TOWARDS AN OBJECTIVE EVALUATION OF TEACHER PERFORMANCE Journal website: http://epaa.asu.edu/ojs/ Manuscript received: 7/23/2017 Facebook: /EPAAA Revisions received: 2/5/2018 Twitter: @epaa_aape Accepted: 2/6/2018 education policy analysis archives A peer-reviewed, independent, open access, multilingual journal Arizona State University Volume 26 Number 43 April 9, 2018 ISSN 1068-2341 Enrollment of Students with Disabilities in Charter Schools: Contemporary National and State Level Findings Lucy Barnard-Brak Marcelo Schmidt & M. Hasan Almekdash Texas Tech University United States Citation: Barnard-Brak, L., Schmidt, M., & Almekdash, M. H. (2018). Enrollment of students with disabilities in charter schools: Contemporary national and state level findings. Education Policy Analysis Archives, 26(43). http://dx.doi.org/10.14507/epaa.26.3276 Abstract: There is no national study examining the rate of enrollment of students with disabilities in charter schools. We examined whether students with disabilities were significantly less likely to enroll in charter schools as compared to non-charter public schools accounting for state level variation using data for the entire national population. We utilized data from the Civil Rights Data Collection under the U.S. Department of Education for the 2011-2012 and 2013-2014 academic years. These nationwide and contemporary data provided school-level numbers of students with disabilities receiving special education services under the Individuals with Disabilities Education Act (IDEA) and charter school status. We performed hierarchical linear modeling to examine for differences in the percentages of students with disabilities under IDEA between charter and non- charter schools, which revealed significantly less students with disabilities enrolled in charter schools at the national and state level. Additionally, we identified and ranked states according to the degree of discrepancy in the percentages of students with disabilities under IDEA between charter and http://epaa.asu.edu/ojs/ http://dx.doi.org/10.14507/epaa.26.3276 Education Policy Analysis Archives Vol. 26 No. 43 2 non-charter schools. Keywords: charter schools; school choice; special education; students with disabilities La matrícula de alumnos con discapacidad en charter schools: Resultados contemporáneos a nivel nacional y estatal Resumen: Hay en el estudio del país examinar el ritmo de la inscripción de los estudiantes con discapacidad en charter escuelas. Se ha comprobado que los estudiantes con discapacidad son menores que la probabilidad de que se inscriban en charter de las escuelas que se comparan a no contables de las cuentas públicas de estado para la variable de estado establecida utilizando fecha para la población de la población. Hemos utilizado fecha de la the Civil Rights Data Collection del Departamento de Educación de los Estados Unidos para el 2011-2012 y 2013-2014 academic years. Estos nacionales y contemporáneos de datos de nivel de referencia de los números de estudiantes con discapacidad reciben servicios de educación especial bajo los Individuals with Disabilities Education Act (IDEA) y charter school status. Se trata de jerárquicas lineales de modelado para examinar las diferencias en los porcentajes de los estudiantes con discapacidad en IDEA entre charter y non-charter de las escuelas, que muestran diferencias significativas de los estudiantes con discapacidad en charter de las escuelas en el nivel nacional y de nivel. Además, hemos identificado y clasificado los estados según el grado de di screpancia en los porcentajes de los estudiantes con discapacidad bajo IDEA entre charter y no charter. Palabras claves: charter schools; escuela elección; educación especial; estudiantes con discapacidad A matrícula de alunos com deficiência em escolas charter: Resultados contemporâneos em nível nacional e estadual Resumo: Há um estudo no país para examinar a taxa de matrícula de alunos com deficiência em escolas charter. Ficou provado que os alunos com deficiência são menores do que a probabilidade de serem matriculados em escolas charter que são comparados a contas públicas públicas não responsáveis pela variável estadual estabelecida usando data para a população da população. Usamos a data da Civil Rights Data Collection do Departamento de Educação dos Estados Unidos para os anos letivos de 2011-2012 e 2013-2014. Esses números de linha de base de dados nacionais e contemporâneos de alunos com deficiências recebem serviços de educação especial de acordo com a Individuals with Disabilities Education Act (IDEA) e o status de escola charter. Trata-se de hierarquias de modelagem linear para examinar as diferenças nas porcentagens de alunos com deficiência no IDEA entre escolas charter e não charter, que mostram diferenças significativas de alunos com deficiências em escolas charter em nível nacional e internacional. de nível. Além disso, identificamos e c lassificamos os estados de acordo com o grau de discrepância nas porcentagens de alunos com deficiências no IDEA entre o afretamento e o não-afretamento. Palavras-chave: escolas charter; escolha escolar; educação especial; alunos com deficiências Enrollment of students with disabilities in charter schools 3 Introduction Charter schools have become a fixture of public education in the United States. These schools are similar to public schools in many ways, but are distinct in the fact that these schools typically have a specific contract or charter in which to operate autonomously (Rhim & McLaughlin, 2007). Charter schools were conceptualized as a remedy to improve educational outcomes by providing an alternative to public schools (Seltzer, 2013). Despite operating with greater autonomy and flexibility, charter schools must adhere to the federal requirement to provide free appropriate public education (FAPE) within the least restrictive environment (LRE) to all children under the Individuals with Disabilities Education Act (Levesque, 2011). The requirements of FAPE and LRE together are what form the core of inclusive practices for children with disabilities. The overarching purpose of IDEA, as it initially passed as the Education of All Handicapped Children Act of 1975, is two-fold. First, IDEA is an anti-discrimination measure conceived in the spirit of the civil rights movement for political and social justice (Harr-Robins et al., 2012). Second, IDEA promotes educating children with disabilities in a similar way to children without disabilities so that these children mature to become self-sufficient adults that require less public assistance and can contribute in meaningful ways (Palmaffy, 2001; Seltzer, 2013). The civil rights movement with respect to education emphasized equity through both legislation and court rulings at the federal level. While Brown vs. Board of Education in 1954 was a seminal, landmark court ruling with respect to the racial desegregation of the schools (Harr-Robins et al., 2012), Section 504 of the Rehabilitation Act of 1973 stated that any recipient receiving federal funds cannot discriminate against individuals with disabilities (Aron & Loprest, 2012). As all public schools receive some amount of federal funding, Section 504 was extended to students with disabilities stating that students should have similar access to the education received by students without disabilities. The Rehabilitation Act was the first time federal civil rights protections included individuals with disabilities (Aron & Loprest, 2012). In 1975, IDEA was passed and provided that children with disabilities were entitled to a free, appropriate public education (FAPE) in the least restrictive environment (LRE) alongside children without disabilities. This phrase from IDEA initiated the path to creating inclusive learning environments for children with disabilities to be educated with their non-disabled counterparts. IDEA as passed