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We would like to thank the Institute for Education Sciences for their support and funding.

Principal Investigators: Brian Jacob & Susan Dynarski University of Michigan Barbara Schneider & Ken Frank Michigan State University Thomas Howell Center for Educational Performance & Information Joseph Martineau Michigan Department of Education.

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We would like to thank the Institute for Education Sciences for their support and funding.

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  1. Principal Investigators: Brian Jacob & Susan Dynarski University of Michigan Barbara Schneider & Ken Frank Michigan State University Thomas Howell Center for Educational Performance & Information Joseph Martineau Michigan Department of Education We would like to thank the Institute for Education Sciences for their support and funding. Please direct any questions to info@michiganconsortium.org 735 S. State Street | Ann Arbor, MI 48109

  2. What is MCER? • Michigan Consortium for Educational Research • IES-funded collaboration between: • University of Michigan • Michigan State University • Michigan Department of Education • Michigan Center for Educational Performance and Information.

  3. Objectives of MCER • Engage stakeholders and education experts in research for the benefit of public education in Michigan • Provide research-based evidence to policymakers in Michigan • Inform national policy initiatives for improving education

  4. Inaugural Research Questions • What is the effect of the Michigan Merit Curriculum on… • course-taking patterns, student achievement • high school graduation, postsecondary attendance • What was the effect of the Michigan Promise Scholarship on… • college entry, college choice, college completion • Do these effects vary by school or student characteristics?

  5. MCER Data Goal

  6. Michigan Merit Curriculum Study • As of 2011, all graduating MI HS students must pass 16 rigorous courses (e.g., Algebra II, Biology, Chemistry, Physics) and complete end-of-course exams to measure content mastery • The evaluation will compare student outcomes before & after the MMC • Standardized test scores, HS graduation • College entry, choice & completion • Random sample of 100+ schools: assess fidelity of implementation • End-of-course exams • Student transcripts • Interviews

  7. Michigan Promise Study • Intervention • $4,000 college scholarship for students with qualifying score on Michigan Merit Exam • Students must maintain 2.5 college GPA • Methods • Regression discontinuity: compare outcomes just above/below MME threshold • College enrollment/choice/persistence (NSC) • Scholarship receipt (Treasury)

  8. Emerging Research Questions • Has mandatory ACT (now part of MME) improved college attendance and choice? • What is the “value-added” of individual schools, once we control for student characteristics and initial achievement? • Are Michigan’s charter schools raising student achievement?

  9. Example studyThe Impact of the Threat of School Sanctions: A Regression Discontinuity Study of Being on a Probationary List Guan K. Saw, I-Chien Chen, Barbara L. Schneider, Kenneth A. Frank Michigan State University

  10. Purpose of Study • This study analyzes the effect of the first stage of school sanctions in Michigan, being on a probationary list. We pay attention to its possible impact on student achievement in high-stakes and low-stakes subjects at school level.

  11. Background • School sanctions, increasingly used as instruments of education policy, have been the focus of debate at federal and state levels. • The goal of sanctions is to incentivize schools that fail to meet academic standards to improve their students’ educational performance. • How does it work to make school change?

  12. Two possible explanations 1. Probations serve as a Social Stigma • Stigmatizing or labeling is a potent tool for guiding individuals to conform to social norms. • For failing schools, stigmatization becomes a motivating factor to make change (Figlio & Rouse, 2006; Ladd & Glennie, 2001; Mintrop, 2004; Sim, 2007, 2009). • “Being on a probationary list” can be seen as a social stigma, which may have a “labeling effect”.

  13. 2. Effects of Sanction Threats • Not only imposing sanctions, but also threatening sanctions can change an individual’s behavior (Lacy & Niou, 2004). • In economic sanctions literature, some argue that sanctions threatened are often more effective than those that are deployed (Drezner, 1999; Drury & Li, 2006; Lacy & Emerson, 2004; Smith, 1996). • In education, the effects of sanction threats on low-performing schools are mixed (Chiang, 2009; Figlio & Rouse, 2006; Springer, 2008; Winters et al., 2010).

  14. Crowding-out hypothesis • There is a growing concern that test-based accountability may cause schools to shift inputs from low-stakes subjects (Corbett & Wilson, 1991; Kohn, 1999; Nichols & Berliner, 2007; Whitford & Jones, 2000). • This crowding-out hypothesis was supported by some qualitative evidence (Au, 2007; Groves, 2002; King & Mathers, 1997; Murillo & Flores, 2002). • In contrast, some quantitative studies report that high-stakes testing policies led to significant gains in low-stakes subjects (Jacob, 2005; Winters, Trivitt, & Greene, 2010).

  15. Michigan Context: PLA and Watch List • Since 2009, Michigan Department of Education (MDE) has annually published a list of the lowest performing 5% schools, named the Persistently Lowest Achieving list (PLA list). • The PLA list is established by certain criteria: (a) 2-year average percent proficiency in math and reading;(b) 4-year slope of percent proficiency in math and reading; (c) whether a school made Adequate Yearly Progress (AYP) status over the past two years; and (d) whether a school had a 4-year graduation rate below 60% for three years in a row.

  16. The PLA list schools have to make significant gains in student achievement within a short time to get off the list. • If not, further sanctions may be imposed including turnaround, restart, and closure of schools. • With labeling and sanction threat effects, we hypothesize that the PLA list schools tend to positively affect student achievement.

