Inequalities in Girls’ High School Sports Participation: How Social Class, Race/Ethnicity, and Gender Route Opportunities to Play and Persist in Athletics

ABSTRACT

High school athletics are understudied sites for social and educational stratification. Participation can offer mental and physical health benefits, improved student retention and graduation rates, the encouragement of pro-social behaviors, resume building, and enhanced social statuses. Despite legal prohibitions against race and gender discrimination in schools, opportunities to play and persist in interscholastic athletics may reflect and amplify existing social and educational stratification processes. Using an exploratory sequential mixed-methods research design, this study centers on girls’ high school sports and considers how gender, race/ethnicity, and social class operate at the individual, interactional, cultural, and institutional levels and encourage proclivities, commitments, and support for participation. We combine qualitative (N = 28 women and 47 total college athletes) and quantitative (N = 4,271 high school students) studies to inquire about how, and to what extent, racial/ethnic and social-class dynamics affect girls playing any and specific high school sports and whether they play persistently. Findings suggest that schools co-construct unequal athletic opportunity structures by nurturing and rewarding a cultivated athletic habitus associated with masculinity, whiteness, and affluent dispositions. These processes disguise athletic advantages and successes as well-earned merit and restrict who is most likely to receive the individual and social benefits of high school sports participation.

KEYWORDS:

• Habitus
• whiteness
• social class
• segregation
• girls’ school sports

Extracurricular activities are understudied sites for social stratification (Friedman 2013; Pericak and Martinez 2022; Tompsett and Knoester 2022, 2023). Yet high school sports participation offers mental and physical health benefits, improved student retention and graduation rates, the encouragement of pro-social behaviors, resume building, and enhanced social statuses (Lopez 2019; Milner and Braddock 2016; NWLC 2015). Moreover, athletics are key sites for contestations over the opportunities to attain such personal and social benefits—and are consequently presumed to offer opportunities for the retrenchment or solidification of social inequalities. In part, these contestations are shaped by gender, racial/ethnic, and social-class forces that operate at the individual, interactional, cultural, and institutional levels to direct and encourage particular proclivities, commitments, and levels of support and encouragement for high school sports participation (Bourdieu 1984; Heffer and Knoester 2021; Messner 2002, 2009; Pericak and Martinez 2022; Tompsett and Knoester 2022, 2023).

The civil rights and feminist movements targeted sports participation and educational opportunities for social uplift. Longstanding racial/ethnic and gender-based exclusion from quality educational and athletic opportunities had symbolically and materially relegated People of Color and women to inferior and less respected activities and institutions—when any opportunities to participate even existed (Martin 2010; Milner and Braddock 2016). Prominent outcomes from these equity and inclusion efforts included mandates to enable racial/ethnic integration in schools via Brown v. Board of Education and subsequently the Civil Rights Act of 1964 and Title IX, which presumably offered educationally based equality for People of Color and women through eliminating barriers to access to educational opportunities and extracurricular activities. Title IX of the Education Amendments of 1972 specifically prohibited sex-based discrimination in U.S. schools—including school-sponsored athletic programs. Although there have been ongoing pushes for useful athletic and educational opportunities for all members of the public, social-class inequalities in sport and educational opportunities have not been so specifically and powerfully addressed (Bourdieu 1984; Hextrum 2021; Hextrum and Cameron 2023; Tompsett and Knoester 2022, 2023). Also, the relevance of, and interplay between, gender, racial/ethnic, and social-class dynamics for high school sports participation remain under-researched.

This study uses an exploratory, sequential mixed-methods design to better understand how gender, racial/ethnic, and social-class forces direct high school sports opportunities and commitments. We combine independently collected qualitative (N = 28 women and 47 total college athletes) and quantitative (N = 4,271 high school students) data sets. We focus on girls’ high school sports participation to inquire about how gender contextualizes girls’ experiences. In doing so, we empirically highlight how girls’ racial/ethnic and social-class backgrounds operate at the individual and institutional levels in routing interscholastic athletic opportunities.

Our conceptual framework emphasizes the undercurrents and salience of developed habitus—socially structured by gender, race/ethnicity, and social class—that encourage particular proclivities, values, understandings, and actions among individuals and are also at work in institutionalized school-sport opportunity structures and levels of support (Bourdieu 1984; Hextrum 2021; Knoester and Allison 2022; Tompsett and Knoester 2022). Our qualitative and quantitative data sets complement one another in offering evidence about the innerworkings and implications of these dynamics, although each data set contains strengths and limitations. The 2015–16 qualitative data derive from dozens of collegiate track and field and crew athletes who provided extensive life-history information about their pre-college contexts and experiences, including their experiences playing many different sports until eventually specializing in their college sport. Life histories offer rich descriptions of the processes and contexts that the respondents experienced prior to attending college. The quantitative data, although they are older (i.e., about a decade prior), provide a unique opportunity to inquire about the generalizability and implications of the emergent life-history themes by analyzing a large cohort of high school students and their individual, family, and school contexts and experiences. Information was reported by students, parents, and administrators. We analyzed the data using multilevel modeling techniques to account for individual-level and school-level contexts that include information about specific sport participation patterns and sport offerings, respectively.

We pursue the following research questions: (1) Who plays high school sports? (2) Which sports do they play? (3) For how long? And (4) How do gender, race/ethnicity, and social class shape sports participation and persistence? We focus on girls because gender extensively structures sport opportunities and experiences. Additionally, within-gender analyses—especially among girls—remain understudied. For example, high schools offer different and segregated sports for girls, with the most celebrated and highly resourced teams (i.e., football) played by boys. Furthermore, White, middle-class girls are overrepresented on high school teams (Hextrum 2021; Hextrum and Sethi 2022; Messner 2002, 2009; Project Play 2022; Zarrett and Veliz 2021). This study seeks to understand how, within highly gender-segregated contexts, race/ethnicity and social class, along with family and school influences, shape athletic experiences for girls.

Our analysis begins with women college athletes’ perceptions and reported life histories about our topics of interest. Then, informed by the qualitative findings, we use quantitative methods to inquire about the extent to which racial/ethnic and social-class forces operate at the individual and school levels to affect the likelihood of girls playing (a) any high school sports, (b) specific high school sports (i.e., basketball, soccer, softball, another team sport, an individual sport), and (c) high school sports persistently (i.e., in both 10^th and 12^th grades).

Overall, our findings suggest that schools co-construct unequal athletic opportunity structures by nurturing and rewarding a cultivated athletic habitus associated with masculinity, whiteness, and affluent dispositions. These processes disguise athletic advantages and successes as well-earned merit and restrict who is most likely to receive the individual and social benefits of high school sports participation. We advance research by illuminating these processes and offering evidence of their links to intensive parenting, segregated experiences, and assumptions of choice, all of which reinforce racial/ethnic, social-class, and gendered social structuring and stratification. Our qualitative findings offer novel and rich descriptions of how these dynamics have operated in people’s lives. The quantitative results are unique in emerging from comprehensive multilevel modeling techniques applied to the study of a large national cohort of high school girls and their high school sports experiences, which go beyond research that relies on descriptive analyses, a regional focus, or estimates that do not consider individual- and school-level effects (NFHS 2022; NWLC 2015; Pericak and Martinez 2022; Zarrett and Veliz 2021).

CONCEPTUAL FRAMEWORK

Social Structure and Habitus

Our conceptual framework draws from classic sociological theorizing of social structure and habitus. We envision gender, race/ethnicity, and social class as socially structuring peoples’ lives and operating at the individual, interactional, cultural, and institutional levels. This structuring does not preclude human agency but directs and situates it based on the gendered, racialized, and social-classed selves emerging from individual-level processes (e.g., internalizations of socialization messages); social pressures, exposures, and norms stemming from social interactions and cultural expectations; and institutions that organize and habituate crucial components of social life (e.g., family, education, sports). The culmination of our socially structured lives, in combination with our idiosyncratic thoughts, activities, and experiences, cultivates a habitus (Bourdieu 1984; Hextrum 2021; Hextrum and Kim 2023; Messner 2002; Risman 2004).

Habitus refers to the durable training that occurs over time, place, and setting. Habitus shapes our cultural orientation and our sense of what is natural, preferred, and expected as we negotiate social life, such as our family situations, peer interactions, and institutions like sports and schools. Habitus, as well as the social structuring forces of gender, race/ethnicity, and social class, influences our capital acquisition and locates us in different positions in social hierarchies with differing abilities to maintain or improve our social standings (Bourdieu 1984; Lareau 2015). Consequently, an individual “choice” to do something, such as play sports, is shaped by countless social forces that include those connected to gender, race/ethnicity, social class, and developed habitus (Bonilla-Silva 2017; Knoester and Fields 2020; Knoester and Randolph 2019; McGovern 2021; Messner 2002, 2009; Tompsett and Knoester 2022).

Individuals compete for social standing within complex and overlapping fields—or multifaceted, active, and bounded settings with unique values, regulations, norms of behavior, and gatekeepers. Within a given field, gatekeepers set the rules and standards for ascendance. Gatekeepers of socially valued fields like schools and sports, often have habitus that align with higher levels of SES, masculinity, and whiteness (Bonilla-Silva 2017; Bourdieu 1984; Friedman 2013; Hextrum 2021; Lareau 2011). Thus, interscholastic gatekeepers may perceive athletic merit based on physical capital expressions and neglect the roles and impacts of socioeconomic, cultural, social-interactional, social-capital, and institutional forces and determined investments—all of which are functions of gender, race/ethnicity, social class, and habitus (Bonilla-Silva 2017; Bourdieu 1984; Eckstein 2017; Friedman 2013; Lareau 2011, 2015).

Gender and High School Sports Participation

Gender is one factor that socially structures sports participation and affects habitus. Title IX is widely seen as the most successful of the civil rights amendments, especially in improving girls’ and women’s access to athletics by modifying the gendering of sports institutions (Hextrum and Cameron 2023; Hextrum and Sethi 2022; Messner 2002, 2009; Milner and Braddock 2016). In 1972, there were nearly 3.7 million boys and only 294,000 girls playing high school sports. By 2022, over 3-million high school girls played sports. Nevertheless, girls have yet to reach the original participation rates of boys: in 2022, there were 4,376,582 high school playing opportunities for boys compared to 3,241,472 for girls (NFHS 2022).

Dominant ideologies position this gap in sports participation as emerging from a lack of interest among girls or simply a result of girls’ choices to pursue non-athletic activities (Cooky 2009). Yet gender as social structure has worked to limit girls’ athletic choices. For example, sexist ideologies—which often perpetuate the notion that girls have less rights to play and thrive in sports, and that their athletic feats deserve less attention, support, and celebration—have been internalized and entrenched in institutional discrimination enabling schools to provide girls with inadequate facilities, treatment, and funding compared to those offered for boys’ sports (Cooky 2009; Lopez 2019; Milner and Braddock 2016). Despite its positive impact on girls’ sports opportunities, Title IX normalized an educationally sponsored sex-segregated athletic system that upholds inherent masculine supremacy, strips leadership opportunities for girls and women, and reinforces biological and binary notions of sex and gender—essentially institutionalizing and reifying sexist assumptions of gendered differences and who can be (most) athletic, powerful, competent, and competitive (Hextrum 2021; Hextrum and Cameron 2023; Hextrum and Sethi 2022; Messner 2009; Milner and Braddock 2016).

Far less attention has been given to the specific family, individual, and school-level mechanisms that may produce within-gender differences in high school athletic participation (Lopez 2019). For instance, athletic participation disaggregated by race/ethnicity and social class suggests that being White and middle class enables girls to play high school sports to a greater extent. It is probable that inequalities in youth sports trickle up into high school participation rates (NWLC 2015; Project Play 2022; Zarrett and Veliz 2021).

Social Class and High School Sports Participation

Social class is another important factor that socially structures sports participation and influences the development of a habitus. Over recent decades, public subsidies for sports and recreation have been drastically cut, especially in lower-income areas, shifting the financial burden to cover athletic costs to individual families (NWLC 2015; Project Play 2022; Zarrett, Veliz, and Sabo 2020). Cuts to community and school funding for sports have meant fewer overall opportunities for lower-income youth to play (NWLC 2015; Project Play 2022).

