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Community Service and Undergraduates’ Social Capital Development

Author: Krista M. Soria orcid logo (University of Idaho)

  • Community Service and Undergraduates’ Social Capital Development

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    Community Service and Undergraduates’ Social Capital Development

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Abstract

The purpose of this study was to examine the effects of community service participation on undergraduates’ social capital development, including whether any potential effects are conditional by students’ race/ethnicity, parents’ or guardians’ education, and social class. I used a propensity score analysis on a multi-institutional sample of students enrolled at 69 colleges and universities in spring 2021 to construct matched pairs of students who did and did not participate in community service (n = 8,964). The results of ordinary least squares regressions suggest that community service has significant and positive effects on students’ social capital development; however, while the results were uniform for students based upon their parents’ or guardians’ education and social class, the results were not uniform based upon students’ race/ethnicity. 

Keywords: community service, undergraduates, social capital, propensity score matching

How to Cite:

Soria, K. M., (2023) “Community Service and Undergraduates’ Social Capital Development”, Michigan Journal of Community Service Learning 29(2). doi: https://doi.org/10.3998/mjcsl.3643

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Published on
22 Dec 2023
Peer Reviewed

From admission to graduation, the system of higher education is concomitantly stratified and stratifying—restricting access and opportunity to students from more privileged backgrounds while also maintaining structural barriers that perpetuate systemic racial, family education, and social class-based disparities in college students’ graduation rates. The evidence is clear: Black, Hispanic, Native Hawaiian, Pacific Islander, American Indian, Alaska Native, multiracial, first-generation, low-income, and working-class students have disproportionately lower rates of enrollment and graduation at four-year colleges and universities compared to White, continuing-generation, middle-class, and wealthy students (Cataldi et al., 2018; National Center for Education Statistics, 2015, 2018a, 2018b, 2019, 2022). One of the means through which higher education reproduces those societal inequities is via the privileging and rewarding of social capital—the social networks, connections, and relationships that provide actual or potential resources or benefits to individuals (Lin, 2001). In higher education, social capital can be mobilized to facilitate students’ admission into colleges and universities, grant students access to academic and career resources, and provide social and emotional support to promote college students’ success (Almeida et al., 2019; Bourdieu & Passeron, 1990; Dika, 2012; Harper, 2008; Luedke, 2020). By and large, traditionally privileged groups, including White, continuing-generation, middle-class, and wealthy students, possess the “right kinds” of social capital desirable in higher education; consequently, those students are more prepared than marginalized students to enter and successfully navigate the system of higher education, a system that was designed to mutually reinforce the power and privilege of their elite status in society (Aries, 2008; Armstrong & Hamilton, 2013; Stuber, 2011).

Institutional interventions and programs may help to disrupt those disparities in social capital and support social capital development among marginalized and disenfranchised college students (Schwartz et al., 2017). For example, mentorship programs, leadership development programs, and student organizations can increase marginalized students’ social capital (Glass, 2022; Glass & Gesing, 2018; Soria & Cole, 2023). Community service participation also appears to be a promising means of facilitating college students’ social capital development (Campbell, 2000; Hart et al., 2008). Indeed, community service participation may even be more effective than other types of co-curricular activities or programs in promoting students’ social capital development (Flanagan et al., 2014); however, at present, researchers have only investigated the social capital benefits of community service participation among youth and older adults while college students have been overlooked (Burnett, 2006; Cuskelly, 2008; Flanagan et al., 2014; Henness et al., 2013). The purpose of this study is therefore to examine whether there are effects of community service participation on college students’ social capital development. Given the disparities in social capital described above, the following research questions guided this study: 1) are there effects of community service participation on undergraduates’ social capital development? 2) are any potential effects of community service participation on social capital development conditional on undergraduates’ race and ethnicity, parents’ or guardians’ education, and social class?

Social Capital and Community Service

Students from different racial and ethnic identities, family education levels, and social classes grow up with distinct knowledge, aspirations, support, and expectations related to higher education (Cataldi et al., 2018; Hines et al., 2019; Maldonado, 2022; Mishra, 2020; Soria, 2015; Yosso, 2005). For students with privileged social identities, higher education is viewed as a birthright, which ultimately leads the same students to have higher aspirations for educational attainment and efficacy in their abilities to meet their educational goals compared to marginalized students (Cataldi et al., 2018; Dufur et al., 2016; Ornstein, 2019; Schroeder & Saw, 2016; Soria & Stubblefield, 2014). In line with those expectations, the parents of privileged students provide a “concierge” experience bolstered by social capital to facilitate their children’s entrance into higher education, including access to exclusive higher education infrastructure (Hamilton et al., 2018). Students from privileged backgrounds also benefit from legacy admissions into institutions and social organizations, attend better-resourced schools with more college preparatory resources, and have ready access to academic and social resources to support their admission to prestigious institutions (e.g., letters of recommendation; Alon, 2009; Armstong & Hamilton, 2013; Bowen & Bok, 1998; Hamilton et al., 2018). Marginalized students experience barriers in accessing the same types of social capital and thus experience greater systemic disadvantages in higher education (Armstong & Hamilton, 2013; Bowen & Bok, 1998; Soria, 2015; Thiem & Dasgupta, 2022).

The disparities in students’ pre-college social capital continue as students are enrolled in higher education. As a function of their “concierge” services, parents of privileged students can easily connect their children to other adults who provide career guidance and opportunities (e.g., internships) and help their children obtain access to elite university programming and experiences not available to all students (Armstong & Hamilton, 2013; Hamilton et al., 2018). Moreover, college students from privileged identities have been intensively socialized to engage independently in behaviors and activities to develop their ongoing social capital while enrolled in higher education. Many of those same students enroll in college already involved in student activities and clubs and can ultimately leverage their social capital to assist with obtaining internships and gateways into prestigious careers regardless of their academic majors (Armstrong & Hamilton, 2013; Hamilton et al., 2018; Stuber, 2011). With their socialization for independence and networking connections, it is easier for privileged students to interact with faculty, staff, and institutional agents who support their development of additional social capital (Dika, 2012; Soria & Stebleton, 2013; Stanton-Salazar, 1997). Thus, marginalized students are structurally disadvantaged when it comes to acquiring social capital in higher education (Armstrong & Hamilton, 2013; Stuber, 2011).