in 1975 acknowledged what had been found twenty years earlier in the Brown decision from 1954 that separate was inherently unequal. Before the passage of IDEA in 1975, “only one in five children with identified disabilities attended public school,” (Aron & Loprest, 2012, p. 100). Additionally, many states specifically excluded students with certain disabilities such as those children with sensory impairments (i.e., being visually or hearing impaired) as well as those children with intellectual disabilities or emotional/behavioral disorders (Aron & Loprest, 2012). While significant strides have been made for students with disabilities, the full realization of IDEA remains elusive as schools struggle with the full inclusion of students with disabilities. Additionally, Blanchett (2009) and others have questioned whether special education placement has not been employed as a mean of resegregating schools by disproportionately identifying children from African American backgrounds as disabled. Given this claim of resegregation, the full realization of IDEA in providing FAPE in the LRE should continue to be vigorously pursued. Theoretical Framework Bagley (2006) noted how the emergence of charter schools has been part of a greater discussion on school choice. As part of this greater debate on school choice, Chubb and Moe (1990) Education Policy Analysis Archives Vol. 26 No. 43 4 proffered a market theory of school choice. Chubb and Moe posited that when parents have choice among schools that schools would compete and produce better options for students as part of a market economy for schools. Parents being provided with more choices for their students has manifested in many ways (i.e., vouchers and specialized/magnet schools). Another manifestation of school choice was the emergence of charter schools. However, Bagley (2006) has noted that schools have begun to influence the selection of students rather than students and their families selecting schools in a period of post-market theory for school choice. Bagley (2006) explains this inversion of school choice, schools are selecting students as the result of the demand for well-performing schools in many cases has outstripped the supply of these schools. Gewirtz (2002) noted this phenomenon of school choice being inverted as being an unintended and unforeseen circumstance of school choice policy being implemented into practice. This inversion of school choice takes place when schools are influencing directly and indirectly the selection of students and their families rather than parents selecting schools for their students amongst schools that compete with each other for students. Welner (2013) discussed twelve ways in which charter schools influence the enrollment of students. These twelve ways were termed the “dirty dozen,” (Welner, 2013, p. 1) implicitly noting this inversion of school choice from its intended path. These dozen ways have varied from the marketing materials that a school produces that, “can send distinct messages about who is welcome and who is not,” (Welner, 2013, p. 3) to burdensome application procedures and access to transportation (Marshall, 2017).. Students with disabilities are often considered less desirable students for charter schools given the emphasis on performance as well as the financing of special education providing few incentives. Charter School Practices Several scholars have questioned whether charter schools consistently provide free appropriate public education to children with disabilities and whether the number of enrollees with special needs is proportional to that of non-charter public schools (e.g., Estes, 2004, 2009; Rhim & McLaughlin, 2007). This speculation seems warranted considering that charter schools have been found to vary in their approach to defining educational services offered to students with disabilities, inadequate in documenting the impact of the services provided, and lacking in special education personnel (Ahearn, Lange, Rhim, & McLaughlin, 2001). Additionally, it has been documented that some charter schools practice cropping out of students with disabilities by steering them to non- chartered, public schools (Lacireno-Paquet, Holyoke, Moser, & Henig, 2002). A similarly discriminatory practice is that of counseling out students from charter schools once it is learned that they have a disability (e.g., Fiore, Harwell, Blackorby, & Finnigan, 2000; Garda, 2012; Rhim, Lange, Ahearn, & McLaughlin, 2007; Welner & Howe, 2005; Zollers & Ramanathan, 1999). In the case of counseling out of students, charter schools may suggest to parents that their school is not an appropriate fit for their child (Fiore et al., 2000) or that the child’s needs exceeded the resources available at the school (Welner & Howe, 2005). While the practice of counseling out students has been questioned by some scholars as lacking empirical evidence (Winters, 2015; Zimmer & Gaurino, 2013) and some have alluded to this phenomenon being more an issue of selection bias on the part of parents (e.g., Booker, Gilpatric, Gronberg, & Jansen, 2007; Hanushek, Kain, Rivkin, & Branch, 2007 ). The issue of selection bias on the part of parents is however, inextricably intertwined with their interactions with a charter school. A charter school offering a chilly reception with a reluctance to provide accommodations will certainly influence whether a parent of a child with a disability decides to enroll. Selection bias is difficult to untangle without taking into account the interactions a parent may have with a charter school. The presence of selection bias, however does not deflect responsibility from charter schools Enrollment of students with disabilities in charter schools 5 in the lower enrollment of students with disabilities. If we were to hypothetically replace the word, disability with race, ethnicity, or gender, we would be disturbed if scholars explained away the lower enrollment of students from ethnic minority backgrounds in any type of school as a function of the selection bias on the part of parents. Disability is just as much an integral part of the fabric of human diversity and the lower enrollment of students with disabilities in charter schools should not be explained away. We should note that charter schools do appear to serve comparatively more students from African American and Latino(a) backgrounds than non-charter schools, which has been attributed to the location of many charter schools in urban areas (Berends, 2015; Epple, Romano, & Zimmer, 2015). However, Frankenberg, Siegel-Hawley, and Wang (2010) note that this has perpetuated segregated school environments for children from African American and Latino(a) backgrounds. Frankenberg et al. (2010) note that for students from African American backgrounds who attend charter schools are actually more “intensely segregated” (p. 4) with 90% to 100% being from under-represented minority backgrounds as compared to their counterparts in traditional public schools. Alternatively, some research has indicated that a more plausible reason for the lower enrollment of students with disabilities at charter schools may simply be that of available resources to serve the needs of children with disabilities as they are directly related to cost, especially for those schools that are operated for-profit (Miron, Urschel, Mathis, & Tornquist, 2010). Many charter schools operate based upon free market principles and as a result, may, “offer a parallel school system for some but not all students,” (Waitoller & Thorius, 2015, p. 30). Indeed, Miron et al. (2010) found that for-profit charter schools did appear to enroll less students with disabilities than non- profit charter schools. From the perspective of charter schools, Kose (2013) notes a child or children with severe enough disabilities could likely bankrupt a charter school when requiring additional personnel as well as