weighted.rank

Conducts weighted ranking on the basis of sums of weights per unique raw score.
Please provide a vector with raw values and an additional vector with the weight of each
observation. In case the weight vector is NULL, a normal ranking is done. The vectors may not
include NAs and the weights should be positive non-zero values.

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 

weighted.rank function

<dl><dt>x</dt>
<dd>A numerical vector</dd>
<dt>weights</dt>
<dd>A numerical vector with weights; should have the same length as x</dd></dl>

Arguments

Weighted rank estimation — weighted.rank

<dl>

<dt>x</dt>
<dd>A numerical vector</dd>


<dt>weights</dt>
<dd>A numerical vector with weights; should have the same length as x</dd>

</dl>

weighted.rank: Weighted rank estimation

Description

Usage

Value

Arguments