While institutional agents (e.g., faculty, staff, advisors) in higher education can disrupt the system of social reproduction by transmitting or negotiating the transmittal of social capital (Dika, 2012; Harper, 2008; Stanton-Salazar, 1997, 2011), students from systemically excluded and marginalized identities are not as likely to network with institutional agents because they experience greater difficulty interacting with faculty and staff and finding networks on campus who share their background and experiences (Soria & Stebleton, 2012, 2013). Students from marginalized backgrounds sometimes experience indifference or hostility from faculty, perceive faculty and administrators as gatekeepers who make education more challenging for students, and fear negative repercussions when meeting with faculty, including feelings of incompetence (Longwell-Grice, 2003; Longwell-Grice & Longwell-Grice, 2007–2008).

Because marginalized students experience more barriers in accessing institutional agents, it is important to examine whether institutional programs and opportunities, such as community service, could be a means for students to experience social capital development. At present, there is a deficit in the community service literature regarding the potential for community service to promote college students’ social capital, although scholars have documented many benefits of community service participation. For instance, undergraduates who participate in community service gain enhanced critical thinking skills, academic skills, civic responsibility, self-efficacy, multicultural awareness, and leadership abilities (Astin & Sax, 1998; Soria & Johnson, 2017; Soria et al., 2013; Soria et al., 2019; Soria et al., 2023). Undergraduates who participate in community service also benefit from enhanced social outcomes, including a higher sense of belonging, interpersonal skills, perspective-taking, and prosocial values (Horn, 2012; Cooks & Schrarrer, 2006; Soria et al., 2012; Soria et al., 2023).

The relational and contextual processes of community service provide in situ learning and development opportunities via social interactions and relationships with others (Cooks & Schrarrer, 2006). Community service participation offers opportunities for students to interact with a wider network of individuals in their community, become aware of their own identities as members of a community with a stake in the greater good, witness how people can work collectively to resolve problems, and connect with their classmates, staff, and faculty in novel settings (Flanagan et al., 2014). Therefore, within the social experience of community service, students may be well-positioned to expand their social networks and connections with others, thereby increasing their social capital.

Indeed, scholars have proposed that the social dynamics present in community service participation may enhance social capital among college students. Campbell (2000) hypothesized that community service participation would increase adolescents’ social capital via norms of reciprocity and thicker networks of social connectedness between youth and adults in their community; however, Campbell did not directly test the hypothesis using measures of social capital but instead analyzed whether individuals who volunteered when they were younger were likely to volunteer in adulthood. Flanagan et al. (2014) used direct measures of social capital and discovered that fifth through 12th grade students who engaged in community service had higher rates of social capital than students who were not as involved. Burnett (2006) and Cuskelly (2008) found that adults who volunteered for community sports clubs generated individual and organizational social capital; however, their samples did not include college students. Similarly, Henness et al. (2013) examined the social capital benefits of community service among rural youth and adults but did not examine the effects of service for college students. Given the critical role of social capital in college students’ success in higher education and beyond, it is therefore important to address the gap in research related to the potential benefits of community service in college students’ social capital development.

Although many researchers over the last three decades have demonstrated the benefits of college students’ participation in community service, few have considered the potential influences of students’ self-selection biases in their analyses. College students who engage in community systematically differ from their peers who do not participate in community service based upon their demographic characteristics, pre-college experiences, and other collegiate experiences (Horn, 2012; Meyer et al., 2019; Soria et al., 2019; Wilson, 2000). Those systematic differences may contribute directly or indirectly to differences in the outcomes associated with students’ participation in community service, making it difficult to gauge the effectiveness of community service above and beyond students’ characteristics or experiences. Therefore, in this study I used quasi-experimental procedures to address and limit the potential biases in students’ participation in community service as I explored whether there are effects of community service participation on undergraduates’ social capital development.

Conceptual Framework

While individual theorists have provided unique nuances to their conceptualizations of social capital, at its core social capital reflects the resources available to individuals in their network (Bourdieu, 1980; Coleman, 1988; Putnam, 1994). Social capital is theorized to have both individual and collective benefits to society (Coppe et al., 2022). In Coleman’s view, individual social capital can only flourish when trust and norms of reciprocity (collective social capital) are present in social organizations or networks. Community service experiences may facilitate norms of trust and reciprocity among participants and community members (Campbell, 2000), providing ideal collective social capital conditions to foster college students’ individual social capital. Bourdieu understood that social inequalities and competition for scarce resources lead individuals to have different levels of access to social capital—those with higher economic and cultural capital are more likely to have advantageous positions in society that provides greater access to valuable social capital. As applied in the present study, I analyzed whether the potential benefits of community service participation were uniform across students from privileged and marginalized identities to examine whether community service could be leveraged to achieve greater parity in students’ social capital development.

Putnam (2000) proposed that there are distinctions in social capital based upon the types of social networks and function of social capital. Bonding social capital in closer networks and relationships (e.g., friendships) can build a sense of support, belonging, community, and solidarity for individuals. Bridging social capital among looser, more distant connections (e.g., faculty, staff, community members) can facilitate access to resources and information. Bonding social capital helps individuals to “get by” while bridging social capital helps individuals to “get ahead” (Putnam, 2000). Bonding social capital is often established among more homogenous groups whereas bridging capital is more likely to occur within heterogeneous groups (Coffé & Geys, 2007). Participation in community service has the potential to facilitate bonding social capital through opportunities to develop friendships and connections with classmates and bridging social capital for students through enhanced opportunities to develop networks among community members, campus staff, or faculty who may also be engaged in those efforts (Campbell, 2013; Flanagan et al., 2014). However, community service may have differential effects on students’ outcomes based upon their race and ethnicity, parents’ or guardians’ education, and social class (Chittum et al., 2022; Soria et al., 2019; Valentine et al., 2021), so it is important to examine whether the effects may be conditional upon those demographic characteristics. Further, it is important to consider the role of institutional agents, such as faculty, in improving marginalized students’ social capital development (Stanton-Salazar, 1997, 2001). These theoretical frameworks provided the rationale for analyzing the potential effects of community service participation on college students’ social capital development, the analytic approaches selected, and the potential benefits of the results for marginalized and disenfranchised students in higher education contexts.

Methodology

Instrument

The Multi-Institutional Study of Leadership (MSL) survey was administered to undergraduate students at 69 U.S. four-year colleges and universities from January to May of 2021. Common psychometric concerns related to self-reported data (i.e., social desirability, halo effect, and item format) are not problematic in the MSL survey (Dugan, 2015; Tyree, 1998). Several changes have also been made over time to improve the psychometric properties of the instrument, including removing and phrasing individual items and constructs (Dugan, 2015; Tyree, 1998). Researchers have used MSL survey data in several studies as they have investigated the outcomes of college students’ participation in community service (Author, in-press-a; Johnson et al., 2017; Kodama & Dugan, 2013; Soria et al., 2013).

