Multiple choice results are inherently probabilistic outcomes, as correct responses reflect a combination of knowledge and guessing, while incorrect responses additionally reflect blunder, a confidently committed mistake. To objectively resolve knowledge from responses in an MC test structure, we evaluated probabilistic models that explicitly…

A study in a university clinic/laboratory investigated adaptive Bayesian scaling as a supplement to interpretation of scores on the Mini-IPIP. A "probability of belonging" in categories of low, medium, or high on each of the Big Five traits was calculated after each item response and continued until all items had been used or until a…

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

This study considers the importance of contextual effects on the quality of assessments on item bias and differential item functioning (DIF) in measurement. Often, in educational studies, students are clustered in teachers or schools, and the clusters could impact psychometric issues yet are largely ignored by traditional item analyses. A…

Examines the question of how large a sample must be in order to produce empirical Bayes estimates which are preferable to other commonly used estimates, such as proportion correct observed score. (Author/RC)

Weber contends that the use of Rasch analysis, principal components analysis, and classical test analysis shows that an instrument designed to measure a "bilevel dimensionality" in probability achievement measures a single latent trait. That interpretation and the use of Rasch and classical analysis to establish unidimensionality are…

Topics concerning latent trait theory are addressed: (1) dimensionality of latent space, local independence, and item characteristic curves; (2) models--equations, parameter estimation, testing assumptions, and goodness of fit, (3) applications test developments, item bias, tailored testing and equating; and (4) advantages over classical…

A scaling procedure is proposed, based on item response theory (IRT), to fit non-hierarchical test structure as well. The binary scores of a test of English were used for calculating the probabilities of answering each item correctly. The probability matrix was factor analyzed, and the difficulty intervals or estimates corresponding to the factors…

This pamphlet reprints three papers and an invited discussion of them, read at a Division 5 Symposium at the 1975 American Psychological Association Convention. The first paper describes a Bayesian tailored testing process and shows how it demonstrates the importance of using test items with high discrimination, low guessing probability, and a…

This primer is an introduction to item response theory (also called item characteristic curve theory, or latent trait theory) as it is used most commonly--for scoring multiple choice achievement or aptitude tests. Written for the testing practitioner with minimum training in statistics and psychometrics, it presents and illustrates the basic…