Early College High School Model
Blueprints Program Rating: Model Plus
A high school model designed to increase students’ access to a postsecondary credential, particularly for underrepresented students. The goal is to minimize challenges in the transition to postsecondary education for students for whom access has historically been problematic.
- Academic Performance
- Post Secondary Education
- Academic Services
- School - Environmental Strategies
- School - Individual Strategies
- Universal Prevention (Entire Population)
- Late Adolescence (15-18) - High School
- Male and Female
- All Race/Ethnicity
- School: Opportunities for prosocial involvement in education
- Enroll in college and attain a college degree while in high school
- Attain a postsecondary degree 2 years after high school graduation in a typical time frame
- Were more likely to earn college credits in high school
- Report stronger college-going high school cultures and support from instructors while in high school
- Were more likely to have access to general college information while in high school (p = .06)
- Enrolled in postsecondary education (2 years after high school graduation in a typical time frame)
- Attained a postsecondary degree (2 years after high school graduation in a typical time frame)
- Took more core college preparatory courses and succeeded in them at the end of 9th grade (Edmunds et al., 2012)
- Had better school attendance and fewer suspensions at the end of 9th grade (Edmunds et al., 2012)
- Earned more college credits by the end of 12th grade (Edmunds et al., 2017)
- Baseline difference on 1 of 6 variables in favor of treatment, though controlled for in analysis
- High attrition on 1 (high school graduation) of 3 outcomes but differential attrition not tested
- Baseline differences (in favor of treatment) on 2 of 13 variables, though controlled for in analysis
- High attrition on 1 (college credits received) of 3 primary outcomes but differential attrition not tested
- : Model Plus
- College credits earned while in high school: The number of college credits transferable to a 4-year college earned though the end of 12th grade was measured. Vocational courses and remedial or developmental courses were excluded as these credits were not transferable to a 4-year college, and “passing” was defined as earning a C or above. Data came from the North Carolina Community College System, and thus excluded 36 students who were enrolled in 4-year colleges while in high school. As for AP exam, data were not available before 2009-2010. However, the authors report sensitivity analyses that excluded 248 students who would have been in 11th and 12th grades (the years in which most students take AP classes) prior to 2009–2010 showed very little difference.
- Graduation from high school: The authors reported 5-year graduation rates (for both treatment and control) because the majority of early colleges are 5 year programs.
- Enrollment in postsecondary education: The authors reported whether a student was ever enrolled in any type of postsecondary education (part-time or full-time). This enrollment could have occurred at any point over the time period from 9th grade through the fall semester of the 6th year after the student started high school.
- Postsecondary credentials: Any type of postsecondary credential, including associate degree, technical credential, or bachelor’s degree, was assessed. The sample only went through the sixth year after students’ entrance into high school (or two years after the student’s expected graduation from high school), allowing two years for students in two-year institutions to complete their degrees.
Continuum of Intervention
A high school model designed to increase students’ access to a postsecondary credential, particularly for underrepresented students. The goal is to minimize challenges in the transition to postsecondary education for students for whom access has historically been problematic.
High school students, including those traditionally underrepresented in postsecondary education
The treatment sample in Haxton et al. (2016) was majority (53%) minority and nearly half of the students (47%) were from low-income families. Findings showed that minority students in the treatment group were more likely to earn a postsecondary degree (of any type) than minority students in the control group. In addition, low-income students who attended an Early College High School were more likely to earn “any” postsecondary degree than low-income students in the control group. For Edmunds et al. (2017), the overall sample was around 40% minority, roughly 40% were first-generation college going, and half were low-income. Findings showed that under-represented minority students in the treatment earned more college credits while in high school, and were more likely to enroll in college and receive a postsecondary credential compared to minority students in the control group. These findings were consistent across other sub-groups, including low-income and first-generation students.
Risk/Protective Factor Domain
Risk and Protective Factors
Brief Description of the Program
An Early College High School (EC) is a high school model that offers enrolled students an opportunity to earn an associate’s degree or up to 2 years of college credits toward a bachelor’s degree during high school at no or low cost to the students. Often referred to as “small schools that blur the line between high school and college” (Edmunds et al., 2017, p. 297), the model is designed to enable students to take college courses while still receiving support from high school staff. Many early college models target students who are traditionally underrepresented in postsecondary education, including minority students, students from low-income families, and students who are in the first generation of their families to go to college.
