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U.S. School District Dropout Prevention and Recovery Practices Linked to Graduation Rate Performance Daniel Princiotta Renee Ryberg Johns Hopkins University School of Education Child Trends Bethesda Policy Research, LLC Presented at the

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Presented at the Annual Meeting of the American Educational Research Association Philadelphia, PA | April 4, 2014

U.S. School District Dropout Prevention and Recovery Practices Linked to Graduation Rate Performance

Daniel Princiotta

Johns Hopkins University School of Education Bethesda Policy Research, LLC

Renee Ryberg

Child Trends

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Introduction

One in five eighth graders drops out during high school5
600,000 public high school students drop out each year3
About 76 percent of U.S. public high school students graduated on-

time in 2008-0911

Costs of the dropout problem are profound
On average, each dropout costs the public sector $209,100 over a lifetime7
Dropouts cost the United States more than $300 billion per year1

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Introduction

Little national research on district factors tied to graduation rates9
Graduation rates vary substantially by district. In 2007-08:10
Los Angeles Unified School District: 49 percent graduation rate
Montgomery County Public Schools in Maryland: 87 percent graduation rate
As of 2012, districts are reporting graduation rates the same way and

being held accountable for graduation rate improvement12

But what are districts doing to help solve the dropout problem, and is

there any evidence of their effectiveness?

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Purpose

Provide a snapshot of the prevalence of certain district-level dropout

prevention practices in the United States

Dropout Early Warning Systems
Informational intervention strategies
Dropout recovery strategies
Compare dropout prevention practices of districts that are over- and

under-performing with respect to graduation rates

An exercise in “benchmarking”6
Identify whether particular practices are tied to better-than-expected district

graduation rate performance

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Data and sample

Fast Response Survey System (FRSS) Dropout Prevention Services and

Programs Survey (2010-11)

Covers a wide array of district dropout prevention services and programs2
FRSS Surveyed 1,090 school districts
When weighted appropriately, represent 13,400 districts nationwide
Common Core of Data (CCD 2008-09)
American Community Survey (ACS) data (2006-2010)
Small Area Income and Poverty Estimates (SAIPE 2003-06)

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Methodology

Step 1: Generate an Averaged Freshman Graduation Rate (AFGR) for all eligible

school districts in the country (10,520 districts, 1,090 FRSS districts)

Step 2: Model AFGR as a function of school district and community predictor

variables, as well as state (9,210 districts, 800 FRSS districts)

Step 3: Generate a predicted graduation rate for each district based on this model
Step 4: Determine the difference between each district’s observed and predicted

graduation rates

Step 5: Classify districts as over- or under-performing (top/bottom 20 percent)
Step 6: Compare dropout prevention practices among over- and under-

performing districts using FRSS sample (130 and 160 districts, respectively, representing 1,820 and 1,850 districts nationwide)

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Methodology: Modeling AFGR

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Predictor variables

CCD (2008-09)
Gender (% male)
Locale (rural, city, suburb, town)
Race/ethnicity
% American Indian/Alaskan Native
% Black
% Hispanic
SAIPE (2003-06)
Poverty Rate
American Community Survey

(2006-10)

Educational Attainment
% less than a high school credential
% w/ a bachelor's degree or higher
Household type (% w/ 2 parents)
Mobility (% moved in past year)

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District dropout prevention practices investigated

Dropout Early Warning Systems (prevalence & indicators used)
Informational intervention strategies. Provide information on:
Financial and employment implications of dropping out
Other educational or training options (alternative schools, GED, job training)
Dropout recovery strategies
Try to determine status of students who did not return to school in the fall
Follow up with school-year dropouts to encourage them to return before the

next school year

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Results

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43 57 Have dropout early warning system Do not have a dropout early warning system

Figure 1. Percentage of districts with a Dropout Early Warning System: 2010–11

41 59

All Districts Over-Peforming Districts

43 57 47 53

Under-Peforming Districts

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Figure 2A. Percentage of DEWS districts that reported using various factors to identify at-risk students to a moderate or large extent: 2010–11

Factors used to identify at-risk students

76 80 86 95 97 76 87 95 97 100 78 81 88 93 97

20 40 60 80 100 Failure on state standardized tests Involvement with criminal justice system Behaviors that warrant suspension or expulsion Truancy or excessive absences Academic failure (grades/credits/retention) Percent All districts Over-performing districts Under-performing districts

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Figure 2B. Percentage of DEWS districts that reported using various factors to identify at-risk students to a moderate or large extent: 2010–11

Factors used to identify at-risk students

63 68 69 76 75 69 86 78 89 80 67 74 74 76 77

20 40 60 80 100 Pregnancy/teen parenthood Homelessness or frequent address change Change in student attitude or life conditions Substance abuse Behaviors that warrant other disciplinary action Percent All districts Over-performing districts Under-performing districts

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Figure 2C. Percentage of DEWS districts that reported using various factors to identify at-risk students to a moderate or large extent: 2010–11