In addition, there is evidence that higher socioeconomic status (SES) families leverage their resources to intensively parent via concerted cultivation, enrolling their children in myriad, high-quality extracurricular activities to develop dispositional characteristics like discipline and competitiveness to socially ascend and to build their college resumes. Higher SES families have increasingly turned to competitive sports—and oftentimes rarely offered and high SES-associated competitive sports (e.g., “country club” sports)—as the preferred extracurricular for securing such benefits (Friedman 2013; Hextrum 2018, 2021; Lareau 2015; Messner 2009).

Altogether, class-based behaviors and unequal family and community funding structures appear to influence participation rates, and these stratifying dynamics are upheld by sports and educational institutions (Hextrum and Kim 2023; Pericak and Martinez 2022; Zarrett and Veliz 2021). Nearly double the proportion of high-income kids play sports compared to low-income kids on a regular basis (U.S. Department of HHS Health and Human Services 2019). Family income also spurs continual school sports participation (Tompsett and Knoester 2022, 2023). Youth from low-income families are six times more likely to quit sports for financial costs than higher-income kids (Project Play 2022).

Financial resources generally enhance the amount and variety of sports available in schools and communities. Higher income settings are more likely to offer various sports and ample support for them, increasing participation (Pericak and Martinez 2022; Project Play 2022; Sabo and Veliz 2008). The most common high school sports played by girls, which are also among the most frequently offered sports, are track (n = 456,697 participants), volleyball (n = 454,153), soccer (n = 374,773), basketball (n = 370,466), and softball (n = 340,923). Rare sports—that have fewer competitors, are clustered in high-income areas, and are still typically offered in college—include water polo (n = 37,379 boys/girls combined), skiing (n = 9,671), equestrian (n = 7,161), rowing (n = 4,589), fencing (n = 4,139), and sailing (n = 916) (Eckstein 2017; Hextrum 2021; NFHS 2022). Diverse sports offerings allow participants to test a variety of sports to find their fit. Playing multiple sports can also enhance sports development (Hextrum 2018, 2021; Tompsett and Knoester 2022).

Race/Ethnicity and High School Sports Participation

Race/ethnicity and persistent racial/ethnic inequalities also structure sports opportunities and commitments and contribute to one’s habitus. Racial/ethnic progress in sports participation has largely been judged by assessing Black athletes’ progress (McGovern 2021). Black athletes have mostly gained greater access to certain sports—track and field, basketball, and men’s football (Martin 2010). Black youth are both pushed and pulled into these sports. Social processes involving the pursuit of culturally valued and celebrated sports with significant Black representation draw Black athletes into a few sports. In addition, racial stacking—placements in particular sports and positions because of racial/ethnic stereotyping—and limited offerings push Black youth into certain sports (Carrington 2013; Hextrum 2021; Zarrett and Veliz 2021).

Racial/ethnic segregation also matters (Hextrum 2021; NWLC 2015; Pericak and Martinez 2022). Children of Color are particularly likely to attend majority-racial/ethnic-minority schools with fewer resources. Consequently, Girls of Color are more likely to attend heavily minoritized schools that are lower funded, have fewer sports, and are less likely to comply with Title IX compared to White girls who are more likely to attend majority White schools with higher funding, more sports, and offerings that are more likely to comply with Title IX (Hextrum and Cameron 2023; NWLC 2015; Pericak and Martinez 2022). Notably, White girls disproportionately play most sports; Black girls remain concentrated in basketball and track; Latinx girls are concentrated in soccer, softball, volleyball, and basketball; other race/ethnicity girls disproportionately play volleyball, basketball, and track (Zarrett and Veliz 2021; Zarrett, Veliz, and Sabo 2020).

But the experiences of Latinx and Asian girls’ high school sport participation are largely underexamined (Lopez 2019; McGovern 2021). Still, evidence suggests that school and community barriers to sports participation are compounded by nativity and immigration statuses. For example, in semi-structured interviews with 31 Latina college students who played high school sports, McGovern (2021) found that class and nativity shape whether Latina girls can play sports, which sports they play, and for how long—with lower-income, first-generation Latina girls having the least opportunity in athletics.

Combined, social structuring and habitus effects seem to have allowed White girls to maintain high representations across all high school sports whereas Girls of Color appear to have fewer chances to play and become concentrated in certain sports (Zarrett and Veliz 2021; Zarrett et al. 2020). In turn, the interrelationship between racial neighborhood segregation and school funding inequalities leads to far fewer sport offerings in majority-minority schools (Pericak and Martinez 2022). Yet the common focus on the racialized social structuring and developed habitus of People of Color ignores how White people, in addition to People of Color, are also racialized—generally, in ways that work to maintain their racial/ethnic privileges. Racial/ethnic segregation separates most White people into racially isolated environments in which they live, study, recreate, and work. In these homogenous environments, they rarely encounter People of Color, and when they do, they are likely to self-isolate, congregating with other White people. White people tend to develop a comfort with other White people and a discomfort with racial/ethnic diversity. They also commonly develop inaccurate understandings of race/ethnicity and racism, learning to deny that their segregation is the result of racialized processes. Instead, they learn that all-White environments are natural and desirable (Bonilla-Silva 2017; Hextrum 2020a, 2020b, 2022).

White people are not only racially segregated—they are also more likely to be overrepresented across all dominant social institutions, including sports and educational institutions. As a result, they feel comfortable and encouraged in most settings as they have had repeated exposure to seeing positive and powerful versions of themselves represented. This overrepresentation conveys a general sense of superiority—or overconfidence—that they can and will be successful in a range of activities. Combined, these factors shape the racial/ethnic attitudes held by White individuals and encoded in White-dominant (i.e., most) institutions. Notably, White behaviors, dispositions, attitudes, and cultures become normative and are used as standards for acceptance and advancement. A White habitus reproduces whiteness or a structural and embodied form of racial power in which “all actors socially regarded as ‘White’ … receive systemic privileges by virtual of wearing the white—or virtually white—outfit, whereas those regarded as nonwhite are denied those privileges” (Bonilla-Silva 2017:271). Thus, whiteness extends beyond individuals as it becomes encoded in institutions (Bonilla-Silva 2017; Hextrum 2020a, 2020b, 2022).

Summary

Nevertheless, popular rhetoric and policies that position athletic participation as an individual choice obscure the familial, community, and school resources and supports required for sports (Hextrum 2021, 2023, 2024; Hextrum and Kim 2023; McGovern 2021). Also, the dynamic interplay between gender, racial/ethnic, social class, and habitus and their influence on our “choices” and routing of our chances for and commitments to sports are largely neglected (Bourdieu 1984; Hextrum 2023). Thus, we apply our conceptual framework and mixed-methods design to reveal evidence and implications of the social forces that result in gendered, classed, and racialized habitus that are institutionalized within school-sport opportunity structures and influence how comfortable, prepared, supported, and committed someone is to persist through athletic winnowing mechanisms.

METHOD

Mixed-Methods Design

We used an exploratory sequential mixed-methods design because this approach optimized our ability to study the underlying mechanisms and directional relationships among individuals, institutions, and social systems (Creswell 2014). Mixed-methods studies allow researchers to explore concepts emerging from a qualitative study and design quantitative models to test themes. Exploratory sequential studies allow data collection to occur in two phases or by different researchers. Typically, the qualitative study is designed and collected independently (Creswell 2014). Once the qualitative research has generated themes and findings, a quantitative study is designed to “develop better measurements with specific samples of populations and to see if data from a few individuals (in qualitative phase) can be generalized to a large sample of population (in quantitative phase)” (Creswell 2014:276).

For our study, the first author designed and conducted a study from 2015 to 2016 examining college access via sport. The first author published findings and continued data analysis in subsequent years (Hextrum 2018, 2020a, 2020b, 2023, 2024). Independently, the second and third authors conducted a quantitative study examining the social factors that affected the chances of a high school student becoming a college athlete (Tompsett and Knoester 2022). After discovering our mutual research interests and expertise, the authors teamed up to design a new, mixed-methods study to deepen our understanding of stratification processes that result in disparities in high school sports participation. The qualitative research offers in-depth interview information and subjective perceptions of life histories about the processes that lead to high school sports participation—centering a large group of college athletes at one university. While the participants were college athletes at the time of the interviews, life-history methodology necessarily explores the lived experiences that lead to one’s eventual social position. As discussed throughout this article, college athletes recounted elaborate, pre-college, athletic and educational experiences traversing multiple sports and social sites to acquire the athletic abilities to matriculate as college athletes. While the participants eventually became college rowers and track and field athletes, all but one participated in elite youth and high school sports reflected in the quantitative dataset (e.g., soccer, football, and basketball).

The quantitative portion of the study enables us to draw upon the rich qualitative findings and consider how these qualitative findings may or may not hold up when used to inform rigorous statistical analyses of the experiences of members of a large high school cohort. Merging datasets and research traditions can enhance rigor and validity for a study by using multiple data sources, modes of analysis, and multiple researchers and perspectives, all of which enhance the trustworthiness (Creswell 2014).

Limitations of mixed methods include misalignment of ontology and epistemology across the study’s design; underdevelopment of qualitative portions; and duplication of data (which occurs when qualitative participants are also included in the quantitative sample) (Creswell 2014; Jones, Torres, and Arminio 2014). By merging data at the interpretation phase, exploratory sequential design minimizes these limitations by ensuring the qualitative research is fully developed, analyzed, and aligned before incorporating quantitative analysis. Merging our research was also aided by our theoretical alignment. Researchers were independently interested in studying stratification and, especially, the roles families, social class, and sports play in maintaining unequal school systems. Our independent, and aligned, expertise in these areas limited possible paradigmatic conflicts in our understanding of how power and inequality shape our society.

Qualitative Study

Initially, the first researcher conducted a critical qualitative study exploring, How does race, class, and gender shape one’s path to and through college via sport? The critical qualitative inquiry examined how power relationships create unequal material conditions, shape subjectivities, become obscured from the public, and are reproduced through ideologies. This approach assumed people’s narratives discussing unequal material conditions can reveal the reproduction of said conditions (Hextrum 2021; Ravitch and Carl 2016). Life-history narratives with college athletes—an interview style allowing participants to freely narrativize and researchers to center narratives in exploring social contexts (Lensmire 2014)—were the primary method.

Research Site

Interviews were conducted at Coastal-U, a highly selective Research-1, public university with 40,000 students. Coastal-U is a top Division I program with nearly 100 team and 200 individual national championships. Coastal-U’s repute for academic and athletic excellence appeals to hopeful college athletes. Participants were recruited from two sports—rowing and track and field—to introduce potential demographic variety. Rowing is one of the most restrictive sports, as rowers learn their sport primarily through private, expensive clubs (Hextrum 2021). Track is widely accessible; it is one of the most common high school sports (NFHS 2022). Combined, these teams represented 25 percent of Coastal-U’s athlete population. Yet life-history interviews revealed that participants played on a range of teams before specializing in track and field and rowing. Their athletic backgrounds included participation in ballet, basketball, equestrian sports, field hockey, golf, gymnastics, ice hockey, karate, skiing, swimming, tennis, volleyball, and water polo—all sports offered by their schools. Qualitative findings interrogated college athletes’ experiences in varied school-sport settings that shaped their eventual specialization and success in two sports.

The first author recruited volunteer participants through purposive sampling and snowball recruitment methods. Participants self-identified their race and gender positions. Twenty-eight identified as women and 19 as men, all cisgender. Eleven identified as People of Color (9 as Black) and 36 identified as White (Table 1). The study’s racial demographics mirrored Coastal-U’s athlete population. Over 70 percent of the athletes were White; 23 percent were Black and concentrated in track, basketball, and football, in their high school sports experiences. Less than 6 percent of the athletes were Asian and less than 1 percent were Latinx. Coastal-U’s demographics reflect national participation rates that continue to show White athletes as the most common participants in college sports with Black athletes as the next most common participants (NCAA 2023). Because of these demographics, discussions of race in the qualitative findings center on the experiences of being White and Black.

