School Punishment and Education: Racial/Ethnic Disparities With Grade Retention and the Role of Urbanicity

Abstract

There are racial/ethnic disparities associated with school punishment practices and academic progress. In addition, research suggests that urban schools have stricter school punishment practices and higher grade retention rates. What remains unknown, however, is the relationship between race/ethnicity, school punishment practices, and retention rates across urban, rural, and suburban schools. Thus, this study draws from the Texas Education Agency’s Public Education Information Management System and Critical Race Theory to investigate if there is link between school punishment practices and academic progress, as well as establishing if there are racial/ethnic disparities in urban, rural, and suburban contexts.

Keywords

suspension programs achievement gap social retention rates

Introduction

Zero-tolerance and other strict school punishment policies have become a common method to address violence occurring within U.S. public schools (Muschert, Henry, Bracy, & Peguero, 2013; Muschert & Peguero, 2010; Skiba, Horner, Chung, & Rausch, 2011). Although zero-tolerance policies are intended to protect students and ensure a safe learning environment, they have received a great deal of scholarly attention with many arguing that they function in problematic and discriminatory ways for racial/ethnic minority students (Carter, Skiba, Arredondo, & Pollock, 2017; Gregory, Skiba, & Noguera, 2010; Kim, Losen, & Hewitt, 2012; Rios, 2011; Shedd, 2015; Skiba et al., 2011). Racially disproportionate school punishment (e.g., referral, suspension, expulsion) can steer students toward social exclusion, educational failure, and lifelong economic hardship (Gregory et al., 2010; Noguera, 2008; Rios, 2011; Shedd, 2015; Skiba et al., 2011). Although research demonstrates that school punishment can have a detrimental impact on educational success, limited studies examine the effects of how the severity of school strictness on educational progress for racial/ethnic minority students varies across areas (i.e., urban, rural, and suburban areas). This gap in the existing literature is particularly pressing, as racial/ethnic minority students who attend urban schools are disproportionately punished, while also facing increased educational hurdles and barriers (Carter et al., 2017; Kozol, 2006, 2012; Rios, 2011; Shedd, 2015; Skiba et al., 2011). Considering that racial/ethnic minority students are projected to represent more than half of the student population and the globally competitive employment market within the next 50 years (U.S. Census Bureau, 2014), ensuring educational progress for racial/ethnic minority students in the U.S. educational system is imperative.

Grade retention occurs when school faculty and administrators decide that a child should not continue on to the subsequent grade or should be held back a grade. Scholars have demonstrated that a number of academic excessive absences, poor grades, and low scores on high-stakes standardized tests, as well as experiences with school punishment, are associated with the administrative decision to withhold a student from advancing to the next grade (Andrew, 2014; Marchbanks et al., 2014; Martin, 2011; Moller, Stearns, Blau, & Land, 2006). Furthermore, scholars argue that grade retention could have serious implications for academic progress and success, as it could be detrimental toward a number of educational and psychological outcomes, including academic motivation and engagement, self-concept and self-esteem, positive peer relationships, achievement, and high school completion (Andrew, 2014; Marchbanks et al., 2014; Martin, 2011; Moller et al., 2006). What remains unknown, however, is if and how school discipline policies contribute to grade retention rates.

This study examines the association between stringent and lenient school punishment practices on grade retention rates across distinct school contexts (i.e., urban, rural, and suburban), as well as the associated racial/ethnic disparities. Using Critical Race Theory (CRT) as a guiding framework, we first review research that conceptualizes the link between punishment practices and educational success, as well as the role that race/ethnicity play across in urban, rural, and suburban school contexts. Using data from the Texas Education Agency’s (TEA) Public Education Information Management System (PEIMS), we address two research questions proposed by this study that remain unanswered by the previous literature:

Research Question 1: Is the relationship between stringent or lenient school punishment practices and retention rates similar in urban, rural, and suburban contexts?

Research Question 2: Are racial/ethnic disparities between school strictness and retention rates evident in urban, rural, and suburban contexts?

Findings indicate that both stringent and lenient school punishment practices have effects on grade retention rates; however, there are important and distinctive nuances that are presented and examined. Finally, this study discusses the policy implications for the complex relationship between stringent or lenient school punishment practices, racial/ethnic inequality, educational success, and school context.

Using CRT as an Analytical Framework in School Punishment Research

CRT, as an analytical framework centralizes race and racism and thus, can be used to analyze the phenomenon this study investigates (Matsuda, Lawrence, Delgado, & Crenshaw, 1993). While first developed by legal scholars as a race-critical challenge to dominant legal paradigms, the use of CRT within education continues to expand, with both CRT and educational scholars arguing the value and need of race-based analysis in educational research (Delgado & Stefancic, 2001; Ladson-Billings, 2004; Ladson-Billings & Tate, 1995; Solorzano & Yosso, 2002). The use of CRT in education has allowed scholars to disrupt prevailing racial ideology driving research in areas such as educational achievement, high-stakes testing, assessment, curriculum, and instruction, among others (Howard, 2008; Ladson-Billings, 2004; Ladson-Billings & Tate, 1995; Solorzano, 1998). Scholars have argued, as we do here, that the use of CRT not only acknowledges the continued salience and presence of race within U.S. public schools but also the detrimental effects that racism has in the lives of youth of color, their experiences within the U.S. educational system, the severity of the inequality they experience while in school, and the lifelong impact of those experiences (Howard, 2008; Ladson-Billings, 2004; Ladson-Billings & Tate, 1995; Rios, 2011; Simson, 2014).

Although CRT should not be understood as an abstract set of principles, as scholarship in this area often differs in subject, argument, and emphasis, CRT scholarship nonetheless has an explicit set of defining tenants.¹ We briefly outline two of these tenants, which we argue are instrumental in understanding the complex relationship between racial/ethnic inequality, disproportionate punishment practices, and retention rates in urban, rural, and suburban schools.

First Tenant: Race and Racism Are Central Aspects of the United States and Its Institutions

CRT is fundamentally rooted in challenging the assertion that race and racism no longer matter in the United States and thus argues that race and racism continue to be fundamental aspects of the U.S. society, particularly in the post–civil rights era. As such, CRT tasks itself with demonstrating the multiple ways in which racist ideology and racism have become institutionalized within all social, political, economic, legal, and educational institutions, and also how racism continues to be embedded within and define everyday experiences, in overt and covert ways (Crenshaw, Gotanda, pellar, & Thomas, 1995; Powell, 1999).