Sample

Each institution administered the survey to a random sample of 4,000 students and the response rate was 21.0% (n = 49,307). The items measuring social capital development were randomly assigned to half of the participants, leaving 15,498 students who responded to all the items used in the analysis. Within that reduced sample, 28.1% (n = 4,482) of students participated in community service in an average month. I used matching procedures (described below) to construct the final sample of 8,964 students (n = 4,482 students who participated in community service, n = 4,482 students who did not participate in community service). The final sample primarily included cisgender women (72.8%), White students (64.3%), continuing-generation students (68.6%), and middle-class, upper professional, or upper middle-class students (68.0%; Table 1). The participants also tended to be younger on average (x̄ = 21.41, s = 5.50).

Table 1

Descriptive Information for Participants

n %
Gender
Man 2,179 24.3
Woman 6,530 72.8
Transgender, non-binary, or genderqueer 255 2.7
Race/Ethnicity
Middle Eastern or Northern African 96 1.1
African American or Black 499 5.6
Asian American 798 8.9
Latinx or Hispanic 639 7.1
Multiracial 933 10.4
White 5,765 64.3
Race not listed 208 2.3
International Status
Domestic student 8,577 95.7
International student 387 4.3
Parents’ Education Level
Continuing-generation (parents/guardians have ≥ a bachelor’s degree or higher) 6,150 68.6
First-generation (parents/guardians have < a bachelor’s degree) 2,814 31.4
Social Class
Low-income or poor 1,588 10.0
Working-class 3,155 19.8
Middle-class 6,874 43.1
Upper professional or upper middle-class 3,970 24.9
Wealthy 361 2.3
Disability
Deaf or hard of hearing 36 0.4
Blind or visual impairment 83 0.9
Speech or language condition 9 0.1
Learning disability 38 0.4
Physical or musculoskeletal (e.g., multiple sclerosis) 20 0.2
Attention deficit disorder or attention deficit hyperactivity disorder 169 1.9
Psychiatric or psychological condition 455 5.1
Neurological condition (e.g., brain injury, stroke) 23 0.3
Medical (e.g., diabetes, severe asthma) 48 0.5
Does not have a disability 7,135 79.6
Disability not listed 60 0.7
Multiple disabilities 888 9.9
Sexual Orientation
Asexual 315 3.5
Bisexual 808 9.0
Gay 133 1.5
Lesbian 130 1.5
Heterosexual 6,607 73.7
Pansexual 107 1.2
Queer 100 1.1
Questioning or unsure 199 1.1
Preferred response not listed 134 1.5
Multiple sexual orientation categories selected 431 4.8
Transfer Status
Transfer 1,580 17.6
Non-transfer 7,384 82.4
Enrollment Status
Full-time 8,580 95.7
Part-time 384 4.3
Class Level
First Year 1,988 22.2
Second Year 1,950 21.8
Third Year 2,334 26.0
Fourth Year and Beyond 2,692 30.1
Employment
Working in an off-campus job unaffiliated with school 2,922 32.6
Working in an on-campus job 2,660 29.7
Residence
Off-campus with partner, spouse, and/or children 630 7.0
Off-campus with parent/guardian or other relative 1,991 22.2
Other off-campus home, apartment, or room 2,631 29.4
College/university residence hall 2,866 32.0
Other on-campus student housing 571 6.4
Fraternity or sorority house 189 2.1
Other residence 86 1.0
Academic Majors
Natural sciences 1,156 12.9
Science, technology, engineering, or mathematics 1,210 13.5
Business or communications 1,608 17.9
Health-related 984 11.0
Education 535 6.0
Humanities 910 10.2
Social sciences 1,843 20.6
Undeclared or other 718 8.0
Carnegie Classification
Baccalaureate 905 10.1
Master’s colleges and universities: small and medium programs 1,489 16.6
Master’s colleges and universities: larger programs 1,372 15.3
Doctoral/professional universities 2,673 29.8
Doctoral universities: High research activity 2,090 23.3
Doctoral universities: Very high research activity 435 4.9
Institutional Size
Under 4,999 1,881 21.0
5,000 to 9,999 1,629 18.2
10,000 to 19,999 2,827 31.5
20,000+ 2,627 29.3
Control
Public 4,296 47.9
Private 4,668 52.1
Institutional Setting
Town or rural 2,248 25.1
Suburb 1,682 18.8
Small city 1,486 16.6
Midsize city 2,020 22.5
Large city 1,528 17.0
  • Note. Less than 10 American Indian, Alaska Native, Native Hawaiian, and Pacific Islander students responded to the items in the survey, so they were removed from analysis to prevent potential identifiability.

Measures

Independent Variable

The survey included an item asking students whether they engaged in any community service in an average month. As noted above, in the original sample, 28.1% (n = 4,482) of students participated in community service in an average month. In follow-up items, students shared additional information about the nature of their participation in community service. Among those who participated in community service, 28.3% participated as part of a class, 9.0% as part of a work-study experience, 57.5% with a campus student organization, 39.1% as part of a community organization unaffiliated with their college or university, and 56.9% on their own. Students could select any of those options, so the totals do not round to 100%.

Covariates

I included pre-college, demographic, collegiate, and institutional variables as covariates in matching procedures because those variables are sources of self-selection biases for participation in community service and may also be associated with their social capital development (Cruce & Moore, 2007; D’Agostino, 2010; Horn, 2012; Marks & Jones, 2004; Mitchell & Donahue, 2009; Schulzetenberg et al., 2020; Soria et al., 2019). In the survey, students identified their race/ethnicity, parents’ or guardians’ education, and social class. I recoded parents’ or guardians’ education into a dichotomous variable (0 = continuing-generation, where parents or guardians have ≥ a bachelor’s degree or higher, and 1 = first-generation, where parents or guardians have < a bachelor’s degree). Students selected their social class in one of five categories (1 = low-income or poor, 2 = working-class, 3 = middle-class, 4 = upper-professional or upper-middle class, 5 = wealthy). Those subjective categories have strong correlations with other items typically used to measure social class (i.e., parents’ or guardians’ income and education; Soria & Barratt, 2012), they have been used extensively in research related to social class (Soria, 2012; Soria & Stebleton, 2013), and students’ subjective social class identification is valid and reliable (Rubin et al., 2014; Soria, 2018).

The pre-college items measured students’ participation in student clubs and organizations, organized sports, community service, and leadership positions while in high school. Students reported the frequency of their participation in those activities on a scale from 0 = never to 3 = very often. I also included two items measuring students’ pre-college social capital development in which students reflected upon the accuracy (1 = not at all accurate to 5 = completely accurate) of the following statements regarding their behaviors prior to college: “I sought out connections with a range of people from different fields” and “I maintained relationships even when I wasn’t sure I would get much from them.”

