mokken (version 1.2)

mokken-package: Mokken Scale Analysis in R

Description

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.

Details

ll{ Package: mokken Type: Package Version: 1.2 Date: 2008-10-18 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.

References

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

Examples

# Personality test
data(acl)               

# Select the items of the scale Communality
acl.com <- acl[,1:10]

# Compute scalability coefficients
coefH(acl.com)

# Investigate the assumption of monotonicity
monotonicity.com <- check.monotonicity(acl.com)
summary(monotonicity.com)
plot(monotonicity.com)

# Investigate the assumption of non-intersecting using method restscore
restscore.com <- check.restscore(acl.com)
summary(restscore.com)
plot(restscore.com)

# Investigate the assumption of non-intersecting using method pmatrix
pmatrix.com <- check.pmatrix(acl.com)
summary(pmatrix.com)
plot(pmatrix.com)

# Partition the entire data set into mokken scales
search.normal(acl.com)

# Compute the reliability of the scale
check.reliability(acl.com)