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When constructed response items are administered repeatedly, "trend scoring" can be used to test for rater drift. In trend scoring, raters rescore responses from the previous administration. Two simulation studies evaluated the utility of Stuart's measure of marginal homogeneity as a way of evaluating rater drift when monitoring trend scoring. In the first study, data were generated based on trend scoring tables obtained from an operational assessment. The second study tightly controlled table margins to disentangle certain features present in the empirical data. In addition to, the paired test was included as a comparison, because of its widespread use in monitoring trend scoring. Sample size, number of score categories, interrater agreement, and symmetry/asymmetry of the margins were manipulated. For identical margins, both statistics had good Type I error control. For a unidirectional shift in margins, both statistics had good power. As expected, when shifts in the margins were balanced across categories, the test had little power. demonstrated good power for all conditions and identified almost all items identified by the test. shows substantial promise for monitoring of trend scoring.

The Angoff method requires experts to view every item on the test and make a probability judgment. This can be time consuming when there are large numbers of items on the test. In this study, a G-theory framework was used to determine if a subset of items can be used to make generalizable cut-score recommendations. Angoff ratings (i.e., probability judgments) from previously conducted standard setting studies were used first in a re-sampling study, followed by D-studies. For the re-sampling study, proportionally stratified subsets of items were extracted under various sampling and test-length conditions. The mean cut score, variance components, expected standard error (SE) around the mean cut score, and root-mean-squared deviation (RMSD) across 1,000 replications were estimated at each study condition. The SE and the RMSD decreased as the number of items increased, but this reduction tapered off after approximately 45 items. Subsequently, D-studies were performed on the same datasets. The expected SE was computed at various test lengths. Results from both studies are consistent with previous research indicating that between 40-50 items are sufficient to make generalizable cut score recommendations.

Since its 1947 founding, ETS has conducted and disseminated scientific research to support its products and services, and to advance the measurement and education fields. In keeping with these goals, ETS is committed to making its research freely available to the professional community and to the general public. Published accounts of ETS research, including papers in the ETS Research Report series, undergo a formal peer-review process by ETS staff to ensure that they meet established scientific and professional standards. All such ETS-conducted peer reviews are in addition to any reviews that outside organizations may provide as part of their own publication processes. Peer review notwithstanding, the positions expressed in the ETS Research Report series and other published accounts of ETS research are those of the authors and not necessarily those of the Officers and Trustees of Educational Testing Service.The Daniel Eignor Editorship is named in honor of Dr. Daniel R. Eignor, who from 2001 until 2011 served the Research and Development division as Editor for the ETS Research Report series. The Eignor Editorship has been created to recognize the pivotal leadership role that Dr. Eignor played in the research publication process at ETS. ETS Research Report Series ISSN 2330-8516 R E S E A R C H R E P O R T Articulation of Cut Scores in the Context of the Next-Generation Assessments Priya Kannan & Adrienne SgammatoEducational Testing Service, Princeton, NJ Logistic regression (LR)-based methods have become increasingly popular for predicting and articulating cut scores. However, the precision of predictive relationships is largely dependent on the underlying correlations between the predictor and the criterion. In two simulation studies, we evaluated the impact of varying the underlying grade-level correlations on the resultant bias in cut scores articulated using the LR method. In Study 1, we compared different articulation methods (LR and equipercentile smoothing), and in Study 2, we evaluated different criteria for linking (e.g., adjacent grade or end of course). The collective results indicate that as correlations became smaller, cut scores articulated using LR-based predictions became increasingly biased when compared to a true value obtained under perfect correlation. The predicted values are significantly biased for lower achievement levels, irrespective of the linking criteria used. Results from these studies suggest that the LR method must be used with caution, particularly when articulating cut scores for multiple achievement levels.

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