In addition to race/ethnicity, parents’ or guardians’ education, and social class, the demographic characteristics used as covariates included students’ gender, citizenship, sexual orientation, disability, and age. The variables related to collegiate experiences include transfer experience, enrollment intensity, class level, employment (on- or off-campus), residence, and academic major. Finally, the institutional variables included Carnegie classification, size, control, and setting.

Dependent Variable

In the survey, students were asked to indicate how accurate the following statements were to their thoughts, beliefs, or actions (scaled 1 = not at all accurate to 5 = completely accurate): a() I intentionally seek out relationships with others who are different than myself; (b) When talking with someone, I look for connections that may either help me or help them; (c) I can effectively build a network in professional fields outside my own; and (d) I’m skilled at identifying mutual interests between colleagues and/or friends. I used those four items to construct a single factor via confirmatory factor analysis (described below) that I named “social capital development.”

Data Analysis

I first used the “lavaan” package in R (Rosseel, 2012) for a confirmatory factor analysis on the social capital development items. The factorial model had acceptable fit (CFI = .981, TLI = .959, RMSEA = .060, SRMR = .028; Kline, 2015). The social capital development factor had good reliability (α = .874, ω = .876), which is congruent with related research (Soria & Cole, 2023).

Next, I utilized propensity score matching techniques using the “MatchIt” package in R (Ho et al., 2021) to match students who participated in community service (the “treatment” group) with students who did not participate in community service (the “control” group). There are multiple methods available to produce matched sets and different methods can yield differences between the treatment and control groups. Austin (2011) recommended that researchers use several methods for the balance of covariates prior to final selection for outcome analysis. After reviewing different methods (i.e., optimal pair matching, optimal pair matching comparing Mahalanobis distances, nearest neighbor, and full matching), I proceeded with nearest neighbor matching because it provided the best overall balances in the covariates after matching, the most similar distributions of propensity scores between the treatment and control groups, and the smallest differences in propensity score distances between the matched pairs (Schultzetenberg et al., 2020). I used binary logistic regression to compute students’ individual propensity scores, the estimated probability that students participated in community service. I used one-to-one nearest neighbor matching without replacement so that students who participated in community service were matched to students who did not participate in community service (Austin, 2011). I used a caliper of 0.20, which represents the maximum tolerated difference between matched subjects (Greifer, 2022a). A caliper of 0.20 can eliminate between 98% and 99% of the bias (Austin, 2011).

When utilizing propensity score matching analyses, it is important to assess the similarity of the covariate distributions (or balance) between the “treatment” and “control” groups (Harder et al., 2010). I examined whether the matching procedures balanced the distribution of variables in both groups (students who did and did not participate in community service) by reviewing the standardized mean differences before and after matching. I detected no imbalances above 0.25 after matching (Austin, 2011; Harder et al., 2010). The percent balance improvement for each covariate was above 0.0 and indicated the balance did not become worse after matching (Greifer, 2022a). The empirical cumulative density functions (eCDF) statistics, which correspond to the difference in the overall distributions of the covariates between the groups, were close to zero, which indicates better balance (Greifer, 2022a). All statistics are reported in Table 2.

Table 2

Balance Statistics

Standardized Mean Differences Pre-Matching Standardized Mean Differences Post-Matching Percent Balance Improvement Empirical Cumulative Density Functions Post-Matching
Propensity Score Distance 0.660 0.033 95.10 0.004
Transfer Status 0.074 –0.001 99.20 0.000
Enrollment Intensity 0.008 –0.011 38.30 0.002
Class Level 0.052 –0.001 97.80 0.001
Academic Major –0.023 –0.007 71.60 0.009
Working Off Campus 0.023 0.007 69.10 0.003
Working On Campus 0.166 0.031 81.50 0.014
Student Clubs or Organizations (H.S.) 0.309 0.024 92.30 0.007
Organized Sports (H.S.) 0.181 0.001 99.50 0.005
Leadership Positions (H.S.) 0.358 0.022 93.80 0.006
Community Service (H.S.) 0.605 0.007 98.90 0.004
Sought Connections (H.S.) 0.266 0.020 92.30 0.005
Maintained Relationships (H.S.) 0.144 0.005 96.50 0.004
Age 0.037 0.022 39.90 0.004
Gender 0.212 –0.018 91.60 0.004
Sexual Orientation 0.021 0.025 17.00 0.005
Citizenship –0.033 –0.007 78.40 0.002
Race/Ethnicity –0.068 –0.008 88.30 0.003
Disability 0.004 –0.001 78.00 0.006
Social Class 0.010 –0.016 56.90 0.004
First-Generation Status 0.091 0.001 99.00 0.005
Residence –0.075 0.005 92.90 0.003
Institutional Size –0.040 –0.014 66.50 0.012
Institutional Control –0.054 –0.005 90.90 0.003
Institutional Setting 0.044 0.011 74.10 0.008
Carnegie Classification –0.151 –0.021 86.10 0.006

I also visually inspected the Love plot of the standardized mean differences and observed that the matching improved balance on all covariates because all were within a threshold of 0.10 (Greifer, 2022b; Figure 1). I inspected the empirical quantile-quartile (eQQ) plots of the covariate for the “treated” units and value of the covariate at the corresponding quantile in the “control” group, kernel density plots of the covariate values and density of the sample at the covariate values, and the histograms of the density of the propensity score distribution before and after matching (Figure 2). The visual evidence suggested that the covariates within the groups differed significantly before matching procedures were implemented. The matching techniques effectively decreased bias by making the groups of students who did and did not participate in community service more similar with regard to the covariates.

Figure 1
Figure 1
Figure 1

Love Plot of Standardized Mean Differences Before and After Matching.

Finally, I analyzed the data using ordinary least squares regressions to examine the relationship between the independent and dependent variables. I did not utilize hierarchical linear modeling techniques, even though the students were enrolled at different institutions. I computed the intraclass correlation coefficient (an estimate of the proportion of between-institution variance compared to within-institution variance) using the bruceR package in R (Bao, n.d.). The coefficient was .010, suggesting greater independence of observations in the different groups of institutions. Scholars utilizing the MSL survey in prior studies have also discovered nominal between-institution differences (Dugan et al., 2013), so ordinary least squares regression is sufficient for the present analysis. I ran 15 separate ordinary least squares regressions—one for all students and 14 separated by students’ race/ethnicity, first-generation status, and social class. In the regression analyses, I also included the same covariates used in matching procedures (Horn, 2012; Soria et al., 2019) and whether or not the community service was completed as a part of a course (i.e., a service-learning class), given the important role of faculty in enhancing the social capital development among marginalized students (Soria & Stebleton, 2013; Stanton-Salazar, 2011). Adding the covariates in regression models post-matching has several benefits, including increasing precision in the estimates, reducing bias from residual imbalance, and generating robust estimates (Greifer, 2022b).

