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Writing is an important component in standardized t ests that are utilized for admission decisions, class placement, and academic or professional development. Placement results of the EPT Writing Test at the un dergraduate level are used to determine whether international students meet Engli sh requirements for writing skills (i.e., Pass); and to direct students to appropriate ESL writin g classes (i.e., 101B or 101C). Practical constraints during evaluation processes i n the English Writing Placement Test (the EPT Writing Test) at Iowa State University, su ch as rater disagreement, rater turnover, and heavy administrative workload, have d emonstrated the necessity to develop valid scoring models for an automated writing evalu ation tool. Statistical algorithms of the scoring engines were essential to predict human raters' quality judgments of EPT essays in the future. Furthermore, in measuring L2 writing performance, previous research has heavily focused on writer-oriented tex t features in students' writing performance, rather than reader-oriented linguistic features that were influential to human raters for making quality judgments. To address the practical concerns of the EPT Writing Test and the existing gap in the literature , the current project aimed at developing a predictive model that best defines human placemen t decisions in the EPT Writing Test. A two-phase multistage mixed-methods design was ado pte in this study within a modelspecification phase and in interconnection with mod el-specification and modelconstruction phases. In the model-specification phase, results of a Mult ifaceted-Rasch-Measurement (MFRM) analysis allowed for selection of five EPT e xp rt raters that represented rating severity levels. Concurrent think-aloud protocols p rovided by the five participants while

Predictive modeling of human placement decisions in an English Writing Placement Test