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DeMars, Christine E. – Educational and Psychological Measurement, 2016
Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were…
Descriptors: Item Response Theory, Models, Thinking Skills, Test Items
DeMars, Christine E.; Jurich, Daniel P. – Educational and Psychological Measurement, 2015
In educational testing, differential item functioning (DIF) statistics must be accurately estimated to ensure the appropriate items are flagged for inspection or removal. This study showed how using the Rasch model to estimate DIF may introduce considerable bias in the results when there are large group differences in ability (impact) and the data…
Descriptors: Test Bias, Guessing (Tests), Ability, Differences
Socha, Alan; DeMars, Christine E. – Educational and Psychological Measurement, 2013
Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for…
Descriptors: Sample Size, Test Length, Correlation, Test Format
DeMars, Christine E. – Educational and Psychological Measurement, 2008
The graded response (GR) and generalized partial credit (GPC) models do not imply that examinees ordered by raw observed score will necessarily be ordered on the expected value of the latent trait (OEL). Factors were manipulated to assess whether increased violations of OEL also produced increased Type I error rates in differential item…
Descriptors: Test Items, Raw Scores, Test Theory, Error of Measurement