individualized technology devices for communication and educational tasks such as for when reading or interacting with materials. Thus, it is not surprising that, “charter schools may selectively market themselves to families,” (Epple, Romano, & Zimmer, 2015, p. 7). In acknowledging this potential for the needs of children with disabilities to exceed resources available at charter schools, special education cooperatives like those utilized across rural school districts have been suggested as a solution (O’Neill & Rhim, 2015). Conversely, it is important to acknowledge the emerging role of niche or haven charter schools in serving the needs of students with disabilities such as charter schools specifically for students with Autism Spectrum Disorder (Decker, Seitz, & Kulwicki, 2015; Eckes, 2015), which may be better prepared to serve the needs of these children more adequately than public non-charter schools. We can acknowledge some merit to this argument but we can not accept it at face value given the history of how individuals with disabilities have been treated by our educational system and broader society, thus we must be cautious in our evaluation of these schools. These haven or niche charter schools can result in school environments that are intensely segregated violating the spirit of a free appropriate public education in the least restrictive environment as well as the civil rights movement. An inclusive learning environment that includes students with and without disabilities is considered an essential component of a free appropriate public education in the least restrictive environment (e.g., Villa & Thousand, 2005; Yell & Christle, 2017). An inclusive learning environment cannot be achieved if all of the students in the school have a disability. Literature on Enrollment In reviewing the literature on the enrollment of children with disabilities in charter schools, it appears that most analyses have been restricted to data particular to select states or regions (e.g., Estes, 2009; Hehir, 2010; Rhim & McLaughlin, 2007; Setren, 2015). For instance, Hehir (2010) Education Policy Analysis Archives Vol. 26 No. 43 6 examined data for the state of Massachusetts and found that significantly fewer students with disabilities were enrolled in charter schools as compared to non-chartered public schools. Rhim and McLaughlin (2007) found a similar discrepancy in the number of students with disabilities enrolled in charter schools as compared to non-chartered public schools in California. However, in examining charter schools in Texas, Estes (2009) found comparable rates of students with disabilities enrolled in charter (12.5%) and non-charter, public (11.5%) schools. Thus, variation in the percentage of children with disabilities enrolled in charter versus non-charter schools does appear to exist by state, which may be a function of how charter schools are authorized and regulated by state. We have reviewed the extant literature and there does not appear to be a peer-reviewed study that examines whether children with disabilities enroll in charter schools at significantly lower rates as compared to non-charter public schools across the nation accounting for state level variations. Three non-peer-reviewed studies were found that examined this issue nationally. The first of which was conducted over fifteen years ago and examined children with disabilities enrolled in charter schools across 27 states (RPP International, 2000). In this study, the authors found that lower percentages of children with disabilities were enrolled in charter schools as compared to non- charter public schools. However, this study did not statistically account for state level variations to provide a national estimate. Additionally, this study retained charter schools designated as especially serving children with disabilities as special education schools and other alternative schools, which would appear to under-estimate the discrepancy in enrollment between charter and non-charter public schools. The second and third non-peer-reviewed studies were a report from the General Accounting Office (GAO, 2012) and a report from the National Center for Special Education in Charter Schools (Rhim, Gumz, & Henderson, 2015), both which descriptively reiterated the under- enrollment of students with disabilities in charter schools. However, neither study accounted for state level variations and included all charter schools even those focusing on serving children with disabilities that again under-estimates the extent of the discrepancy (GAO, 2012; Rhim, Gumz, & Henderson, 2015). More recently, Losen, Keith II, Hodson, and Martinez (2016) presented evidence that at the national level, enrollment percentages of students with disabilities at charter schools is lower than that of non-charter schools, yet their analysis did not examine state level data or account for these variations. Accounting statistically for this state level variation is important as states provide the legislative and regulatory framework that governs charter schools (Rhim, Ahearn, & Lange, 2007). Nationally, Miron et al. (2010) did examine the enrollment of students with disabilities amongst charter schools comparing non-profit and for-profit charter schools. The lack of data regarding traditional public schools precluded the comparison of enrollment between charter versus non-charter public schools. Purpose The purpose of this study was to examine the number of students with disabilities enrolled in charter versus non-charter public schools. We examined the percentage of students receiving special education services by school comparing charter and non-charter public schools across the United States. The current study provides the most complete and contemporary examination of the enrollment patterns of students with disabilities comparing charter and non-charter schools across the United States. Using data from the Civil Rights Data Collection, data from 46 states including the District of Columbia were available for analysis to provide a nationally representative picture. Additionally, the analyses conducted omit charter schools designated as special education or alternative schools from analyses to obtain a more accurate estimate as to the possible discrepancy Enrollment of students with disabilities in charter schools 7 of students with disabilities enrolled between charter versus non-charter public schools. Finally, in the current study, we have identified and ranked states by the degree of any discrepancy in the enrollment of students with disabilities between charter versus non-charter public schools. Method Sample Data were obtained from the public use 2011-2013 and 2013-2014 Civil Rights Data Collections under the Department of Education Office of Civil Rights (U.S. DOE, 2012 , 2016), which surveyed approximately 95,500 schools across the fifty states including the District of Columbia representing approximately 50 million students from pre -K to 12th grade in public schools during the 2011-2012 academic year. Given the purpose of the current study, schools that were designated as special education or alternative schools (and thus students) were subsequently removed from analyses. For the 2011-2012 school year data, the resultant sample size was 48,767,882 students across 88,497 schools in the United States retaining approximately 98% of the total sample. Approximately 5.4% (n = 4,800) being charter schools and 94.6% (n = 83,677) being non-charter schools. Of the students, approximately 11.9% (n = 5,795,018) were students receiving special education services under IDEA. We retained approximately 95% of the sample of students receiving special education services despite removing schools designated as special education or alternative schools. Only approximately 5% (n = 2,849) of special education schools were charter schools located across 26 states