Assessing the Impact of Academic Preparation, Finances and Social Capital on Postsecondary Education Enrollment
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This dissertation examines how academic preparation, finances, and social capital impact postsecondary education enrollment in the United States. The author analyzes data from a longitudinal study to test hypotheses about the effects of social networks, norms, information access, and support systems on college participation. Results support the role of social capital, showing that variables like peer influences, community involvement, and parental engagement positively influence enrollment even after accounting for academic and financial factors. The study aims to inform policies that can increase postsecondary access and reduce inequities.
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Assessing the Impact of Academic Preparation, Finances and Social Capital on Postsecondary Education Enrollment
- 1. Assessing the Impact of Academic Preparation, Finances and Social Capital on Postsecondary Education Enrollment Iria Puyosa A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy CSHPE – University of Michigan 2009 Doctoral Committee: Professor Stephen L. DesJardins, Chair Professor Mary E. Corcoran Professor Edward P. St. John Associate professor Deborah Faye Carter
- 15. Outcome Variable Postsecondary education enrollment status Fall 1992 – Winter 1993 Not enrolled 5,000 (42,7%) Enrolled in a 2-year institution 2,306 (19,7%) Enrolled in a 4-year institution 4,399 (37,6%)
- 29. Impact of Social Capital, Academic Preparation and Financial Factors on Postsecondary Education Enrollment
Notas do Editor
- Multi-causality of enrollment outcomesCrucial role of financesInteraction between family background, community resources, and school resourcesInequalities in academic preparation opportunities Inequalities in ability to payIntervening role of social capital
- The multinomial logit is a non-linear regression model. It allows effects of the independent variables to differ for each outcome. The MNLM estimates simultaneously binary logits for all possible comparisons among the outcome categories using maximum likelihood estimation (Long, 1997). In fact, a MNLM allows estimating different effects of predictors in each distinct outcome category (Raundenbush and Bryk, 2002). The multinomial logit is a non-linear regression model. It allows effects of the independent variables to differ for each outcome. The MNLM estimates simultaneously binary logits for all possible comparisons among the outcome categories using maximum likelihood estimation (Long, 1997). In fact, a MNLM allows estimating different effects of predictors in each distinct outcome category (Raundenbush and Bryk, 2002). The multinomial logit is a non-linear regression model. It allows effects of the independent variables to differ for each outcome. The MNLM estimates simultaneously binary logits for all possible comparisons among the outcome categories using maximum likelihood estimation (Long, 1997). In fact, a MNLM allows estimating different effects of predictors in each distinct outcome category (Raundenbush and Bryk, 2002). The multinomial logit is a non-linear regression model. It allows effects of the independent variables to differ for each outcome. The MNLM estimates simultaneously binary logits for all possible comparisons among the outcome categories using maximum likelihood estimation (Long, 1997). In fact, a MNLM allows estimating different effects of predictors in each distinct outcome category (Raundenbush and Bryk, 2002).
- The Haussman likelihood test confirms the null hypothesis that the odds for any outcome are independent of the other categories (IIA assumption).Likelihood ratio and Wald tests indicate that all variables significantly affect the outcome.