expl.detect: 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).Usage
expl.detect(data, score, nclusters, N.est = NULL, seed = 897, bwscale = 1.1)
Arguments
data
An $N \times I$ data frame of dichotomous responses.
Missing responses are allowed.
score
An ability estimate, e.g. the WLE
nclusters
Number of clusters in the analysis
N.est
Number of students in a (possible) validation of the DETECT index.
N.est students are drawn at random from data.
bwscale
Bandwidth scale factor
Value
- A list with followinmg entries
- detect.unweightedUnweighted DETECT statistics
- detect.weightedWeighted DETECT statistics. Weighting is done
proportionally to sample sizes of item pairs.
- clusterfitFit of the cluster method
- itemclusterCluster allocations
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.