Comparison of SPRT and Sequential Bayes Procedures for Classifying Examinees Into Two Categories Using a Computerized Test

Abstract

Many testing applications focus on classifying examinees into one of two categories (e.g., pass/fail) rather than on obtaining an accurate estimate of level of ability. Examples of such applications include licensure and certification, college selection, and placement into entry-level or developmental college courses. With the increased availability of computers for the administration and scoring of tests, computerized testing procedures have been developed for efficiently making these classification decisions. The purpose of the research reported in this article was to compare two such procedures, one based on the sequential probability ratio test and the other on sequential Bayes methodology, to determine which required fewer items for classification when the procedures were matched on classification error rates. The results showed that under the conditions studied, the SPRT procedure required fewer test items than the sequential Bayes procedure to achieve the same level of classification accuracy.

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 — 110 citations exist upstream.

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