Factors used to identify at-risk students

33 51 60 62 72 39 49 66 62 63 43 54 61 64 65

20 40 60 80 100 Migrant status Limited English proficiency Mental health problems Involvement with social services or foster care Learning disability as indicated in an IEP Percent All districts Over-performing districts Under-performing districts

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53 29 18 71 24 5 57 38 6 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts

Figure 3. Percentage of districts that provide information about the employment or financial consequences of dropping out to likely dropouts: 2010–11

Percent * *

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Figures 4A-D. Percentage of districts that provide information about schooling options to likely dropouts: 2010–11

43 25 32 65 22 13 49 39 12 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts Percent * * 63 19 18 79 14 7 70 26 3 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts Percent *

Alternative Schools or Programs Combined Job Training + GED Programs

Percent 51 23 26 68 24 8 58 35 7 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts

GED or Adult Ed Programs

29 31 40 49 32 20 33 42 25 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts Percent *

Job Training Programs

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Figure 5. Percentage of districts that try to determine the status of students who did not return to school in the fall as expected: 2010–11

Percent 73 14 12 78 13 9 77 19 4 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts

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36 34 30 55 31 14 37 52 11 20 40 60 80 100 All students Some students No students All districts Overperforming districts Underperforming districts

Figure 6. Percentage of districts that follow up with school-year dropouts to encourage them to return before the next school year : 2010–11

Percent * *

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Conclusion: Implications

Six in 10 districts lack DEWS, leaving substantial room for

improvement in targeting dropout prevention efforts efficiently

Investigate address changes and homelessness as DEWS indicators,

particularly in districts with high student mobility/homelessness

Substantial overlap exists between over-performing and under-

performing district practices

It’s not just about what practices are in place, but how well they are

implemented

It is critical to systematically apply dropout prevention and recovery

strategies to all relevant students

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Conclusions: Future research directions to address current limitations

Running analysis using new 2010-11 cohort graduation rates
Use updated predictor variables aligned with new base year
Strengthen over/under-performing metric by
Generating school-level predictions and then aggregating to district level
Incorporating student achievement data from EDFacts into model
Impute missing district-level data
Investigate additional district dropout prevention practices
Cross-check district-reported survey responses with outside data
Perform a multivariate investigation into dropout prevention practices

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References

1. Alliance for Excellent Education (2008). Dropouts, diplomas, and dollars: U.S. High schools and the nation’s economy. Washington, DC: Author.
2. Carver, P.R. and Lewis, L. (2011). Dropout Prevention Services and Programs in Public School Districts: 2010–11 (NCES 2011–037). U.S. Department of Education,

National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.

3. Chapman, C., Laird, J., Ifill, N., and KewalRamani, A. (2011). Trends in High School Dropout and Completion Rates in the United States: 1972–2009 (NCES 2012–006).

U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved December 19, 2012 from http://nces.ed.gov/pubsearch.

4. Dynarski, M., Clarke, L., Cobb, B., Finn, J., Rumberger, R., and Smink, J. (2008). Dropout Prevention: A Practice Guide (NCEE 2008–4025). Washington, DC: National

Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc.

5. Hurst, D., Kelly, D., & Princiotta, D. (2004). Educational Attainment of High School Dropouts 8 Years Later. Washington, DC: U.S. Department of Education, Institute
f Education Sciences, National Center for Education Statistics.
6. Jerald, C. D. (2008). Benchmarking for Success: Ensuring US Students Receive a World-Class Education. Washington, DC: National Governors Association, Council of

Chief State School Officers, and Achieve, Inc.

7. Levin, H. M., Belfield, C., Muennig, P., & Rouse, C. (2007). The Costs and Benefits of an Excellent Education for All of America’s Children. New York: Teachers College,

Columbia University.

8. Neild, R., Balfanz, R., & Herzog, L. (2007). An early warning system. Educational Leadership 65(2), 28–33.
9. Rumberger, R. W. and Lim, S. A. (2008). Why Students Drop Out of School: A Review of 25 Years of Research. Santa Barbara: California Dropout Research Project.
10. Sable, J., Plotts, C., and Mitchell, L. (2010). Characteristics of the 100 Largest Public Elementary and Secondary School Districts in the United States: 2008–09 (NCES

2011–301). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.

11. Stillwell, R., Sable, J., and Plotts, C. (2011). Public School Graduates and Dropouts From the Common Core of Data: School Year 2008–09 (NCES 2011–312). U.S.

Department of Education. Washington, DC: National Center for Education Statistics. Retrieved December 19, 2012 from http://nces.ed.gov/pubsearch.

12. U.S. Department of Education (2012). States Report New High School Graduation Rates Using More Accurate, Common Measure [Press release]. Retrieved

December 19, 2012 from http://www.ed.gov/news/press-releases/states-report-new-high-school-graduation-rates-using-more-accurate-common-measur.

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Contact Information

Daniel Princiotta

Ph.D. Student Johns Hopkins School of Education dprinciotta@jhu.edu Principal Research Scientist Bethesda Policy Research, LLC (240) 621-0079 dprinciotta@bethesdapolicyresearch.com @dprinciotta

Renee Ryberg

Research Analyst Child Trends (240) 223-9200 rryberg@childtrends.org