Table 1. Participants’ Characteristics from the Women College Athlete Study

Participant	Sport	Race	Home State	Class	Median Family Income	Hometown demographics % White	Hometown demographics % Black	Caregivers’ Highest Ed Level	Athletic Participation	High School	# of Sports High School
Amanda	Crew	White	WA	Middle	$99,597	77.10 percent	13.30 percent	Advanced	Recreational, private club, school	Public	22
Brittany	Track	Black	CA	Middle	$74,869	30.80 percent	20.70 percent	BA	Recreational, private club, camps, private coach, school	Public	24
Camilla	Crew	White	CA	Upper Middle	$109,592	67.80 percent	1.20 percent	Professional	Recreational, private club, camps, school	Public	23
Captain America	Crew	White	CO	Upper Middle	$111,901	97.30 percent	0.00 percent	Professional	Recreational, private club, camps, private coach, school	Private	23
Casey	Crew	White	CA	Upper Middle	$167,561	89.80 percent	0.60 percent	BA	Recreational, private club, school	Public	24
Chantae	Track	Black	CA	Lower	$60,665	67.40 percent	8.20 percent	HS	Recreational, private club, private coach, school	Public	19
Chelsea	Crew	White	CA	Middle	N/A*	N/A*	N/A	BA	Recreational, private club, camps, school	Private	30
CM	Track	White	CA	Upper Middle	$109,762	50.80 percent	2.10 percent	BA	Recreational, private club, camps, school	Public	24
Cooper	Crew	White	WA	Upper Middle	$91,149	92.30 percent	0.80 percent	BA	Recreational, private club, camps, school	Private	16
Ella	Crew	White	Germany	Middle	N/A	N/A	N/A	Professional	Recreational, private club, camps, school	Public	N/A**
Imani	Track	Black/Spanish Japanese	CA	Middle	$81,498	62.10 percent	10.80 percent	HS	Recreational, private club, camps, private coach, school	Private	24
Josephine	Track	Black	CA	Upper Middle	$81,498	62.10 percent	10.80 percent	BA	Recreational, private club, camps, private coach, school	Private	6
Joy	Crew	White	MI	Upper Middle	96,210	72.40 percent	7.60 percent	Professional	Recreational, private club, camps, school	Private	27
Kalie	Crew	White	WA	Upper Middle	$99,597	77.10 percent	13.30 percent	BA	Recreational, private club, camps, school	Private	21
Kayla	Track	Chicana White	CA	Middle	$97,628	59.40 percent	5.90 percent	HS	Recreational, school	Public	23
Laura	Crew	White	CT	Upper Middle	$211,313	93 percent	0.40 percent	BA	Recreational, private club, camps, school	Private	34
Lisa	Crew	White	Germany	Middle	N/A	N/A	N/A	BA	Recreational, private club, camps, school	Public	N/A
London	Track	White/Middle Eastern	CA	Upper Middle	$136,150	85.20 percent	0.50 percent	BA	Recreational, private club, camps, school	Private	24
Monique	Crew	White	CA	Upper Middle	$167,561	89.80 percent	0.60 percent	HS	Recreational, private club, camps, school	Public	24
Morgan	Crew	White	NY	Middle	$120,860	87.60 percent	2.70 percent	Advanced	Recreational, private club, camps, school	Public	26
Noelle	Crew	White	CA	Upper Middle	$69,944	90.10 percent	0.20 percent	BA	Recreational, private club, school	Public	22
Sanya	Track	White	CA	Lower	$114,764	82.90 percent	1.80 percent	BA	Recreational, school	Public	24
Savannah	Crew	White	CA	Lower	$92,192	79.90 percent	0.70 percent	HS	Recreational, private club, school	Public	24
Sophia	Crew	White	NJ	Upper Middle	117,727	39.80 percent	6.60 percent	Advanced	Recreational, private club, school	Private	31
Stella	Crew	White	HI	Upper Middle	85,837	21.50 percent	2.50 percent	Advanced	Recreational, private club, school	Private	34
Taylor	Track	White	CA	Upper Middle	$109,693	81.60 percent	1.60 percent	BA	Recreational, private club, camps, school	Public	26
Vera	Track	Black/Haitian/German	Germany	Middle	N/A	N/A	N/A	HS	Recreational, private club, camps, school	Public	N/A
Victoria	Crew	White	Canada	Upper Middle	N/A	N/A	N/A	BA	Recreational, private club, camps, school	Public	27

*Data for hometown median income and racial demographics came from the U.S. Census Bureau (2016) data. No adequate comparison exists for countries represented in this study. As a result, no median income or demographics are listed for international student athletes in the study.

**Data unavailable for some international college athletes.

Only three people in the study (all women) identified as low income, the rest identified as middle- and upper-middle class. Class categories were created by contextualizing their self-identification through several follow-up questions. Participants described their neighborhood and school, their caregiver’s educational level, their extracurricular activities, and whether their family needed them to supplement the household income. U.S. Census Bureau data—family median income and racial demographics—were also matched to respondents’ hometowns to further contextualize their self-identifications.

To recruit, research opportunities were presented at team practices and flyers were disseminated across campus. Participants were asked to share the flyers with their teammates. Participants received no compensation. The life-history interviews took place in person in a private office. Interviews averaged 3 hours and totaled 145 hours. To maintain participants’ confidentiality participants selected pseudonyms.

The first author’s positionality influenced recruitment. As a former Division I non-revenue college athlete, she developed quick rapport with participants. Participants often used institutional jargon and at times included her in their narratives, saying: “you get it.” As a researcher, she countered these moments saying, “please assume I know nothing about … ” As a White, cisgender woman her race/ethnicity and gender also influenced participant recruitment and responses. White athletes candidly shared racially coded accounts, assuming she shared their racial ideology. Men athletes often moderated or apologized for their sexist language.

Coding

Throughout, the first author documented emerging insights with field notes and research memos. After transcribing the interviews verbatim, she began open coding and read each interview while identifying any possible theme, repetition, or pattern (Ravitch and Carl 2016). She coded the interviews with Dedoose software. Next, she used fixed coding to reexamine excerpted text for recurring patterns (Creswell 2014). Fixed codes were informed by Bordieuan theory (social, cultural, and economic capital); critical whiteness studies (structural racism, racist ideologies, racialized interactions, and racial identity); and gender institutionalism (structural sexism, sexist ideologies, gendered interactions, and gender identity). She then used axial coding to group the codes into categories (Jones et al. 2014). During this phase, she reinterpreted passages of coded texts to refine the categories and identify higher order themes.

In determining whether a qualitative insight arose to the level of a theme, the first author examined in-group similarities and differences in racialized and gendered experiences (e.g., comparing whether a certain theme resonated across the majority of White participants or the majority of male participants) and evaluated this insight across different groups for conflicting instances (e.g., if White and Black athletes shared an experience). These comparisons helped determine the salience of race/ethnicity, social class, and gender in the participants’ life trajectories (Lensmire 2014). The entire process was iterative as insights deepened through rereading and refinement. She then challenged her initial themes by rereading each narrative in its entirety for conflicting or confirming statements; checking insights against the literature; and discussing initial findings with participants and members of the athletic community including former athletes and athletic academic support personnel (Ravitch and Carl 2016) (Table 2).

Table 2. Summary of Findings from the Women College Athlete Study

Codes, Definitions, and Themes	Representative Quotes and Evidence
THEME 1: Intensive Parenting (Fixed code: Habitus | Lower-order codes: Components of habitus)
Parental expectations and socialization – Parents value and expect sports participation; believe sports develop dispositions and skills needed for academic and career success	– “My family really, really values [sports] because I think it teaches you a lot. It teaches you competitiveness, determination, it teaches you how to compete, and how to be mentally tough, and it also teaches you how to work with other people in different environments.” (Brittany) – “Sports were encouraged … I was definitely pushed into sports … my parents pushed me into sports because they saw that I could excel. But also, just because they wanted me to be doing stuff. Have extracurriculars, have interests … . But yeah, but I don’t think I was pushed into sports for the sake of sports, but for the sake of being a well-rounded person. Having interests and drive.” (Noelle)
Parental investments – Parents invest time, money, and their emotions in child’s sport career	– “My mom really supported me, in my rowing, and she would drive me to practice every day … . And [rowing] is a very expensive sport. I’m really lucky that my parents could afford to send me [to a private rowing club].” (Casey) – “To get me through a year of ski racing at the semi-lower level was about the same as going to private school, so like 25–30 thousand dollars a year. And you only race for half of the year.” (Victoria) – “I could [play sports] because of my family’s financial situation. [Money] didn’t impact which sport I did.” (Brittany) – “My Dad is one of those people who doesn’t want to know how your friends are doing. He wants to know how you’re doing in your sport.” (CM)
Durable training – Investments and expectations carried across sport levels (club through high school), to different sports (basketball, swimming, etc.), and settings (club, school sports, etc.)	– “Besides basketball, soccer, ballet … a year of softball, a year of lacrosse, a year of volleyball … to row I had to quit basketball, our middle school team. I started [rowing] the spring of my eighth grade. Which is kinda like the winter/springtime when you start. So I quit basketball. But I did end up trying that next fall to do rowing and soccer. But that was physically exhausting. So I stopped soccer and I just 100 percent did rowing.” (Cooper) – “Probably grade four, I dropped down to three sports. I did figure skating, I did soccer in the summer, and I did ski racing in the winter. And ski racing has always been my main sport until … I quit ski racing to be a rower.” (Victoria)
THEME 2: Racial/Ethnic and Gender Segregation (Fixed codes: Structural racism and sexism | Lower-order codes: Segregation mechanisms—law, policy, rules, organization, resources)
Racial/ethnic segregation: Neighborhoods	Neighborhood characteristics—Race: 25 lived in communities 50 percent or more white population; 21 lived in communities with 67 percent or more white population; 16 of 28 lived in communities with 80 percent or more white population Descriptions of their neighborhood – “I kinda grew up in the “bubble” that’s what we call it. [There’s no] “homelessness” or people struggling.” (Camilla) – “I had the most sheltered up brining … it’s literally as suburbia as you can get … And, a lot of, money-centered behavior and pressure to ‘keep up that perfect family image.’” (CM) – “[Q]uintessential small-town America.” (Joy) – “I just grew up in this community that wasn’t diverse at all … [an] affluent part of [the East Coast] … there’s a lot of people in my neighboring towns who are in the one percent. It was very wealthy. And people dress like it, and they drove Jeeps. It was all about how you were perceived by other people.” (Laura)
Racial/ethnic segregation: Sports	– “I think, rowing is such a White-washed sport. I mean, our entire team [is White]. Just looking at my team. I’m like, ‘Wow. We’re also 75 percent Blonde.’ I think that unfortunately, sports are very racialized.” (Morgan) – “Obviously there are certain sports that are more racially geared, like basketball is more African American. I can’t think of anyone on the team who isn’t White. Crew is very Caucasian-based. And then gymnastics may have more Asians. [Race] is not really something I’ve noticed.” (Sophia)
Racial/ethnic segregation: Schools	Majority-white schools had plentiful sport opportunities. Sports sponsored at their high school: average, 24 sports. Diverse and niche sports opportunities like Badminton, squash, curling, rugby, equestrian activities, ultimate frisbee, lacrosse, field hockey, and ice hockey.
Gender segregation: Separate and unequal/fewer sports, different sports, and less resources for girls	– “My [track] coach … he was also a football coach, so, I had asked him, we were supposed to have practice, and like, he wasn’t there, and I’d seen him in the weight room with the football players and I had asked him, ‘Are we having practice?’ And he’s like, ‘Oh you go here?’ And I was like ‘OK, seriously? I don’t have a coach, what do you want me to do? You want me to just sprint five days a week and never have any jump practice? What do you expect me to do?’” (Brittany) – “There was more emphasis on the male sports in my high school … And sports like women’s rowing or women’s field hockey, we don’t really get the credit that football and basketball do. Like we put in the same amount of effort, in practice, I mean in different ways. Both for the gender, we’re both equally trying as hard, but men just get that extra advantage for being a dude than the females.” (Captain America)
THEME 3: Individualizing Inequality: Choice (Fixed codes: White habitus | feminine athletic habitus)
White habitus: Universalism, race neutral, denial of racism, comfortable in most settings	– “Where I’m from is very much just White people. But [race] never been a problem for me, I don’t have a problem with anything. People are people are people … I don’t think [race], plays a factor.” (Cooper) – “[Race is] definitely something I notice in rowing. There’s no diversity at all … . I don’t think [my race has] been to my advantage, but it’s definitely been something I’ve noticed.” (Amanda) – “I am on a team with all White people. So [race] doesn’t really come up. Because we’re all White … Everyone looks exactly the same, in my entire boat … . It’s very homogenous” (Casey)
Feminine athletic habitus: Accept gender segregated, different, and unequal athletic opportunities; sense of inferiority to men’s athletic performance	– “There’s this one guy on the team … .who would say some things about women’s rowers that weren’t so great. Something about girls steady-state being really slow. And they shouldn’t be rowing … . . I understand we’re slower, but, it doesn’t make it less hard, or less worthy … . men are faster than women, but that doesn’t make them better … . . Because it is a time. So it’s easy to say faster is better.” (Camilla) – “It’s like the idea of a girl doing something that—would be considered better than probably 99 percent of people, in general. And I think I like that. It’s annoying to me still that there’s always boys, that no matter what, the boys are going to be faster. The ones on my team and stuff. That bothers me. But, it’s just the way it is. But I think as a female athlete, I’m still able to say that I’m better than most people at this.” (Kayla) – “When we had to do track in high school and we had to race and then guys were like, ‘Oh [Vera] runs track let’s see if we can race her.’ They just think they’re better just because they’re male. And then when I jump further or when I run faster they’re surprised … that women can be very athletic and good at what they’re doing.” (Vera)

All 47 participants were included in determining the qualitative themes. For this study, the experiences of women athletes are centered (N = 28), focusing on the elements of their narratives that are discrete from men athletes. This allowed us to consider within-gender differences along racial/ethnic and social class lines (Lensmire 2014). In isolation, the qualitative themes presented here are not meant to be generalizable. Instead, we designed a mixed-methods study to examine whether these insights resonated in a larger population. The qualitative findings highlight three themes—intensive parenting, racial/ethnic and gender segregation, and “choices” to play certain sports—all of which unfolded within contexts that were socially structured by gender, social class, and race/ethnicity and linked to developed habitus.

