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| Bayesian Statistics | 2 |
| Error of Measurement | 2 |
| Item Response Theory | 2 |
| Predictor Variables | 2 |
| Ability | 1 |
| Difficulty Level | 1 |
| Regression (Statistics) | 1 |
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| Fox, Jean-Paul | 2 |
| Glas, Cees A. W. | 2 |
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Download full textFox, Jean-Paul; Glas, Cees A. W. – 2000
This paper focuses on handling measurement error in predictor variables using item response theory (IRT). Measurement error is of great important in assessment of theoretical constructs, such as intelligence or the school climate. Measurement error is modeled by treating the predictors as unobserved latent variables and using the normal ogive…
Descriptors: Bayesian Statistics, Error of Measurement, Item Response Theory, Predictor Variables
Fox, Jean-Paul; Glas, Cees A. W. – 1998
A two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that this offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and…
Descriptors: Ability, Bayesian Statistics, Difficulty Level, Error of Measurement
