plotDerivative

Plots the scores obtained via the first order derivative of the regression model
in dependence of the norm score. The results indicate the progression of the
norm scores within each age group. The regression based modeling approach
relies on the assumption of a linear progression of the norm scores.
Negative scores in the first order derivative indicate a violation of this
assumption. Scores near zero are typical for bottom and ceiling effects in the raw data.
The regression models usually converge within the range of the original
values. In case of vertical and horizontal extrapolation, with increasing
distance to the original data, the risk of assumption violation increases
as well.
ATTENTION: plotDerivative is currently still incompatible with reversed raw
score scales ('descent' option)

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 

plotDerivative function

<dl><dt>model</dt>
<dd>The model from the bestModel function or a cnorm object</dd>
<dt>minAge</dt>
<dd>Age to start with checking</dd>
<dt>maxAge</dt>
<dd>Upper end of the age check</dd>
<dt>minNorm</dt>
<dd>Lower end of the norm score range, in case of T scores, 25 might be good</dd>
<dt>maxNorm</dt>
<dd>Upper end of the norm score range, in case of T scores, 25 might be good</dd>
<dt>stepAge</dt>
<dd>Stepping parameter for the age check, usually 1 or 0.1; lower
values indicate higher precision / closer checks</dd>
<dt>stepNorm</dt>
<dd>Stepping parameter for norm scores</dd>
<dt>order</dt>
<dd>Degree of the derivative (default = 1)</dd></dl>

Arguments

Plot first order derivative of regression model — plotDerivative

<dl>

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


<dt>minAge</dt>
<dd>Age to start with checking</dd>


<dt>maxAge</dt>
<dd>Upper end of the age check</dd>


<dt>minNorm</dt>
<dd>Lower end of the norm score range, in case of T scores, 25 might be good</dd>


<dt>maxNorm</dt>
<dd>Upper end of the norm score range, in case of T scores, 25 might be good</dd>


<dt>stepAge</dt>
<dd>Stepping parameter for the age check, usually 1 or 0.1; lower
values indicate higher precision / closer checks</dd>


<dt>stepNorm</dt>
<dd>Stepping parameter for norm scores</dd>


<dt>order</dt>
<dd>Degree of the derivative (default = 1)</dd>

</dl>

plotDerivative: Plot first order derivative of regression model

Description

Usage

Arguments

See Also

Examples