with the states of California (n = 33) and Ohio (n = 32) having the most. Approximately 6% (n = 297) of alternative schools were charter schools located across 28 states with the states of California ( n = 33) and Ohio (n = 32) having the most. We did not discern a geographical pattern in the intersection of charter school status with being an alternative or special education school. We also should note that an additional approximately 1.5% (n = 746,923) were students receiving special education services under Section 504 of the Rehabilitation Act. For the 2013-2014 school year data, the resultant sample size was 49,209,558 students across 88,950 schools in the United States retaining approximately 98% of the total sample. Approximately 6.4% (n = 5,674) being charter schools and 93.6% (n = 83,276) being non- charter schools. Of the students, approximately 11.8% (n = 5,840,158) were students receiving special education services under IDEA. We retained approximately 96% of the sample of students receiving special education services despite removing schools designated as special education or alternative schools. Only approximately 5% (n = 108) of special education schools were charter schools located across 23 states with the states of Florida (n = 33) and Ohio (n = 32) having the most. Approximately 6% (n = 297) of alternative schools were charter schools located across states with the 26 states of Arizona (n = 53) and Florida (n = 52) having the most. We did not discern a geographical pattern in the intersection of charter school status with being an alternative or special education school. We also should note that an additional approximately 1.8% (n = 910,092) were students receiving special education services under Section 504 of the Rehabilitation Act yet, we focused on students receiving special education services under IDEA . In the current study, we focused on students receiving special education services under IDEA.Additionally, we retained schools regardless of magnet school status. Tables 1 provides the descriptive statistics for the data for both 2011-2012 and 2013-2014 school years. Education Policy Analysis Archives Vol. 26 No. 43 8 Table 1 Descriptive statistics by school and years 2011-2012 2013-2014 n % n % Number of Charter Schools 5,293 5.5 6,054 6.3 Number of students attending charter schools 2,035,111 4 2,562,793 5 Number of students in special education schools 2,625 2.7 2,121 2.2 Number of students attending special education schools 416,809 0.8 357,174 0.7 Number of alternative schools 4,870 5.1 4,444 4.7 Number of students attending alternative schools 602,119 1.2 501,964 1.8 Number of students receiving special education services under IDEA 6,082,911 12.2 6,104,340 12.1 Measures All measures were obtained from the Office of Civil Rights Data Collection , which collects data biennially. We conservatively excluded from our analyses states that did not have charter school laws for both school years of data collection (i.e., 2011 -2012 and 2013-2014) or had substantial changes in their charter school law between rounds. Table 2 provides a reason for each state excluded from analyses. The percent of students receiving special educa tion services under IDEA was calculated by taking the total number of students receiving special education services under IDEA divided by the total number of students in each school. The average percentage of students receiving special education services under IDEA was 12.4% (SD = 0.07) per school. The distribution of this variable was mostly normal with exception of slight negative skew with approximately 4.3% (n = 3,787) of schools reporting no students receiving special education services under IDEA. As a result of this modest non-normal distribution, we interpreted fixed effects with robust standard errors (Garson, 2013). Table 2 Reason for exclusion from analyses by state State Exclusion Reason Alabama No charter school law for 2011-2012 & 2013-2014 Kentucky No charter school law for 2011-2012 & 2013-2014 Maine No charter school law for 2011-2012 & 2013-2014 Mississippi According to the National Alliance for Public Charter Schools, the state of Mississippi enacted a “significant overhaul” (p. 96) of charter schools in 2013 limiting the ability to compare between 2011-2012 and 2013-2014.† Montana No charter school law for 2011-2012 & 2013-2014 Nebraska No charter school law for 2011-2012 & 2013-2014 North Dakota No charter school law for 2011-2012 & 2013-2014 South Dakota No charter school law for 2011-2012 & 2013-2014 Vermont No charter school law for 2011-2012 & 2013-2014 Washington No charter school law for 2011-2012 West Virginia No charter school law for 2011-2012 & 2013-2014 †(NAPCS, 2016). Enrollment of students with disabilities in charter schools 9 Analyses We employed hierarchical linear modeling techniques given that data were nested within states. Hierarchical linear modeling techniques permit for state -level variation in school data to be accounted. As charter schools were not consistently clustered under local education agencies as compared to typical public schools, we were unable to examine the influence of local education agency as a distinct level. Therefore, the model consisted of two levels. For the first level, we examined for school-level variation in the percent of students receiving special education services under IDEA. For the second level, we examined for state -level variation in the percent of students receiving special education services under IDEA as schools nested within states. Analyses were conducted using HLM (v. 7.0; Raudenbush, Bryk, & Congdon, 2011). After evaluating the unconditional or intercept-only model via χ2 deviance testing, we provide the equations for the full, conditional model’s level 1 and 2 equations along with the combined equation below: Level 1: Percent_IDEA = β0 + β1(Charter_School) + r Level 2: β0 = γ00 + u0 β1 = γ10 + u1 Combined: Percent_IDEA = γ00 + γ10(Charter_School) + u0 + u1(Charter_School) + r In these equations, the coefficient term β0 represents the average percentage of students served under IDEA, β1 introduces the dummy-coded variable of charter school status covariate, and r is the state level random effect. The term, γ00 is the grand mean or intercept and u0 is the state-level random effect. The term, γ10 estimates the effects of the covariate of charter school status on the intercept and u1 is the state-level random effect according to charter school status. To measure effect size, we calculated a Cohen’s d value based upon HLM parameter estimates using the following formula by Raudenbush, Spybrook, Liu, and Congdon (2005): d = γ/(√(τ00+ σ^2)) In this equation, τ00 represents the between-class variance (i.e., variance between states) and σ 2 is the within-class or residual variance (i.e., variance within states), which when added together equal the total variance (Raudenbush & Bryk, 2002). This equation is functionally equivalent to the Hedges, Pustejovsky, and Shadish (2012) equation. Given how large the CRDC samples were, results tended to be statistically significant (p < .01), which may be considered an artifact of standard errors being incredibly small as standard error (in its most basic form) is standard deviation divided by the square root of the sample size. Our large s ample size drives down the values for the standard errors, thus many mean differences can appear statistically significant. As a result, we have emphasized practical significance through use of effect sizes, namely Cohen’s d values. Cohen’s d values of |.20|, |.50|, |.80| and greater are considered as small, medium, and large values respectively (Cohen, 1988). For the purposes of the study, d values of -.20 and greater were considered practically significant as being at least small according to Cohen (1988) . Education Policy Analysis Archives Vol. 26 No. 43 10 Results We first