Figure 2
Figure 2
Figure 2

Histograms of the Density of the Propensity Score Distribution Before and After Matching.

Results

The results of the first ordinary least squares regression analysis for all students suggests that community service participation has significant (p < 0.05) and positive effects on students’ social capital development (Table 3). The results also suggest that community service has heterogeneous effects on students’ social capital development by their race and ethnicity. There were no statistically significant effects of community service on the social capital development of Middle Eastern or Northern African students’ social capital development. There were also no statistically significant effects of community service on the social capital development among students who did not have a race or ethnicity listed in the survey. However, there were statistically significant (p < 0.05) and positive effects of community service on African American or Black, Asian American, Latinx or Hispanic, multiracial, and White students’ social capital development. Among both continuing-generation and first-generation students, community service participation had significant (p < 0.05) and positive effects on their social capital development. Finally, the results suggested there are statistically significant (p < 0.05) and positive effects of community service participation on low-income or poor, working-class, middle-class, upper professional or upper middle-class, and wealthy students’ social capital development. All effects held controlling for the covariates used in propensity score matching and for the additional independent variable of whether the service completed was within a service-learning class.

Table 3

Regression Analyses for College Students’ Social Capital Development

B (SE) p 95% CI(B) R2
All students 0.237 (0.020) *** 0.199, 0.276 0.481
Middle Eastern or Northern African 0.233 (0.226) –0.217, 0.683 0.610
African American or Black 0.282 (0.081) ** 0.124, 0.441 0.536
Asian American 0.182 (0.068) ** 0.049, 0.315 0.513
Latinx or Hispanic 0.219 (0.080) ** 0.061, 0.376 0.453
Multiracial 0.237 (0.023) *** 0.192, 0.282 0.471
White 0.321 (0.056) *** 0.211, 0.432 0.548
Race Not Listed 0.174 (0.129) –0.080, 0.429 0.508
Continuing-generation (parents/guardians have ≥ a bachelor’s degree or higher) 0.242 (0.023) *** 0.197, 0.288 0.484
First-generation (parents/guardians have < a bachelor’s degree) 0.227 (0.036) *** 0.156, 0.298 0.477
Low-income or poor 0.293 (0.067) *** 0.161, 0.426 0.442
Working-class 0.292 (0.045) *** 0.204, 0.380 0.511
Middle-class 0.202 (0.030) *** 0.143, 0.261 0.496
Upper professional or upper middle-class 0.238 (0.038) *** 0.163, 0.312 0.469
Wealthy 0.148 (0.075) * 0.011, 0.293 0.482
  • Note. * p < .05, ** p < .01, *** p < .001. Each separate model includes covariates, but only the focal coefficients are presented.

Discussion, Limitations, and Recommendations

The results of this study suggest that community service participation has significant and positive effects on undergraduates’ social capital development. Most students who participate in community service benefit from being more likely to intentionally seek out relationships with others from diverse backgrounds, build networks across professional fields, and identify shared interests between colleagues and/or friends for the benefit of mutual support. However, the results are not uniform across students based upon their race or ethnicity; in particular, community service did not have significant effects on social capital development among Middle Eastern or Northern African students or students who did not have a race or ethnicity listed in the survey. By and large, while community service participation may be a useful means for some students from marginalized identities to develop social capital in higher education—including African American or Black, Asian American, Latinx or Hispanic, multiracial, first-generation, low-income, and working-class students—it also appears as though community service participation also enhances social capital among privileged students as well.

The inconsistent results by race/ethnicity may be the result of several factors. Notably, the sample sizes were smaller among Middle Eastern or Northern African students and students who did not have a race or ethnicity listed on the survey, which reduces the statistical power and ability to find an effect. The size of the effects of community service were also smaller among some students of color compared to White students. Some community service experiences may reinforce structural injustices, reaffirm White privilege, and reinforce the subjugation of racially minoritized people and communities (Cahuas & Levkoe, 2017; Cann & McCloskey, 2017; Castillo-Montoya et al., 2021). While the survey did not capture details about the community service spaces students served in this study, racially minoritized students may encounter unexamined racist perspectives in predominantly White community service spaces where critical lenses and perspectives are not employed (Mitchell & Donahue, 2009; Mitchell et al., 2012). In those circumstances, the act of centering and prioritizing White students’ experiences and outcomes serves not only to reinforce the power and privilege of Whiteness in higher education, but it diminishes the potential developmental outcomes for racially minoritized students, who are often overburdened with teaching their peers about issues like racism and privilege, may struggle to navigate the complexities of service in White-framed “needy or problematic” communities that may include their own communities of upbringing, and endure unchallenged deficit assumptions about racially minoritized communities (Mitchell et al., 2012). In the service of Whiteness, racially minoritized students’ development outcomes may be neglected, contributing to the social reproduction of ongoing social capital disparities (Bourdieu, 1980).

The specific nature of students’ community service participation—including duration, type of organization, community, and other contextual factors—is unknown, which limits understanding of whether some of those factors may be supportive of students’ social capital development. Although I included the independent variable of whether students’ community service was completed in the context of a service-learning course, I observed that service-learning was not significantly associated with students’ social capital development in any of the 15 models. While faculty are institutional agents who can impart social capital among marginalized students (Soria & Stebleton, 2013; Stanton-Salazar, 2011), it may be the case that faculty-related social capital development has greater limitations than community-related social capital development. I encourage researchers to continue to explore the effects of different types of community service, including service conducted in the context of service-learning courses, on students’ social capital development.

Some have proposed that community service experiences expand undergraduates’ trust in community institutions (e.g., emergency services), trust in individuals, feelings of reciprocity, and networks, which can promote social capital (Coleman, 1988; D’Agostino, 2010). Within service environments, students are likely to work alongside the community partners—adults, staff, volunteer managers, or supervisors—who may actively mentor students to increase their social capital by intentionally connecting them with other networks (Swaminathan, 2007). It may be the case that community service participants received opportunities to engage with individuals from different backgrounds, which can facilitate bridging social capital (Putnam, 2000; Tinkler & Tinkler, 2020); however, the survey did not capture the composition of the communities in which students served, so it may also be the case that students served in homogenous communities or worked alongside others from similar backgrounds, which could diminish bridging social capital. Students may have served alongside others with a similar passion or interest in resolving a societal or community problem, which can facilitate bridging social capital (Putnam, 2000). While those two types of social capital were not distinguished in this study, I encourage researchers to explore the nuances of bridging and bonding capital and whether both may be potential outcomes of community service.

