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Reflection On Acuity As An English II Predictor

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Reflection 27: Acuity as an English II Predictor Methods Linear regression allows researchers to analyze cause and effect or predictive relationships among variables (Creighton, 2007). For this assignment, I set out to conduct a regression analysis in hopes of answering two questions: (1) is there a relationship between student scores on Acuity, our school-wide interim testing program, and their performance on the English II state assessment?, and (2) if the relationship is significant, can Acuity results be used to predict student performance on the state test? To conduct this analysis I used the 2015 Partnership for Assessment of Readiness for College and Careers (PARCC) English II assessment results as well as the Acuity data from the first semester screener which was given in December 2014. This test was the last comprehensive Acuity exam given to English II students during the 2014-2015 school year (a PARCC-issued practice test was used during third nine-weeks exams). My student sample included all students who had both a December Acuity score in the Acuity online system and a Spring 2015 PARCC score in School Status, the school’s database for teacher and student information. With these requirements, the total sample size included 64 students. To run my statistical analysis, I created a Google Sheet document with three columns including the student name (1), their percent correct on the December 2014 Acuity Diagnostic test (2), and their scale score for the Spring

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