examined the unconditional or null model, where we included no covariates in the model examining state variation in the percentage of students receiving special education services under IDEA. For the 2011-2012 CRDC data, ICC results indicate that approximately 7.1% of the variance in the percent of students receiving special education services under IDEA may be accounted by variations in states. Thus, there appears to be a small variation in the percentage of students receiving special education services under IDEA that may be accounted for by state. For the 2013-2014 CRDC data, we found a similarly small variation with 7.2% of the variance in the percent of students receiving special education services under IDEA being accounted for by state. We next included the covariate of whether the school was a charter or non-charter public school. This model appeared to fit the data significantly better than the unconditional model for both 2011-2012 data, χ2(2) = 650.62, p < .001 and 2013-2014 data, χ2(2) = 1,224.22, p < .001. The charter schools were associated with a significantly lower percentage of students receiving special education services under IDEA as compared to non- charter schools, β = -.0246, p < .001, d = -0.34 for the 2011-2012 data as well as the 2013-2014 data, β = -.0256, p < .001, d = -0.33. For 2011-2012 data, nationally, charter schools had an average of 9.8% of students receiving special education services under IDEA while non-charter schools had an average of 12.2% of students receiving special education services under IDEA. For 2013 -2014 data, nationally charter schools had an average of 9.9% of students receiving special education services under IDEA while non-charter schools had an average of 12.4% of students receiving special education services under IDEA. Finally, we merged 2011 -2012 and 2013-2014 CRDC data and then calculated a difference score in the percent age of students receiving services under IDEA between rounds of data collection by school. Results indicated no statistically significant difference in the percentage of students enrolled receiving services under IDEA between 2011 - 2012 and 2013-2014 school years, β = -.0014, p = .37. Table 3 provides the complete estimates of fixed effects with robust standard errors for 2011 -2012, 2013-2014, and merged data indicating the difference in percentages of students enrolled receiving services under IDEA. Enrollment of students with disabilities in charter schools 11 Table 3 Fixed effects with robust standard errors Fixed Effect Coefficient Standard Error p-value 2011-2012 data for Percent of IDEA enrollment For INTERCEPT1, β0 INTERCEPT2, u00 0.128404 .003011 < .001 For CHARTER_SCHOOL slope, β1 INTERCEPT2, u10 -0.024600 .005195 < .001 2013-2014 data for Percent of IDEA enrollment For INTERCEPT1, β0 INTERCEPT2, u00 0.128903 .003141 < .001 For CHARTER_SCHOOL slope, β1 INTERCEPT2, u10 -0.025616 .003891 < .001 Merged 2011-2012 and 2013-2014 data for Difference in Percent of IDEA enrollment For INTERCEPT1, β1 INTERCEPT2, u00 0.000044 0.000590 0.941 For CHARTER_SCHOOL slope, β1 INTERCEPT2, u10 -0.001370 0.001485 0.362 We should note that we additionally examined the percent age of students who were not White as a covariate in our analyses, in particular including the interaction of this variable with charter school status. Results indicated that the percentage of students who were not White and charter school status was not significantly associated with the percent age of students receiving special education services under IDEA, β = 2.85, p = .97. By individual state, some states had larger differences in the percent age of students receiving special education services under IDEA between charter and non -charter schools. For the 2011-2012 data, these effect sizes ranged from Oklahoma with a d = -1.53 (CI95: -1.71; -1.34) to Virginia with a d = 0.87 (CI95: -0.23; 1.96). For the 2013-2014 data, these effect sizes ranged from Kansas with a d = -1.18 (CI95: -1.22; -1.13) to Virginia with a d = 0.65 (CI95: 0.57; 0.74). Table 4 provides the effect size in Cohen’s d value as ranked by state along with 95% confidence intervals for the 2011-2012 and 2013-2014 data. This variation between charter and non-charter schools across states would appear to be consistent with other studies indicating variation across locales and states (e.g., Betts & Tang, 2016; Epple, Romano, & Zimmer, 2015). Figure 1 provides the forest plot of each Cohen’s d value with 95% confidence intervals by state for each round of data collection (e.g., 2011-2012 and 2013-2014 school years). From the forest plot, the wider 95% confidence intervals for some states are more apparent such as for Virginia for both rounds of data and Iowa for the 2011-2012 data. Yet, for most states, data were consistent across both rounds of data collection. Education Policy Analysis Archives Vol. 26 No. 43 12 Table 4 States ranked by degree of enrollment discrepancy for students with disabilities 2011-2012 CRDC data 2013-2014 CRDC data State Rank d 95% CI Rank D 95% CI Oklahoma 1 -1.53 -1.71 -1.34 2 -0.99 -0.97 -1.01 New Hampshire 2 -1.34 -1.89 -0.78 4 -0.84 -0.79 -0.89 Delaware 3 -0.98 -1.21 -0.75 28 -0.22 -0.13 -0.31 Missouri 4 -0.93 -1.12 -0.74 8 -0.66 -0.65 -0.67 Arkansas 5 -0.92 -1.21 -0.62 7 -0.69 -0.67 -0.70 New Jersey 6 -0.69 -0.81 -0.57 5 -0.80 -0.78 -0.81 Tennessee 7 -0.62 -0.79 -0. 46 26 -0.26 -0.24 -0.28 Kansas 8 -0.58 -1.15 -0.02 1 -1.18 -1.13 -1.22 Colorado 9 -0.57 -0.70 -0.43 3 -0.87 -0.86 -0.88 Oregon 10 -0.56 -0.70 -0.42 16 -0.34 -0.33 -0.35 South Carolina 11 -0.55 -0.77 -0.32 19 -0.32 -0.30 -0.34 Wisconsin 12 -0.52 -0.66 -0.38 9 -0.62 -0.61 -0.63 Connecticut 13 -0.51 -0.76 -0.27 11 -0.45 -0.42 -0.47 Alaska 14 -0.50 -0.68 -0.32 15 -0.41 -0.39 -0.42 California 15 -0.45 -0.49 -0.42 17 -0.34 -0.33 -0.34 Louisiana 16 -0.45 -0.58 -0.32 20 -0.31 -0.30 -0.32 Indiana 17 -0.44 -0.68 -0.20 33 -0.08 -0.04 -0.11 Florida 18 -0.43 -0.50 -0.36 10 -0.61 -0.60 -0.61 Idaho 19 -0.42 -0.66 -0.18 18 -0.33 -0.31 -0.35 Utah 20 -0.41 -0.50 -0.33 29 -0.22 -0.21 -0.23 Michigan 21 -0.37 -0.44 -0.31 24 -0.27 -0.26 -0.27 District of Columbia 22 -0.36 -0.36 -0.35 35 -0.04 -0.02 -0.05 Arizona 23 -0.34 -0.42 -0.26 12 -0.43 -0.42 -0.43 Nevada 24 -0.33 -0.59 -0.07 14 -0.41 -0.39 -0.43 New York 25 -0.33 -0.45 -0.21 25 -0.27 -0.26 -0.28 Massachusetts 26 -0.32 -0.47 -0.17 27 -0.24 -0.22 -0.25 Rhode Island 27 -0.31 -0.53 -0.09 36 -0.03 -0.003 -0.05 New Mexico 28 -0.26 -0.39 -0.12 23 -0.27 -0.25 -0.29 Georgia 29 -0.17 -0.34 0.01 34 -0.08 -0.06 -0.09 Hawaii 30 -0.17 -0.48 0.15 13 -0.41 -0.39 -0.43 Texas 31 -0.12 -0.22 -0.02 30 -0.21 -0.20 -0.22 Illinois 32 -0.11 -0.33 0.10 38 0.12 0.13 0.11 Wyoming 33 -0.10 -0.99 0.79 6 -0.75 -0.69 -0.82 North Carolina 34 -0.06 -0.23 0.12 22 -0.30 -0.29 -0.31 Pennsylvania 35 -0.04 -0.20 0.12 31 -0.12 -0.10 -0.13 Iowa 36 -0.01 -1.04 1.02 21 -0.31 -0.15 -0.47 Ohio 37 0.002 -0.11 0.12 32 -0.08 -0.07 -0.09 Minnesota 38 0.06 -0.06 0.18 39 0.29 0.32 0.27 Maryland 39 0.42 0.23 0.61 37 0.11 0.12 0.10 Virginia 40 0.87 -0.23 1.96 40 0.65 0.74 0.57 * Shading indicates states with an under-enrollment of students receiving services under IDEA. Enrollment of students with disabilities in charter schools 13 Oklahoma New Hampshire Delaware Missouri Arkansas New Jersey Tennessee Kansas Colorado Oregon South Carolina Wisconsin Connecticut Alaska California Louisiana Indiana Florida Idaho Utah Michigan DC Arizona Nevada New York Massachussetts Rhode Island New Mexico Georgia Hawaii Texas Illinois Wyoming North Carolina Pennsylvania Iowa Ohio Minnesota Maryland Virginia -2.2 -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Cohen's d value 2011-2012 CRDC data 2013-2014 CRDC data Figure 1. Forest plot of Cohen’s d value by state with 95% confidence intervals For approximately 24% of states in 2011-2012 (n = 12) and 20% of states (n = 10) in 2013-2014 school, there was no practically