Assessing Essential Dimensionality of Real Data

摘要

The purpose of this article is to validate the capability of DIMTEST to assessessential dimensionality of the model underlying the item responses of real test s as opposed to simulated tests. A variety of real test data from different sources are used to assess essential dimensionality. Based on DIMTEST results, some test data are assessed as fitting an essential unidimensional model while others are not. Essential unidimensional test data, as assessed by DIMTEST, are then combined to form two-dimensional test data. The power of Stout's statistic T is examined for these two-dimensional data. It is shown that the results of DIMTEST on real tests replicate findings from simulated tests in that the statistic T discriminates well between essential unidimensional and multidimensional tests. It is' also highly sensitive to major abilities while being insensitive to relatively minor abilities influencing item responses.