11 Association of Research Libraries Research Library Issues 290 — 2017 their research topic, choose keywords, evaluate articles and websites, and learn other key research skills. We gathered Internet IDs for students who met with a peer research consultant. Outcomes measures: Students’ learning outcomes. The dependent variables of interest in this study included first-year students’ self-reported development of three learning outcomes: critical thinking and analytical skills, written communication skills, and reading comprehension skills. In the SERU survey, students were asked to indicate their skill levels when they started at the university and their current skill levels on a scale from 1 (very poor) to 6 (excellent). We subtracted students’ skills when they started at the university from their current skills to develop variables measuring students’ growth or regression in those areas. Data Analyses We utilized propensity score matching techniques in SPSS 23.0 using the procedures outlined by Thoemmes.30 We began by using binary logistic regression to compute propensity scores for individual students. We used the binary variable of using the library (yes or no) as a dependent variable and the independent covariates listed above in the regressions to calculate the probabilities of using a library resource at least once. Next, we used 1:1 nearest neighbor matching, meaning that each student in the treatment condition is matched to a student in the untreated condition who has the most similar estimated propensity score. We matched without replacement and discarded all units that fell outside of the area of common support to avoid extrapolation to units that were so dissimilar that no comparisons could be made to other units.31 Next, it was important to check whether the matching procedures balanced the distribution of variables in both the treatment and control groups. We examined the standardized mean differences (the mean differences between the two groups divided by the