buildFunction

Build regression function for bestModel

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 <https://cnorm.shinyapps.io/cNORM/>.

Wolfgang Lenhard

cNORM

Continuous Norming

Alexandra Lenhard

Sebastian Gary

WPS publisher 

buildFunction function

<dl><dt>raw</dt>
<dd>name of the raw score variable</dd>
<dt>k</dt>
<dd>the power degree for location</dd>
<dt>t</dt>
<dd>the power degree for age</dd>
<dt>age</dt>
<dd>use age</dd>
<dt>covariates</dt>
<dd>use covariates</dd></dl>

Arguments

Build regression function for bestModel — buildFunction

<dl>

<dt>raw</dt>
<dd>name of the raw score variable</dd>


<dt>k</dt>
<dd>the power degree for location</dd>


<dt>t</dt>
<dd>the power degree for age</dd>


<dt>age</dt>
<dd>use age</dd>


<dt>covariates</dt>
<dd>use covariates</dd>

</dl>

buildFunction: Build regression function for bestModel

Description

Usage

Value

Arguments