regressionFunction

The method builds the regression function for the regression model,
including the beta weights.
It can be used to predict the raw scores based on age and location.

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 

regressionFunction function

<dl><dt>model</dt>
<dd>The regression model from the bestModel function or a cnorm object</dd>
<dt>raw</dt>
<dd>The name of the raw value variable (default 'raw')</dd>
<dt>digits</dt>
<dd>Number of digits for formatting the coefficients</dd></dl>

Arguments

Regression function — regressionFunction

<dl>

<dt>model</dt>
<dd>The regression model from the bestModel function or a cnorm object</dd>


<dt>raw</dt>
<dd>The name of the raw value variable (default 'raw')</dd>


<dt>digits</dt>
<dd>Number of digits for formatting the coefficients</dd>

</dl>

regressionFunction: Regression function

Description

Usage

Value

Arguments

See Also

Examples