Unpacking High-Impact Practices in the Arts: Predictors of College, Career, and Community Engagement Outcomes

ABSTRACT

Research on high-impact practices (HIPs) demonstrates positive links to student learning and development, but generally does not focus on discipline-specific activities, such as working with an artist in the community and portfolio completion. This study seeks to identify beneficial HIPs for arts training through an analysis of 23,916 arts alumni from 77 postsecondary institutions. A series of regression models suggest that HIP participation was associated with gains in academic abilities and career skills, higher levels of college satisfaction, more successful job searches, greater likelihood of employment in the arts and avocational arts practice, and more frequent arts community involvement.

KEYWORDS:

• Arts alumni
• higher education
• high-impact practices
• career outcomes
• community involvement

Acknowledgements

The authors would like to thank Savannah Bastian and Samantha Silberstein for their vital assistance in preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The SNAAP data set is proprietary and not currently available for free public use. Syntax for all analyses included in this article are available from the authors upon request.

Notes

1 To verify the academic abilities and career skills scales, we conducted confirmatory factor analysis to determine scale properties with AMOS 24.0. Strong model fit was reflected by GFI greater than .85, CFI greater than .90, RMSEA less than .06, PCLOSE greater than .05. Full details are available upon request.

2 We also used a disaggregated coding of non-white racial/ethnic identities: Asian (4.5% of arts alumni), Hispanic or Latino (3.7%), Black or African American (2.3%), other (1.9%), American Indian or Alaska Native (0.5%) and Native Hawaiian or other Pacific Islander (0.2%). Across the selected outcomes, few contrasts were statistically significant with this more detailed coding, and the overall pattern is inconsistent. For example, Asian alumni reported lower levels of college satisfaction and were less likely to perform art in their personal time relative to white alumni, but were also more likely to currently work in an arts-related occupation. Results for these analyses are available upon request.

3 Predictive margins (or adjusted predictions) are statistics calculated from model estimates where some covariates are fixed at different values (Williams 2012). In Figure 1, the margins represent the average values of the college outcome variables if all observations were treated as if they had reported the selected type or level of HIP participation. This approach contrasts with making model predictions by setting covariates at fixed values (e.g., means), which has the limitation of referring to nonexistent values with categorical predictors.