Adolescents are surrounded by people who have expectations about their college-going potential. Yet, few studies have examined the link between these multiple sources of college-going expectations and the actual status of students in postsecondary education years later. The study draws on data collected in the 2002–2006 Educational Longitudinal Study and employs an underutilized statistical technique (cross-classified multilevel modeling) to account for teacher reports on overlapping groups of students (typical of high school research). Results showed that positive expectations of students, parents, English, and mathematics teachers in the 10th grade each uniquely predicted postsecondary status 4 years later. As a group, the four sources of expectations explained greater variance in postsecondary education than student characteristics such as socioeconomic status and academic performance. This suggests positive expectations are additive and promotive for students regardless of their risk status. Teacher expectations were also found to be protective for low income students. Implications for future expectancy research and equity-focused interventions are discussed.
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In order to investigate if using a two-level CCREM was appropriate in modeling our data, we compared other similar, competing models taking into account possible school-level variability in our outcome measure. We ran unconditional models of A) students nested within two teachers nested within schools (using CCREM) and B) students nested within a single teacher nested within schools (not using CCREM but a standard three-level multilevel model). To guide model selection, we considered the variability at the school-level (using the school-level intraclass correlation or ICC), the model Akaike information criterion (AIC), and the Schwarz Bayesian information criterion (SBC; also known as BIC) as done by Meyers and Beretvas (2006). Smaller values of the AIC and SBC indicate better model fit.
Our unconditional, baseline CCREM model had an AIC of 10,782. Competing model A had a very similar AIC of 10,781 and model B had a much higher AIC of 10,892. Using SBC, the two-level CCREM (SBC = 10,774) fit better than the competing models (SBCA = 10,802; SBCB = 10,909). Meyers and Beretvas (2006) indicated that SBC may be more accurate in determining model fit for cross classified models compared to AIC.
The variability at the school level was statistically significant in model A (p = .04) and model B (p < .001) but the proportion of variance accounted for at the school-level was relatively small (ICCA = 0.03 and ICCB = 0.06). In addition, we conducted further analyses using the competing models but included student-level covariates. With the inclusion of student-level controls, school-level variance for both competing models became nonsignificant (both ps > 0.05). Based on the AIC, SBC, and as a result of school-level variability being relatively small (and later nonsignificant), we opted to present the results of the two-level CCREM.
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This research was supported by a grant from the American Education Research Association which receives funds from its “AERA grants program” from the National Science Foundation under its NSF Grant #DRL-0941014. Opinions reflect those of the authors and do not necessarily reflect those of the granting agencies. The authors would also like to thank Rhona S. Weinstein for her contribution to the study.
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Gregory, A., Huang, F. It Takes a Village: The Effects of 10th Grade College-Going Expectations of Students, Parents, and Teachers Four Years Later. Am J Community Psychol 52, 41–55 (2013). https://doi.org/10.1007/s10464-013-9575-5
- Postsecondary education
- Cross-classified multilevel modeling