Multistage Testing in Heterogeneous Populations: Some Design and Implementation Considerations.

A central challenge in international large-scale assessments is adequately measuring dozens of highly heterogeneous populations, many of which are low performers. To that end, multistage adaptive testing offers one possibility for better assessing across the achievement continuum. This study examines the way that several multistage test design and implementation choices can impact measurement performance in this setting. To attend to gaps in the knowledge base, we extended previous research to include multiple, linked panels, more appropriate estimates of achievement, and multiple populations of varied proficiency. Including achievement distributions from varied populations and associated item parameters, we design and execute a simulation study that mimics an established international assessment. We compare several routing schemes and varied module lengths in terms of item and person parameter recovery. Our findings suggest that, particularly for low performing populations, multistage testing offers precision advantages. Further, findings indicate that equal module lengths-desirable for controlling position effects-and classical routing methods, which lower the technological burden of implementing such a design, produce good results. Finally, probabilistic misrouting offers advantages over merit routing for controlling bias in item and person parameters. Overall, multistage testing shows promise for extending the scope of international assessments. We discuss the importance of our findings for operational work in the international assessment domain.

Checking Equity: Why Differential Item Functioning Analysis Should Be a Routine Part of Developing Conceptual Assessments.

We provide a tutorial on differential item functioning (DIF) analysis, an analytic method useful for identifying potentially biased items in assessments. After explaining a number of methodological approaches, we test for gender bias in two scenarios that demonstrate why DIF analysis is crucial for developing assessments, particularly because simply comparing two groups' total scores can lead to incorrect conclusions about test fairness. First, a significant difference between groups on total scores can exist even when items are not biased, as we illustrate with data collected during the validation of the Homeostasis Concept Inventory. Second, item bias can exist even when the two groups have exactly the same distribution of total scores, as we illustrate with a simulated data set. We also present a brief overview of how DIF analysis has been used in the biology education literature to illustrate the way DIF items need to be reevaluated by content experts to determine whether they should be revised or removed from the assessment. Finally, we conclude by arguing that DIF analysis should be used routinely to evaluate items in developing conceptual assessments. These steps will ensure more equitable-and therefore more valid-scores from conceptual assessments.