics and biometrics, and analyzing model fit. The package offers both
distribution-free modeling using Taylor polynomials and parametric modeling
using the beta-binomial distribution. Originally developed for achievement
tests, it is applicable to a wide range of mental, physical, or other test
scores dependent on continuous or discrete explanatory variables. The package
provides several advantages: It 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 even supports conventional norming. It generates
norm tables including confidence intervals. It also includes methods for
addressing representativeness issues through Iterative Proportional Fitting.

Wolfgang Lenhard

cNORM

Continuous Norming

Alexandra Lenhard

Sebastian Gary

WPS publisher 

getNormScoreSE function

<dl><dt>model</dt>
<dd>a cnorm object</dd>
<dt>type</dt>
<dd>either '1' for the standard error senso Oosterhuis et al. (2016) or '2' for
the RMSE (default)</dd></dl>

Arguments

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

<dl>

<dt>model</dt>
<dd>a cnorm object</dd>


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

</dl>

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

Value

Arguments

References