Racial/ethnic inequality is not only manifested in a variety of ways within schools, but schools themselves are crucial mechanisms in maintaining and reinforcing structural inequality and prevailing racial ideologies (Ferguson, 2001; Giroux, 2003; Lewis, 2003; Lewis & Diamond, 2015). The U.S. educational system is wrought with policies and practices that demonstrate the ways in which racism and inequality is institutionalized within and permeates public schools, including biased standardized testing, lack of access to educational resources, harsh discipline policies, and negative day-to-day teacher–student interactions that lead to educational disengagement, particularly for students of color (Howard, 2008; Rios, 2011, 2017; Simson, 2014). Although such policies and practices may appear race-neutral, their well-documented disproportionate impact on students of color questions that neutrality (Howard, 2008; Rios, 2011; Simson, 2014).

Second Tenant: Challenging Ahistorical Approaches

CRT moves away from an ahistorical approach by urging that analyses of U.S. society consider historical context, arguing that doing so is absolutely necessary because current racial inequalities and manifestations of racial privilege and disadvantage are directly linked to past historical periods (Bell, 1992; Crenshaw et al., 1995; Haney-Lopez, 1997). This includes racial phenomena such as income inequality, mass imprisonment, housing inequality and residential segregation, and differences in access to education, among others. In this sense, race and racism are intricately connected to material realities, past and present, as resources continue to be distributed along racial lines, resulting in little incentive to eradicate institutional and systemic racism (Crenshaw et al., 1995; Haney-López, 1997; Powell, 1999).

The importance of this for our study is twofold. On one hand, unpacking the historical legacy of race-based stereotypes is essential when considering how behavior is policed and punished in today’s public schools. Scholars have demonstrated that school practices that result in unequal educational experiences for youth of color are rooted in a complex web of racialized images and stereotypes about criminality and criminal behavior, which continue to intersect with current and past systems of punishment and surveillance (Howard, 2008; Rios, 2011, 2017; Simson, 2014; Tonry, 1995, 2010) With the continued implementation of zero-tolerance policies into U.S. public schools, long-standing associations of “Blackness” and “Latinoness” with criminality are further solidified, with youth of color being placed at higher risk for detention, suspension, and in-school ticketing (Giroux, 2003). Scholars have shown that the perceptions that teachers and other school personnel have of students of color play a critical role in how discipline is handled within schools. Research has demonstrated that not only do youth of color feel surrounded by challenges while in school because of their skin color, youth of color also often express being subjected to stigmatization and increased harassment and surveillance from teachers and other school personnel (Howard, 2008; Ladson-Billings, 2004; Ladson-Billings & Tate, 1995; Rios, 2011; Simson, 2014; Tonry, 1995, 2010).

On the other hand, although formal and legal residential segregation mechanisms were eliminated decades ago, the practices and ideologies that sustain residential segregation in the post–civil rights era have not gone away but have simply become more covert. Our study investigates educational disparities across three distinct contexts making the interrogation of racialized space and residential segregation crucial, particularly because of the cyclical relationship between residential patterns and characteristics of local schools, including racial make-up, resource availability, and drop-out and retention rates (Howard, 2008; Ladson-Billings, 2004; Ladson-Billings & Tate, 1995; Rios, 2011; Simson, 2014; Tonry, 1995, 2010). Contemporary patterns of residential segregation, and their accompanying resource, educational, and wealth inequality, have not only been well-documented in the social science literature but both CRT and other social science scholars have also demonstrated that they are supported and sustained by a long history of institutional and structural inequality (Massey & Denton, 1993; Moore, 2008; powell, 1999). As Moore (2008) notes, racialized space “functions as a central element of institutional racism” (p. 25) by reinforcing the prevailing racial hierarchy and functioning as a tool through which privilege, power, and wealth is reproduced (see also Lipsitz, 2011).

Understanding the ways in which racial/ethnic inequality manifests within schools has a long academic tradition. In the following sections, we discuss various areas of academic scholarship that highlight the importance of needing to understand the complex relationship between racial/ethnic inequality, disproportionate punishment practices, and retention rates in urban, rural, and suburban schools.

Racial/Ethnic Inequality and Punishment in U.S. Schools

Racial/ethnic disparities are historic and persistent within the U.S. educational system (Carter et al., 2017; Portillos, González, & Peguero, 2012; Rios, 2011, 2017; Shedd, 2015; Skiba et al., 2011). Schools continue to attempt to maintain school safety through multiple efforts including implementing numerous school punishment policies. Although intended to be an unbiased and neutral way to ensure school safety and safe learning environments, school punishment policies have been increasingly scrutinized in recent years by a large area of academic research (Kim et al., 2012; Muschert et al., 2013; Rios, 2011, 2017; Shedd, 2015; Skiba et al., 2011). One of the most consistent findings in this body of literature is the high degree of racial disparities in school discipline. For more than 40 years, research has demonstrated that African American youth are over-represented in office referrals, suspensions, and expulsions (Beger, 2003; Children’s Defense Fund, 1975; Fenning & Rose, 2007; Skiba, 2004, 2013; Skiba & Rausch, 2006). Even after studies control for misbehavior, racial/ethnic minority adolescents continue to face disproportionate discipline experiences while in school with African American and Latina/o American students being particularly at an increased risk of being punished at school (Peguero & Shekarkhar, 2011; Portillos et al., 2012; Rios, 2011; Skiba et al., 2011). Rios (2011) argues that disproportionate school punishment practices place Black/African American and Latina/o American students on a pipeline toward the juvenile and criminal justice system.

The Importance of Place for Educational Success, School Punishment, and Racial/Ethnic Inequality

Because this study investigates the potential significance of school context (i.e., urban, rural, and suburban) for the link between stringent or lenient school punishment, grade retention, and racial/ethnic inequality, a discussion of the continued importance of racialized space is essential. Although formal mechanisms of residential segregation, such as red-lining and racially discriminatory lending practices, have long been eliminated, covert mechanisms of residential segregation remain prevalent in the post–civil rights era.

Although formal and legal residential segregation mechanisms were eliminated decades ago, the practices and ideologies that sustain residential segregation in the post–civil rights era have not gone away but have simply become more covert. Contemporary patterns of residential segregation, and their accompanying resource, educational, and wealth inequality, have not only been well-documented in the social science literature but they are also supported and sustained by a long history of institutional and structural inequality (Massey & Denton, 1993; Moore, 2008; powell, 1999).

Urban communities have often been characterized by social isolation, heightened police surveillance, perceived family dysfunction, high rates of unemployment and poverty, high rates of violent crime, and overcrowded and underfunded schools. Not only are urban communities more likely to be occupied by racial/ethnic minorities, they are also more likely to be pathologized in such a way that urban disorder, poverty, and crime are made synonymous with racial/ethnic minorities (Lipsitz, 2011; Massey & Denton, 1993; Nolan, 2011; powell, 1999; Tonry, 1995, 2010).

