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Item Selection Using an Average Growth Approximation of Target Information Functions.
Peer reviewed Peer reviewed
Luecht, Richard M.; Hirsch, Thomas M. – Applied Psychological Measurement, 1992
Derivations of several item selection algorithms for use in fitting test items to target information functions (IFs) are described. These algorithms, which use an average growth approximation of target IFs, were tested by generating six test forms and were found to provide reliable fit. (SLD)
Descriptors: Algorithms, Computer Assisted Testing, Equations (Mathematics), Goodness of Fit
Computerized Adaptive Testing Using the Partial Credit Model: Effects of Item Pool Characteristics and Different Stopping Rules.
Peer reviewed Peer reviewed
Dodd, Barbara G.; And Others – Educational and Psychological Measurement, 1993
Effects of the following variables on performance of computerized adaptive testing (CAT) procedures for the partial credit model (PCM) were studied: (1) stopping rule for terminating CAT; (2) item pool size; and (3) distribution of item difficulties. Implications of findings for CAT systems based on the PCM are discussed. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Computer Simulation, Difficulty Level
Influence of Prior Distributions on Detection of DIF.
Peer reviewed Peer reviewed
Cohen, Allan S.; And Others – Journal of Educational Measurement, 1991
Detecting differential item functioning (DIF) on test items constructed to favor 1 group over another was investigated on parameter estimates from 2 item response theory-based computer programs--BILOG and LOGIST--using data for 1,000 White and 1,000 Black college students. Use of prior distributions and marginal-maximum a posteriori estimation is…
Descriptors: Black Students, College Students, Computer Assisted Testing, Equations (Mathematics)