Description of Program
An Early College High School (EC) is a high school model that offers enrolled students an opportunity to earn an associate’s degree or up to 2 years of transferable college credits toward a bachelor’s degree during high school at no or low cost to the students. In early colleges, all students take a curriculum that includes the high school courses necessary for entrance into a four-year university (thus ensuring an academically rigorous course of study) and teachers are expected to receive support in implementing instructional strategies designed to prepare students for the level of thinking they will need to do in college. Often referred to as “small schools that blur the line between high school and college” (Edmunds et al., 2017, p. 297), the model is designed to enable students to take college courses while still receiving support from high school staff. Many early college models target students who are traditionally underrepresented in postsecondary education, including minority students, students from low-income families, and students who are in the first generation of their families to go to college. Thus, while some early colleges are structured as 4-year high schools, most allow students five years to complete the curriculum having recognized that students who are members of the target populations may not always be able to complete all of the necessary credits in only four years.
The early college is a comprehensive school reform model that focuses explicitly and purposefully on preparing all of its students for college. Core “design” principles include: 1) partnering with colleges and universities for enrolled high school students to take college courses; 2) providing opportunities to take college-level courses to all students, not only those who are academically advanced – with some models specifically focusing on dropouts or students at-risk of dropping out of high school; 3) giving students a wide variety of academic and social supports—from personalized relationships to academic tutoring, advising, and help with study skills, time management, self-advocacy, other college “life skills,” and college preparation. In addition, early colleges provide students with supports in the formal transition to college, such as assistance in completing college applications and financial aid forms. Some early colleges also have other design principles for adults in the school (for example, professional development focused on a common vision and a collaborative, learning environment for staff).
According to this model, all students are required to take college courses. For most, this starts in the ninth grade when they might take physical education or college success skills, often in classes composed only of early college high school students. In 10th grade, most early college students begin to take core academic courses along with regular college students. By 11th and 12th grade, students take the majority of their courses on the college campus along with regular college students. Many early colleges are actually located on a 2-year or 4-year college campus. For Haxton et al. (2016) and Edmunds et al. (2017), all sites included schools of choice in which resident pupils applied to enroll in schools for which they were not zoned.
Among the characteristics cited in the literature associated with college access and success are academic preparation, early access to courses that carry college credit, a college-going culture, and assistance with logistical preparation. The model is expected to provide college exposure, rigorous academics, and student supports, which in turn is expected to promote improved high school outcomes, including high school achievement and graduation. Students’ high school outcomes, including completing sufficient postsecondary credits while in high school, may lead them to engage in further college education or lead directly to college degree attainment. The framework also acknowledges that student background characteristics may affect student outcomes both during and after high school, and may also moderate the program effects on student outcomes.
Brief Evaluation Methodology
Haxton et al. (2016) and Edmunds et al. (2012, 2017) each conducted a multisite randomized controlled trial (using lottery assignments) to evaluate the effects of the Early College High School model on students’ high school graduation and postsecondary access and completion rates. Haxton et al. (2016) recruited from a national sample of 17 lotteries across 10 schools and 3 cohorts of students graduating from high school in 2005-06, 2006-07, and 2007-08, which resulted in a sample size of 2,458 students. Edmunds et al. (2012, 2017) included students who applied to one of 12 early college schools in North Carolina, and included 18 cohorts of students who enrolled in ninth grade in the 2005-06, 2006-07, 2007-08 and 2008-09 school years (n=1,689). Outcomes in Edmunds et al. (2012) included course taking patterns and success, attendance, suspension, and college aspirations at the end of 9th grade. Meanwhile, primary outcomes for Haxton et al. (2016) and Edmunds et al. (2017) included high school graduation rates, as well as college enrollment and completion rates up to 6 years after students completed 9th grade (or 2 years after high school graduation in a typical time frame).
Outcomes (Brief, over all studies)
Haxton et al. (2016) found that, as compared to the control group, treatment students were more likely to be enrolled in college and attain a college degree while in high school. In addition, compared to the control group, treatment students were more likely to attain a postsecondary degree after high school. At the end of 9th grade, Edmunds et al. (2012) found that compared to the control group, students in the treatment group had significantly higher school attendance and lower suspension rates at the end of 9th grade. Edmunds et al. (2017) found that, as compared to the control group, treatment students earned more college credits while in high school, and 2 years after high school graduation in a typical time frame, treatment students were more likely to be enrolled in postsecondary education and attain a postsecondary degree.