There are additional limitations to the study that provide rich opportunities for future research. The survey was administered during the COVID-19 pandemic, which likely limited students’ ability to participate in different types of community service and may have also hampered their social capital development. In the sample, 28.1% of students participated in community service, although in a previous administration of the MSL survey in 2018, the average was 42.3%. The selection of covariates in the logistic regression is subjective and propensity score matching reduces the sample size for the outcome analysis, which can introduce bias in the final models. While I used propensity score matching to address self-selection bias, the generalizations derived from self-selection in response to the MSL survey should be factored into cautious interpretations of the results.

Further, the data are limited by students’ self-reported responses to items related to their community service participation and social capital development. Critics have proposed that students may be overconfident or inaccurate in their self-assessment of experiential learning (Porter, 2011; Porter et al., 2011). The sample also included college students who were attending four-year institutions, so the results may not be generalizable to students enrolled at other institutions. The limited diversity of the sample may also make it difficult to generalize the results to students enrolled at campuses with higher demographic diversity. Consequently, I recommend that scholars continue exploring the benefits of community service participation among more diverse samples of college students enrolled at different types of institutions.

Conclusion

The results of this study provide some evidence for the potential ways in which community service participation can enhance undergraduates’ social capital development. While those results are promising, less than one-third of the students in the original sample participated in community service and not all students equally benefited from community service participation in terms of their social capital development. Therefore, I encourage higher education administrators, faculty, and staff to consider the ways in which community service opportunities could be more intentionally leveraged to promote students’ social capital development, especially among marginalized students.

References

1 Almeida, D. J., Byrne, A. M., Smith, R. M., & Ruiz, S. (2019). How relevant is grit? The importance of social capital in first-generation college students’ academic success. Journal of College Student Retention: Research, Theory, & Practice, 23(3), 539–559.  http://doi.org/10.1177/1521025119854688

2 Alon, S. (2009). The evolution of class inequality in higher education: Competition, exclusion, and adaptation. American Sociological Review, 74(5), 731–755.

3 Aries, E. (2008). Race and class matters at an elite college. Temple University Press.

4 Armstrong, E. A., & Hamilton, L. T. (2013). Paying for the party: How college maintains inequality. Harvard University Press.

5 Astin, A. W., & Sax, L. J. (1998). How undergraduates are affected by service participation. Journal of College Student Development, 39(3), 251–263.

6 Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(2), 399–424.  http://doi.org/10.1080/00273171.2011.568786

7 Bao, H.-W.-S. (n.d.). bruce R: BRoadly Useful Convenient and Efficient R functions. https://search.r-project.org/CRAN/refmans/bruceR/html/bruceR-package.html

8 Bowen, W. G., & Bok, D. (1998). The shape of the river: Long-term consequences of considering race in college and university admissions. Princeton University Press.

9 Bourdieu, P. (1980). Le capital social [Social capital]. Actes de La Recherche En Sciences Sociales, 31, 2–3.  http://doi.org/10.3406/arss.1979.2654

10 Burnett, C. (2006). Building social capital through an “active community club.” International Review for the Sociology of Sport, 41(3–4), 283–294.  http://doi.org/10.1177/1012690207078381

11 Cahuas, M. C., & Levkoe, C. Z. (2017). Towards a critical service learning in geography education: Exploring challenges and possibilities through testimonio. Journal of Geography in Higher Education, 41(2), 246–263.  http://doi.org/10.1080/03098265.2017.1293626.

12 Campbell, D. (2000). Social capital and service learning. PS: Political Science & Politics, 33(3), 641–646.  http://doi.org/10.2307/420872.

13 Cann, C. N., & McCloskey, E. (2017). The poverty pimpin’ project: How whiteness profits from black and brown bodies in community service programs. Race Ethnicity and Education, 20(1), 72–86.  http://doi.org/10.1080/13613324.2015.1096769.

14 Castillo-Montoya, M., Zastoupil, G., & Newton, A. (2021). Developing critical consciousness: The gains and missed opportunities for Latinx college students in a sport-based critical service-learning course. Michigan Journal of Community Service Learning, 27(1), 33–58.

15 Cataldi, E. M., Bennett, C. T., Chen, X., & Simone, S. A. (2018). First-generation students: College access, persistence, and postbachelor’s outcomes. U.S. Department of Education.

16 Chittum, J. R., Enke, K. A. E., & Finley, A. P. (2022). The effects of community-based and civic engagement in higher education: What we know and questions that remain. American Association of Colleges and Universities.

17 Coffé, H., & Geys, B. (2007). Toward an empirical characterization of bridging and bonding social capital. Nonprofit and Voluntary Sector Quarterly, 36(1), 121–139.

18 Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95–120.  http://doi.org/10.1086/228943

19 Cooks, L., & Scharrer, E. (2006). Assessing learning in community service learning: A social approach. Michigan Journal of Community Service Learning, 13(1), 44–55.

20 Coppe, T., Thomas, L., Pantic, N., Froehlich, D. E., Sarazin, M., & Raemdonck, I. (2022). The use of social capital in teacher research: A necessary clarification. Frontiers in Psychology, 13, 866571.  http://doi.org/10.3389/fpsyg.2022.866571

21 Cruce, T. M., & Moore, J. V. (2007). First-year students’ plans to volunteer: An examination of the predictors of community service. Journal of College Student Development, 48(6), 655–673.

22 Cuskelly, G. (2008). Volunteering in community sport organizations: Implications for social capital. In M. Nicholson and R. Hoye (Eds.), Sport and social capital (pp. 187–204). Routledge.

23 D’Agostino, M. J. (2010). Measuring social capital as an outcome of service learning. Innovative Higher Education, 35, 313–328.  http://doi.org/10.1007/s10755-010-9149-5

24 Dika, S. L. (2012). Relations with faculty as social capital for college students: Evidence from Puerto Rico. Journal of College Student Development, 53(4), 596–610.

