Publication Date
| In 2015 | 4 |
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| Models | 3 |
| Computation | 2 |
| Correlation | 2 |
| Item Response Theory | 2 |
| Statistical Analysis | 2 |
| Academic Achievement | 1 |
| Achievement Gains | 1 |
| Bayesian Statistics | 1 |
| Classification | 1 |
| Cluster Grouping | 1 |
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| Journal of Educational and… | 4 |
Author
| Browne, Michael W. | 1 |
| Castellano, Katherine E. | 1 |
| Drechsler, Jörg | 1 |
| Ho, Andrew D. | 1 |
| Liang, Longjuan | 1 |
| Magis, David | 1 |
Publication Type
| Journal Articles | 4 |
| Reports - Research | 4 |
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Showing all 4 results
Liang, Longjuan; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2015
If standard two-parameter item response functions are employed in the analysis of a test with some newly constructed items, it can be expected that, for some items, the item response function (IRF) will not fit the data well. This lack of fit can also occur when standard IRFs are fitted to personality or psychopathology items. When investigating…
Descriptors: Item Response Theory, Statistical Analysis, Goodness of Fit, Bayesian Statistics
Magis, David – Journal of Educational and Behavioral Statistics, 2015
The purpose of this note is to study the equivalence of observed and expected (Fisher) information functions with polytomous item response theory (IRT) models. It is established that observed and expected information functions are equivalent for the class of divide-by-total models (including partial credit, generalized partial credit, rating…
Descriptors: Item Response Theory, Models, Statistics, Computation
Drechsler, Jörg – Journal of Educational and Behavioral Statistics, 2015
Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model should be preferred from a theoretical point of view if…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Educational Research, Statistical Bias
Castellano, Katherine E.; Ho, Andrew D. – Journal of Educational and Behavioral Statistics, 2015
Aggregate-level conditional status metrics (ACSMs) describe the status of a group by referencing current performance to expectations given past scores. This article provides a framework for these metrics, classifying them by aggregation function (mean or median), regression approach (linear mean and nonlinear quantile), and the scale that supports…
Descriptors: Expectation, Scores, Academic Achievement, Achievement Gains

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