predictBeta

Predicts beta coefficients in dependence of age

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 

predictBeta function

<dl><dt>model</dt>
<dd>An `betaContinuous` model output</dd>
<dt>x</dt>
<dd>A vector specifying the raw scores</dd>
<dt>group</dt>
<dd>A vector specifying the group variables for each raw score</dd></dl>

Arguments

Predicts beta coefficients in dependence of age — predictBeta

<dl>

<dt>model</dt>
<dd>An `betaContinuous` model output</dd>


<dt>x</dt>
<dd>A vector specifying the raw scores</dd>


<dt>group</dt>
<dd>A vector specifying the group variables for each raw score</dd>

</dl>

predictBeta: Predicts beta coefficients in dependence of age

Description

Usage

Value

Arguments

Examples