sirt (version 1.14-0)

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 = NULL, 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, sum score or mean score

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.

seed

Random seed

bwscale

Bandwidth scale factor

Value

A list with followinmg entries A list with followinmg entries

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.