Institution-Wide Analysis of Academic Outcomes Associated with Participation in UGR: Comparison of Different Research Modalities at a Hispanic-Serving Institution

Abstract

Most studies on the benefits of participation in undergraduate research (UGR) use data from student participants in undergraduate research programs (URPs), which offer a limited number of positions. In reality, however, the majority of UGR students participate in undergraduate research not in programs (URNPs). The authors conducted an institution-wide study at a Hispanic-serving institution to examine the relationship between academic success and participation in these two UGR modalities. Although there were some differences between URPs and URNPs, participation in research at this institution was largely equitable and inclusive, with UGR demographics that reflected those of the institution, and it was positively associated with increased benefits along multiple academic metrics, regardless of UGR modality. Importantly, these increases were observed for both first time in college and transfer students.

Keywords: UGR; UGR experiences; equity; high-impact educational practices; inclusion; research apprenticeship; undergraduate research.

FIGURE 1.. Matching Process and Comparison of Matching Variables

Note: (A) Schematic describing the matching process for the two cohorts of participants. (B) Comparison of the distribution of students by entry status. (C) Tukey box plot with outliers for GPA in the first term. The median is shown as a line in the box, p values for t-tests are denoted with asterisks (*p < .05).

FIGURE 2.. Demographic and Socioeconomic Indicators Are Comparable between Research and Non-research Groups

Note: (A) Comparison of socioeconomic status using receipt of need-based federal Pell grants as an indicator for financial need. (B) Comparison of race and ethnicity distributions. (C) Comparison of gender distribution. Note that this information was recorded by the institution on a binary scale.

FIGURE 3.. Undergraduate Researchers Attempt and Earn More SCH and Maintain Higher GPAs Than Non-researchers

Note: (A) Comparison of SCH earned. ANOVA of mean earned SCH was F(3, 495) = 37.8685, p < .0001 for cohort 1 and F(3, 574) = 29.5521, p < .0001 for cohort 2. (B) Comparison of the ratio of SCH earned/attempted. ANOVA of mean SCH earned/attempted was F(3, 495) = 19.0459, p < .0001 for cohort 1 and F(3, 574) = 15.6375, p < .0001 for cohort 2. (C) Comparison of cumulative GPAs. ANOVA of mean cumulative GPA was F(3, 495) = 11.9355, p < .0001 for cohort 1 and F(3, 574) = 18.6413, p < .0001 for cohort 2. (D) Comparison of the change in GPA (ΔGPA). ΔGPA was calculated by subtracting the cumulative GPA from the GPA in the first term for each subject. ANOVA of mean ΔGPA was F(3, 495) = 22.3667, p < .0001 for cohort 1 and F(3, 574) = 12.6895, p < 0.0001 for cohort 2. Between-groups comparisons for data in this figure is provided in Table 3.

FIGURE 4.. Participation in Research Experiences Is Positively Correlated with Increased Graduation Rates and Higher Enrollment in Further Education

Note: (A) Comparison of four-year graduation rates for URP, URNP, and their respective matched controls. Results from chi-square analysis were χ²[3, N = 499] = 37.24, p < .0001* for cohort 1 and χ²[3, N = 578] = 41.881, p < .0001* for cohort 2. (B) Comparison of five-year graduation rates for cohort 1 (χ²[3, N = 499] = 59.364, p < .0001*). The five-year graduation rate data for cohort 2 were not available at the time of the analysis. (C) Comparison of rates of enrollment into further education. Note that for students in cohort 1 this analysis includes all students who graduated within five years (N = 311) whereas for cohort 2 it includes students who graduated within four years (N = 263). Results from chi-square analysis were χ²[3, N = 311] = 18.499, p = .0003* for cohort 1 and χ²[3, N = 263] = 6.051, p = .1091 for cohort 2.

FIGURE 5.. Participation in Research for Both FTC and Transfer Students Is Correlated with Positive Academic Outcomes

Note: (A) Comparison of four-year graduation rates for FTC and transfer students. (B) Comparison of cumulative GPA for FTC and transfer students. ANOVAs for FTC students were F(3, 367) = 21.2725, p < .0001 for cohort 1 and F(3, 427) = 15.0598, p < .0001 for cohort 2. ANOVAs for transfer students were F(3, 122) = 11.2544, p < .0001 for cohort 1 and F(3, 143) = 4.0315, p = .0087 for cohort 2. (C) Comparison of ΔGPA for FTC and transfer students. ΔGPA was calculated by subtracting the cumulative GPA from the GPA in the first term for each subject. ANOVAs for FTC students were F(367) = 17.9411, p < .0001 for cohort 1 and F(3, 427) = 8.0396, p < .0001 for cohort 2. ANOVAs for transfer students were F(3, 122) = 4.7143, p = .0038 for cohort 1 and F(3, 143) = 4.9033, p = .0028 for cohort 2. (D) Comparison of rates of enrollment into further education for FTC and transfer students. For cohort 1, the analysis includes students who graduated within five years (N = 224 for FTCs; N = 86 for transfer students). For cohort 2 the analysis includes students who graduated within four years (N = 163 for FTCs; N = 100 for transfer students).