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Yao, Lili; Haberman, Shelby J.; Zhang, Mo – ETS Research Report Series, 2019
Many assessments of writing proficiency that aid in making high-stakes decisions consist of several essay tasks evaluated by a combination of human holistic scores and computer-generated scores for essay features such as the rate of grammatical errors per word. Under typical conditions, a summary writing score is provided by a linear combination…
Descriptors: Prediction, True Scores, Computer Assisted Testing, Scoring
Haberman, Shelby J.; Yan, Duanli – Educational Testing Service, 2011
Continuous exponential families are applied to linking test forms via an internal anchor. This application combines work on continuous exponential families for single-group designs and work on continuous exponential families for equivalent-group designs. Results are compared to those for kernel and equipercentile equating in the case of chained…
Descriptors: Equated Scores, Statistical Analysis, Language Tests, Mathematics Tests
Sinharay, Sandip; Haberman, Shelby J.; Jia, Helena – Educational Testing Service, 2011
Standard 3.9 of the "Standards for Educational and Psychological Testing" (American Educational Research Association, American Psychological Association, & National Council for Measurement in Education, 1999) demands evidence of model fit when an item response theory (IRT) model is used to make inferences from a data set. We applied two recently…
Descriptors: Item Response Theory, Goodness of Fit, Statistical Analysis, Language Tests
Haberman, Shelby J. – Educational Testing Service, 2011
Alternative approaches are discussed for use of e-rater[R] to score the TOEFL iBT[R] Writing test. These approaches involve alternate criteria. In the 1st approach, the predicted variable is the expected rater score of the examinee's 2 essays. In the 2nd approach, the predicted variable is the expected rater score of 2 essay responses by the…
Descriptors: Writing Tests, Scoring, Essays, Language Tests