Quantitative Study

Consequently, in the quantitative portion of the study, we looked to assess the extent of evidence of the broader existence and implications of the patterns suggested by the emergent qualitative themes in an examination of high school sports participation patterns among a large cohort of 10^th-grade students from the Educational Longitudinal Study (ELS). We used indicators of the relevance of presumptive racial/ethnic, social-class, and (implicitly) gender dynamics, and their assumed connections to habitus, for individuals, families, and institutions—and their connections to (high school) sport opportunities and commitments—in considering four questions: (a) Who plays high school sports? (b) Which sports do they play? (c) For how long? And (d) How do SES, race/ethnicity, and gender matter for these processes?

We drew upon unique individual-level and school-level data from 2002 through 2004 from the ELS, that offers nationally representative information about over 10,000 10^th-grade students from 752 high schools in 2002. Information was provided by students, parents, and school administrators and re-interviews occurred in 2004, 2006, and 2012. We focused on girls’ race/ethnicity, percentage of White students in their schools, percentage of low-income students in their schools, number of school sports, and family SES as predictors of girls’ likelihoods of (a) participating in any school sport in the 10^th grade; (b) playing the specific sports of basketball, soccer, softball, another team sport, and an individual sport; and (c) persisting in playing a high school sport in 10^th and 12^th grades. Our sample included 4,271 girls across 559 schools for which we had the relevant information for our study design.

Dependent Variables

Descriptive statistics for all variables used in the quantitative analysis are presented in Table 3. The dependent variables were dichotomous indicators of high school sports participation. School sports participation (1 = yes) signified girls’ participation in any school sport in the 10^th grade. Due to contestations about what counts as a sport under Title IX, we excluded cheerleading and drill from the present study (NFHS 2022; Zdunek 2020). Specific sports participation includes separate indicators that are available from the ELS for playing basketball (1 = yes), soccer (1 = yes), softball (1 = yes), another team sport (1 = yes), and an individual sport (1 = yes). Persistent sports participation indicates participation in a school sport in both the 10^th and 12th grades (1 = yes).

Table 3. Descriptive Statistics for All Variables Used in the Quantitative High School Cohort Analysis

Predictor Variables	M(SD)/%	Dependent Variables	M(SD)/%
Individual Level
(White)	62%	10^th grade athlete	46%
Black	11%	–basketball	11%
Latinx	14%	–soccer	9%
Asian	8%	–softball	14%
Other race/ethnicity	5%	–other team	28%
Family SES	.10 (.76)	–individual sport	12%
(Married)	63%	Persistent athlete	26%
Cohabit	16%
Single	21%
# of siblings	2.28 (1.52)
Physical disability	2%
Cognitive disability	7%
Played (another) sport^a	23–25%
Played (other) sports^a	12–20%
School Level
% White	.61 (.31)
% low SES families	1.56 (.83)
School size	3.25 (1.82)
# of school sports	8.29 (2.51)
Private	24%
(Urban)	32%
Suburban	48%
Rural	20%
(Northeast)	18.56%
Midwest	27%
South	37%
West	19%

N = 4,271; ^aThese variables are unique measures used for predicting each specific sport.

Independent Variables

Independent variables included both individual-level (i.e., race/ethnicity, family SES) and school-level (i.e., percentage of low-SES families, percentage White, number of school sports) characteristics. Race/ethnicity consisted of mutually exclusive dummy variables (i.e., White as reference category; Black, Latinx, Asian, other race/ethnicity) that reflected self-identifications of race/ethnicity. Family SES was a variable constructed by the ELS based on family income, father’s education, mother’s education, father’s occupation, and mother’s occupation. School-level SES (1 = 0–20 percent; 4 = >75 percent) indicated the reported percentage of students receiving free or reduced lunch by school administrators, percent White was a continuous variable calculated as the percentage of roughly 12 to 24 sampled students in each school who identified as White, and number of school sports was the number of girls’ school sports reported by school administrators.

Control Variables

Control variables contained individual-level (i.e., family structure, disability statuses, [other] sports participation) and school-level (i.e., school size, type, and geographic location) variables, too. Family structure included parental marital status (i.e., dummy variables for married—used as reference category, cohabiting, and single) and number of siblings. Physical (1 = yes) and cognitive (1 = yes) disability statuses are also considered. In addition, as control variables for the specific sports analyses, we used indicators of girls’ participation in one (1 = yes) or more than one (1 = yes) other school sport(s). School-level control variables were reported by administrators and signify school size (1 = <100; 7 = 700+ class size), private school status (1 = yes), level of urbanicity (i.e., dummies for urban—used as reference, suburban, and rural), and region (dummies for Northeast—used as reference, Midwest, South, and West).

Analytic Strategy

We used multilevel logistic regression analyses with nested models to consider racial/ethnic disparities at the individual and school levels in any school sports participation, specific school sports participation, and persistent school sports participation. Then, we focused on school-level SES, number of school sports, and family SES in successive models.

QUALITATIVE FINDINGS

Interviews with college athletes revealed that social class, race/ethnicity, and gender and their links to habitus seemed to affect the opportunities and commitments to play and persist in sports and pointed to how the relevant social processes appeared to operate via (a) intensive parenting, (b) racial/ethnic and gender segregation, and (c) assumptions of individualized “choices” (Table 2). Higher SES, reflected in families, schools, and communities, granted girls access to a wide array of high-quality sports. Resource-abundant communities were majority White and cultivated a White habitus that elevated whiteness as the default, universal human status and denied the workings of structural racism. Whiteness limited chances for even high-SES Athletes of Color. Furthermore, gender segregation immersed girls in markedly different sport worlds than boys. Combined, these barriers led to fewer athletic choices for all girls, and especially for lower-income girls, and for Girls of Color. Gender, race/ethnicity, and social class operated at the individual, interactional, and cultural levels, in part via developed habitus.

Intensive Parenting

Intensive parenting references by college athletes in their interviews were common, and higher family SES clearly enabled more extensive parenting investments. Yet, no significant racial/ethnic or gender differences were apparent in how middle-class families pursued sports. Both middle-class Families of Color and White families believed that sports developed the dispositions and skills girls needed for academic and career success and encouraged sports participation for them. Still, success in competitive sports—and the corresponding habitus that middle-class parents perceived would translate into other social domains—required intensive capital, instrumental support, and emotional investments. These contributions undergirded a durable training as youth participated for many years (e.g., starting at age five, on average), across multiple sports, and multiple levels (e.g., recreational, competitive club teams, private coaching, and high school teams) (Table 1).

Participants connected their early and repeated exposure to sports as vital to igniting their athletic careers. Victoria (White, upper-middle class) recalled, “I started young in a lot of sports, like three or four … And my mom’s philosophy was just get me into as many sports as I could.” Victoria went on to list the sports she played, recalling how her schools had every sport, even “badminton,” which she joined, before focusing on Nordic skiing.

Captain America (White, upper-middle class) saw sports as interwoven with her family, childhood, and development:

My mom put me in the pool when I was a few months old, so I immediately was exposed to sports—there’s never a point in my childhood where I wasn’t doing anything … If its swimming in the summertime, or soccer in the fall, or, basketball in the winter. I basically played every single sport there was … In the environment that I grew up, it was more of, rewarding your accomplishments … [which is] a contributor to my personality today. I cannot lose … even though, I [no longer] have a parental figure constantly watching over me [or] being proud every time I come home from practice or a game.

Participants said their parents had explicit conversations about how sports can cultivate the skills needed to succeed in school and careers. Like Captain America stated, parents believed sports encouraged a competitive spirit, drive, commitment, and work ethic. Here are two examples:

Brittany (Black, middle class): My family really, really values [sports] because I think it teaches you a lot. It teaches you competitiveness, determination, it teaches you how to compete, and how to be mentally tough, and it also teaches you how to work with other people in different environments. Sportsmanship, teamwork, yeah, I think it helps a lot, with how you develop, and how you grow up.

Imani (mixed race, middle class): [My parents] wanted me to always have something constructive to do … they just wanted me to grow up with structure.

Achieving the benefits of sport required parental investments of time, money, effort, and emotional support. Participants typically spent 10 to 20 hours per week on their sport since age five. Their exposure to sport was not restricted to one venue—rather they played on recreational, school, and club teams. Middle- and upper-middle-class participants recollected that their parents could make these investments, enabling their future athletic commitments and success. Cooper (White, upper-middle class) described her upbringing as,

just blessed. I haven’t had problems … if it’s for academics or sports and it’s something that I’m committed to, my parents are usually like, “we’ll figure out a way to do it.” … . Growing up, my goal had always been to [become a college athlete]. I was like, “I’m going to go for soccer.” Or, “I’m going to go for basketball.” … . My parents were like, “Cooper, whatever you need to be happy and succeed … we’ll do a second mortgage on the house,” whatever that means.

Participants recalled their families spending thousands of dollars per year on equipment, transportation, club membership dues, tournament fees, private coaching, camps, and lodging and travel expenses. Families learned that private clubs offered superior training and development. Twenty-six of 28 girls played for private clubs to supplement their school teams.

Four of the Black women (all track athletes) discussed how their parents intervened in their school teams and hired private coaches. One such athlete, Imani (mixed race, middle class), explained the importance of private coaching to her development: “My Dad would always go to my school practice, every day, and watch what we were doing. And he thought it was less intense than the club track workouts … So he hired a private coach for me, and I would go to his practice like after my track practice.” The private coach gave her workouts to do on her own and a diet regime to lose weight—all to improve her 400-meter times.

Most of the middle- and upper-class athletes never had to consider whether their family could afford their sport participation. In contrast, the three lower-income women in the study vividly recounted how their family’s finances limited their athletic development. Chantae (Black, lower class) believed her family’s finances were a detriment. Chantae explained that by fifth grade she saw differences in the families that had resources, support, and connections to facilitate their children’s athletic success and those that did not. She felt that her peers took the sport “more serious” than she did and could attend all the practices, meets, and extra training sessions she had to sometimes miss:

My mom does hair, so her money was really inconsistent. [S]he’ll have a good week when everyone comes in and gets their hair done. And then other weeks, she wouldn’t. And so during club season, you have to pay for [the club]. And pay for your way to the meet. And have money to spend [at the meet]. [The club] didn’t give me anything.

Chantae said she had to miss training sessions on weeks her mom could not pay the club dues. Similarly, Savannah (White, lower class) only accessed rowing because she received a scholarship that covered the club membership—the scholarship did not cover travel costs. She recalled running a car wash to pay her travel expenses. Sanya (White, lower class) took on a tutoring job to pay her track and field costs. “I tutored at night … [My schedule was] 6 am practice, 7 am class … then practice from 2–5:30[pm]. And then I’d tutor from 6–8[pm]”

Sanya, Savannah, and Chantae persisted in sports despite their family’s financial status. However, they were the exceptions, and all expressed how their athletic development suffered. Furthermore, they all benefited from living within or in proximity to White, affluent suburbs. Their sports careers began and were subsidized by de facto racially segregated and well-funded, White, suburban schools.

