Exploratory DETECT Analysis
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.
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)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.
|
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
|
estsample |
Optional vector of subject indices that defines the estimation sample |
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
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.
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