mokken (version 2.4)

aisp: Automated Item Selection Procedure (AISP) for Mokken Scale Analysis

Description

Returns a vector with as many elements as there are items, indicating the scale an item belongs to

Usage

aisp(X, search="normal", lowerbound=.3, alpha=.05, popsize=20, maxgens=default.maxgens, pxover=0.5, pmutation=0.1, verbose=FALSE)

Arguments

X

matrix or data frame of numeric data containing the responses of nrow(X) respondents to ncol(X) items. Missing values are not allowed

search

Type of item selection procedure: "normal": Mokken's automated item selection procedure (Molenaar and Sijtsma, 2000; Sijtsma and Molenaar, 2002); "ga": item selection using a genetic algorithm (Straat, va

lowerbound

numeric scaling criterium; 0 <= lowerbound < 1. The default is 0.3.

alpha

Type I error level. The default is 0.05.

popsize

Size of the population of items in genetic. algorithm The default is 20.

maxgens

Number of generations in genetic algorithm. The default is 10^(log2(ncol(X)/5)) * 1000.

pxover

Cross-over probability in genetic algorithm. The default is 0.5.

pmutation

Mutation probability in genetic algorithm. The default is 0.1.

verbose

Logical, indicating whether should output to the screen the results of the model. If FALSE, no output is produced. The default is TRUE.

Value

• An indicator vector of length J. Each entry refers to an item. Items with same integer belong to the same Mokken scale. A zero indicates an unscalable item. If n is the largest integer, then n Mokken scales were found.

Details

The number of Mokken scales cannot exceed ncol(X)/2. Procedure may be slow for large data sets. Especially if the genetic algorithm is used. aisp replaces the function search.normal is earlier versions.

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. Straat, J. H., van der Ark, L. A., and Sijtsma, K. (2008). Comparing optimization algorithms for item selection in Mokken scale analysis. Paper submitted for publication. Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. http://www.jstatsoft.org

See Also

coefH, check.iio, check.monotonicity, check.pmatrix, check.reliability,check.restscore

Examples

data(acl)

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

# Partition these 10 items into mokken scales using Mokken's automated item selection procedure.
scale <- aisp(Communality)
coefH(Communality[,scale==1])

# Partition these 10 items into mokken scales using a genetic algorithm.
scale <- aisp(Communality,"ga",maxgens=1000)
coefH(Communality[,scale==1])