Rural communities, while often suffering from similar limitations in terms of wealth and access to resources as urban communities, have not been pathologized to the same degree in terms of violence and crime (Roscigno, Tomaskovic-Devey, & Crowley, 2006; Rumberger, 2011). Suburban communities, however, have not only been historically White and affluent, they are often portrayed as exclusive, having high levels of social and familial cohesion, low levels of violent crime, and having well-resourced schools with highly qualified teachers (Lipsitz, 2011; Massey & Denton, 1993).

Because schools are influenced by the resource and wealth distribution of the communities in which they are located, they are also influenced by the racial/ethnic dynamics and ideologies found in those same communities. Thus, social science research has also made it evident that the multifaceted inequality associated with urban, rural, and suburban educational disparities are symbiotic with racial/ethnic inequality, both in terms of school punishment and educational inequality (Blau, 2003; Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Moore, 2008; Roscigno et al., 2006).

Although the formal root of racial segregation in U.S. public schools was overturned over 60 years with Brown v. Board of Education of Topeka (1954), scholars have demonstrated that U.S. public schools have become increasingly resegregated within the last 30 years (Diem, Cleary, Ali, & Frankenberg, 2014; Orfield, Kucsera, & Siegel-Hawley, 2012). This is an alarming trend, as scholars have demonstrated a clear link between patterns of resegregation and unequal educational opportunities and outcomes, as residential segregation often isolates and limits wealth and other economic and educational resources (Frankenberg, 2009; Frankenberg & Lee, 2002; Lipsitz, 2011; Massey & Denton, 1993; powell, 1999).

Educational disparities, in terms of resource allocation, between urban, rural, and suburban contexts have been well-documented (Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Roscigno et al., 2006). For example, research demonstrates that the influence of per-pupil expenditure is significant, such that students attending urban and rural schools are at a disadvantage, which ultimately contributes to rural and urban gaps in achievement and attainment (Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Roscigno et al., 2006). Schools in urban communities often face limited financial and community resources, have lower rates of academic achievement, have lower college enrollment rates, and are more likely to be plagued by gang violence (Rios, 2011, 2017). In contrast to this, schools located outside urban areas are often coveted by families with children due to their higher levels of academic achievement, higher college enrollment rates, safer learning environments, and more financial resources (Lewis & Diamond, 2015; Nolan, 2011; Shedd, 2015).

However, emerging empirical evidence suggests that rural and suburban schools are no longer as racially homogeneous as they were in the past, with more Black/African American and Latina/o American students increasingly attending suburban schools than in past decades (Chapman, 2013; Tefera et al., 2011). Scholars, such as Tefera and colleagues (2011) and Frankenberg and Orfield (2012) warn, however, that this shift is deceiving. For example, Tefera and colleagues argue that racial/ethnic minority students continue to attend highly segregated schools, despite their non-urban locations, indicating that structural and institutional inequality continue to create tangible advantages for some students and not others (see also Lewis & Diamond, 2015). Similarly, Frankenberg and Orfield argue that although the populations of suburban communities have become more racially and ethnically diverse in recent decades, some suburban communities are beginning to witness similar patterns of White flight and decrease of investment that created suburban communities in the first place. Such patterns are alarming as previous patterns of White flight created the very urban schools that face serious educational disadvantages today (Frankenberg & Lee, 2002; Orfield et al., 2012).

Thus, scholars argue that community characteristics also become a defining factor in how urban, suburban, and rural schools approach and implement school discipline practices (Blau, 2003; Lewis, 2003; Lewis & Diamond, 2015; Roscigno et al., 2006; Shedd, 2015; Tyson, 2011). Scholars have consistently demonstrated that harsh disciplinary practices are more prevalent in urban schools, who have higher concentrations of racial/ethnic minority students (Rios, 2011, 2017; Shedd, 2015).

Studies over time have shown exclusionary punishment policies to have a profound impact on students in numerous ways, particularly for racial/ethnic minority students who are at increased risk for school punishment. Not only does the disparate use of punishment practices contribute to inequality in the availability of educational opportunities, it also limits access to necessary academic resources (Bradley & Renzulli, 2011; Gregory et al., 2010; Kim et al., 2012; Rios, 2011; Shedd, 2015). A clear link between harsh school punishment and educational failure for racial/ethnic minority youth has been established by scholars—research dating as far back as the 1980s, to today, highlight the association between exclusionary punishment rates and poor test scores, academic failure, high school dropout, and juvenile justice involvement (Bradley & Renzulli, 2011; Gregory et al., 2010; Kim et al., 2012; Rios, 2011; Shedd, 2015). By design, punishment policies remove students from the classroom and limit their opportunities to obtain necessary classroom instruction by placing punished students in short-term, or possibly long-term, settings such as alternative education discipline sites, and in-school and out-of-school suspension (Kim et al., 2012; Losen & Skiba, 2010; Marchbanks et al., 2014). For example, Gregory et al. (2010) highlight the link between the so-called “achievement gap” and racial/ethnic disparities in school punishment, referring to the racial/ethnic achievement gap and the racial/ethnic “discipline gap” as representing “two sides of the same coin” (p. 59).

Furthermore, it also appears that there are racial/ethnic disparities associated with grade retention (Andrew, 2014; Marchbanks et al., 2014; Martin, 2011; Moller et al., 2006). It is well established that schooling and educational mechanisms shape learning opportunities for students. Educational mechanisms, such as ability grouping, could facilitate or restrict learning opportunities (Hallinan, 1991; Kerckhoff, 1993; Kozol, 2006, 2012; Oakes, 2005). It is also arguable that grade retention is another category of a sorting mechanism that is less uniform and standardized than ability grouping (Andrew, 2014; Marchbanks et al., 2014; Martin, 2011; Moller et al., 2006). The decision to retain a given student is frequently made at the school-level, in the absence of standardized guidelines, giving teachers and school administrators broad discretionary power when determining which students should be held back (Andrew, 2014; Marchbanks et al., 2014; Martin, 2011; Moller et al., 2006).

The implications of urban, rural, and suburban, as well as racial/ethnic, educational inequalities and school punishment are complex. According to Wilson (1987, 1996, 2009), these patterns of school and community segregation produce social isolation and inequality, whereby residents of heavily poor (and often racial/ethnic minority) communities lack sustained contact with mainstream individuals and institutions, and instead have contact mainly with those of similarly impoverished status within their communities. Zubrinsky-Charles (2003) and Wilson (1987, 1996, 2009) also note that restricted interactions with, and segregation from, mainstream society inside one’s own community often limits knowledge and access to resources. Racial/ethnic minority urban community populations suffer decreased life chances and reduced community viability as residents find it difficult to realize common goals (Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Massey & Sánchez, 2010; R. D. Peterson & Krivo, 2010; Roscigno et al., 2006; Wilson, 1987, 1996, 2009; Zubrinsky-Charles, 2003). As Lewis and Diamond (2015) note,

While the mechanisms that structure schooling have changed, there remain deep and persistent quality differences between the schools that the average white and black, Latino students attend . . . moreover, they also affect the quality of experience that black and Latino students have even they are in the same schools as white students. (pp. 8-10, emphasis in original)

Although the link between school punishment and grade retention, and the resulting racial/ethnic disparities in both, are well established, the potential significance of school context (i.e., urban, rural, and suburban) in the relationship between stringent or lenient school punishment and grade retention remains unknown (American Psychological Association, Presidential Task Force on Educational Disparities, 2012; Jimerson & Ferguson, 2007; Marchbanks et al., 2014).