Racial/Ethnic and Gender Segregation: Unequal Resources in Neighborhoods, Sports, and Schools

The second emergent theme captured how racial/ethnic and gender segregation influenced access to community and school resources.

Racial/Ethnic Segregation

While the United States no longer supports de jure or overt segregation, study findings reflect how de facto residential and schooling segregation generated racially segregated sporting experiences that also evidenced social-class differences. Racial/ethnic segregation substantiates a White habitus (Bonilla-Silva 2017). White, affluent families used their class standing to purchase homes in White-majority suburbs with higher ranking public schools and more high-quality sporting infrastructures. Athletes recounted abundant and high-quality sport opportunities reflected in their neighborhood, athletic, and educational infrastructures.

Participants generally described their hometowns as safe, affluent, and replete with athletic and academic resources:

CM (White, upper-middle class): “I had the most sheltered upbringing … it’s literally as suburbia as you can get … And, a lot of money-centered [behavior and pressure to] keep up that perfect family image.”

Camilla (White, upper-middle class): “I kinda grew up in the “bubble” that’s what we call it … [we never saw] “homelessness [or] people struggling.”

Monique (White, upper-middle class): “Where I grew up—it’s a very affluent rich area.”

Joy (White, upper-middle class): “I grew up in quintessential small-town America.”

Census data confirmed participants’ accounts of their upbringings. The average hometown median income for the athletes was $109,025. Their communities were also majority White; 89 percent of the participants lived in towns with 50 percent or more White populations. Moreover, nearly 60 percent of the athletes lived in towns with greater-than-80-percent-White populations. Immersed in White, affluent communities, participants learned to conflate whiteness with affluence, safety, and accessibility to social goods such as high-ranking schools and competitive sports.

Participants described how their parents actively moved to White suburbs for better schools and opportunity. Morgan (White, middle class) conflated her hometown’s demographics with academic success: “My town is 97 percent White … we’re a suburban town off of the city. The main employer in that city is the University. And all of the University professors live in this suburban town because we have the best schools.”

Casey (White, upper-middle class) said her family moved to a suburb that was 90 percent White because the area was known for “very nice [public] schools … . Most of the schools, had ample funding. I came from a very affluent community … Resources were always readily available to me. I had a lot of support, especially from counselors and teachers.”

While Casey and Morgan stayed in public schools, 50 percent of athletes attended private schools in their suburbs (Table 1). They learned that private schools offered even greater educational and athletic advantages. Captain America (White, upper-middle class), who grew up in a town that was 97 percent White with a median family income of $111,000, recalled how her parents enrolled her in a “private Catholic school” because “it was known for athletics and academics.” Her parents felt the public school in her suburb was insufficient because “its reputation wasn’t exactly great for academics or athletics. And I think that my parents thought that if I was put in a better situation, like going to [private school] … it would channel me into [or make it] easier to get into college.”

Chantae (Black, lower class) was one of the few low-income participants in the study. She had access to sports after she transferred to a majority-White, higher income school:

I went to the same school, up until the fifth grade, which was an all-Black school. In a little ghetto neighborhood. And then I moved into … an all-White school [because] my mom wanted me to be in a better neighborhood … just more opportunities that were offered to me. [at the new school]

Chantae’s new school had a track team, which she joined. And she recalled more serious workouts; starting in sixth grade, “we had practice every day after school.” She was also exposed to a variety of track events. “I started off doing everything … . So I’ve literally done the 100, 200, 400, 4 × 1, 4 × 4, and then in high school I started jumping. I started doing triple jump and long jump. And I’m a jumper now.”

Hometown and school demographics directed who played sports. Participants recalled their sports teams as mostly White, with few People of Color who were often concentrated in certain sports or positions. Noelle (White, upper-middle class) played a variety of sports growing up and noted how,

the [racial demographics] varied by sport. Like soccer teams were very much Hispanic and very good … . Basketball, mostly White … my swim team, not so diverse … mostly White … rowing, it was all White. I can think of one girl that I competed against that was not White … And I always thought, “Why is she the only one I’ve ever seen?” … Because it is a very elitist sport, it’s going to be at the elite universities. I think it has positive routes. But right now, it’s kinda blocked for a lot of people.

Camilla and Monique (White, upper-middle class) linked their neighborhood demographics to their athletic opportunities:

Camilla (White, upper-middle class): “I wouldn’t have been exposed to rowing, maybe, if I had grown up in [a nearby racially and economically diverse city].”

Monique (White, upper-middle class): “The more I think about rowing the more I think: I’m not that great at rowing, I just had the privilege to row. It’s so difficult for people to get the chance to row … I’m sure there are more athletic people than I am, but they just didn’t have the chance.”

Black and White families alike moved to White-majority suburbs or transferred to White-majority schools for superior athletic and academic experiences for their children. White-majority communities, schools, and sports were perceived to develop an athletic habitus aligned with advantages. White participants recounted no sense of loss living and recreating in a racially homogenous environment; rather, White-majority areas were perceived as positive and beneficial to social advancement. As a result, the emerging habitus links whiteness to expanded access to valued social goods like safe neighborhoods, high-ranking schools, and plentiful sports opportunities (Bonilla-Silva 2017). Relatedly, virtually all sports were perceived as appropriate for White girls but only a handful of sports contained substantial numbers of People of Color.

White, suburban areas offered more sports than lower-income and racially diverse communities. On average, participants attended high schools with 24 sports, including both girls’ and boys’ teams. Four, attended schools with 30+ sports (Table 1). The scope of sports offered encompassed popular and niche ones. The most frequently offered sports at participants’ high schools were volleyball (86 percent of schools), basketball (82 percent), cross-country (82 percent), golf (82 percent), track and field (82 percent), and soccer (79 percent). Participants attended schools that commonly offered other “country club sports,” in addition to golf, such as tennis (75 percent), swimming (79 percent), lacrosse (57 percent) water polo (43 percent), and field hockey (29 percent). In fact, their abundant athletic activities included niche sports not yet sponsored by most colleges: squash, badminton, rugby, yoga, equestrian, ultimate frisbee, sailing, judo, canoe, paddling, curling, kayaking, and surfing.

Gender Segregation

Yet girls in White, affluent suburbs still encountered gender-based sport barriers and discouragement. Participants recalled some inter-gender play at young ages, most of these experiences were in informal encounters at recess or recreational sports outside of school. Kayla (White/Chicana, middle class) characterized her elementary school years as “very tomboyish” because “I was really into basketball and soccer. I would dress like a boy every day. Play basketball at recess with the boys … [And] you do the mile, in PE and stuff. And I’d be so competitive. I’d have to beat all the boys.” Kayla also played co-ed recreational soccer and basketball through community leagues until she joined school teams in middle school. From then on, her sports were gender segregated. Cooper (White, upper-middle class) described the transition between co-ed to segregated sports:

As a little kid, in mixed basketball and soccer, co-ed, yeah, you don’t think of it because we’re all friends. You don’t think anything like, “Oh it’s a boy,” quite yet. Then, as you get a little older. I think like fifth grade, and then sixth, seventh and eighth it’s divided in all the sports … So you never really think like, “Oh, men’s soccer and women’s soccer. That’s just the way it is.“You never really question it.

As girls advanced in their sport, they began to question gender segregation, namely unequal resource distribution across boys’ and girls’ teams. A common observation was the amount of attention paid to boys’ sports, even when girls’ teams had better-performing seasons:

Captain America (White, upper-middle class): “There was more emphasis on the male sports in my high school … And sports like women’s rowing or women’s field hockey, we don’t really get the credit that football and basketball do. Like we put in the same amount of effort, in practice … we’re both equally trying as hard, but men just get that extra advantage.”

Casey (White, upper-middle class): “[In high school] there was a lack of respect for the women. We had to prove ourselves ten-times over, before [the men’s team] would give us the time of day. It’s like, “Honey, you do realize we just won three national championships? … Yeah, we’re not as fast as you guys, but in terms of for our sport, we’re freakin” fast. Please afford us the same respect that you would any men’s crew.’

Others discussed how the school offered more physical resources for the boys’ squads.”

CM (White, upper-middle class) recalled gender-based scheduling differences, a subtle but effectual mode of gender discrimination (Cooky 2009): “In high school, girls always raced first. And boys would race second. Because it was like the boys’ race was more exciting … [and] our boys’ team wouldn’t even cheer for us because they were getting ready for their race.” Taylor (White, upper-middle class) recalled how her girls’ track program “didn’t get to use the weight room” because the football team had priority. Taylor’s family had a membership to a private gym that she went to for weightlifting. “I would go on my own. But still, some girls can’t do that. Some girls can’t drive themselves to the gym.”

Brittany’s (Black, middle-class) high school track experience also suffered because of football. Her track coach was “also a football coach.” She remembers how “we were supposed to have practice, and like, he wasn’t there, and I’d seen him in the weight room with the football players and I had asked him, ‘Are we having practice?’ And he’s like, ‘Oh you go here?’ And I was like ‘OK, seriously? I don’t have a coach.’” Without a dedicated track coach, Brittany’s family decided to “pay for [private] training … twice a week.”

Many recalled how their athletic achievements were devalued in comparison to boys’ performance. Chelsea (White, middle class) recalled how boys’ squads would say, “‘Girl rowers suck. Girls are not fast.’ … [or] in hockey … ‘women’s hockey’s a lot slower than men’s hockey,’” implying that boys are physically superior, have better athletic programs, and higher levels of status and support.

Casey (White, upper-middle class) also encountered sexism when her athletic performance encroached on male terrain:

My sophomore year. Varsity boys laughed at me when I was deadlifting. I was deadlifting twice my body weight. And I could barely do it. And they were laughing me. So I was like, “Screw you.” And I slapped on another couple plates. And lifted it … . And then, [my senior] year, the varsity men, called us cows and mooed at us [while we were training] … it just became a general term to refer to a varsity girl as a “cow” and moo at them as they passed by.

Affluent families could partially offset systemic gender inequalities by not relying on school-sponsored resources. Many rowers paid for private clubs because their schools lacked teams. Taylor’s family paid a gym membership so she could access a weight room. And Brittany’s family purchased private coaching lessons when her school refused to fund a dedicated track coach. Despite these class advantages, girls encountered forms of overt sexism they could not offset with financial resources. As the final section reveals, athletes immersed in racial/ethnic- and gender-segregated environments produced racialized and gendered associations of athletic success, widening opportunities for White and male athletes and winnowing opportunities for White girls and Girls of Color.

Individualizing Inequality: Sport Ascendance and “Choice”

The extracurricular nature and presumed meritocracy of sports result in common understandings of participation as a function of individual choice. But one’s social structural experiences and habitus affect how one navigates different aspects of social life, including sports. For White athletes, their racialized habitus generally expanded and normalized their athletic opportunities whereas Athletes of Color encountered, and felt, barriers in White-dominated sports. Girls’ habitus were socially structured by internalized assumptions of male supremacy that led them to view their unequal treatment as normal and accept fewer resources, fewer chances to play, and a resignation to be grateful for what they had. White girls faced the least harm as their race/ethnicity led to better treatment, relatively speaking, and more chances to play sports compared to Girls of Color.

Immersed in White-dominant neighborhoods and schools, White youth developed a White habitus based on a sense of universalism—seeing themselves as the default (human) race, unencumbered by racial barriers (Bonilla-Silva 2017). All higher SES participants played various youth sports. Yet White athletes played an array of athletic activities—including those like basketball, football, and track, which were often associated with People of Color—up and through high school. The primary reason for selecting a sport for White athletes was athletic fit or a better future in one sport over another. Yet Black athletes gravitated to track and ended their careers in other sports sooner than White athletes. The primary reasons Black athletes gave did not highlight athletic ability but instead racial/ethnic representation.

When asked whether their race expanded or limited their chances to play sports, White athletes generally said, “no.”

Cooper (White, upper-middle class): “Where I’m from is very much just White people. But [race] never been a problem for me, I don’t have a problem with anything. People are people are people … But I definitely noticed that I am, the majority, kind of thing. But no, I don’t think so. I don’t think [race], plays a factor.”

Savannah (White, lower class): “I don’t really think that I’ve been disadvantaged at all … . I don’t think that [my race] really affected me in any significant way.”