significant difference (i.e., may have been statistically significant at the .05 level or less but a d value less than |.20|) in the percent age of students receiving special education services under IDEA between charter and non-charter schools. For these states, there was no practically significant discrepancy in the enrollment of students receiving services under IDEA between charter and non -charter schools. With regard to under- enrollment of students receiving services under IDEA, for the 2011-2012 school year data, 55% of states (n = 28) and 59% of states (n = 30) for the 2013-2014 data, there was a practically Education Policy Analysis Archives Vol. 26 No. 43 14 significant lower percentage of students receiving special education services under IDEA between charter schools versus non-charter schools (e.g., a d value greater than -.20). States that revealed a practically significant discrepancy between charter and non -charter schools ranged from d = -1.53 (Oklahoma) to d = -0.26 (New Mexico) for the 2011-2012 data and d = 1.18 (Kansas) to d = -0.21 (Texas) for the 2013-2014 data. Figures 2 and 3 provides a map of all 50 states plus the District of Columbia with corresponding shading according to the degree of the effect size for the 2011-2012 and 2013-2014 CRDC data respectively. Figure 2. CRDC map for 2011-2012 data Enrollment of students with disabilities in charter schools 15 Figure 3. CRDC map for 2013-2014 data Discussion In the current study, we found that charter schools were associated with a significantly lower percentage of students receiving special education services under IDEA as compared to non-charter schools with a small effect size of d = -.30 (CI95: -.37; .16). So, while charter schools had an average of 9.7% of students receiving special education services under IDEA, non - charter schools had a significantly higher average of 12.6% of students receiving special education services under IDEA. In accounting for state level variation, our results indicate that nationally there is a significant but small discrepancy in the enrollment of students with disabilities under IDEA in charter versus non-charter public schools. When disaggregating the data by individual states, it appears that the discrepancy in the enrollment of students with disabilities under IDEA varies widely from Oklahoma with a d = -1.53 (CI95: -1.71; -1.34) to Washington with a d = 1.02 (CI95: 1.00; 1.05). Please refer to Table 3 for complete information. We would suggest that both extremes of the d values are problematic (e.g., a d value of -1.00 or +1.00). A d value of -1.00 would indicate that a significantly fewer number of students with disabilities under IDEA enrolled in charter versus non -charter schools. A d value of +1.00 would indicate that a significantly higher number of students with disabilities under IDEA enrolled in charter versus non-charter schools. The issue of a practically significant (i.e., d > .20) percentage of more students with disabilities under IDEA enrolled in charter versus non -charter public schools defies the spirit of inclusion as inclusion requires both students with and without disabilities. So, while it may be well-intentioned for some charter schools to provide students with disabilities specialized learning environments to serve these needs, there is a well - Education Policy Analysis Archives Vol. 26 No. 43 16 documented body of literature that indicates better educational outcomes for students with disabilities in inclusive learning environments (e.g., Dessemontet, Bless, & Morin, 2012; Farrell, Dyson, Polat, Hutcheson, & Gallannaugh, 2007; Rouse & Florian, 2006) . This examination, however, is beyond the scope of the current study. The primary concern remains that charter schools in a little over half of all states for both rounds of data collection (55% and 58% respectively for the 2011 -2012 and 2013-2014 data sets) had a lower percentage of children with disabilities receiving services under IDEA as compared to non-charter public schools. These differences in over half of states were practically significant with a d value greater than 0.20. These results are not surprising given previous state level analyses but the current study does provide the most complete and contemporary examination of this issue. The maps (Figures 2 and 3) do appear to indicate that states located in the Western part of the United States appear to be more likely to have an under-enrollment of students with disabilities in charter schools as compared to non-charter, public schools. Both the states of California and Massachusetts (states specifically mentioned in the literature review) continued to have a practically significant discrepancy i n the number of children with disabilities receiving services under IDEA enrolled in charter schools for both the 2011 -2012 and 2013- 2014 data. Thus, the results of Hehir (2010) for the state of California as well as Rhim and McLaughlin (2007) for the state of Massachusetts continue to persist with respect to the under - enrollment of students with disabilities in charter schools. Funding Differences in the enrollment of students with disabilities in charter schools according to state may be a function of the varied funding allocation mechanisms for special education across states. States in tandem with local education agencies provide approximately 91% of funding for special education services under IDEA while the federal government contributes the remaining 9% (Ahearn, 2010). Verstegen (2011) delineates four main allocation mechanisms for funding special education consisting of: per pupil; cost reimbursement; unit; and census. The majority of states appear to use a per pupil funding mechanism for spec ial education that is weighted accordingly followed by cost reimbursement (i.e., after the fact), unit (i.e., number of students served), and census (i.e., total number of students in the district) allocation mechanisms. There appeared to be no associated pattern of the enrollment of students with disabilities in charter schools versus traditional public schools with state funding mechanism used for special education. Future research should further examine for patterns in state differences in the enrollment of students with disabilities between charter versus traditional, public schools. One reason for this lack of a clear connection between state funding mechanism for special education utilized and the enrollment of students with disabilities in charter versus traditional public schools is that IDEA requires “placement neutral” (Ahearn, 2010, p. 1) funding. In placement neutral funding, school districts cannot differentially fund special education according to learning environment so as not to reward the seg regation of students with disabilities (Ahearn, 2010). As a result, for -profit charter schools would not have a means to increase funding according to placement thereby supplementing their bottom line. Maintaining an inclusive learning environment would receive the same amount of special education funding as an environment without inclusive supports. It would not be surprising if a charter school was reluctant to maintain an inclusive learning environment. In combination with the mandate of IDEIA has long been under-funded by the federal government (Ahearn, 2010), charter schools may have limited incentive to actively recruit students with disabilities given the costs associated with doing inclusion well. As a result, it is not surprising when students with Enrollment of students with disabilities in charter schools 17 disabilities who are higher functioning or have less severe disabilities that are easier to include are creamed from the top by charter schools (Lacireno-Paquet, Holyoke, Moser, & Henig, 2002). These students would appear to be much more likely to be placed in less inclusive environments with less supports. Limitations