Current Study

Research demonstrates that there are limited resources available for urban and rural schools to facilitate educational opportunities, progress, and success; moreover, urban and rural schools have decreased access to resources and increased hurdles toward educational success for their students (Kim et al., 2012; Kozol, 2006, 2012; Lewis & Diamond, 2015; Morris, 2012). Urban schools often have high concentrations of low-income students and racial/ethnic minority students. Research suggests that rural schools are similar to urban schools in terms of resources but instead mostly consist of low-income students and White American students (Kozol, 2006, 2012; Morris, 2006, 2012; Rios, 2011; Roscigno et al., 2006; Shedd, 2015). On the contrary, suburban schools are often characterized as safer, better organized, affluent, having increased access to financial and community resources, and school climates that are more conducive to learning (Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Roscigno et al., 2006). Even though zero-tolerance and stringent school punishment practices have become common across all schools (Casella, 2003; Kupchik, 2010), it is also evident that zero-tolerance and stringent school punishment practices have a disproportionate impact on students who attend urban schools and are racial/ethnic minorities, and their educational progress (Kim et al., 2012; Rios, 2011; Shedd, 2015; Skiba et al., 2011).

This study addresses two primary questions about the relationship between stringent or lenient school punishment practices and educational disparities that remain unanswered by the previous literature. First, is the relationship between stringent or lenient school punishment practices and retention rates similar in urban, rural, and suburban contexts? Second, are racial/ethnic disparities between school strictness and retention rates evident in urban, rural, and suburban contexts? This study seeks to contribute to school punishment research by examining if there is link between stringent or lenient school punishment practices and grade retention as well as establishing if there are racial/ethnic disparities in urban, rural, and suburban contexts.

Method

Data

Our analyses rely upon two separate data sources. The first, PEIMS, is maintained by the TEA. The dataset includes a myriad of individual-level variables for all public school students in the state. Indicators include discipline events, academic measures, and basic demographics. Because the data are maintained at the individual level, access to the data is limited and highly secure.

The second data source, the Texas Academic Excellence Indicator System (AEIS) is also maintained by TEA. AEIS contains data at the campus and district levels; as such, access is less controlled and is available from the TEA website. Measures available include summary measures such as student and teacher demographics, student/teacher ratios, and expenditures by category. In one instance where AEIS data were unreliable for the students in our model, percent of students on free/reduced lunch, the PEIMS data were aggregated to provide a more accurate picture.

The data are focused on the seventh grade cohorts from the 1999-2000, 2000-2001, and 2001-2002 school years as shown in Figure 1. More than 900,000 students are included and are followed through to 2006-2007 school year—at least to each cohort’s expected graduation year.

Figure 1.

Student cohorts tracked in study.

Dependent variable: Retention

We are interested in explaining what predicts the grade retention rate of schools. For our measure, a student is considered as being retained if she or he is in the same grade in the following year that she or he was in the current year. Marchbanks and colleagues (2014) demonstrate the considerable social costs associated with school discipline and grade retention. For each year, the percent of students at a school in our cohort that are retained is computed by simply dividing the number of students who are retained by the total number of students. The resulting proportion serves as the analyses’ dependent variable.

Independent variables: School strictness, race/ethnicity, and urbanicity

The methodology detailed in Booth et al. (2012) is used to create the variable of interest: school strictness. Using this approach, a logistic regression is performed at the individual level to predict the likelihood that each student will encounter exclusionary discipline in a given academic year. By campus, these probabilities are then averaged together to create an expected discipline rate. This value is then compared with the actual discipline rate for the campus to create a measure of school strictness. This approach provides an unbiased estimate of the degree of discipline at a campus compared with what the characteristics of the students and campus would suggest.

There is reason to think schools that are unusually strict or overly lenient may both lead to poor academic performance and ultimate grade retention. As such, the absolute value of the strictness is utilized. In addition, the analyses use an interaction between a dummy variable for schools having less-than-expected discipline and the absolute value. Using this framework, the extent to which simple deviations from the expectations of school discipline relate to grade retention rates is examined, while also exploring if effect varies in strength between more lenient campuses and more strict campuses.

An additional key focus of this research is the degree to which the campus’s urbanicity is related to grade retention rates. As a measure of urbanicity, the U.S. Department of Agriculture’s 2003 county classifications are used.² The categories were collapsed into rural (non-metro, not adjacent to metro area), suburban (non-metro, adjacent to metro area), and urban (metro).

This study also explores the extent to which the percent of students that are Asian, Native American, African American, and Latina/o American affects grade retention rates. For the cohorts used here, Texas co-mingled race/ethnicity and treated Latina/o American as its own standalone category.

Control variables: School characteristics

Previous studies have established that a number of school characteristics (i.e., proportion of students who receive free or reduced lunch, size, student–teacher ratio, diversity of teachers, classification, and context) are associated with stringent or lenient school punishment practices, racial/ethnic educational disparities, and grade retention rates (Andrew, 2014; Bradley & Renzulli, 2011; Gregory et al., 2010; Kim et al., 2012; Kozol, 2006, 2012; Lareau, 2011; Lewis & Diamond, 2015; Losen & Skiba, 2010; Marchbanks et al., 2014; Martin, 2011; Moller et al., 2006; Roscigno et al., 2006; Skiba et al., 2011).

Here, we utilize the free or reduced lunch rates at a school as a proxy for poverty. To examine the effects of teacher diversity, we utilize the Greenberg measure of diversity calculated as ( $1 - % {Teachers}_{African American}^{2} - % {Teachers}_{Latino}^{2} - % {Teachers}_{White}^{2} - % {Teachers}_{Other race}^{2}$ ). This measure was originally conceived to measure the linguistic diversity of an area (Greenberg, 1956). However, the variable also captures racial/ethnic diversity as well. A perfectly homogeneous school would have a value of 0, while a perfectly diverse campus would have a value of 0.75.