25 Dufur, M. J., Parcel, T. L., Hoffmann, J. P., & Braudt, D. B. (2016). Who has the advantage? Race and sex differences in returns to social capital at home and at school. Research in Social Stratification and Mobility, 45, 27–40.  http://doi.org/10.1016%2Fj.rssm.2016.07.001

26 Dugan, J. P. (2015). The measurement of socially responsible leadership: Considerations in establishing psychometric rigor. Educational, Cultural, and Psychological Studies, 12, 23–42.  http://doi.org/10.7358/ecps-2015-012-duga

27 Flanagan, C. A., Kim, T., Collura, J., & Kopish, M. A. (2014). Community service and adolescents’ social capital. Journal of Research on Adolescence, 25(2), 295–309.  http://doi.org/10.1111/jora.12137

28 Glass, L. E. (2022). Social capital and first-generation college students: Examining the relationship between mentoring and college enrollment. Education and Urban Society, 55(2), 143–174.  http://doi.org/10.1177/00131245221076097

29 Glass, C. R., & Gesing, P. (2018). The development of social capital through international students’ involvement in campus organizations. Journal of International Students, 8(3), 1274–1292.  http://doi.org/10.5281/zenodo.1254580

30 Greifer, N. (2022a). Assessing balance. https://cran.r-project.org/web/packages/MatchIt/vignettes/assessing-balance.html

31 Greifer, N. (2022b). Estimating effects after matching. https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html

32 Hamilton, L., Roska, J., & Nielsen, K. (2018). Providing a “leg up”: Parental involvement and opportunity hoarding in college. Sociology of Education, 91(2), 111–131.  http://doi.org/10.1177/0038040718759557

33 Harder, V. S., Stuart, E. S., & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15(3), 234–249.  http://doi.org/10.1037/a0019623

34 Harper, S. R. (2008). Realizing the intended outcomes of Brown: High-achieving African American male undergraduates and social capital. American Behavioral Scientist, 51(7), 1030–1053.  http://doi.org/10.1177/0002764207312004

35 Hart, D., Matsuba, M. K., & Atkins, R. (2008). The moral and civic effects of learning to serve. In L. P. Nucci & D. Narvaez (Eds.), Handbook of moral and character education (pp. 484–499). Routledge.

90 Henness, S. A., Ball, A. L., & Moncheski, M. (2013). A community development approach to service-learning: Building social capital between rural youth and adults. In M. Calvert, M. Emery, & S. Kinsey (Eds.), Youth programs as builders of social capital: New Directions for Youth Development (no. 138, pp. 75–95).  http://doi.org/10.1002/yd.20059

36 Hines, E. M., Cooper, J. N., & Corral, M. (2019). Overcoming the odds: First-generation Black and Latino male collegians’ perspectives on pre-college barriers and facilitators. Journal for Multicultural Education, 13(1), 51–69.  http://doi.org/10.1108/JME-11-2017-0064

37 Ho, D., Imai, K., King, G., Stuart, E., Whitworth, A., & Greifer, N. (2021). Package “MatchIt.” https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf

38 Horn, A. S. (2012). The cultivation of a prosocial value orientation through community service: An examination of organizational context, social facilitation, and duration. Journal of Youth and Adolescence, 41, 948–968.  http://doi.org/10.1007/s10964-011-9714-y

39 Johnson, M., Dugan, J. P., & Soria, K. M. (2017). Try to see it my way: What influences social perspective taking among college students? Journal of College Student Development, 58(7), 1035–1054.  http://doi.org/10.1353/csd.2017.0082

40 Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford.

41 Kodama, C. M., & Dugan, J. P. (2013). Leveraging leadership efficacy for college students: Disaggregating data to examine unique predictors by race. Equity & Excellence in Education, 46(2), 184–201.  http://doi.org/10.1080/10665684.2013.780646

42 Lin, N. (2001). Social capital: A theory of social structure and action. Cambridge University Press.

43 Longwell-Grice, R. (2003). Get a job: Working class students discuss the purpose of college. College Student Affairs Journal, 23(1), 40–53.

44 Longwell-Grice, R., & Longwell-Grice, H. (2007–2008). Testing Tinto: How do retention theories work for first-generation, working-class students? Journal of College Student Retention: Research, Theory and Practice, 9(4), 407–420.  http://doi.org/10.2190%2FCS.9.4.a

45 Luedke, C. L. (2020). Lifting while we climb: Undergraduate students of color communal uplift and promotion of college-going within their communities. The Review of Higher Education, 43(4), 1167–1192.  http://doi.org/10.1353/rhe.2020.0016

46 Maldonado, S. (2022). Parental social capital and educational inequality. Fordham Law Review, 90, 2599–2620.

47 Marks, H. M., & Jones, S. R. (2004). Community service in the transition: Shifts and continuities in participation from high school to college. The Journal of Higher Education, 75(3), 307–339.

48 Meyer, M., Neumayr, M., & Rameder, P. (2019). Students’ community service: Self-selection and the effects of participation. Nonprofit and Voluntary Sector Quarterly, 48(6), 1162–1185.  http://doi.org/10.1177/0899764019848492

49 Mishra, S. (2020). Social networks, social capital, social support, and academic success in higher education: A systematic review with a special focus on “underrepresented” students. Educational Research Review, 29, 100307.  http://doi.org/10.1016/j.edurev.2019.100307

50 Mitchell, T. D., & Donahue, D. M. (2009). “I do more service in this class than I ever do at my site”: Paying attention to the reflections of students of color in service-learning: New solutions for sustaining and improving practice. In J. R. Strait & M. Lima (Eds.), The future of service-learning: New solutions for sustaining and improving practice (pp. 172–190). Stylus Publishing.

51 Mitchell, T. D., Donahue, D. M., & Young-Law, C. (2012). Service learning as a pedagogy of whiteness. Equity & Excellence in Education, 45(4), 612–629.  http://doi.org/10.1080/10665684.2012.715534.

52 National Center for Education Statistics. (2015). Postsecondary attainment: Differences by socioeconomic status. The condition of education. U.S. Department of Education.

53 National Center for Education Statistics. (2018a). 2015–2016 National Postsecondary Student Aid Study (NPSAS:16). U.S. Department of Education.

54 National Center for Education Statistics. (2018b). Immediate college enrollment rate. The condition of education. U.S. Department of Education.

55 National Center for Education Statistics. (2019). Indicator 23: Postsecondary graduation rates. U.S. Department of Education. https://nces.ed.gov/programs/raceindicators/indicator_red.asp

56 National Center for Education Statistics. (2022). College enrollment rates. The condition of education. U.S. Department of Education. https://nces.ed.gov/programs/coe/indicator/cpb.

57 Ornstein, A. (2019). Wealth, legacy, and college admission. Society, 56, 335–339.  http://doi.org/10.1007/s12115-019-00377-2

58 Porter, S. R. (2011). Do college student surveys have any validity? The Review of Higher Education, 35(1), 45–76.  http://doi.org/10.1353/rhe.2011.0034

59 Porter, S. R., Rumann, C., & Pontius, J. (2011). The validity of student engagement survey questions: Can we accurately measure academic challenge? In S. Herzog & N. A. Bowman (Eds.), New Directions for Institutional Research (no. 150): Validity and limitations of college student self-report data (pp. 87–98). Wiley.  http://doi.org/10.1002/ir.391

60 Putnam, R. D. (1994). Social capital and public affairs. Bulletin of the American Academy of Arts and Sciences, 47(8), 5–19.  http://doi.org/10.2307/3824796

61 Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.