White dominant spaces cultivate racial comfort for White people in which they do not learn about their racial identity and corresponding privileges (Bonilla-Silva 2017). While White athletes could name that their athletic environments were majority White, they saw these demographics as race neutral. As such, they were unable to recognize how their race/ethnicity may have advantaged them in their ability to participate in a White-dominant sport.

For instance, Amanda (White, middle class) was encouraged to play “tall people sports” due to her taller-than-average height. She recalled she had immediate advantages in basketball. She was taller than most of her competitors and became “really, really good” at the sport. She won several youth league and school tournaments. As a sixth grader she became the youngest member on her varsity middle school team. Looking back, she admits that this selection should have indicated her athletic potential. Instead, she felt like she was the worst person on the team and none of the coaches encouraged her to keep playing. So, she quit and joined another “tall person” sport: rowing. She joined a local private club team. Right away her height drew the attention of the coaches:

From the first day of practice I heard, “I’m going to be good.” So instantly I’m hearing this “potential” or “you have so much potential.” Because I was already huge in seventh grade. And [the coaches] were like, “Wow we have so much time to build her into this great athlete.”

In discussing why she joined and persisted in rowing, Amanda said she felt comfortable in the sport because of the character, disposition, and unity of her team—a virtually all-White team:

You just have this instant bond because—it’s such a different type of commitment to yourself and to your teammates. Especially in high school. No other sports were working as hard as we were with rowing. We just had very similar sense of humor. Very similar work ethics … Rowing kind of weeds out the people that don’t want to work that hard. That don’t want to have respect for themselves, and their coaches, and each other. By the end of the year, the people that stuck around through all of that. Those were my people.

From Amanda’s perspective, the defining barriers in her sport were intrinsic characteristics like height, work ethic, and deference to authority. Several social processes become disguised and mutated by reducing Amanda’s athletic choices to individual attributes. First, Amanda had plentiful opportunities in her community to try a variety of sports. These material advantages immediately afforded her the chance to develop and improve her ability regardless of any physical proclivities. Second, her interactions with coaches—whether telling her to join one sport or the other or that she had “potential” to excel—shaped which sport she ultimately committed to. As she noted, she may have been a successful basketball player—a sport that has a greater representation of Black athletes than rowing—but she was not encouraged to stay. Instead, her rowing club, made up of exclusively White coaches and rowers, identified her physical talents and encouraged her to remain in the sport. While Amanda may have attributed her decision to row to her height, this choice cannot be disentangled from her White habitus. She felt comfortable in this environment—“those were my people”—a comfort that enabled her persistence in a grueling sport. She rowed from seventh through 12th grade and her team only had one Black rower during Amanda’s tenure. This absence of racial/ethnic diversity largely went unquestioned and unrecognized.

Whereas White people felt comfortable in all sport settings, Black athletes spoke candidly about how their race/ethnicity shaped their athletic choices. Black athletes became experts in whiteness so they could persist in sport. Yet their expertise never fully enabled them to adopt the White habitus emerging in their White schools and communities.

Josephine (Black, upper-middle class) attended White-dominant private schools and played on White-majority teams. She never felt “fully herself” in these environments. In middle school, Josephine joined a majority-Black private track club coached by a Black woman. The first time Josephine attended practice, she watched from the sidelines and noticed how “they just looked like they were on it. They were running hard and they wanted to be there.” She remembered how seeing a group of women that looked like her and were working hard together drew her to the program. Josephine recalled how she told her mom that day, “I want to run [and] to be with these girls. It was powerful because it was all girls. It was a girl coach, girl staff … I liked the feeling of being comfortable with powerful women.” This was Josephine’s first experience with an all-women athletic space that was also majority Black. Her teammates and her coach taught her that Black women could be athletically successful.

Chantae (Black, lower class) who transferred from a Black-majority school to a White-majority school, felt at her new school and track program she “need[ed] to do more than everybody else, just because it’s kinda harder for you [because of your race].” Brittany (Black, middle class) felt rebuked by her classmates in her magnet program; she reminded herself that she just had to get to the end of the school day because “you get to practice and you see people who look like you” who, unlike her classmates, included her in their group athletic work.

All five Black women athletes also remembered a coach pulling them toward running, jumping, or sprinting events. Vera (mixed race, middle class), a slender, six-foot tall woman had numerous athletic options aligned to her body type. Starting in elementary school, Vera excelled in all forms of athletic activity. She enjoyed running long and short distances, but admitted her coaches and competitors contributed to why she became a jumper.

I do think some coaches thought, “Oh she has Black in her so maybe she can be fast,” because they know that some Black people with a certain body type are really good [at track]. [And that] kind of influences what people think of you. For example, once [at] a meet, we were running hurdles and then, the [race official] said to me, “Oh, I bet you’ll win.” I was the only Black girl in the race. Or half Black. And I was thinking, he said that probably because I’m Black. And I didn’t end up winning.

Imani (mixed race, middle class) said she tried lots of “White” sports but found her fit in the racially diverse track environment. “I tried out a bunch of different sports. I even tried golf. I did tennis. I did some dance classes … I did a lot of swimming and karate.” When I asked if her race influenced her athletic decisions, she replied, “Yeah, it did.” She chose sprinting because “a lot of the distance runners were Caucasian” whereas a lot of sprinters are “African American [because we] have really good sprinting genes.” These assessments reflect racial slotting in which athletes are funneled into certain sports or events based on race (Carrington 2013).

Girls of Color were further limited by their gender. In fact, all women athletes in the study discussed how their athletic choices and accomplishments were secondary to male sports. Within segregated and unequal athletic environments, girls internalized that maleness remained the default in sport—a standard they could never reach—even when they were absent.

Girls learned from an early age that “beating the boys” signified athletic status. For many, they were encouraged to continue in sport because of initial athletic proclivities—displayed in school settings like informal play at recess or during the physical-education (PE) mile test—were comparable or superior to their male classmates.

Chantae discovered her interest in track because she was “so fast” in PE class, “blowing everyone out, beating all the guys.” Her speed was also celebrated on the playground where she played “flag football” with the guys “because I was so fast.” London (White, upper-middle class) was encouraged by a teacher to join track because of her performance in PE:

[In] elementary school, I remember on Thursday, I’d wake up and be like, “OK. Today’s the day. It’s the mile.” And I always thought it was really cool cause I beat all the guys and I always thought that was exciting … . And the PE teacher at school told my mom that I should try out for a club track team or pursue track. And so, I listened and we did.

Importantly, in integrated athletic events beating other girls did not spawn the same celebration of athletic achievement. Girls did not retain the standard of athleticism.

As girls developed their athletic careers and moved into gender-segregated competition, male supremacy in sport only increased. CM, who spent much of her life competing against—and often beating—boys, still learned that her athletic performance was subpar. “As a girl, boys always swim faster, jump faster, run faster. Whatever it is, they’re always better … That’s just what you’re told, so it was normal.” Similar, Captain America felt “blessed” that she was different from other girls—she believed her body type more resembled a masculine “build.”

All 28 women athletes said segregated sports create gender-equity. This tacit acceptance of men’s inherent physicality was imbued with complex feelings of their own ability.

Cooper (White, upper-middle class): “In the rowing world, the guys’ times, [are] just a minute faster. And my competitive side is like, “so what they’re a minute faster. Be a minute faster.” But for some reason we can’t get it down that low … And it’s just like, as long as there are guys there, for some reason we can’t be the fastest. It’s separated so it’s OK. And that’s just the way it is. They are better than us and they deserve it if they really are. Because we train our butts off. So if they’re able to beat us, we know they’re working hard too.”

Cooper, like other top-women athletes, felt grateful for the opportunity to be part of, and compete for, national championships, world championships, and Olympic medals. The social markers of membership on the best team in the nation, in the most competitive sports division and conference, and even receiving an athletic scholarship, were not enough to free women from the burden of demonstrating their right to athletic participation.

Girls recalled their non-athlete male peers challenging them to athletic “tests”—undermining girls’ athletic confidence and achievement. Morgan described how a high school male peer constantly teased her about how her sport must be “easy”—the implication being if a girl can do it, it must not be that hard. So she challenged him to a race. Vera also faced threats to her athleticism from her non-athlete male classmates:

When we had to do track in high school and we had to race, guys were like, “Oh, Vera runs track; let’s see if we can race her.” They just think they’re better because they’re male. And then when I jump further or when I run faster, they’re surprised.

In these accounts, women’s athleticism was seen as immediately sub-par based on their gender. Even non-athlete males—by virtue of their masculinity—believed they were physically superior to elite-performing girls.

As extracurricular activities, sports are often positioned as a “choice” children make based on their initial interest and aptitude. In resource-rich communities, White people developed a particular habitus steeped in racial advantage. Middle- and upper-middle-class athletes, across racial groups, tried numerous sports at an early age. White athletes continued to participate in a broad array of athletic activities—including ones associated with Black communities like basketball, football, and track—up to and through high school. White athletes did not have to consider how their race may impact their educational and athletic opportunities. In contrast, Black people were acutely aware of their racial identity and position, especially in majority-White contexts. Black athletes gravitated to track and ended their careers in other sports sooner than White athletes. The primary reason Black athletes gave for choosing track was not athletic ability but racial representation. Thus, racial segregation differently shaped the athletic trajectories of Black and White athletes.

Girls’ athletic trajectories were also shaped by their gender. The girls who persisted to college did so often by utilizing their family’s financial privilege—hiring private coaches or attending private clubs when their schools lacked adequate resources for girls’ teams. They also persisted through overt and covert messages that signified maleness as the default, superior standard in sport. The constant comparison to maleness—an always-out-of-reach standard for girl athletes—may dissuade some girls from seeing a future for themselves in sport. Combined, these racial/ethnic and gender barriers seemed to have had a more severe impact on Girls of Color who had fewer opportunities and support to play and persist in sports than White girls.

QUANTITATIVE FINDINGS

To further scrutinize and test the suggested patterns and implications of gender, race/ethnicity, and family SES at the individual and school levels—and especially their connections to parenting practices, (school) sports offerings, and pursuits of particular sports participation patterns—we next employed quantitative research methods. Because of the profound social structuring of gender, including via gender-segregated sports experiences and even markedly different sports offerings for different genders, we restricted our analysis to the experiences of high school girls (NFHS 2022).

Based on our conceptual framework and the qualitative findings that brought to light the SES-based, racialized, and gendered experiences of girls at the individual and community/school levels through themes of intensive parenting, racial/ethnic and gender segregation, and perceptions of “choices” to play sports, we examined five hypotheses:

Hypothesis 1: Compared to Girls of Color, White girls will be more likely to participate in any high school sports, participate in every specific sport (except for basketball, compared to Black girls), and participate more persistently in high school sports.

Hypothesis 2: The percentage of White students in a school will be positively associated with any high school sports participation, participation in every specific sport, and persistent school sports participation.

Hypothesis 3: School-level SES will be positively associated with any high school sports participation, participation in every specific sport, and persistent school sports participation.

Hypothesis 4: Number of school sport offerings will be positively associated with any high school sports participation, participation in every specific sport, and persistent school sports participation.

Hypothesis 5: Family SES will be positively associated with any high school sports participation, participation in every specific sport, and persistent school sports participation.

Any High School Sports Participation

We begin by predicting the likelihood that girls would participate in high school sports in the 10^th grade. The results from this analysis are presented in Table 4. As shown in Model 1, consistent with Hypothesis 1, White girls are more likely than girls from other racial/ethnic backgrounds to participate in high school sports. Specifically, the exponentiated coefficients indicate that the odds of playing school sports for Black (OR = .68, p < .01), Latinx (OR = .62, p < .001), Asian (OR = .70, p < .05), and other race/ethnicity (OR = .63, p < .01) girls range from 30 to 40 percent lower than those for White girls. Also, as predicted by Hypothesis 2, there is evidence that the percentage of White students in a school is positively associated with girls’ high school sports participation (OR = 2.13, p < .001). In Model 2, as anticipated by Hypothesis 3, there is evidence that school-level SES (OR = .82, p < .01) is positively associated with girls’ school sports participation. Furthermore, in Model 3, there is support for Hypothesis 4; number of school sports is positively associated with school sports participation (OR = 1.08, p < .001). Finally, as presented in Model 4 and predicted by Hypothesis 5, family SES (OR = 1.50, p < .001) is positively associated with girls’ likelihood of playing high school sports.