The total campus size is included to control for any effects associated with campus size. The campus’s student–teacher ratio is also included. Furthermore, dummies for the campus type are included. The types included are pure high school (base category), pure junior high, combination junior and senior high school, and elementary through junior or high school. To measure the extent to which the faculty mirror their students’ race/ethnicity, a student–teacher racial incongruence measure detailed by Marchbanks and colleagues (2014) is utilized. This value is computed by

{[\begin{array}{l} {({% Faculty}_{Black} {- % Students}_{Black})}^{2} \\ + {({% Faculty}_{Latina / o American} {- % Students}_{Latina / o American})}^{2} \\ + {({% Faculty}_{White} {- % Students}_{White})}^{2} \\ + {({% Faculty}_{Other Race} {- % Students}_{Other Race})}^{2} \end{array}]}^{0.5}

Analysis Plan

For the models in this project, generalized linear models (GLM) using a logistic link function was carried out using Stata 13. GLM was favored over the more commonly used ordinary least squares (OLS) regression, because the dependent variable represents a proportion of individuals who are retained in a given grade. GLM accounts for this value by being constrained to be between 0 and 1. In addition, this method is not limited by the same assumptions of normality in the error term as OLS. Hardin and Hilbe (2011) note that

The traditional linear model is not appropriate when assuming that data are normally distributed is unreasonable or if the response variable has a limited outcome set. Furthermore, in many instance in which homoscedasticity is an untenable requirement, the linear model is again inappropriate. The GLM allows these extensions to the linear model. (p. 17)

The favorable traits of GLM make it the appropriate analysis technique for this project. Table 2 displays the results of the relationships between race/ethnicity, school strictness, and pertinent school factors for urban grade retention rates. In the baseline Model 1 of Table 2, urban school strictness is regressed on grade retention rates. In Model 2 of Table 2, race/ethnicity is added to the analysis. In Model 3, school control variables are added to the analyses. The same analytical plan to examine the relationships between school strictness, race/ethnicity, and school factors for the occurrence of rural and suburban grade retention rates is presented in Tables 3 and 4, respectively.

Results

Descriptive Statistics

As presented in Table 1, approximately 10% of urban students are retained while 4.4% and 4.5% of rural and suburban students are retained, respectively. In terms of absolute school strictness, urban students attend slightly more extreme schools than their rural and suburban counterparts. However, it is also appears that there are slightly more lenient schools in urban contexts than rural and suburban contexts. Urban schools also have the highest proportion of racial/ethnic minorities while rural and suburban schools are predominately White. Regarding school characteristics, it appears that urban schools are larger, have higher student-teacher ratios, and have more teacher diversity than rural and suburban schools.

Table 1.

Summary Values of Variables in Models.

Variable	Range	Urban		Suburban		Rural
Variable	Range	M	SD	M	SD	M	SD
Retention rate	0-1	0.097	0.172	0.044	0.116	0.045	0.126
% African American students	0-100	15.87	20.37	8.82	12.055	6.024	11.247
% Latino/a students	0-100	39.748	30.811	29.225	26.36	37.827	28.162
% Asian students	0-49.3	2.137	4.073	0.355	0.962	0.291	0.629
% Native American students	0-21.2	0.349	0.693	0.455	1.353	0.445	0.999
Strictness (absolute value)	0-0.99	0.134	0.161	0.116	0.131	0.115	0.135
Strictness (for those with less than expected)	0-0.99	0.086	0.16	0.073	0.125	0.074	0.125
School size	1-4,979	806.465	746.831	318.193	289.304	285.998	312.998
Student–teacher ratio	0.2-61.9	13.971	4.38	11.244	2.699	10.687	3.31
Junior high	0-1	0.383	0.486	0.238	0.426	0.209	0.407
Junior high and high school	0-1	0.113	0.317	0.18	0.384	0.12	0.325
Elementary to high school	0-1	0.198	0.398	0.311	0.463	0.403	0.491
Student–teacher racial incongruence	0-141.4	42.526	25.247	37.069	25.61	43.767	26.248
% free/reduced lunch	0-100	50.225	29.664	50.334	21.21	53.939	22.974
Teacher diversity measure	0-0.75	0.272	0.193	0.134	0.135	0.13	0.146

Urban School Strictness and Grade Retention

Table 2 presents the regression analysis of school strictness, race/ethnicity, and grade retention rates in urban schools. The baseline Model 1 explores the role of race/ethnicity in association with grade retention rates within urban schools. African American (β = 0.017, p ⩽ .001), Latina/o American (β = 0.013, p ⩽ .001), and Native American (β = 0.082, p ⩽ .001) students are related to higher rates of grade retention in urban schools while Asian students are associated with lower rates (β = −0.038, p ⩽ .001).

Table 2.

Generalized Linear Model Coefficients (Logit Link Function) and Standard Errors for Grade Retention Rate in Texas Urban Schools.

	Model 1			Model 2			Model 3
	b	OR	SE	b	OR	SE	B	OR	SE
% African American students	0.017***	1.017	0.001	0.014***	1.014	0.001	0.011***	1.011	0.001
% Latino/a students	0.013***	1.013	0.001	0.012***	1.012	0.001	0.009***	1.010	0.002
% Asian students	−0.038***	0.962	0.009	−0.020*	0.980	0.008	−0.005	0.995	0.007
% Native American students	0.082***	1.085	0.032	0.070*	1.072	0.029	0.053	1.054	0.028
Strictness (absolute value)	—	—	—	1.311***	3.709	0.241	1.468***	4.341	0.235
Strictness (for those with less than expected)	—	—	—	0.996***	2.709	0.236	0.393	1.482	0.229
School size	—	—	—	—	—	—	0.000***	1.000	0.000
Student–teacher ratio	—	—	—	—	—	—	0.015**	1.015	0.006
Junior high	—	—	—	—	—	—	−1.920***	0.147	0.067
Junior high and high school	—	—	—	—	—	—	−0.517***	0.596	0.074
Elementary to high school	—	—	—	—	—	—	−0.549***	0.578	0.051
Student–teacher racial incongruence	—	—	—	—	—	—	0.004***	1.004	0.001
% free/reduced lunch	—	—	—	—	—	—	0.002	1.002	0.001
Teacher diversity measure	—	—	—	—	—	—	0.282	1.325	0.151
Constant	−3.063***	—	0.069	−3.327***	—	0.068	−2.916***	—	0.104
Total (n)	11,894			11,894			11,894

Note. OR = odds ratio.

p ⩽ .05. **p ⩽ .01. ***p ⩽ .001.

In Model 2 of Table 2, school strictness measures are added to the analysis of grade retention rates in urban schools. At this stage of the analysis, as urban school strictness deviates from expected values, grade retention rates also increase within that school (β = 1.311, p ⩽ .001). Results also indicate that urban schools with strictness less than expected is also associated with a further increase in grade retention rates (β = 0.996, p ⩽ .001). It also appears evident that race/ethnicity still matters in this analysis of grade retention rates in urban schools. African American (β = 0.014, p ⩽ .001), Latina/o American (β = 0.012, p ⩽ .001), and Native American (β = 0.070, p ⩽ .05) students are linked to higher grade retention rates in urban schools, while Asian students are still associated with lower retention rates (β = −0.020, p ⩽ .05).

