Literaturnachweis - Detailanzeige
| Autor/inn/en | Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. |
|---|---|
| Institution | Regional Educational Laboratory Southeast (ED); National Center for Education Evaluation and Regional Assistance (ED) |
| Titel | Testing the Importance of Individual Growth Curves in Predicting Performance on a High-Stakes Reading Comprehension Test in Florida. REL 2014-006 |
| Quelle | (2014), (77 Seiten)
PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) |
| Zusatzinformation | ORCID (Petscher, Yaacov) Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Response to Intervention; Achievement Gains; High Stakes Tests; Prediction; Accountability; Elementary School Students; Secondary School Students; Reading Comprehension; Reading Achievement; Inferences; Computation; Scores; Least Squares Statistics; Regression (Statistics); Correlation; Reading Ability; Age Differences; Progress Monitoring; Benchmarking; Comparative Analysis; Data Analysis; Student Characteristics; Bayesian Statistics; State Standards; Florida; Dynamic Indicators of Basic Early Literacy Skills (DIBELS); Florida Comprehensive Assessment Test; SAT (College Admission Test); Woodcock Reading Mastery Test |
| Abstract | Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for individual students and groups of students. Monitoring student progress is central to accountability systems in general and is useful for measuring how well students respond to instruction or intervention. Progress monitoring entails tracking individual growth across the academic year. Thus, it is important to understand why individual students differ on an outcome beyond what can be known by accounting for performance on a status assessment. This study examines the relations among descriptive measures of growth (simple difference and average difference) and inferential measures (ordinary least squares and empirical Bayes) for students in grades 3-10 and considers how well such measures statistically explain differences in end-of-year reading comprehension after controlling for student performance on a mid-year status assessment. The study also looks at how the results change when controlling for initial (fall) and final (spring) status and when the relations among individual growth curves, status, and end-of-year reading comprehension performance depend on end-of-year reading comprehension performance. Four appendices present: (1) Unstandardized regression coefficients for each model by grade and controlling for status; (2) Unstandardized multiple quantile regression process plots centering time at the initial (fall) status on the Florida Assessments for Instruction in Reading; (3) Unstandardized multiple quantile regression process plots centering time at the midyear (winter) status on the Florida Assessments for Instruction in Reading; and (4) Unstandardized multiple quantile regression process plots centering time at the final (spring) status on the Florida Assessments for Instruction in Reading. (Contains 9 notes, 28 figures, and 22 tables.) [For the summary of this report, see ED544677.] (ERIC). |
| Anmerkungen | Regional Educational Laboratory Southeast. Available from: Florida Center for Reading Research, Florida State University, 2010 Levy Avenue Suite 100, Tallahassee, FL 32310. Tel: 850-644-9352; e-mail: rel-se@fsu.edu; Web site: http://rel-se.fcrr.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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