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Very few studies have examined issues of work-life balance among faculty of different racial/ethnic backgrounds. Utilizing data from Harvard University's Collaborative on Academic Careers in Higher Education project, this study examined predictors of work-life balance for 2953 faculty members from 69 institutions. The final sample consisted of 1059 (36%) Asian American faculty, 512 (17%) African American faculty, 359 (12%) Latina/o faculty, and 1023 (35%) White/Caucasian faculty. There were 1184 (40%) women faculty and 1769 (60%) men faculty. The predictors of worklife balance included faculty characteristics, departmental/institutional characteristics and support, and faculty satisfaction with work. While African American women faculty reported less work-life balance than African American men, the reverse was true for Latina/o faculty. In addition, White faculty who were single with no children were significantly less likely to report having work-life balance than their married counterparts with children. Faculty rank was a significant positive predictor of work-life balance for all faculty. Notably, the findings highlight the importance of department and institutional support for making personal/family obligations and an academic career compatible. Institutional support for making personal/family obligations and an academic career compatible was consistently the strongest positive predictor of perceived work-life balance for all faculty. In addition, satisfaction with time spent on research had positive associations with work-life balance for all faculty, highlighting how faculty from all racial/ethnic backgrounds value being able to spend enough time on their own research.

Few college experiences elicit as much controversy as racial/ethnic student organizations. Critics argue that these student groups promote racial division and segregation, whereas supporters counter these claims and suggest instead that they facilitate college adjustment, learning, and growth. Clearly, some students are quite predisposed to participate (or not participate) in these organizations, which can lead to significant challenges when trying to determine the impact of this form of engagement. The present study used multilevel propensity score matching analyses to explore the relationships between racial/ethnic student organizations and post-college civic outcomes within a 10-year longitudinal sample of 8,634 alumni from 229 institutions. The results indicate that participation is significantly and positively associated with numerous civic behaviors and attitudes 6 years after graduation. Moreover, these findings are similar regardless of race/ethnicity, gender, or institution.

Although used frequently in related fields such as K-12 education research, educational psychology, sociology, and social survey research, latent class analysis (LCA) has been infrequently used in higher education. This article provides higher education researchers with a pedagogical application of LCA to classify entering freshmen based on their pluralistic orientation. This study utilized data on entering freshmen at a racially diverse institution on the West coast. LCA was used to estimate latent profile probabilities, classify freshmen into latent classes, and relate latent class probabilities to covariates. The findings indicated that a four-class model was the best fitting model: high pluralistic orientation; high-disposition, low-skill; low-disposition, high-skill; and low pluralistic orientation. Similar to previous research, the findings indicated that the probability of being classified into one group versus the other was dependent upon a student's race/ethnicity and intended major. This approach can aid college administrators in their program planning and targeted interventions around issues of diversity.

According to prevailing theory and anecdotal evidence, the congruence between institutional attributes and students' needs, interests, and preferences plays a key role in promoting college satisfaction and retention. However, this assertion has received little direct empirical attention, and the few available studies appear to have some key limitations. This study examined the factor structure and predictive validity of a newly developed student-institution fit instrument, which was designed to avoid the problems in previous research. Confirmatory factor analyses identified several interrelated dimensions of fit, and these dimensions can be combined into a single overall fit index. Moreover, a six-factor structure of student-institution fit is similar at two institutions that differ in terms of size, control, type, region, and religious affiliation. Structural equation modeling analyses show that student-institution fit is associated with greater college satisfaction and lower social isolation; fit also has a positive, indirect effect on intent to persist. Implications for practice and future research are discussed.

Using data from the 2004-2007 National Study of Living Learning Programs, the only national dataset offering longitudinal information on outcomes associated with living-learning (L/L) program participation, this study investigated the role of L/L programs and other college environments in the professional outcome expectations of women in science, technology, engineering, and mathematics (STEM) majors. Specifically, we examined an overall measure of professional outcome expectations, along with participants' anticipation of the chances that they will "get a good job in their field," "achieve success in their career," and "combine a professional career with having a balanced personal life." Findings indicated that attending a coeducational STEM L/L program and discussing academic and career issues with peers were positively related with three of the outcome measures. Additional findings spoke to the importance of self-efficacy and interactions with diverse peers in the development of professional outcome expectations among women in STEM. Implications are presented for higher education institutions' efforts to support coeducational and women-only STEM-related L/L programs, peer and faculty interactions, and diverse peer interactions.

This paper delves into a facet of socioeconomic diversity relatively unaddressed in the literature: student reports of cross-class interaction ("reported CCI"). Previous research has found that student interaction across social class is a significant predictor of cross-racial interaction, but it is unknown whether the actual socioeconomic heterogeneity of a student body is significantly related to reported CCI. We use hierarchical linear modeling to identify predictors of reported CCI in the 2003 Freshman/2007 College Student Survey from the UCLA Higher Education Research Institute. In the final model, students who attended more socioeconomically diverse institutions and more racially diverse institutions reported higher levels of CCI. Findings suggest that reported CCI is linked to the actual socioeconomic heterogeneity of a student body. Measures of racial diversity (percent of students of color and diversity engagement), both at the institutional and student level, also predicted reported CCI. Thus, reported CCI is likely influenced by the racial diversity of a student body and other aspects of the campus racial climate, in addition to socioeconomic diversity. Implications for campus climate, diversity, and equity research are discussed.

The educational and societal benefits of promoting meaningful interracial interactions during college are well-established. While most previous studies have examined the relationship between interracial interactions and college student outcomes among all students, much less is known about the extent to which these effects depend upon student characteristics and, more specifically, their precollege experiences. Drawing upon Gurin et al.'s ("Harv Educ Rev" 72:330-366, 2002) theoretical framework, this paper explores whether and how the impact of college interracial interactions might vary depending upon students' precollege exposure to diversity. Hierarchical linear modeling analyses were conducted on a 4-year longitudinal sample of 3,098 undergraduates from 28 colleges and universities. Regardless of the type of outcome and type of precollege diversity measure, the relationship between college interracial interactions and various outcomes (college satisfaction, emotional well-being, and race-related perceptions) were stronger among students who had had greater precollege exposure to racial/ethnic diversity. Implications for higher education research and practice are discussed.

The purpose of this article is to provide higher education researchers with an illustrative example of meta-analysis utilizing hierarchical linear modeling (HLM). This article demonstrates the step-by-step process of meta-analysis using a recently-published study examining the effects of curricular and co-curricular diversity activities on racial bias in college students as an example (Denson, "Rev Educ Res" 79:805-838, 2009). The authors present an overview of the meta-analytic approach and describe a meta-analysis from beginning to end. The example includes: problem specification; research questions; study retrieval and selection; coding procedure; calculating effect sizes; visual displays and summary statistics; conducting HLM analyses; and sensitivity analyses. The authors also offer guidelines and recommendations for improving the conduct and reporting of research which in turn can provide the information necessary for future and more comprehensive meta-analytic reviews. (Contains 7 tables, 6 footnotes, and 5 figures.)

In most multi-campus studies of college impact that have been conducted over the past four decades, investigators have relied on ordinary least squares (OLS) regression as the analytic method of choice. Recently, however, some investigators have advocated the use of Hierarchical Linear Modeling (HLM), a method specifically designed for analyses that involve both individual (student) and aggregate (institutional) level measures. Cross-validation analyses using a national database show that the two methods yield an equally good "fit" with empirical data. Existing OLS software has the advantage of enabling one to perform path analytical causal modeling; HLM has the advantage of yielding a more conservative estimate of the significance of institution-level effects.