In Model 3 of Table 2, school characteristics are added to the analysis of grade retention rates in urban schools. While controlling for other school factors, deviations from expected levels of school strictness remains associated with increased grade retention rates (β = 1.468, p ⩽ .001). Even while controlling for other urban school characteristics, African American (β = 0.011, p ⩽ .001) and Latina/o American (β = 0.009, p ⩽ .001) students are coupled with higher grade retention rates in urban schools. It does appear that controlling for other school factors in this analysis does explain away the relationships between Native American and Asian students and grade retention rates in urban schools.

Findings also indicate that some urban school characteristics contribute to grade retention rates within urban schools. As school size increases, grade retention rates in urban schools increase (β = 0.001, p ⩽ .001), although of little substantive significance. As school student–teacher ratio increases, grade retention rates in urban schools increase (β = 0.015, p ⩽ .01). Urban junior high schools (β = −1.920, p ⩽ .001), urban schools that sustain both junior high and high school (β = −0.517, p ⩽ .001), and urban schools that sustain elementary through junior high or high school (β = −0.549, p ⩽ .001) have lower grade retention rates than urban high schools. As the student–teacher racial/ethnic incongruence increases, grade retention rates in urban schools increase (β = 0.004, p ⩽ .001). Worth noting is that school strictness measures have the highest effect on grade retention rates in urban schools while accounting for the school characteristics controlled for in these analyses.

Rural School Strictness and Grade Retention

Table 3 displays the regression analysis of school strictness, race/ethnicity, and retention rates in rural schools. The baseline Model 4 explores the role of race/ethnicity in association with retention rates within urban schools. African American (β = 0.035, p ⩽ .001) and Latina/o American (β = 0.020, p ⩽ .001) students have higher retention rates in rural schools.

Table 3.

Generalized Linear Model Coefficients (Logit Link Function) and Standard Errors for Grade Retention Rate in Texas Rural Schools.

	Model 4			Model 5			Model 6
	b	OR	SE	b	OR	SE	B	OR	SE
% African American students	0.035***	1.035	0.007	0.020***	1.020	0.006	0.020*	1.020	0.009
% Latino/a students	0.020***	1.020	0.004	0.017***	1.017	0.003	0.018***	1.018	0.005
% Asian students	0.107	1.113	0.122	0.067	1.070	0.098	0.015	1.015	0.113
% Native American students	0.116	1.122	0.093	0.128	1.136	0.078	0.158*	1.171	0.072
Strictness (absolute value)	—	—	—	2.403***	11.051	0.684	2.619***	13.724	0.717
Strictness (for those with less than expected)	—	—	—	0.700	2.013	0.708	0.591	1.805	0.694
School size	—	—	—	—	—	—	0.000	1.000	0.000
Student–teacher ratio	—	—	—	—	—	—	0.047**	1.048	0.017
Junior high	—	—	—	—	—	—	−1.464***	0.231	0.288
Junior high and high school	—	—	—	—	—	—	−0.424	0.654	0.243
Elementary to high school	—	—	—	—	—	—	−0.841***	0.431	0.199
Student–teacher racial incongruence	—	—	—	—	—	—	−0.001	0.999	0.003
% free/reduced lunch	—	—	—	—	—	—	−0.007	0.993	0.005
Teacher diversity measure	—	—	—	—	—	—	0.331	1.393	0.814
Constant	−4.253***	—	0.245	−4.451***	—	0.225	−3.969***	—	0.396
Total (n)	1,573			1,573			1,573

Note. OR = odds ratio.

p ⩽ .05. **p ⩽ .01. ***p ⩽ .001.

In Model 5 of Table 3, school strictness measures are added to the analysis of grade retention rates in rural schools. At this stage of the analysis, as rural school strictness deviates from its expected value, grade retention rates also increase within that rural school (β = 2.403, p ⩽ .01). It also appears evident that race/ethnicity still matter when analyzing grade retention rates in rural schools. African American (β = 0.020, p ⩽ .01) and Latina/o American (β = 0.017, p ⩽ .001) students have higher grade retention rates in rural schools.

In Model 6 of Table 3, school characteristics are added to the analysis of grade retention rates in rural schools. While controlling for other school factors, deviations from expected levels in rural school strictness remains associated with increased grade retention rates (β = 2.619, p ⩽ .001). As student–teacher ratios increase, grade retention increases in rural schools (β = 0.047, p ⩽ .01). Findings also indicate that rural junior high schools (β = −1.464, p ⩽ .001) and rural schools that sustain elementary through junior or high school (β = −0.841, p ⩽ .001) have lower grade retention rates than rural high schools. Even while controlling for other urban school characteristics, African American (β = 0.020, p ⩽ .05), Latina/o American (β = 0.018, p ⩽ .001), and Native American (β = 0.158, p ⩽ .05) students are still linked to higher grade retention rates in rural schools. It is also important to highlight that school strictness measures have the greatest effect on grade retention rates in rural schools, while considering the school characteristics controlled for in this study.

Suburban School Strictness and Grade Retention

Table 4 presents the regression analysis of school strictness, race/ethnicity, and grade retention rates in suburban schools. The baseline Model 7 explores the role of race/ethnicity in association with grade retention rates within urban schools. African American (β = 0.029, p ⩽ .001) and Latina/o American (β = 0.006, p ⩽ .001) students have higher grade retention rates in suburban schools.

Table 4.

Generalized Linear Model Coefficients (Logit Link Function) and Standard Errors for Grade Retention Rate in Texas Suburban Schools.

	Model 7			Model 8			Model 9
	B	OR	SE	B	OR	SE	b	OR	SE
% African American students	0.029***	1.029	0.005	0.021***	1.021	0.004	0.019***	1.019	0.006
% Latino/a students	0.016***	1.016	0.003	0.013***	1.013	0.003	0.012**	1.012	0.004
% Asian students	0.017	1.017	0.052	−0.003	0.997	0.046	−0.026	0.974	0.047
% Native American students	0.031	1.031	0.035	0.017	1.017	0.030	0.025*	1.026	0.030
Strictness (absolute value)	—	—	—	2.576***	13.149	0.526	2.547***	12.773	0.557
Strictness (for those with less than expected)	—	—	—	0.357	1.428	0.573	0.467	1.595	0.617
School size	—	—	—	—	—	—	0.000	1.000	0.000
Student–teacher ratio	—	—	—	—	—	—	0.014	1.014	0.026
Junior high	—	—	—	—	—	—	−1.386***	0.250	0.194
Junior high and high school	—	—	—	—	—	—	−0.600***	0.549	0.165
Elementary to high school	—	—	—	—	—	—	−0.817***	0.442	0.150
Student–teacher racial incongruence	—	—	—	—	—	—	−0.001	0.999	0.003
% free/reduced lunch	—	—	—	—	—	—	0.001	1.001	0.004
Teacher diversity measure	—	—	—	—	—	—	−0.028	0.972	0.555
Constant	−3.898***	—	0.147	−4.127***	—	0.132	−3.710***	—	0.373
Total (n)	3,456			3,456			3,456

Note. OR = odds ratio.

p ⩽ .05. **p ⩽ .01. ***p ⩽ .001.