In the current study, we did not disaggregate by type of disabilities as these numbers were suppressed in many cases due to low sample sizes per school. Thus, we were not able to evaluate discrepancies in charter school enrollments according to type of disabilities. Previous literature has indicated that charter schools tend to retain certain types of students with disabilities, which we were unable to replicate in the current study (Fiore e t al., 2000). Additionally, we conservatively excluded from our analyses states that did not have charter school laws for both school years of data collection (i.e., 2011 -2012 and 2013-2014) or had substantial changes in their charter school law between ro unds (see Table 1). Thus, findings may not be considered generalizable to these or all states but the current study does represent the most nationally representative study available. In the current study, we also excluded schools designated as special education or alternative schools as these schools would ostensibly have higher frequencies of students with disabilities and these schools appear to be more prevalent as charter versus non-charter, public schools. These schools, however, do not promote inclusi on by predominantly serving children with special education needs and potentially excluding the typically developing students. Additionally, the CRDC did not contain data on private schools, which precluded their inclusion in our analyses, which would appe ar to provide some interesting comparisons (Berends, 2015). Finally, throughout the current study, we reiterate the term, students with disabilities under IDEA as there could be students with disabilities enrolled in these schools under 504 or not receiving special education services at all. These students not receiving services under IDEA but rather 504 or no services at all would generally be considered as having less severe disabilities, thus requiring less services, thus would ostensibly be less costly to educate for charter schools. Conclusion In conclusion, the current study found that nationwide, charter schools enrolled lower percentages of students with disabilities as compared to non -charter, public schools. Some authors have suggested that this phenomenon would largely appear to exist as the result of a selection bias on the part of parents occurring with respect to charter schools (e.g., Booker, Gilpatric, Gronberg, & Jansen, 2007; Hanushek, Kain, Rivkin, & Branch, 2007). This selection bias may be somewhat reciprocal in nature such that parents of children with disabilities may prefer non-charter, public schools given the larger scale of personnel and services provided by these schools in the context of a school district as the local education ag ency. Ultimately though, selection bias is inextricably intertwined between the parents and their interactions with the charter school, who may steer parents of children with disabilities away with a chilly or reluctant reception. Yet, the impact of selection bias is difficult to accurately examine without knowledge of the interactions a parent may have with a charter school. Additionally, if disability were replaced with race, ethnicity, or gender, we would not let the issue of selection bias on the part of parents have such credence in contemporary literature. If charter schools systematically under-enrolled students from ethnic minority backgrounds, selection bias would not be a viable argument in contemporary literature. The far greater and more pernicious bias that confronts the charter school research is one of confirmation bias (see Nickerson, 1998) among researchers. There are scholars associated Education Policy Analysis Archives Vol. 26 No. 43 18 with certain organizations that promote charter schools or more broadly, free market solutions for education (Powell, Oct. 2016). These potential conflicts of interest can create a situation where scholars are seeking evidence (e.g., variables and samples) that confirm the agenda of these organizations (which may align with the researcher’s as well). This is c onfirmation bias, which we are all culpable to committing. Confirmation bias is not dishonest but it can create some substantial blindspots in research. While media such as Education Week have recently started to discuss this issue of potential conflicts o f interest in charter school research (Powell, 2016), noting the use of organizations that are pro-charter schools to fund research rather than the charter schools themselves, scholarly journals appear to have trailed in the discussion and acknowledgement of this issue in charter school research. We suggest that at the very least, scholarly journals should require researchers to acknowledge in their articles the nature of their relationships with organizations that promote charter schools. To model such beh avior, we conclude with a statement of our own. Conflict of Interest Statement The authors have no conflict of interest. References Ahearn, E. (2010). Financing special education: State funding formulas. In Project Forum, National Association of State Directors of Special Education (p. 1-50). Alexandria, VA: National Assocation of State Directors of Special Education. Ahearn, E. 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IGI Global. https://doi.org/10.4018/978-1-5225-2520-2.ch002 Zimmer, R. W., & Guarino, C. M. (2013). Is there empirical evidence that charter schools “push out” low-performing students? Educational Evaluation and Policy Analysis, 35(4), 461-480. https://doi.org/10.3102/0162373713498465 Zollers, N. J., & Ramanathan, A. K. (1998). For-profit charter schools and students with disabilities: The sordid side of the business of schooling. Phi Delta Kappan, 80(4), 297. https://doi.org/10.5642/lux.201301.27 http://ocrdata.ed.gov/Downloads/2009-10CRDCDefinitions.doc https://doi.org/10.14507/epaa.v19n21.2011 https://doi.org/10.1111/1467-9604.12076 https://doi.org/10.3102/0013189X15584772 https://doi.org/10.3102/0162373717690830 https://doi.org/10.4018/978-1-5225-2520-2.ch002 https://doi.org/10.3102/0162373713498465 Education Policy Analysis Archives Vol. 26 No. 43 22 About the Author Lucy Barnard-Brak Texas Tech University lucy.barnard-brak@ttu.edu https://orcid.org/0000-0001-7239-0720 Lucy Barnard-Brak is an Associate Professor of Educational Psychology and Leadership in the College of Education at Texas Tech University. She earned her Ph.D. from the same institution after she became an Assistant Professor at Baylor University before returning to Texas Tech University. Her research pivots on examining the educational experiences and outcomes of students with disabilities. She has published over 100 articles. Marcelo Schmidt Texas Tech University marcelo.schmidt@ttu.edu https://orcid.org/0000-0003-1057-6284 Marcelo Schmidt is the Director of Assessment & Accredication for the College of Education at Texas Tech University. Dr. Schmidt has a PhD in Educational Psychology from Texas Tech University and a Master’s in Education from the University of Texas-Pan American. His areas of interest are outcomes assessment, improvement science in educational settings, and children with disabilities. M. Hasan Almekdash Texas Tech University hasan.almekdash@ttu.edu M. Hasan Almekdash is a doctoral candidate in the College of Education at Texas Tech University. mailto:lucy.barnard-brak@ttu.edu https://orcid.org/0000-0001-7239-0720 mailto:marcelo.schmidt@ttu.edu https://orcid.org/0000-0003-1057-6284 mailto:hasan.almekdash@ttu.edu Enrollment of students with disabilities in charter schools 23 education policy analysis archives Volume 26 Number 43 April 9, 2018 ISSN 1068-2341 Readers are free to copy, display, and distribute this article, as long as the work is attributed to the author(s) and Education Policy Analysis Archives, it is distributed for non- commercial purposes only, and no alteration or transformation