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Kaplan, David; Chen, Jianshen – Psychometrika, 2012
A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…
Descriptors: Intervals, Bayesian Statistics, Scores, Prior Learning
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Ligtvoet, Rudy – Psychometrika, 2012
In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable. However, the PCM is very restrictive with respect…
Descriptors: Simulation, Item Response Theory, Comparative Analysis, Scores
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Krishnan, T. – Psychometrika, 1973
Scores on different items of a test are often combined linearly (sometimes with equal weights) to form the test score, mainly for the sake of computational convenience. In this paper, the author studies the consequences of doing this, and also studies the problems involved in finding a suitable linear combination. (Author/RK)
Descriptors: Algorithms, Definitions, Discriminant Analysis, Psychometrics
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van der Ark, L. Andries – Psychometrika, 2005
The sum score is often used to order respondents on the latent trait measured by the test. Therefore, it is desirable that under the chosen model the sum score stochastically orders the latent trait. It is known that unlike dichotomous item response theory (IRT) models, most polytomous IRT models do not imply stochastic ordering. It is unknown,…
Descriptors: Data Analysis, Item Response Theory, Scores, Models