As for risk and protective factors, Edmunds et al. (2012) reported that a significantly higher proportion of students in the treatment group were taking core college preparatory courses and succeeding in them at the end of 9th grade, compared to students in the control group. Meanwhile, Haxton et al. (2016) found that, as compared to the control group, treatment students were more likely to earn college credits in high school, and report stronger college-going cultures and support from instructors in their high schools. Also, treatment students were more likely to have access to general college information than the control group, yet this effect was marginally significant (p = .06). In contrast, treatment students were less likely than control group counterparts to take at least one Advanced Placement (AP) course and pass at least one AP exam.
Haxton et al. (2016) found that, as compared to the control group, treatment students were more likely to:
As for risk and protective factors, Haxton et al. (2016) found that, as compared to the control group, treatment students:
Edmunds et al. (2017) found that, as compared to the control group, treatment students were more likely to have:
In terms of risk & protective factors, compared to the control group, students in the treatment group:
In Haxton et al. (2016), effect sizes for behavioral outcomes ranged from small (OR = 1.13) to large (OR = 35.37). Effect sizes for risk and protective factors ranged from small (OR = 1.31) to large (OR = 18.61). In Edmunds et al. (2017), effect sizes were not reported.
Haxton et al. (2016)
Edmunds et al. (2012, 2017)
Program Information Contact
Julie Edmunds, Ph.D., Evaluator
Program Director for Secondary School Reform, SERVE Center
University of North Carolina at Greensboro
Phone: (336) 315-7415
Clarisse Haxton, Ph.D., Evaluator
Research, Evaluation, and Assessment (REA) Department
Palo Alto Unified School District
Phone: (650) 833-4229 ext. 6914
Blueprints Certified Studies
Berger, A., Turk-Bicakci, L., Garet, M., Knudson, J., & Hoshen, G. (2014). Early College, Continued Success: Early College High School Initiative Impact Study. Washington, DC: American Institutes for Research.
Berger, A., Turk-Bicakci, L, Garet, M, Song, M., Knudson, J., Haxton, C., . . . Cassidy, L. (2013). Early College, Early Success: Early College High School Initiative Impact Study. Washington, DC: American Institutes for Research.
Haxton, C., Song, M., Zeiser, K., Berger, A., Turk-Bicakci, L., Garet, M. S., . . . Hoshen, G. (2016). Longitudinal findings from the Early College High School Initiative Impact Study. Educational Evaluation and Policy Analysis, 38(2), 410-430.
Edmunds, J. A., Bernstein, L., Unlu, F., Glennie, E., Willse, J., Smith, A. & Arshavsky, N. (2012). Expanding the start of the college pipeline: Ninth-grade findings from an experimental study of the impact of the Early College High School Model. Journal of Research on Educational Effectiveness, 5(2), 136-159.
Edmunds, J. A., Unlu, F., Glennie, E., Bernstein, L., Fesler, L., Furey, J., & Arshavsky, N. (2017). Smoothing the transition to postsecondary education: The impact of the early college model. Journal of Research on Educational Effectiveness, 10(2), 297-325.
Recruitment: To be eligible for this retrospective study, an EC (Early College High School) had to meet the following criteria for at least 1 of 3 school years (2005–2006 through 2007–2008): (a) enrolled students in Grades 9 to 12; (b) had high school graduates; (c) were oversubscribed and used lotteries in their admission processes for incoming ninth graders; and (d) retained the lottery records. The potential study sample was restricted to all Early College High Schools that were open by fall 2007 to ensure that students in the study would have had the opportunity to complete at least 2 years of college by the end of data collection (e.g., 2012–2013). Of the 154 Early College High Schools open nationwide by fall 2007, about two-thirds were not eligible for the study because they were undersubscribed. Of the remaining models, 10 met the criteria for inclusion in this study.
Assignment: The sample included 17 lotteries across 10 sites and 3 cohorts, with 1,044 students randomly assigned to treatment and 1,414 students randomly assigned to control. Treatment students were lottery applicants who were offered enrollment either through the initial lottery or from a randomized waitlist prior to the first day of school, and control students were lottery applicants who were not offered enrollment. Two schools in the first two cohorts conducted the lottery themselves.
Attrition: Only 2,207 students had high school graduation data (an attrition rate of 10%), and the reasons were not provided (See Table 3). The authors reported no attrition (n = 2,458 at the 2-year follow-up) for the postsecondary outcomes (i.e., college entrance and completion). In terms of risk & protective factors, students from Cohort 1 were not surveyed because the authors were not confident in students’ ability to recall their high school experiences multiple years after leaving high school. The survey response rate was 94% for treatment students and 88% for control students. Non-response-adjusted survey weights were applied to all analyses of survey data.
