Optimal and Nonoptimal Computer-Based Test Designs for Making Pass–Fail Decisions

Abstract

Now that many credentialing exams are being routinely administered by computer, new computer-based test designs, along with item response theory models, are being aggressively researched to identify specific designs that can increase the decision consistency and accuracy of pass–fail decisions. The purpose of this study was to investigate the impact of optimal and nonoptimal multistage test (MST) designs, linear parallel-form test designs (LPFT), and computer adaptive test (CAT) designs on the decision consistency and accuracy of pass–fail decisions. Realistic testing situations matching those of one of the large credentialing agencies were simulated to increase the generalizability of the findings. The conclusions were clear: (a) With the LPFTs, matching test information functions (TIFs) to the mean of the proficiency distribution produced slightly better results than matching them to the passing score; (b) all of the test designs worked better than test construction using random selection of items, subject to content constraints only; (c) CAT performed better than the other test designs; and (d) if matching a TIF to the passing score, the MST design produced a bit better results than the LPFT design. If an argument for the MST design is to be made, it can be made on the basis of slight improvements over the LPFT design and better expected item bank utilization, candidate preference, and the potential for improved diagnostic feedback, compared with the feedback that is possible with fixed linear test forms.