AI Article Synopsis
- Norming psychological tests is critical for accurately interpreting test scores, but traditional methods can create biases or need large samples to be effective.
- Continuous norming methods—including inferential, semi-parametric, and parametric techniques—aim to address these challenges.
- A review of 121 publications revealed that most studies favored simplified parametric norming, but not all accounted for key distributional factors, and the comparison of norming approaches showed that parametric norms offered the highest precision in real data applications.
Article Abstract
Norming of psychological tests is decisive for test score interpretation. However, conventional norming based on subgroups results either in biases or require very large samples to gather precise norms. Continuous norming methods, namely inferential, semi-parametric, and (simplified) parametric norming, propose to solve those issues. This article provides a systematic review of continuous norming. The review includes 121 publications with overall 189 studies. The main findings indicate that most studies used simplified parametric norming, not all studies considered essential distributional assumptions, and the evidence comparing different norming methods is inconclusive. In a real data example, using the standardization sample of the Need for Cognition-KIDS scale, we compared the precision of conventional, semi-parametric, and parametric norms. A hierarchy in terms of precision emerged with conventional norms being least precise, followed by semi-parametric norms, and parametric norms being most precise. We discuss these findings by comparing our findings and methods to previous studies.
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| http://dx.doi.org/10.1177/10731911241260545 | DOI Listing |
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