Sample: Sample characteristics were reported by condition. Among the treatment group, 51.8% were female; 52.5% were minority; 21.6% were first-generation college going; and 47.3% were low-income students. Among the control group, 52.9% were female; 53.7% were minority; 19.9% were first-generation college going; and 45.2% were low-income students.
Measures: Haxton et al. (2016) gathered administrative data from Early College High Schools, districts, and state departments of education on high school outcomes. The specific data sources for these variables differed by site, and in some sites, they were able to obtain data for the same measure from multiple sources. For high school graduation, the percentage of students earning a high school diploma or general equivalency diploma (GED) was measured.
Data for postsecondary enrollment and degree attainment came from the National Student Clearinghouse (NSC; 2015), which collects data from higher education institutions across the country and covers more than 98% of all student enrollments in public and private colleges and universities. The authors assumed that students for whom the NSC could not find matching records did not attend college or attain a postsecondary degree. For college enrollment and college degree attainment, whether students enrolled in college or earned any degree by fall 2013 (the end of study data collection) was examined, including either during or after high school. College degree meant any postsecondary credential, including certificates, associate’s degrees, or bachelor’s degrees. College enrollment and degree attainment outcomes were measured in three ways: 1) outcomes at any point in the study period; 2) cumulative outcomes by Year 4, Year 5, and Year 6; and 3) outcomes after high school (after Year 4). Year 4 reflected the period when students would traditionally be in their final year of high school. For students on a traditional trajectory, Year 5 was the year immediately following high school graduation. Year 6 (i.e., 2 years after high school graduation in a typical time frame) was the last year for which the authors had data for all students in the study. The study analyzed college outcomes at different points in time to parse out the timing of the effects, and thus examine whether the treatment impact was concentrated in the high school years or whether the impact persisted after high school.
The authors also collected data on students’ high school experiences through a student survey. The survey was administered in 2012—after their expected high school graduation—to 1,416 randomly selected students in the two oldest cohorts. Students’ experiences during high school were measured through college exposure and student supports. For college exposure, students’ college course-taking and credit accumulation as well as their Advanced Placement (AP) course-taking and exam passage in high school were measured. For student supports, the college-going culture in the school (a 1 to 4 scale based on three survey items, α reliability = .80), instructor support (a 1 to 4 scale based on six items, α reliability = .88) and whether students had access to general information about college were measured.
To estimate the overall program effects across lotteries, Haxton et al. (2016) constructed a two-level model that took into account the clustering of students within lotteries. The treatment indicator was group-mean centered at the student level to make sure the comparisons of treatment students and control students were made within, rather than across, lotteries and thus produced unbiased estimates. The authors modeled the intercept as a random effect to take into account the clustering of student outcomes within lotteries and modeled the treatment effect as fixed at the lottery level because the number of lotteries in the study was too small to generate stable estimates of the variation in treatment effects across lotteries. Across all analyses, all models included the following student background characteristics as covariates: gender, race and ethnicity, first-generation college-going status, low-income status, and academic achievement prior to high school.
The authors also used multiple imputation to address the potential selection bias caused by missing covariates. The primary impact analyses used imputed covariate values and excluded students with missing outcomes.
Haxton et al. (2016) also conducted moderation analyses. The models incorporated an interaction between treatment status and a given student characteristic into the student-level model. The lottery-level estimate for the interaction term captured the average difference in the treatment effect on high school graduation between student subgroups (e.g., female and male) across all lotteries in the study sample. Authors explored whether the effects of being admitted to the program differed significantly by gender, minority status, first generation college-going status, low-income status, or level of prior mathematics and ELA achievement. These analyses were conducted for the three key outcomes: 1) high school graduation, 2) college enrollment, and 3) college degree attainment.
Intent-to-Treat: All participants were analyzed according to the condition in which they were assigned and all available data were utilized in the analysis which is in line with intent-to-treat protocol. Multiple imputation was conducted for missing covariates, and included all background and outcome variables available from both the extant data and the student survey data. Researchers generated 10 imputed data sets, conducted all analyses using each imputed data set separately, and then combined estimates across the 10 data sets, taking into account the uncertainty in imputed values both within and across the imputed data sets. Students with missing outcome data were excluded from the analysis.
Implementation Fidelity: Not reported.
Baseline Equivalence: Baseline equivalence was tested on a variety of background characteristics including gender, minority status (i.e., non-White), eligibility for free or reduced-price lunch, first-generation status, and English language arts (ELA) and mathematics test scores prior to high school, and there was a significant difference in English language arts scores in favor of treatment.
