expl.detect: Exploratory DETECT Analysis

In sirt: Supplementary Item Response Theory Models

Exploratory DETECT Analysis

Description

This function estimates the DETECT index (Stout, Habing, Douglas & Kim, 1996; Zhang & Stout, 1999a, 1999b) in an exploratory way. Conditional covariances of itempairs are transformed into a distance matrix such that items are clustered by the hierarchical Ward algorithm (Roussos, Stout & Marden, 1998). Note that the function will not provide the same output as the original DETECT software.

Usage

expl.detect(data, score, nclusters, N.est=NULL, seed=NULL, bwscale=1.1,
    smooth=TRUE, use_sum_score=FALSE, hclust_method="ward.D", estsample=NULL)

Arguments

data	An N \times I data frame of dichotomous or polytomous responses. Missing responses are allowed.
score	An ability estimate, e.g. the WLE, sum score or mean score
nclusters	Maximum number of clusters used in the exploratory analysis
N.est	Number of students in a (possible) validation of the DETECT index. N.est students are drawn at random from data.
seed	Random seed
bwscale	Bandwidth scale factor
smooth	Logical indicating whether smoothing should be applied for conditional covariance estimation
use_sum_score	Logical indicating whether sum score should be used. With this option, the bias corrected conditional covariance of Zhang and Stout (1999) is used.
hclust_method	Clustering method used as the argument method in stats::hclust.
estsample	Optional vector of subject indices that defines the estimation sample

Value

A list with following entries

detect.unweighted	Unweighted DETECT statistics
detect.weighted	Weighted DETECT statistics. Weighting is done proportionally to sample sizes of item pairs.
clusterfit	Fit of the cluster method
itemcluster	Cluster allocations

use_sum_score

References

Roussos, L. A., Stout, W. F., & Marden, J. I. (1998). Using new proximity measures with hierarchical cluster analysis to detect multidimensionality. Journal of Educational Measurement, 35, 1-30.

Stout, W., Habing, B., Douglas, J., & Kim, H. R. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement, 20, 331-354.

Zhang, J., & Stout, W. (1999a). Conditional covariance structure of generalized compensatory multidimensional items, Psychometrika, 64, 129-152.

Zhang, J., & Stout, W. (1999b). The theoretical DETECT index of dimensionality and its application to approximate simple structure, Psychometrika, 64, 213-249.

See Also

For examples see conf.detect.

sirt documentation built on May 29, 2024, 8:43 a.m.