Complex Composites: Issues That Arise in Combining Different Modes of Assessment

Abstract

Data from the California Learning Assessment System are used to examine certain characteristics of tests designed as the composites of items of different modes. The characteristics include rater severity, test information, and definition of the latent variable. Three different assessment modes-multiple-choice, open-ended, and investigation items (the latter two are referred to as performance-based modes)-were combined in a test across three different test forms. Rater severity was investigated by incorporating a rater parameter for each rater in an item response model that then was used to analyze the data. Some rater severities were found to be quite extreme, and the impact of this variation in rater severities on both total scores and trait level estimates was examined. Within-rater variation in rater severity also was examined and was found to have significant variation. The information contribution of the three modes was compared. Performance-based items provided more information than multiple-choice items and also provided greatest precision for higher levels of the latent variable. A projection-like method was applied to investigate the effects of assessment mode on the definition of the latent variable. The multiple-choice items added information to the performance-based variable. The results of the analysis also showed that the projection-like method did not practically differ from the results when the latent trait was defined jointly by both the multiple-choice and the performance-based items. Index terms: equating, linking, multiple assessment modes, polytomous item response models, rater effects.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 39 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.