Differential Attrition: There was high attrition (10%) for high school graduation rates, but tests for differential attrition were not conducted. However, the authors reported no attrition on the postsecondary outcome measures.
Posttest: Haxton et al. (2016) reported no significant difference between treatment and control in high school graduation rates.
As for risk and protective factors, Haxton et al. (2016) found that, as compared to the control group, treatment students were more likely to earn “any” college credits (OR = 8.04) and 1 year of college credit (OR = 18.61) in high school, and to report stronger college-going cultures (ES = .32) and support from instructors (ES = .32) in their high schools. Treatment students were also more likely to have access to general college information in school than the control group (OR = 1.31), though this effect was marginally significant (p = .06). In contrast, students in the treatment group, compared to the control group, were less likely to take at least one Advanced Placement (AP) course (OR = 0.23) and pass at least one AP exam (OR = 0.19).
Long-Term: At the posttest (2 years after high school graduation in a typical time frame), Haxton et al. (2016) found that being admitted to the program had a statistically significant positive impact on college enrollment: 80.9% of treatment students had at least one record of college enrollment, roughly 9 percentage points higher than the 72.2% college enrollment rate for control students (OR = 1.63). Impacts in favor of treatment were detected for college enrollment by Year 4 (OR = 5.41), Year 5 (OR = 1.72), and Year 6 (OR = 1.73). When examining enrollment by school type (2 vs. 4-year colleges), compared to the control group, students in the treatment group were more likely to enroll in a 2-year college (OR = 2.33), and enroll in a 2-year college by Year 4 (OR = 6.82), Year 5 (OR = 2.85) and Year 6 (OR = 2.46). In addition, compared to control, students in the treatment group were more likely to enroll in a 4-year college by Year 4 (OR = 2.92) and Year 6 (OR = 1.32).
Meanwhile, being admitted to the program had a positive impact on degree attainment. By the end of the 2012–2013 academic year, 24.9% of treatment students earned postsecondary degrees compared with 4.7% of control students (OR = 6.71). Specifically, compared to control, students in the treatment group were more likely to have earned an Associate’s degree (OR = 12.14), a Bachelor’s degree (OR = 3.80), “any degree” by Year 4 (OR = 35.37), “any degree” by Year 5 (OR = 21.38), and “any degree” by Year 6 (OR = 14.66). The program effect on college degree attainment (in favor of the treatment group) was significantly stronger for minority students and low income students, and students who entered high school with higher math and ELA scores. The program effects were weaker for female students on high school graduation and college enrollment, meaning that male students benefited more from the program on these outcomes, but these effects were marginally significant (p < .1).
Recruitment: The schools recruited for this study were located in rural and urban settings in all regions of North Carolina. The authors reported that while demographically similar to traditional schools, the early colleges included in this study were smaller than the traditional schools in their counties, had much lower enrollments of students with disabilities, and enrolled students with higher initial levels of achievement. To participate in the study, schools had to have more applicants than available slots and had to agree to use a lottery to randomly assign students. Schools entered the study on a rolling basis, and as long as they continued to use the lottery, they could continue to contribute cohorts of students to the study.
Assignment: Edmunds et al. (2017) reported a total of 1,689 students who applied to 12 different early colleges across North Carolina and enrolled in ninth grade in the 2005–2006, 2006–2007, 2007–2008, and 2008–2009 school years were enrolled in the lottery (though Edmunds et al., 2012, reported 1,607 students were in the lottery, so the exact sample size is unclear). These 12 schools enrolled a total of 18 cohorts of students, with 5 schools enrolling multiple cohorts. Beginning with the 2007–2008 cohort, the research team began conducting the lotteries (though 2 schools in the first cohort conducted their own lottery). Lottery assignments operated as follows. Each applicant who met the school’s eligibility criteria was assigned a random number and the list of students was ordered from lowest to highest, with the lowest numbers being selected into the early college until all available slots were filled. As a result, 953 students were assigned to treatment and 736 were assigned to the control condition (see Figure 2).
Attrition: Using 1,607 as the randomized sample, Edmunds et al. (2012) reported attrition rates of 18% (1315/1607) for algebra, and less than 1% for college prep math courses (1607/1607), college prep English courses (1607/1607), and absences/suspensions/plans to attend a 4-year college (1604/1607). Meanwhile, Edmunds et al. (2017) reported, “Because we use extant administrative data, we are able to include almost all students from the original lottery samples in our analyses” (p. 304). Overall attrition rates, however, varied according to the outcome measures. For enrollment in postsecondary education and attainment of a postsecondary credential, overall attrition rates were 2% (1651/1689). For high school graduation, it was 6% (1594/1689), and for college credits attained in high school, it was 15% (1437/1689).