In Model 8 of Table 4, school strictness measures are added to the analysis of grade retention rates in suburban schools. At this stage of the analysis, as suburban school strictness deviates from expected values, grade retention rates increase within that school (β = 2.576, p ⩽ .001). African American (β = 0.021, p ⩽ .001) and Latina/o American (β = 0.013, p ⩽ .001) students are linked to higher grade retention rates in suburban schools.

In Model 9 of Table 4, school characteristics are added to the analysis of grade retention rates in suburban schools. While controlling for other school factors, suburban school strictness deviations from expected values remain associated with increased grade retention rates (β = 2.547, p ⩽ .001). Findings also indicate that suburban junior high schools (β = −1.386, p ⩽ .001), suburban schools that sustain junior high and high school (β = −0.600, p ⩽ .001), and suburban schools that sustain elementary through junior or high schools (β = −0.817, p ⩽ .001) have lower grade retention rates than suburban high schools. Even while controlling for other suburban school characteristics, African American (β = 0.019, p ⩽ .01) Latina/o American (β = 0.012, p ⩽ .01), and Native American (β = 0.025, p ⩽ .05) students have higher grade retention rates in suburban schools. It is also important to highlight that school strictness measures have the greatest effect on grade retention rates in suburban schools, while considering the school characteristics controlled for in this study.

Discussion

School districts across the United States have implemented policies that make grade promotion contingent on students’ ability to meet specific academic performance goals (Andrew, 2014; Fenning & Rose, 2007; Jacob & Lefgren, 2009; Jimerson, 2001; Jimerson & Ferguson, 2007). However, there are a variety of organizational and structural factors that influence grade retention rates. Using a conceptual framework rooted in CRT, this study set out to answer two questions about the relationship between racial/ethnic inequality, stringent or lenient school punishment practices, and retention rates in urban, rural, and suburban schools. First, is the relationship between stringent or lenient school punishment practices and grade retention rates similar in urban, rural, and suburban contexts? Second, are racial/ethnic disparities evident in the relationship between school strictness and grade retention rates across urban, rural, and suburban school contexts? Our results suggest that a complex relationship exists between racial/ethnic inequality, stringent or lenient school punishment practices, and grade retention rates, which is evident across urban, rural, and suburban school contexts.

Relationship Between Level of Strictness and Grade Retention

The results generated by this study suggest that as urban schools deviate from the expected level of strictness, by either being too strict and punitive or being too lenient in their discipline practices, they have higher retention rates. Similarly, divergence from the expected level of discipline in suburban and rural schools was shown to be associated with increased retention rates. In all three school contexts, this association was significant, even after accounting for additional school characteristics.

This is a notable finding for several reasons. First, this finding confirms existing school climate literature, which suggests that overly punitive and overly lenient discipline practices can not only create tense and negative learning environments but can also affect even those students who were not retained or subject to school discipline practices (Fenning & Rose, 2007; Pellerin, 2005; Perry & Morris, 2014). Second, this finding lends strong evidence to the argument that overly punitive or overly lenient discipline practices have other hidden costs, among them the possibility of fracturing important social relationships, including relationships among students and between students and teachers (Pellerin, 2005; Perry & Morris, 2014). Although disciplinary policies are deemed necessary for preserving school safety by ensuring that non-misbehaving students can learn without disruption, disciplinary policies have the potential to harbor anxiety, distrust, and fear among all students (Arum, 2003; McNeely, 2002; Noguera, 2003; Perry & Morris, 2014). Thus, findings directly contradict the argument that overly punitive (and in some instances, overly lenient) school discipline practices create safe, orderly learning environments in which all students can thrive academically (Arum, 2003; McNeely et al., 2002; Noguera, 2003; Perry & Morris, 2014; Rodney, Crafter, Rodney, & Mupier, 1999; Silberglitt, Jimerson, Burns, & Appleton, 2006).

Racial/Ethnic Disparities Between School Strictness and Grade Retention

The findings generated by this study suggest that, across urban, rural, and suburban contexts, significant racial/ethnic disparities exist between school strictness and grade retention rates, which continue to remain significant even after accounting for additional school characteristics, such as availability of school resources. Furthermore, our findings suggest that students of color continue to be at heightened risk for grade retention and school punishment, regardless of whether their school is located in an urban, rural, or suburban community and regardless of the disciplinary practices of any particular school. Specifically, having higher percentages of Black/African American and Latina/o students in urban, rural, and suburban schools was shown to be associated with higher grade retention rates.

CRT scholars have long argued that race and racism continue to be defining characteristics of American life and directly influence the disenfranchisement of people of color. In this sense, this finding is significant for several reasons. Most importantly, this finding suggests that while the number of students of color in some suburban schools and hyper-segregated, underfunded rural schools continue to increase, the educational experiences of students of color continue to be shaped by race/ethnicity and cannot solely be mitigated or canceled out by increasing school integration or a changing school context for any one particular student (Frankenberg, 2009; Howard, 2008; Rios, 2011 Simson, 2014).

Furthermore, it can be argued that this finding demonstrates that racial/ethnic inequality is not accidental, blatant, easy to observe, or only found within any given individual school or community. Instead, racial/ethnic inequality in grade retention and school punishment in urban, rural, and suburban schools is rooted in the systemic nature of deeply engrained, past and present racial/ethnic inequality (Blau, 2003; Ferguson, 2001; Giroux, 2003; Howard, 2008; Lewis, 2003; Lipsitz, 2011; Moore, 2008; Rios, 2011, 2017; Simson, 2014; Tonry, 1995, 2010). This is particularly true given that this study sought to find other potential explanations for racial and ethnic inequality in retention rates and school punishment, including considering additional school characteristics. When viewed through a race-critical lens, this finding becomes pronounced when we consider that, within our sample, schools in suburban and rural communities were predominately White with students of color making up smaller percentages of the total student body than in urban schools (Costenbader & Markson, 1994; Nolan, 2011; Rios, 2011; Skiba, 2004; Welch & Payne, 2010).

