Understanding item parameters in personality scales: An explanatory item response modeling approach
Although item parameters are essential in psychometric frameworks, such as item response theory (IRT), few theories are available to guide their psychological interpretation. Moreover, the meaning of item parameters is generally harder to interpret for personality scales than for ability scales. The...
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Veröffentlicht in: | Personality and individual differences 2018-07, Vol.128, p.1-6 |
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Sprache: | eng |
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Zusammenfassung: | Although item parameters are essential in psychometric frameworks, such as item response theory (IRT), few theories are available to guide their psychological interpretation. Moreover, the meaning of item parameters is generally harder to interpret for personality scales than for ability scales. The current study provides a comprehensive way to interpret personality item parameters, Generalized Linear and Nonlinear Models (GLNMMs). The GLNMMs model the effect of item features on psychometric properties, such as item discrimination and item location. Distinct from previous studies which use a two-step approach, the GLNMMs produce smaller standard errors of item features' coefficients, and allow examination of person covariates. The current study examines four item features – negative wording, subtlety, social desirability, and miscomprehension. In general, item discrimination negatively correlated with item subtlety, social desirability, and miscomprehension; item easiness positively correlated with subtlety and miscomprehension, and negatively correlated with negative wording.
•Item features are correlated with item parameters in item response modeling.•Negative wording shows positive relation with item discrimination.•Subtlety associates with lower item discrimination and higher item easiness.•Miscomprehension correlates with lower item discrimination and higher easiness.•Social desirability has mixed relations with item parameters. |
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ISSN: | 0191-8869 1873-3549 |
DOI: | 10.1016/j.paid.2018.02.012 |