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).
expl.detect(data, score, nclusters, N.est=NULL, seed=NULL, bwscale=1.1)
An \(N \times I\) data frame of dichotomous or polytomous responses. Missing responses are allowed.
An ability estimate, e.g. the WLE, sum score or mean score
Number of clusters in the analysis
Number of students in a (possible) validation of the DETECT index.
N.est
students are drawn at random from data
.
Random seed
Bandwidth scale factor
A list with following entries
Unweighted DETECT statistics
Weighted DETECT statistics. Weighting is done proportionally to sample sizes of item pairs.
Fit of the cluster method
Cluster allocations
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.
For examples see conf.detect
.