mokken-package {mokken} | R Documentation |
Mokken scale analysis is a scaling procedure for both dichotomous and polytomous items. It consists of an item selection algorithm to partition a set of items into Mokken scales and several methods to check the assumptions of two nonparametric item response theory models: the monotone homogeneity model and the double monotonicity model.
Package: | mokken |
Type: | Package |
Version: | 1.4 |
Date: | 2008-12-03 |
License: | GPL Version 2 or later |
The package contains principal functions for Mokken scale analysis. Version 0 was introduced in Van der Ark (2007). Version 1 includes estimation of reliability statistics. In subversions small bugs were repaired. Thanks are due to Daniel van der Palm and J. Hendrik Straat for contributing R code; to Patrick Mair, Rudy Ligtvoet, and J. Hendrik Straat for testing the software; to Michael Dewey, Michael Kubovy, Jue Huang, and Na Yang for reporting bugs; to Robert J. Mokken for lending his last name.
L. Andries van der Ark Maintainer: L. Andries van der Ark <a.vdark@uvt.nl>.
Mokken, R. J. (1971) A Theory and Procedure of Scale Analysis. Berlin, Germany: De Gruyter.
Molenaar, I.W. and Sijtsma, K. (2000) User's Manual MSP5 for Windows [Software manual]. Groningen, The Netherlands: IEC ProGAMMA.
Sijtsma, K, and Molenaar, I. W. (2002) Introduction to nonparametric item response theory. Thousand Oaks, CA: Sage.
Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. http://www.jstatsoft.org
# Personality test data(acl) # Select the items of the scale Communality Communality <- acl[,1:10] # Compute scalability coefficients coefH(Communality) # Investigate the assumption of monotonicity monotonicity.list <- check.monotonicity(Communality) summary(monotonicity.list) plot(monotonicity.list) # Investigate the assumption of non-intersecting ISRFs using method restscore restscore.list <- check.restscore(Communality) summary(restscore.list) plot(restscore.list) # Investigate the assumption of non-intersecting ISRFs using method pmatrix pmatrix.list <- check.pmatrix(Communality) summary(pmatrix.list) plot(pmatrix.list) # Investigate the assumption of IIO using method restscore check.iio(Communality) # Compute the reliability of the scale check.reliability(Communality) # Partition the the scale into mokken scales search.normal(Communality)