Sample: The overall sample was 41% male, and the racial/ethnic composition of the students was 60.2% white, 26.7% black, and 8.3% Hispanic. Forty-one percent of the students were first-generation college going and 50.7% were eligible for free or reduced lunch. Participating schools (n=12) served about 142 students (on average); and had a teacher turnover rate of 17% and a novice teacher rate of 31.2%.
Edmunds (2012) used administrative data to measure college-prep math courses taken in 9th grade, defined as taking/passing Algebra I, Geometry, or Algebra II. Administrative records were also used to measure 9th grade school attendance and the percentage of students who had been suspended at least once in 9th grade. Students’ plans to attend a 4-year college were assessed using data from a survey that accompanied each state-mandated end-of-course exam during the period of the study.
Edmunds et al. (2017) used administrative data collected by 3 primary sources: 1) the North Carolina Department of Public Instruction, 2) the National Student Clearinghouse, and 3) the North Carolina Community College System.
Analysis: Edmunds et al. (2012) conducted an ITT analysis. Primary impact estimates were obtained from a multivariate linear regression model, with a fixed Treatment X Block (school-level) interaction term. Authors calculated an overall ITT impact estimate by averaging these block-specific effects, weighting them proportionally to the total number of students (treatment and control) in each block. In addition, several sensitivity and specification tests were run (none of which yielded results substantively different from those yielded by the primary analytic strategy). Authors adjusted for multiple comparisons for all core outcomes assessing main effects using the Benjamini-Hochberg multiple comparisons correction.
Edmunds et al. (2017) used multivariate linear regression models that included lottery indicators (or lottery fixed effects), a treatment indicator capturing the initial group to which a student was randomly assigned, and baseline student characteristics including demographic characteristics such as gender, race/ethnicity, age, free/reduced price lunch status, whether a student was retained prior to eighth grade, and eighth-grade academic performance. The statistical inference took into account clustering of students within schools by calculating cluster-robust standard errors. The models were estimated using weights that were based on students’ probability of being selected into the early college. This was done because some schools conducted a stratified lottery, which led to different probabilities of selection into the treatment condition. Similar models were run on sub-group analyses, which examine results by race/ethnicity, income, and first-generation status.
In addition to the ITT analysis, Edmunds et al. (2012) presented instrumental variable estimates to represent the average effect of attending an early college (local average treatment effect [LATE]).
Intent-to-Treat: All participants were analyzed according to the condition in which they were assigned and all available data were utilized in the analysis, which is in line with intent-to-treat protocol. The authors also stated they conducted an ITT analysis.
Implementation Fidelity: Not reported.
Baseline Equivalence: Out of 13 baseline variables, including 9 socio-demographic (Black, Hispanic, White; gender; first-generation college, free/reduced price lunch eligibility; disabled/impaired, gifted, retained) and 4 pretest scores (8th grade reading and math scores; percent passed reading and math in 8th grade), 2 significant differences were detected between treatment and control at baseline – being retained in elementary or middle school (effect size = 0.37) and passing the eighth-grade math exam (effect size = 0.16), both in favor of the treatment group.
Differential Attrition: Not conducted. Attrition was >5% for 1 (college credits received) of the 3 outcomes; otherwise attrition was low.
Posttest: In the ITT analysis, Edmunds et al. (2012) found that compared to the control group, students in the treatment group had significantly higher school attendance and lower suspension rates at the end of 9th grade. Though the LATE estimates were similar and presented in Table 3, no significance levels were reported. Edmunds et al. (2017) found that treatment students earned significantly more college credits than control students while in high school. That is, by the end of 12th grade, treatment students had earned an average of 21.6 transferable college credits compared to an average of 2.8 credits earned by the control group. Edmunds et al. (2017) reported no impact on five-year high school graduation rates
Long-Term: At the posttest (2 years after high school graduation in a typical time frame), treatment students were more likely to be enrolled in postsecondary education than control students. Specifically, by the beginning of the 6th year after entering 9th grade, 89.9% of the treatment group had enrolled in postsecondary education at least once (including enrollment while in high school), compared to 74.3% of the control group. In addition, more treatment students attained some sort of postsecondary credential compared to the control group; 30.1% of treatment group students, compared to 4.2% of the control group, had attained a postsecondary credential.