Other School Characteristics Affecting Grade Retention

Although not the central focus of this article, the results generated some interesting findings regarding the control variables that were added to the final models for all three school contexts. For urban schools, the school size, student–teacher ratio, and teacher–student racial congruent variables were all significantly associated with higher retention rates. All three of these variables center on school organization characteristics. These findings are significant because they indicate that disparities exist, in part because of the schools that student attend, not necessarily because of individual students’ behaviors and backgrounds (Ferguson, 2001; Lee & Burkam, 2003; Nolan, 2011).

Similarly, for urban and suburban schools, junior high, combined junior high and high school, and elementary to high school, schools were significantly associated with higher retention rates when compared with the based group of pure high schools. For rural schools, junior high and elementary to high school schools were significantly associated with higher retention rates. Such findings suggest that students continue to be retained at all grade levels and across all school contexts. Although retention, as an educational intervention is used by school districts across the United States, is intended to allow retained students to improve on the necessary skills to succeed academically, research on the advantages to retention has been mixed and is largely driven by the grade level in which a student was retained. On the one hand, scholars have demonstrated that grade retention in elementary school is sometimes more successful, as it gives previously low-achieving students more opportunities to succeed academically (Chen, Hughes, & Kwok, 2014; Im, Hughes, Kwok, Puckett, & Cerda, 2013). On the other hand, scholars have also found that retention during middle school and high school substantially influences high school dropout (Andrew, 2014; Jacob & Lefgren, 2009; Jimerson, 2001; Jimerson & Ferguson, 2007; Silberglitt et al., 2006). Peterson and Hughes (2011) note, “Schools and teachers believe that retention in itself is the solution to help struggling students” but should instead be one strategy among many when trying to help a low-achieving student succeed academically (p. 163).

Limitations and Future Research

This study is not without its own limitations. First, results suggest that a complex relationship exists between race/ethnicity, stringent or lenient school punishment practices, and retention rates in urban, rural, and suburban contexts. However, it is important to note that because of the way in which we operationalized our Strictness (absolute value) and Strictness (for those with less than expected), we were unable to fully capture which school context is more punitive and which is more lenient (e.g., whether or not urban schools are more punitive in their punishment practices than suburban or rural schools). This is due, in part, to availability of data from both the Texas AEIS and PEIMS, both of which are maintained by the TEA. This is also due to the scant availability of existing research on school strictness. Thus, alternate operationalizations of strictness and the use of data from multiple sources is needed in order to fully capture and assess differences in strictness by school context.

Second, there are number of alternatives to address school misbehavior and disorder that were not accounted for in this study. There are a number of researchers who denote that a restorative justice approach would allow students to make amends to those they harmed without criminalizing them in the process (Portillos et al., 2012; Rios, 2011, 2017; Shedd, 2015). A restorative justice approach could also utilize a number of formats to meet the needs of students and communities, and develop solutions agreeable to all parties involved (Portillos et al., 2012; Rios, 2011, 2017; Shedd, 2015). Future research should compare if restorative justice policies are less likely to be associated with disproportionate punishment and referrals for racial/ethnic minority students, especially within urban schools.

Third, a number of studies have suggested that educational achievement, tests scores, and high-stake testing policies are associated with grade retention, especially in urban and predominately racial/ethnic minority schools (Gregory et al., 2010; Nelson, 2017; Nelson & Grace, 2016; Vasquez, Williams, McNeil, & Lee, 2011). These measures were not attainable throughout all the grade levels considered in these analyses. Of course, future research should further examine how educational achievement, tests scores, and high-stake testing policies are moderating the revealed relationships between stringent school discipline practices and grade retention, as well as the racial/ethnic disparities, across urban, suburban, and rural schools. Despite the limitations associated with this study, we do provide evidence to set forth an agenda for the continued exploration of the connections between stringent and lenient school punishment practices on grade retention across distinct school locations.

Conclusion and Implications

There are several important policy implications. First, because the results presented here suggest that racial/ethnic disparities exist in both stringent or lenient school punishment practices and retention rates across urban, rural, and suburban contexts, our results underscore the need to further scrutinize the ways in which educational interventions and both stringent or lenient school punishment practices are carried out by schools and teachers. Second, the results presented here also suggest that students are being retained at all grade levels, regardless of school characteristics and thus, suggests that one should carefully consider the long-term costs of retention for students at all grade levels, particularly for students of color who are already at increased risk for low educational achievement. Early educational interventions not only affect academic achievement in subsequent grade levels, they also have the potential to affect and exacerbate long-term educational achievement and employment successes. Thus, results stress the importance of understanding educational interventions within broader life contexts.

Footnotes

Acknowledgements

Gratitude is extended for the helpful comments and constructive suggestions from the editor and blind reviewers throughout the development of this research manuscript.

Authors’ Note

The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the authors and do not necessarily reflect those of the Department of Justice and are not endorsed by the Texas Education Agency, the Texas Higher Education Coordinating Board, or the State of Texas. John M. Eason is now affiliated with University of Wisconsin–Madison.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Portions of this project were supported by Grant 2012-JF-FX-4064 awarded by the Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice.

Notes

Author Biographies

Anthony A. Peguero is an associate professor of sociology and criminology as well as research affiliate of the Center for Peace Studies and Violence Prevention at Virginia Tech. His research interests involve youth violence, socialization and marginalization, schools, and the adaptation of the children immigrants. He is also a member of the Racial Democracy, Crime, and Justice Network which holds the goal of advancing research on the intersection of race, crime, and justice.

Kay S. Varela is a doctoral candidate in the Department of Sociology at Texas A&M University. Her research focuses on theories of race and ethnicity; crime and punishment; the intersections of race, class, and gender and their impact on public and personal safety and risk assessment; and juvenile justice.

Miner P. ‘‘Trey’’ Marchbanks III, PhD, is an associate research scientist at the Public Policy Research Institute at Texas A&M University. His work focuses upon the intersection of juvenile justice and education.

Jamilia J. Blake is a licensed psychologist and associate professor in the Department of Educational Psychology at Texas A&M University. She is an affiliate faculty with TAMU’s Race & Ethnic Studies Institute and TAMU’s Center for Disability and Development. Her research examines exclusionary discipline among Black females and bullying and victimization among socially marginalized populations. She is a member of American Psychological Association’s Committee of Ethnic Minority Affairs which seeks to promote scientific understanding of the roles of culture and ethnicity in psychology.

John Major Eason is an associate professor in the Department of Sociology at the University of Wisconsin, Madison. His research interests include crime, health, race, punishment, and rural/urban community processes. He is also a member of the Racial Democracy, Crime, and Justice Network which holds the goal of advancing research on the intersection of race, crime, and justice.

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