Table 4. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing a School Sport in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−.39**	(.13)	.68	−.43**	(.13)	.65	−.41**	(.13)	.66	−.24	(.13)	.78
Latinx	−.48***	(.12)	.62	−.45***	(.12)	.64	−.45***	(.12)	.64	−.27*	(.12)	.76
Asian	−.35*	(.14)	.70	−.35**	(.14)	.70	−.34*	(.14)	.71	−.39**	(.14)	.68
Other race/ethnicity	−.46**	(.16)	.63	−.49**	(.16)	.61	−.49**	(.16)	.61	−.42*	(.16)	.66
Family SES										.41***	(.05)	1.50
Cohabit										−.33**	(.10)	.72
Single										−.24**	(.09)	.78
# of siblings										−.02	(.02)	.98
Physical disability										−.20	(.24)	.82
Cognitive disability										−.34*	(.14)	.71
School Level
% White	.76***	(.17)	2.13	.24	(.19)	1.27	.16	(.19)	1.17	.16	(.19)	1.17
% low SES families				−.19**	(.05)	.82	−.08	(.06)	.92	−.00	(.06)	1.00
School size				−.11***	(.02)	.90	−.12***	(.03)	.89	−.13***	(.03)	.88
# of school sports							.07***	(.02)	1.08	.06**	(.02)	1.06
Private							.46***	(.12)	1.58	.22	(.12)	1.24
Suburban	−.23*	(.09)	.80	−.20*	(.09)	.82	−.08	(.09)	.92	−.08	(.09)	.93
Rural	−.30*	(.12)	.74	−.31**	(.12)	.73	.01	(.13)	1.01	.03	(.13)	1.03
Midwest	−.08	(.12)	.93	−.02	(.12)	.98	−.02	(.11)	.98	.01	(.12)	1.01
South	−.33**	(.11)	.72	−.25*	(.11)	.78	−.25*	(.11)	.78	−.29**	(.11)	.75
West	−.14	(.13)	.87	−.08	(.13)	.92	−.09	(.13)	.92	−.12	(.13)	.89
Variance component	0.24			0.20			0.15			0.16

N = 4,271; OR signifies odds ratio; *p < .05; **p < .01; ***p < .001.

Participation in Specific Sports

The results from predicting girls’ participation in high school basketball, soccer, softball, another team sport, and an individual sport are displayed in Tables 5–9, respectively. As presented in the Model 1 results across these Tables, there is rather consistent support for Hypothesis 1. In fact, there is much more consistent support for this hypothesis in preliminary models that omit the percentage of White students as a school-level indicator (results not shown)—suggesting that some of the racial/ethnic differences in sports participation are a function of racially segregated schools. In these models, only Black girls—in basketball—are significantly more likely to play any specific sport, compared to White girls. In the displayed models, after accounting for racial/ethnic segregation, we found evidence that White girls are more likely to participate in every sport except basketball compared to Latinx girls (OR = .48, p < .01; OR = .44, p < .001; OR = .68, p < .05; OR = .72, p < .05; OR = .59, p < .05, respectively). White girls are also more likely than Asian girls to participate in soccer (OR = .51, p < .05) and softball (OR = .32, p < .001). Moreover, as anticipated, Black girls are more likely than White girls (OR = 2.26; p < .001) to play basketball but are less likely than White girls to play soccer (OR = .13, p < .001), softball (OR = .65, p < .05), and individual sports (OR = .59, p < .05). Other race/ethnicity girls are less likely than White girls to play soccer (OR = .30, p < .01) and individual sports (OR = .55, p < .05). Largely in line with Hypothesis 2, a school’s percentage of White students is positively associated with the likelihood of girls playing basketball (OR = 3.00; p < .001), other team sports (OR = 2.11; p < .001), and individual sports (OR = 2.10; p < .01).

Table 5. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing Basketball in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	.82***	(.18)	2.26	.75***	(.19)	2.11	.74***	(.19)	2.10	1.06***	(.20)	2.90
Latinx	−.19	(.18)	.83	−.12	(.21)	.89	−.13	(.21)	.88	.09	(.22)	1.10
Asian	−.48	(.28)	.62	−.35	(.29)	.70	−.33	(.29)	.72	−.23	(.30)	.80
Other race/ethnicity	.49*	(.22)	1.63	.46*	(.22)	1.59	.46*	(.22)	1.58	.73**	(.23)	2.07
Family SES										.05	(.08)	1.05
Cohabit										−.43*	(.17)	.65
Single										−.22	(.14)	.80
# of siblings										−.00	(.04)	1.00
Physical disability										.10	(.40)	1.10
Cognitive disability										−.11	(.24)	.90
Played other sport										1.36***	(.13)	3.88
Played other sports										1.81***	(.14)	6.11
School Level
% White	1.10***	(.28)	3.00	.56	(.31)	1.74	.56	(.31)	1.75	.49	(.32)	1.63
% low SES families				−.03	(.09)	.97	−.02	(.09)	.98	.04	(.09)	1.04
School size				−.27***	(.04)	.76	−.20***	(.05)	.82	−.17**	(.05)	.84
# of school sports							−.03	(.03)	.97	−.07**	(.03)	.93
Private							.24	(.17)	1.28	.05	(.18)	1.05
Suburban	.19	(.15)	1.29	.24	(.15)	1.27	.31*	(.16)	1.36	.34*	(.16)	1.41
Rural	.59**	(.18)	1.81	.49**	(.17)	1.62	.60**	(.21)	1.83	.63**	(.21)	1.89
Midwest	.20	(.18)	1.22	.22	(.17)	1.25	.22	(.17)	1.25	.28	(.17)	1.32
South	−.20	(.18)	.82	−.14	(.17)	.87	−.15	(.17)	.86	−.04	(.18)	.96
West	.10	(.21)	1.11	.18	(.21)	1.20	.16	(.21)	1.18	.24	(.21)	1.27
Variance component	0.45			0.32			0.32			0.28

N = 4,271; OR signifies odds ratio; *p < .05; **p < .01; ***p < .001.

Table 6. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing Soccer in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−2.01***	(.38)	.13	−1.99***	(.38)	.14	−1.95***	(.38)	.14	−1.88***	(.39)	.15
Latinx	−.72**	(.23)	.48	.69**	(.23)	.50	−.67**	(.23)	.51	−.54*	(.23)	.58
Asian	−.67*	(.27)	.51	−.73**	(.27)	.48	−.73**	(.27)	.48	−.70*	(.27)	.50
Other race/ethnicity	−1.15**	(.38)	.32	−1.16**	(.38)	.31	−1.15**	(.38)	.32	−1.13**	(.38)	.32
Family SES										.23*	(.09)	1.26
Cohabit										−.12	(.18)	.89
Single										−.03	(.16)	.97
# of siblings										.06	(.04)	1.07
Physical disability										−.02	(.39)	.98
Cognitive disability										.36	(.22)	1.44
Played other sport										.56***	(.14)	1.76
Played other sports										.96***	(.14)	2.61
School Level
% White	.30	(.30)	1.35	−.09	(.35)	.92	−.13	(.35)	.88	−.20	(.35)	.82
% low SES families				−.30**	(.11)	.74	−.20	(.11)	.82	−.16	(.12)	.85
School size				.00	(.04)	1.00	−.04	(.05)	.97	−.01	(.05)	.99
# of school sports							.09**	(.03)	1.09	.07*	(.03)	1.07
Private							.28	(.20)	1.31	.13	(.20)	1.14
Suburban	−.37*	(.14)	.69	−.33*	(.14)	.72	−.25	(.16)	.78	−.24	(.16)	.78
Rural	−.99***	(.21)	.37	−.92***	(.21)	.40	−.68**	(.24)	.51	−.69**	(.24)	.50
Midwest	−.27	(.18)	.76	−.21	(.19)	.81	−.21	(.19)	.81	−.16	(.19)	.85
South	−.23	(.18)	.79	−.16	(.18)	.85	−.15	(.18)	.86	−.09	(.18)	.92
West	−.21	(.22)	.81	−.20	(.22)	.82	−.17	(.22)	.84	−.15	(.22)	.86
Variance component	0.35			0.34			0.34			0.34

N = 4,271; OR signifies odds ratio *p < .05; **p < .01; ***p < .001.

Table 7. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing Softball in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−.43*	(.19)	.65	−.48*	(.19)	.62	−.48*	(.20)	.62	−.56**	(.21)	.57
Latinx	−.39*	(.18)	.68	−.37*	(.18)	.69	−.37*	(.18)	.69	−.38	(.20)	.69
Asian	−1.14***	(.27)	.32	−1.10***	(.28)	.33	−1.11***	(.28)	.33	−1.14***	(.29)	.32
Other race/ethnicity	−.19	(.22)	.82	−.22	(.23)	.80	−.22	(.23)	.80	−.20	(.24)	.82
Family SES										−.22**	(.08)	.80
Cohabit										.03	(.15)	1.03
Single										.18	(.13)	1.19
# of siblings										.02	(.03)	1.02
Physical disability										−.48	(.41)	.62
Cognitive disability										−.30	(.22)	.74
Played other sport										1.09***	(.13)	2.96
Played other sports										2.09***	(.12)	8.12
School Level
% White	.21	(.24)	1.23	.02	(.28)	1.02	.03	(.28)	1.03	−.16	(.29)	.86
% low SES families				.02	(.08)	1.02	.01	(.08)	1.01	−.01	(.09)	.99
School size				−.10**	(.03)	.91	−.11*	(.04)	.89	−.07	(.05)	.93
# of school sports							.00	(.02)	1.00	−.02	(.03)	.98
Private							−.09	(.16)	.92	−.16	(.17)	.86
Suburban	.08	(.13)	1.08	.09	(.13)	1.10	.07	(.14)	1.07	.08	(.14)	1.09
Rural	.26	(.15)	1.29	.21	(.16)	1.23	.16	(.18)	1.17	.13	(.19)	1.14
Midwest	−.19	(.15)	.82	−.19	(.15)	.83	−.19	(.15)	.83	−.19	(.15)	.82
South	−.32*	(.15)	.72	−.31*	(.15)	.73	−.31*	(.15)	.74	−.19	(.15)	.83
West	−.47*	(.19)	.63	−.44*	(.18)	.65	−.43*	(.19)	.65	−.39*	(.19)	.68
Variance component	0.28			0.25			0.26			0.25

Notes: N = 4,271; OR signifies odds ratio *p < .05; **p < .01; ***p < .001.

Table 8. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing Another Team Sport in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−.22	(.14)	.80	−.24	(.14)	.79	−.21	(.14)	.81	.01	(.16)	1.01
Latinx	−.32*	(.14)	.72	−.30*	(.14)	.74	−.29*	(.14)	.75	−.02	(.15)	.98
Asian	−.29	(.16)	.75	−.31	(.16)	.73	−.31	(.16)	.73	−.23	(.17)	.80
Other race/ethnicity	−.13	(.17)	.88	−.15	(.17)	.86	−.15	(.17)	.86	.05	(.19)	1.05
Family SES										.25***	(.06)	1.28
Cohabit										−.24*	(.12)	.78
Single										−.15	(.10)	.86
# of siblings										−.03	(.03)	.97
Physical disability										−.07	(.28)	.94
Cognitive disability										−.06	(.16)	.94
Played other sport										1.30***	(.09)	3.66
Played other sports										2.24***	(.11)	9.41
School Level
% White	.75***	(.19)	2.11	.34	(.22)	1.40	.26	(.22)	1.30	.18	(.23)	1.19
% low SES families				−.19**	(.06)	.82	−.09	(.07)	.92	−.02	(.07)	.98
School size				−.06*	(.03)	.94	−.09**	(.03)	.91	−.06	(.04)	.94
# of school sports							.09***	(.02)	1.09	.07**	(.02)	1.07
Private							.35**	(.13)	1.42	.18	(.14)	1.20
Suburban	−.18	(.10)	.84	.15	(.10)	.86	−.05	(.10)	.95	−.06	(.11)	.94
Rural	−.17	(.13)	.85	−.15	(.13)	.86	.15	(.15)	1.16	.17	(.15)	1.19
Midwest	−.19	(.13)	.83	−.13	(.12)	.88	−.13	(.12)	.88	−.10	(.13)	.91
South	−.49***	(.12)	.61	−.42**	(.12)	.65	−.42**	(.12)	.66	−.41**	(.13)	.66
West	−.29*	(.15)	.75	−.26	(.14)	.77	−.25	(.14)	.78	−.27	(.15)	.77
Variance component	0.26			0.24			0.20			0.19

N = 4,271; OR signifies odds ratio *p < .05; **p < .01; ***p < .001.

