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2015 - The Effect of Summer on Value-added Assessments of Teacher and School Performance

Attribution: Gregory, Palardy J., & Peng, Luyao
Researchers: Luyao PengPalardy J. Gregory
University Affiliation: University of California Riverside
Email: gjpalardy@gmail.com
Research Question:
  1. To what extent are value added assessments (VAA) estimates of teacher and school performance affected by summer learning differences?
  2. Can any summer effect be ameliorated without biannual assessments (i.e., fall and spring) using control covariates that are typically available to school districts, such as student demographics and contextual characteristics of classrooms and schools?
  3. To what degree does including summer in VAA estimates result in biases against teachers and schools serving low income and ethnic minority children?
Published: 1
Journal Name or Institutional Affiliation: Education Policy Analysis Archives
Journal Entry: Vol. 23, No. 92, Pp. 1-26
Year: 2015
Findings:
  1. A substantial portion of the variance in YoY (Year over Year) VAA estimates originates from summer and that summer variance alters the quintile rankings of a high percentage of teachers and schools.
  2. The summer effect invariably underestimated the performance of teachers and schools in the lowest quintile of summer change and overestimated the performance of teachers and schools in the highest quintile of summer change.
  3. Including an extensive number of demographic and contextual variables does not substantively reduce the summer effects on VAA estimates. These results suggest that twice-annual assessment may be necessary to remove the summer effects from VAA estimates.
  4. Including summer in VAA estimates results in systematic biases against schools serving higher concentrations of students who qualify for FRL. Students from low SES families tend to have greater declines in reading achievement over summer, but learn at similar rates as other students during the school year.
Keywords: Academic AchievementAccountabilityMethodologies: QuantitativeAnalysis Methods: Multilevel Models Sampling Frame:2,251 first grade students students, 682 classrooms, and 168 schools
Sampling Types: Nationally RepresentativeAnalysis Units: ClassroomSchoolStudentData Types: Quantitative-Longitudinal
Data Description:
  • Authors use Early Childhood Longitudinal Study- Kindergarten (ECLS-K)
  • A 3-level HLM was used so that teachers are effectively compared to other teachers working within the same school. This helps separate teacher effects from school effects. Levels one, two and three corresponds to students, classrooms, and schools respectively.
  • DV:
    • Year-over-Year (YoY) reading and math gains (Spring 1st grade minus spring kindergarten) or school-year (SY) achievement gains in reading or math (spring 1st grade minus fall 1st grade).
  • IV:
    1. Student level
      • Spring kindergarten math, spring kindergarten reading, fall 1st grade math, fall 1st grade reading, spring 1st grade math, spring 1st grade reading, free or reduced lunch, female, Asian, Black, Hispanic, other, White, age, non-English home language, disability, days absent, summer school
    2. Classroom Level
      • free lunch, proportion minority, proportion female, proportion disability, proportion non-English home language, mean age
    3. School level
      • free lunch, proportion minority, proportion female, proportion disability, proportion non-English home language, mean age
Theoretical Framework:
Relevance:
Archives: K-12 Integration, Desegregation, and Segregation Abstracts
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