62 Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.

63 Rubin, M., Denson, N., Kilpatrick, S., Matthews, K. E., Stehlik, T., & Zyngier, D. (2014). “I Am Working-Class”: Subjective Self-Definition as a Missing Measure of Social Class and Socioeconomic Status in Higher Education Research. Educational Researcher, 43(4), 196–200.  http://doi.org/10.3102/0013189X14528373

64 Schroeder, B., & Saw, G. (2016). Racial and ethnic gaps in postsecondary aspirations and enrollment. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(5), 5882.  http://doi.org/10.7758/RSF.2016.2.5.04

65 Schulzetenberg, A. J., Wang, Y.-C., Hufnagle, A., Soria, K. M., Maruyama, G., & Johnson, J. (2020). Improving outcomes of underrepresented college students through community-engaged employment. International Journal of Research on Service-Learning and Community Engagement, 8(1), Article 11.  http://doi.org/10.37333/001c.18719

66 Schwartz, S. E. O., Kanchewa, S. S., Rhodes, J. E., Cowdy, G., Stark, A. M., Horn, J. P., Parnes, M., & Spencer, R. (2017). “I’m having a little struggle with this, can you help me out?”: Examining impacts and processes of a social capital intervention for first-generation college students. American Journal of Community Psychology, 61(1–2), 166–178.  http://doi.org/10.1002/ajcp.12206

67 Soria, K. M. (2012). Creating a successful transition for working-class first-year students. The Journal of College Orientation and Transition, 20(1), 44–55.

68 Soria, K. M. (2015). Welcoming blue collar scholars into the ivory tower: Developing class-conscious strategies for students’ success. National Resource Center for the First-Year Experience and Students in Transition.

69 Soria, K. M. (2018). Counting class: Assessing social class identity using quantitative measures. In G. L Martin & B. Elkins (Eds.), New Directions for Student Services, 162: Social class identity in student affairs (pp. 49–61). Wiley.  http://doi.org/10.1002/ss.20261

70 Soria, K. M., & Cole, A. (2023). Leadership education and first-generation students’ social capital development. Journal of Student Affairs Research and Practice, 3(1), 1–19.  http://doi.org/10.1080/26906015.2023.2171823

71 Soria, K. M., & Barratt, W. (2012, June). Examining class in the classroom: Utilizing social class data in institutional and academic research. Association for Institutional Research Forum, New Orleans, LA.

72 Soria, K. M., Hufnagle, A. S., Lopez-Hurtado, I., & Do, T. (2019). Exploring the differential effects of service-learning on students’ sense of belonging: Does social class matter? International Journal of Research on Service-Learning and Community Engagement, 7(1), Article 8.  http://doi.org/10.37333/001c.11486

73 Soria, K. M., & Johnson, M. (2017). High-impact educational practices and the development of college students’ pluralistic outcomes. College Student Affairs Journal, 35(2), 100–116.  http://doi.org/10.1353/csj.2017.0016

74 Soria, K. M., Mitchell, T. D., & Roberts, B. (2023). Leading change to ensure a better world: College students’ participation in community service. Journal of Higher Education Outreach and Engagement, 27(2).

75 Soria, K. M., Nobbe, J., & Fink, A. (2013). Examining the intersections between undergraduates’ engagement in community service and development of socially responsible leadership. Journal of Leadership Education, 12(1), 117–140.

76 Soria, K. M., & Stebleton, M. J. (2012). First-generation students’ academic engagement and retention. Teaching in Higher Education, 17(6), 1–13.  http://doi.org/10.1080/13562517.2012.666735

77 Soria, K. M., & Stebleton, M. J. (2013). Social capital, academic engagement, and sense of belonging among working-class college students. College Student Affairs Journal, 31(2), 139–153.

78 Soria, K. M., & Stubblefield, R. (2014). First-year college students’ strengths awareness: Building a foundation for student engagement and academic excellence. Journal of the First-Year Experience and Students in Transition, 26(2), 69–88.

79 Soria, K. M., Troisi, J. N., & Stebleton, M. J. (2012). Reaching out, connecting within: Community service and sense of belonging among college students. Higher Education in Review, 9, 65–85.

80 Stanton-Salazar, R. D. (1997). A social capital framework for understanding the socialization of racial minority children and youths. Harvard Educational Review, 67(1), 1–40.

81 Stanton-Salazar, R. D. (2011). A social capital framework for the study of institutional agents and their role in the empowerment of low-status students and youth. Youth and Society, 43(3), 1066–1109.  http://doi.org/10.1177/0044118X10382877

82 Stuber, J. (2011). Inside the college gates: How class and culture matter in higher education. Rowman and Littlefield.

83 Swaminathan, R. (2007). Educating for the “real world”: The hidden curriculum of community service-learning. Equity & Excellence in Education, 40(2), 134–143.  http://doi.org/10.1080/10665680701246450

84 Thiem, K. C., & Dasgupta, N. (2022). From precollege to career: Barriers facing historically marginalized students and evidence-based solutions. Social Issues and Policy Review, 16(1), 212–251.  http://doi.org/10.1111/sipr.12085

85 Tinkler, A., & Tinkler, B. (2020). Building social capital through community-based service-learning in teacher education. Journal of Community Engagement and Higher Education, 12(2), 44–58.

86 Tyree, T. M. (1998). Designing an instrument to measure the socially responsible leadership using the social change model of leadership development. Dissertation Abstracts International, 59(06), 1945. (AAT 9836493)

87 Wilson, J. (2000). Volunteering. Annual Review of Sociology, 26, 215–240.  http://doi.org/10.1146/annurev.soc.26.1.215

88 Valentine, J., Price, D., & Yang, H. (2021). High-impact practices and gains in student learning: Evidence from Georgia, Montana, and Wisconsin. Lumina Foundation.

89 Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race, Ethnicity, and Education, 8(1), 69–91.  http://doi.org/10.1080/1361332052000341006

Author Bio

Dr. Krista M. Soria is an assistant professor in leadership and counseling at the University of Idaho. She is a critical quantitative researcher seeking to investigate the most equitable and inclusive programmatic practices and institutional conditions to prepare students to engage in the complexities of social change. She examines how higher education institutions can create a more supportive campus climate for students from diverse backgrounds, whether programs are equitably accessible and beneficial to all students, and how we can create structural conditions to support students’ development and success.