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|Title:||New techniques for the dimensionality assessment of standardized test data|
|Doctoral Committee Chair(s):||Stout, William F.|
|Department / Program:||Statistics|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Subject(s):||Education, Tests and Measurements
|Abstract:||A new index based on the conditional covariance of item scores given a latent variable is defined and investigated to assess dimensionality in educational and psychological test data. This index successfully detects the test dimensionality in both identifying the number of dimensions present in the test and identifying the items contributing to each dimension by means of cluster analysis. In addition, this index remarkably quantifies lack of unidimensionality in the test data, and the asymptotic behavior of this index under unidimensionality provides theoretical justification.
A new significance test based on a kernel smoothing technique, is developed to detect dimensional discrepancy of item pairs. In comparison to existing procedures a simulation study of this method reveals a reasonable type 1 error rate with respect to its nominal level of significance as well as excellent power performance.
The unidimensional parametric item and ability calibration procedures, BILOG and LOGIST, are examined to reveal what is actually being estimated as unidimensional ability when data is not unidimensional. Ability estimation accuracy is also investigated in terms of average standard error. Both BILOG and LOGIST appear to provide a composite of underlying latent traits as their presumed unidimensional ability estimate. In doing so, the average standard errors are relatively invariant to the degree of lack of unidimensionality, however the direction of the composite being measured best changes systematically and by a large amount, according with varying amount of multidimensionality.
|Rights Information:||Copyright 1994 Kim, Hae-Rim|
|Date Available in IDEALS:||2011-05-07|
|Identifier in Online Catalog:||AAI9512427|