Table 9. Results from Multilevel Logistic Regressions Predicting the Likelihood of Playing an Individual Sport in 10^th Grade

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−.52*	(.22)	.59	−.51*	(.22)	.60	−.48*	(.22)	.62	−.23	(.23)	.80
Latinx	−.52*	(.20)	.59	−.48*	(.20)	.62	−.44*	(.20)	.64	−.12	(.21)	.88
Asian	.40*	(.19)	1.49	.32	(.19)	1.38	.30	(.19)	1.35	.47*	(.20)	1.59
Other race/ethnicity	−.59*	(.28)	.55	−.60*	(.28)	.55	−.60*	(.28)	.55	−.43	(.28)	.65
Family SES										.37***	(.08)	1.45
Cohabit										−.20	(.17)	.82
Single										−.14	(.14)	.87
# of siblings										−.06	(.04)	.94
Physical disability										.29	(.37)	1.33
Cognitive disability										−.46	(.25)	.63
Played other sport										1.30***	(.12)	3.68
Played other sports										1.51***	(.13)	4.52
School Level
% White	.74**	(.26)	2.10	.18	(.29)	1.20	.16	(.29)	1.17	.08	(.30)	1.09
% low SES families				−.42***	(.09)	.66	−.30**	(.10)	.74	−.19	(.10)	.83
School size				−.02	(.03)	.98	−.11*	(.04)	.90	−.07	(.05)	.93
# of school sports							.13***	(.03)	1.14	.10***	(.03)	1.11
Private							.17	(.17)	1.19	−.12	(.17)	.89
Suburban	−.06	(.13)	.94	−.02	(.13)	.98	.03	(.13)	1.03	.06	(.14)	1.07
Rural	−.33*	(.17)	.72	−.24	(.17)	.79	−.01	(.19)	.99	.01	(.20)	1.01
Midwest	.08	(.17)	1.08	.16	(.17)	1.18	.18	(.16)	1.20	.27	(.17)	1.32
South	.08	(.16)	1.09	.19	(.16)	1.20	.22	(.16)	1.25	.33*	(.17)	1.39
West	.36	(.19)	1.43	.38*	(.19)	1.46	.43*	(.19)	1.53	.51**	(.19)	1.66
Variance component	0.27			0.23			0.19			0.16

N = 4,271; OR signifies odds ratio *p < .05; **p < .01; ***p < .001.

Model 2 results also indicate consistent support for Hypothesis 3, with a school’s percentage of low SES families being negatively associated with the likelihood of girls playing soccer (OR = .74, p < .01), other team sports (OR = .82, p < .01), and individual sports (OR = .66, p < .001). Similarly, as anticipated by Hypothesis 4, the number of school sports is positively associated with the likelihood of playing soccer (OR = 1.09, p < .01), other team sports (OR = 1.09, p < .001), and individual sports (OR = 1.14, p < .001), as presented in the Model 3 results. Finally, as Hypothesis 5 predicted, family SES is positively associated with girls’ likelihood of playing soccer (OR = 1.26, p < .05), other team sports (OR = 1.28, p < .001), and individual sports (OR = 1.45, p < .001). Unexpectedly, a negative association between family SES (OR = .80, p < .01) and playing softball emerged; although, this finding became apparent only after taking into account participation in another sport or sports—suggesting that only after taking into account the ability to play other sports, softball may be more likely to be recognized as a working-class sport than other sports.

Persistence in Playing High School Sports

Last, we consider the likelihood of girls persisting in playing high school sports in both the 10^th and 12^th grades (Table 10). As shown in Model 1 and in line with Hypothesis 1, Black (OR = .64, p < .01) and Latinx (OR = .59, p < .01) girls are less likely than White girls to persist in playing high school sports (Asian and other race/ethnicity girls are only less likely before accounting for racial/ethnic segregation). In addition, percentage of White students at a school is positively associated with persistent school sports participation (OR = 2.66; p < .001).

Table 10. Results from Multilevel Logistic Regressions Predicting the Likelihood of Persisting in Playing School Sports

	Model 1			Model 2			Model 3			Model 4
Variables	b	SE	OR	b	SE	OR	b	SE	OR	b	SE	OR
Individual Level
Black	−.45**	(.15)	.64	−.50**	(.16)	.61	−.48**	(.16)	.62	−.28	(.18)	.75
Latinx	−.53**	(.14)	.59	−.49**	(.14)	.62	−.49**	(.14)	.62	−.21	(.16)	.81
Asian	−.17	(.16)	.84	−.17	(.16)	.85	−.16	(.16)	.85	−.13	(.19)	.88
Other race/ethnicity	−.25	(.18)	.78	−.29	(.18)	.75	−.30	(.18)	.74	−.21	(.20)	.81
Family SES										.48***	(.07)	1.61
Cohabit										.19	(.13)	.83
Single										−.29*	(.11)	.75
# of siblings										−.04	(.03)	.96
Physical disability										−.01	(.31)	.99
Cognitive disability										−.73***	(.20)	.48
Played other sport										2.40***	(.11)	11.06
Played other sports										2.25***	(.13)	9.50
School Level
% White	.98***	(.20)	2.66	.31	(.22)	1.37	.23	(.22)	1.26	−.02	(.24)	.98
% low SES families				−.25***	(.06)	.78	−.14*	(.07)	.87	−.05	(.07)	.96
School size				−.14***	(.03)	.87	−.14***	(.03)	.87	−.12**	(.04)	.89
# of school sports							.06**	(.02)	1.06	.03	(.02)	1.03
Private							.44**	(.12)	1.55	.23	(.14)	1.26
Suburban	−.24*	(.10)	.79	−.19	(.10)	.83	−.06	(.10)	.94	−.08	(.11)	.92
Rural	−.18	(.13)	.84	−.18	(.13)	.84	.15	(.15)	1.17	.21	(.16)	1.23
Midwest	−.11	(.13)	.90	−.03	(.12)	.97	−.02	(.12)	.98	.11	(.13)	1.12
South	−.32*	(.12)	.73	−.22	(.12)	.80	−.22	(.12)	.80	−.12	(.13)	.89
West	.03	(.15)	1.03	.08	(.14)	1.09	.09	(.14)	1.09	.18	(.15)	1.20
Variance component	0.24			0.19			0.11			0.10

N = 4,271; OR signifies odds ratio *p < .05; **p < .01; ***p < .001.

As anticipated by Hypothesis 3 and shown in Model 2, attending a school with a higher proportion of low-income families (OR = .78, p < .001) is negatively associated with the likelihood of persisting in school sports. Furthermore, as Hypothesis 4 predicted and Model 3 displays, number of school sports (OR = 1.06, p < .01) is positively associated with girls’ likelihood of persisting in playing high school sports. Finally, consistent with Hypothesis 5, family SES (OR = 1.61, p < .001) is positively associated with persisting in school sports.

DISCUSSION

This study utilized theories of social structure and habitus and a mixed-methods approach to reveal evidence of how presumed “choices” to pursue high school sports participation as an extracurricular activity are directed and oftentimes constrained by gender, racial/ethnic, and social-class structuring processes that are also linked to habitus. Collectively, these social forces emerge in social interactions; cultural and subjective attitudes; classist, racist, and sexist ideologies; and institutional mechanisms. Schools remain a nexus for interlacing complex areas of social life, revealing how agentic actions like whether to play sports are constrained by stratification and other social forces (Bourdieu 1984; Hextrum 2021, 2023; Tompsett and Knoester 2022, 2023).

Across the study, there is complementary qualitative and quantitative empirical evidence of race/ethnicity, social class, and gender shaping girls’ decisions to play sports, what sports to play, and for how long. Qualitative themes highlighted how intensive parenting and segregation patterns influenced girls’ “choices” to play sports. Parenting decisions and resource allocations proceeded in gendered and racially segregated contexts, built upon social class settings. These forces decreased the overall chances for all girls to access athletics, but this was especially true for lower-income girls and Girls of Color. Inequalities were magnified in niche sports like rowing that were associated with affluence and whiteness. Quantitative findings extended evidence of the presence and impact of these themes by identifying apparent racial/ethnic inequalities at the individual and school levels, family and school-level SES disparities, and number of sports made available among a large cohort of high school students. These processes seemed to connect to the likelihood of playing high school sports and persisting in playing. The inequalities were also consistent across specific sports, highlighting that resource allocations are generally useful and necessary for enhanced sports participation experiences.

Nevertheless, there are some limitations of the study to note. Qualitative evidence emerged from studies of college athletes—future work should consider the experiences of nonathletes and individuals from a wider span of age ranges. Also, the quantitative evidence necessarily drew upon the experiences of students who were in high school nearly two decades ago. Thus, there is a need to update the present study with information from more recent high school sports participation experiences and corresponding life-history information from sampled students. There are also limitations in the use of a measure of racial/ethnic segregation based on only 12 to 24 sampled students per school and the availability of only some specific sports indicators. Future research should seek to obtain more complete school demographic and sports participation information.

Still, the present study markedly advances our understandings of the social structuring of gender, race/ethnicity, and social class—and related habitus—and the means and extent to which they lead into different high school sports experiences for girls. It does so with the use of a novel mixed-methods approach and unique data from college athletes, high school students, parents, and school administrators. The qualitative evidence offers detailed descriptions of the college athletes’ experiences and the quantitative approach benefits from a novel analysis of a large cohort of high school students, drawing on measures of their individual and school-level situations to better understand how social contexts matter in the students’ lives. Altogether, there is clear and consistent evidence of gender, race/ethnicity, and social class—including in their contributions to habitus—socially structuring high school sports participation experiences such that whiteness and higher levels of SES lead to advantages in the likelihoods of playing any high school sports, playing most specific sports, and persisting in playing high school sports.

Due to the outsized social rewards associated with athletic participation, especially for girls, expanding authentic and generative scholastic sports opportunities remains a vital social justice project (Cooky 2009; Milner and Braddock 2016). Despite social stereotypes, girls of all backgrounds are intrinsically interested in sports, especially when provided with authentic and enriching opportunities (Cooky 2009; Lopez 2019; Messner 2009). Expecting girls to access sport only if they overcome and persist through institutionalized gender inequalities will never generate true equity (Messner 2009). Furthermore, social-class and racial/ethnic inequalities continue to restrict playing opportunities among girls (NWLC 2015; Pericak and Martinez 2022). In fact, segregation remains a potent reproductive feature for maintaining educational and social stratification. Institutionalized racial/ethnic, social-class, and gender segregation becomes normalized only through the widespread cultural devaluing and dehumanizing of certain groups, positioned as contaminating, harming, or limiting the dominant group in some way through their presence (Bonilla-Silva 2017; Risman 2004). Thus, any reforms to expand athletic access need to address both material (e.g., increasing public funding for school sports) and symbolic inequalities (e.g., increasing positive representation of diverse genders, social classes, and racial/ethnic group members in a variety of sports).

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Kirsten Hextrum

Kirsten Hextrum is an assistant professor of language, culture, and society, graduate faculty in Women, Gender, & Sexuality Studies, and the coordinator of the College Student Services Administration Program at Oregon State University. Her research explores the reproduction and contestation of state power at the nexus of school, sports, and communities. Her book, Special Admission: How College Athletic Recruitment Favors White Suburban Athletes (Rutgers University Press, 2021) contradicts the national belief that college sports provide an avenue for upward mobility. Prior to becoming faculty, she worked in academic support services for college athletes and competed as a Division I athlete.

Chris Knoester

Chris Knoester is a professor of sociology at The Ohio State University. He specializes in the sociology of family and of sport. He is a North American Society for the Sociology of Sport Research Fellow, the chair of research for the Ohio State Sports and Society Initiative, and the principal investigator of the National Sports and Society Survey. He has published nearly fifty peer-reviewed studies and his expertise has been featured in outlets including The New York Times, The Washington Post, The Wall Street Journal, Bloomberg, Forbes, US News & World Report, and The Conversation.

James Tompsett

James Tompsett is a PhD candidate in sociology at The Ohio State University. His research interests include inequality, sociology of education, and sociology of sport. James also teaches Research Methods at Ohio State. His dissertation investigates vulnerability in higher education, and his published research findings have been featured in AP News, Times News UK, Yahoo News, MSN, St Louis Post Dispatch, Houston Chronicle, and The Conversation.