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subscore (version 3.3)

Computing Subscores in Classical Test Theory and Item Response Theory

Description

Functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) , (2) Haberman's subscoring methods (Haberman, 2008) , and (3) Yen's objective performance index (OPI; Yen, 1987) . It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 ; Yen, 1993 ).

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Version

Install

install.packages('subscore')

Monthly Downloads

413

Version

3.3

License

GPL (>= 2)

Maintainer

Shenghai Dai

Last Published

May 24th, 2022

Functions in subscore (3.3)

subscore.corr

Computing correlation indices for subscores and the total score.
subscore.sx

Computing subscores using Haberman's method based on both observed total scores and observed subscores.
TIMSS11G8M.data

The 2011 TIMSS Grade 8 Mathematics Assessment Dataset
scored.data

Sample scored data
CTTsub

This main function estimates true subscores using different methods based on original CTT scores.
Yen.OPI

Estimating true subscores using Yen's OPI
subscore.Wainer

Estimating true subscores using Wainer's augmentation method
subscore.s

Computing subscores using Haberman's method based on observed subscores.
data.prep

This function prepares data into a required list format
subscore.x

Computing subscores using Haberman's method based on observed total scores.
test.data

A list of objects that include both test information and subscores.
Yen.Q3

Computing Yen's Q3 statistic for unidimensional Rasch, 1-, 2-, and 3-PL logistic IRT models