  17. Michigan Context: Watch List • In addition to the PLA list (bottom 5%), MDE also publishes a “Watch list” of schools in the lowest quintile (6-20%), which were identified as being in danger of falling under the 5% mark. • This does not affect the PLA ranking but provides an alert to these schools to keep them out of the PLA category. • Without a strong threat of further sanctions, we expect that the labeling effect of being on the watch list is relatively limited.

  18. Data Sources • Our longitudinal school-level data constructed with: • (1) Michigan Educational Assessment System (MEAS); • (2) Common Core of Data (CCD). • We only focus on a sample of regular high schools (only 333 schools ranked by state-wide achievement base percentile ranking).

  19. Measures • Treatment 1: Being on 2008-09 PLA list (<5%)Treatment 2: Being on 2008-09 Watch list (6-20%) • Forcing variable: State-wide achievement base percentile ranking • Outcomes: % of students met proficiency level in (a) high-stakes subjects: math, reading, writing,(b) low-stakes subjects: science, social studies • Covariates: % of free/reduced lunch students, % of black students, school size, and pupil teacher ratio.

  20. Analytic Method • We employ a regression discontinuity design (RDD) method. This is a quasi-experimental design, in which treatment status depends on whether an observed covariate exceeds a fixed threshold(Lee & Card, 2008; Shadish, Campbell, & Cook, 2002). • In our case, the fixed threshold is the cutoff (5% or 20%) of percentile ranking for being on the PLA or watch lists.

  21. RD Analysis of PLA List • RD Analysis of Watch List PLA list Percentile ranking 0 5 10 100 Watch list Percentile ranking 100 0 5 20 35

  22. RDD with covariates • Given the imbalance of covariates between treatment and control groups, we include the covariates in the RD models, which can (Imbens & Lemieux, 2008): (1) reduce small sample bias; (2) improve precision if covariates correlated with potential outcomes (as in analyses of randomized experiments)

  23. Findings: PLA List Mathematics Reading Writing Science Social Studies Cutoff = 5% <5% (PLA list) = 19 schools 5-10% (Control group) = 19 schools Figure 1a. Percentage of Students Met Proficiency Level in 2011, by Tier 2 Percentile Rank in 2008-09

  24. Effects of Being on the PLA List (2008-2009)

  25. RDD analyses show a positive “list” effect on all subjects 2010-2011. • In models with presence of covariates, only the positive “list” effect on writing achieves statistical significance. • No negative effect on low-stakes subjects of science and social studies was observed.

  26. Findings: Watch List Mathematics Reading Writing Science Social Studies Cutoff = 20% 5-20% (Watch list) = 54 schools 20-35% (Control group) = 51 schools Figure 1b. Percentage of Students Met Proficiency Level in 2011, by Tier 2 Percentile Rank in 2008-09

  27. Effects of Being on the Watch List (2008-2009)

  28. We found no effect of being on the watch list for all subjects in 2010-2011. • This finding holds across estimation models using different bandwidths (5%, 10%, and 15% below and above cutoff).

  29. Robustness Test • We created a pseudo PLA list using the previous school year data (2007-08), before the state policy mandating assignment of low-performing schools to a probationary list had been enacted. • Then, we compare the results of 2008-09 PLA list effects to 2007-08 pseudo PLA list effects. • We expect that there would be no effect of being on the pseudo PLA list since these schools did not receive a labeling treatment or real threat of sanctions.

  30. Findings: Pseudo PLA List Mathematics Reading Writing Cutoff = 5% <5% (Pseudo PLA list) = 18 schools 5-10% (Control group) = 17 schools Figure 1c. Percentage of Students Met Proficiency Level in 2010, by Pseudo Tier 2 Percentile Rank in 2007-08

  31. Effects of Being on the Pseudo PLA List (2007-08)

  32. Results from the 2007-08pseudo list analysis indicate no “list” effect on all subjects in 2009-2010. • This finding further testifies the robustness of the effects of 2008-2009 PLA list.

  33. Conclusion • Being on PLA list as a social stigma combined with possible following incremental sanctions may spur school improvement. • Without a real and strong threat of further sanctions, just being labeled by the watch list does not stimulate school performance. • Crowding-out hypothesis was not supported by our data.

  34. Limitation and Discussion • Limitations: (1) given three years of data, we only can claim a short term effect of PLA list.(2) the limited variables in the data set provide no information to uncover real changes being implemented in the schools. • Puzzles:(1) Are the PLA effects stronger for writing? Why? (2) What organizational processes occurred in the schools placed on the PLA list?

  35. …take home message… Within the school sanctioning context: • The labeling effect is limited. • The threat of sanctions does motivate low-ranked probationary schools to make changes. • Crowding-out effects may not occur but spillover effects may be present.

  36. “On the List” Example of Evaluation of Institution • State implemented • Involves state data as well as school composition (common core) • Apply to Michigan Merit Curriculum • Graduate students • Data • Collaborative faculty

  37. Principal Investigators: Brian Jacob & Susan Dynarski University of Michigan Barbara Schneider & Ken Frank Michigan State University Thomas Howell Center for Educational Performance & Information Joseph Martineau Michigan Department of Education We would like to thank the Institute for Education Sciences for their support and funding. Please direct any questions to info@michiganconsortium.org 735 S. State Street | Ann Arbor, MI 48109

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