Outlier Detection in High‐Stakes Certification Testing

Abstract

Recent developrnents of person‐Jit analysis in computerized adaptive testing (CAT) are discussed. Methods from stutistical process control are presented that have been proposed to classify an item score pattern as fitting or misjitting the underlying item response theory model in CAT. Most person‐fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used, Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high‐stakes certification test, the empirical analysis showed that dizerent types of misfit can be distinguished. Further applications using statistical process control methods to detect misfitting item score patterns are discussed.

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 — 44 citations exist upstream.

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