is made in the wo rk. More details of this Creative Commons license are available at http://creativecommons.org/licenses/by-nc-sa/3.0/. All other uses must be approved by the author(s) or EPAA. EPAA is published by the Mary Lou Fulton Institute and Graduate School of Education at Arizona State University Articles are indexed in CIRC (Clasificación Integrada de Revistas Científicas, Spain), DIALNET (Spain), Directory of Open Access Journals, EBSCO Education Research Complete, ERIC, Education Full Text (H.W. Wilson), QUALIS A1 (Brazil), SCImago Journal Rank; SCOPUS, SOCOLAR (China). Please send errata notes to Audrey Amrein-Beardsley at audrey.beardsley@asu.edu Join EPAA’s Facebook community at https://www.facebook.com/EPAAAAPE and Twitter feed @epaa_aape. http://www.doaj.org/ mailto:audrey.beardsley@asu.edu https://www.facebook.com/EPAAAAPE Education Policy Analysis Archives Vol. 26 No. 43 24 education policy analysis archives editorial board Lead Editor: Audrey Amrein-Beardsley (Arizona State University) Editor Consultor: Gustavo E. Fischman (Arizona State University) Associate Editors: David Carlson, Lauren Harris, Eugene Judson, Mirka Koro-Ljungberg, Scott Marley, Iveta Silova, Maria Teresa Tatto (Arizona State University) Cristina Alfaro San Diego State University Amy Garrett Dikkers University of North Carolina, Wilmington Susan L. Robertson Bristol University Gary Anderson New York University Gene V Glass Arizona State University Gloria M. Rodriguez University of California, Davis Michael W. Apple University of Wisconsin, Madison Ronald Glass University of California, Santa Cruz R. Anthony Rolle University of Houston Jeff Bale OISE, University of Toronto, Canada Jacob P. K. Gross University of Louisville A. G. Rud Washington State University Aaron Bevanot SUNY Albany Eric M. Haas WestEd Patricia Sánchez University of University of Texas, San Antonio David C. 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Wolgemuth University of South Florida Rachael Gabriel University of Connecticut Amanda U. Potterton University of Kentucky Kyo Yamashiro Claremont Graduate University Enrollment of students with disabilities in charter schools 25 archivos analíticos de políticas educativas consejo editorial Editor Consultor: Gustavo E. Fischman (Arizona State University) Editores Asociados: Armando Alcántara Santuario (Universidad Nacional Autónoma de México), Jason Beech, (Universidad de San Andrés), Angelica Buendia, (Metropolitan Autonomous University), Ezequiel Gomez Caride, (Pontificia Universidad Católica Argentina), Antonio Luzon, (Universidad de Granada), José Luis Ramírez, Universidad de Sonora) Claudio Almonacid Universidad Metropolitana de Ciencias de la Educación, Chile Ana María García de Fanelli Centro de Estudios de Estado y Sociedad (CEDES) CONICET, Argentina Miriam Rodríguez Vargas Universidad Autónoma de Tamaulipas, México Miguel Ángel Arias Ortega Universidad Autónoma de la Ciudad de México Juan Carlos González Faraco Universidad de Huelva, España José Gregorio Rodríguez Universidad Nacional de Colombia, Colombia Xavier Besalú Costa Universitat de Girona, España María Clemente Linuesa Universidad de Salamanca, España Mario Rueda Beltrán Instituto de Investigaciones sobre la Universidad y la Educación, UNAM, México Xavier Bonal Sarro Universidad Autónoma de Barcelona, España Jaume Martínez Bonafé Universitat de València, España José Luis San Fabián Maroto Universidad de Oviedo, España Antonio Bolívar Boitia Universidad de Granada, España Alejandro Márquez Jiménez Instituto de Investigaciones sobre la Universidad y la Educación, UNAM, México Jurjo Torres Santomé, Universidad de la Coruña, España José Joaquín Brunner Universidad Diego Portales, Chile María Guadalupe Olivier Tellez, Universidad Pedagógica Nacional, México Yengny Marisol Silva Laya Universidad Iberoamericana, México Damián Canales Sánchez Instituto Nacional para la Evaluación de la Educación, México Miguel Pereyra Universidad de Granada, España Ernesto Treviño Ronzón Universidad Veracruzana, México Gabriela de la Cruz Flores Universidad Nacional Autónoma de México Mónica Pini Universidad Nacional de San Martín, Argentina Ernesto Treviño Villarreal Universidad Diego Portales Santiago, Chile Marco Antonio Delgado Fuentes Universidad Iberoamericana, México Omar Orlando Pulido Chaves Instituto para la Investigación Educativa y el Desarrollo Pedagógico (IDEP) Antoni Verger Planells Universidad Autónoma de Barcelona, España Inés Dussel, DIE-CINVESTAV, México José Luis Ramírez Romero Universidad Autónoma de Sonora, México Catalina Wainerman Universidad de San Andrés, Argentina Pedro Flores Crespo Universidad Iberoamericana, México Paula Razquin Universidad de San Andrés, Argentina Juan Carlos Yáñez Velazco Universidad de Colima, México José Ignacio Rivas Flores Universidad de Málaga, España javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/819') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/820') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/4276') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/1609') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/825') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/797') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/823') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/798') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/555') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/814') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/2703') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/801') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/826') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/802') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/1617') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/3264') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/804') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/816') Education Policy Analysis Archives Vol. 26 No. 43 26 arquivos analíticos de políticas educativas conselho editorial Editor Consultor: Gustavo E. Fischman (Arizona State University) Editoras Associadas: Kaizo Iwakami Beltrao, (Brazilian School of Public and Private Management - EBAPE/FGV, Brazil), Geovana Mendonça Lunardi Mendes (Universidade do Estado de Santa Catarina), Gilberto José Miranda, (Universidade Federal de Uberlândia, Brazil), Marcia Pletsch, Sandra Regina Sales (Universidade Federal Rural do Rio de Janeiro) Almerindo Afonso Universidade do Minho Portugal Alexandre Fernandez Vaz Universidade Federal de Santa Catarina, Brasil José Augusto Pacheco Universidade do Minho, Portugal Rosanna Maria Barros Sá Universidade do Algarve Portugal Regina Célia Linhares Hostins Universidade do Vale do Itajaí, Brasil Jane Paiva Universidade do Estado do Rio de Janeiro, Brasil Maria Helena Bonilla Universidade Federal da Bahia Brasil Alfredo Macedo Gomes Universidade Federal de Pernambuco Brasil Paulo Alberto Santos Vieira Universidade do Estado de Mato Grosso, Brasil Rosa Maria Bueno Fischer Universidade Federal do Rio Grande do Sul, Brasil Jefferson Mainardes Universidade Estadual de Ponta Grossa, Brasil Fabiany de Cássia Tavares Silva Universidade Federal do Mato Grosso do Sul, Brasil Alice Casimiro Lopes Universidade do Estado do Rio de Janeiro, Brasil Jader Janer Moreira Lopes Universidade Federal Fluminense e Universidade Federal de Juiz de Fora, Brasil António Teodoro Universidade Lusófona Portugal Suzana Feldens Schwertner Centro Universitário Univates Brasil Debora Nunes Universidade Federal do Rio Grande do Norte, Brasil Lílian do Valle Universidade do Estado do Rio de Janeiro, Brasil Flávia Miller Naethe Motta Universidade Federal Rural do Rio de Janeiro, Brasil Alda Junqueira Marin Pontifícia Universidade Católica de São Paulo, Brasil Alfredo Veiga-Neto Universidade Federal do Rio Grande do Sul, Brasil Dalila Andrade Oliveira Universidade Federal de Minas Gerais, Brasil