search.normal {mokken}R Documentation

Automated Item Selection Algorithm for Mokken Scale Analysis

Description

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

Usage

search.normal(X, lowerbound = 0.3, alpha = 0.05)

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
lowerbound numeric scaling criterium; 0 <= lowerbound < 1
alpha Type I error level

Details

The number of Mokken scales cannot exceed ncol(X)/2. Procedure may be slow for large data sets.

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.

Author(s)

L. A. van der Ark a.vdark@uvt.nl

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

See Also

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

Examples

data(acl)
# Partition all 212 items into mokken scales (may take some time).
scale <- search.normal(acl)      

# investigate monotonicity for all items in the first scale.
monotonicity.list <- check.monotonicity(acl[,scale==1])

# summary of the results
summary(monotonicity.list)

[Package mokken version 1.4 Index]