Accession Number : ADA137741

Title :   An Application of a Multidimensional Extension of the Two-Parameter Logistic Latent Trait Model.

Descriptive Note : Research rept.,


Personal Author(s) : McKinley,R L ; Reckase,M D

PDF Url : ADA137741

Report Date : Aug 1983

Pagination or Media Count : 54

Abstract : A study was conducted to investigate the feasibility of a multidimensional IRT model. A two-parameter logistic IRT model and a multidimensional extension of that model were selected for this research. The design of the study employed two stages. The first stage consisted of generating simulation data to fit the multidimensional model, applying the model to the data, and comparing the resulting estimates to the known parameters. The unidimensional model was also applied to these data. In addition to comparing the parameter estimates to the true parameters, the fit of the unidimensional and multidimensional models to the data were compared. The second state of the study employed real response data. Items were selected from various subtests of a large test so as to simulate shorter tests with varying numbers of dimensions. Both models were applied to these data, and the resulting estimates were used to evaluate the fit of the models to the data. The results of the analyses of the simulation data indicated that the parameters of the multidimensional model could be accurately estimated. The results of the goodness of fit analyses indicated that the multidimensional model could more adequately model simulated multidimensional response data than could the unidimensional model. The results of the analyses of the real data indicated that the multidimensional model also more adequately modeled multidimensional real data than did the unidimensional model. It was concluded that the use of a multidimensional model does seem to be feasible, and that more research was warranted.

Descriptors :   *Mathematical models, *Test methods, *Experimental data, Feasibility studies, One dimensional, Two dimensional, Three dimensional, Parametric analysis, Estimates, Simulation, Factor analysis, Tables(Data)

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE