Applying many-facet Rasch modeling in the assessment of creativity

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Paul Silvia, Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Creativity assessment with open-ended production tasks relies heavily on scoring the quality of a subject’s ideas. This creates a faceted measurement structure involving persons, tasks (and ideas within tasks), and raters. Most studies, however, do not model possible systematic differences among raters. The present study examines individual rater differences in the context of a planned-missing design and its association with reliability and validity of creativity assessments. It applies the many-facet Rasch model (MFRM) to model and correct for these differences. We reanalyzed data from 2 studies (Ns = 132 and 298) where subjects produced metaphors, alternate uses for common objects, and creative instances. Each idea was scored by several raters. We simulated several conditions of reduced load on raters where they scored subsets of responses. We then compared the reliability and validity of IRT estimated scores (original vs. IRT adjusted scores) on various conditions of missing data. Results show that (a) raters vary substantially on the lenient-severity dimension, so rater differences should be modeled; (b) when different combinations of raters assess different subsets of ideas, systematic rater differences confound subjects’ scores, increasing measurement error and lowering criterion validity with external variables; and (c) MFRM adjustments effectively correct for rater effects, thus increasing correlations of scores obtained from partial with scores obtained with full data. We conclude that MFRM is a powerful means to model rater differences and reduce rater load in creativity research.

Additional Information

Psychology of Aesthetics, Creativity, and the Arts, 13(2), 176-186
Language: English
Date: 2019
creativity, assessment, raters, many-facet Rasch model, planned missing data

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