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Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
Assessing the Impact  of Academic Preparation, Finances         and Social Capital  on Postsecondary Education Enrollment
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Assessing the Impact of Academic Preparation, Finances and Social Capital on Postsecondary Education Enrollment

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Iria Puyosa …

Iria Puyosa

A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
CSHPE – University of Michigan
2009

Published in: Education
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  • 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.
  • Transcript

    • 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
    • 2. The Problem: Access to PSE Since the early 1990s, no notable progress has been observed in postsecondary education enrollment in the United States. Participation in postsecondary education still varies by ethnicity and family income. Gaps in college participation between high and low income students widened between 1994 and 2004 and still persist today. (Source: The National Center for Public Policy and Higher Education)
    • 3. S c h o o l S tr u c tu r a l F e a tu r e s In s tr u c tio n a l S c h o o l C a p a c ity A c a d e m ic P r e p a r a tio n E x p e n d itu r e s C o n tr o l C o u r s e O ffe r in g s G PA L o c a liz a tio n T e a c h in g Q u a lity T e s t S c o re s A d m is s io n ____________ ________________ H ig h S ta n d a r d s C u r r ic u lu m P o v e r ty L e v e l C o u n s e llin g M in o r ity P r o p o r tio n E d u c a tio n a l O u tc o m e M e r it A id H S D ro p o u t C o m m u n ity S o c ia l P o s t - s e c o n d a ry e d u c a t io n Tax B ase C a p ita l V a lu e o f E d u c a t io n * None * J o b P r e p a r a tio n * 2 y e a rs c o lle g e P e rs o n a l B a c k g ro u n d * 4 y e a rs c o lle g e G ender Race F in a n c e s ___________ C o lle g e C o s ts / E n ro llm e n t P a r e n ta l E d u c a tio n F a m ily C o n tr ib u tio n + F in a n c ia l A id ____________ (A m o u n t a n d T y p e s ) In c o m e
    • 4. Social Capital Social capital is structurally embedded in society, community, and groups Social capital can be mobilized by individuals Individuals’ goals drive social capital mobilization • (Cf. Lin 2001a; 2001c)
    • 5. Social Capital Social capital enhances individual social action outcomes because it facilitates the flow of information, helps to exert influence on decision makers, certifies an individual’s social credentials, and reinforces identity and group recognition. (Cf. Lin 2001a; 2001c)
    • 6. S o c ia l C a p it a l S u p p o rt B r id g e s N e e d A id M e r it A id F a m ily C o n t r ib u t io n s I n f o r m a t io n C u r r ic u lu m G PA T e s ts F in a n c e s A c a d e m ic P r e p a r a t io n P S E E n r o llm e n t
    • 7. Social Capital Effects on PSE Access to Relevant Information Helps individuals to deal with problems of asymmetric information Enhances efficiency in decision-making by allowing the use of copying and pooling mechanisms
    • 8. Social Capital Effects on PSE Attainment Norms Enforcement • Confluence of several reinforcing rules may facilitate individuals’ adoption of educational attainment norms • Positive attainment norms are better enforced when an individual lives in a community with networks closure
    • 9. Social Capital Effects on PSE • Support for Navigating the System • • Social networks connecting students with either institutional counselors or informal mentors are instrumental for guiding these students in navigating the application and admission processes
    • 10. Hypotheses 1. Social capital affects individuals’ educational postsecondary education enrollment decisions. 2. Information asymmetries among students from different socioeconomic backgrounds are related to inequalities in access to postsecondary education. 3. Resources available through social networks increase the probability of enrollment in postsecondary education beyond what would be expected given a student’s socioeconomic background.
    • 11. Hypotheses 4. Students’ preferences and actual postsecondary education alternatives are constrained by socioeconomic background 5. Enforcement of high attainment norms increases the probability of enrollment in postsecondary education beyond what should be expected given a student’s socioeconomic background
    • 12. Hypotheses 6. Support for navigating the admission system increases the probability of enrollment in PSE beyond what should be expected given a student academic preparation 7. Students’ self-assessment of their probability of career success affects postsecondary education enrollment outcomes regardless of academic credentials
    • 13. Data Advantages • Nationally representative sample • Longitudinal data from 8th grade to 8 years after expected high school graduation • Feasibility of examining underlying relationships among variables related to postsecondary education enrollment
    • 14. Data Disadvantages Some social capital constructs are not measured by variables included in the dataset Entails adjusting theoretical constructs to available variables and measurement scales Extensive data transformation is necessary for creating variables that correspond to the conceptual framework
    • 15. Outcome Variable Postsecondary education enrollment status Fall 1992 – Winter 1993 Enrolled Enrolled Not enrolled in a in a 5,000 2-year institution 4-year institution (42,7%) 2,306 4,399 (19,7%) (37,6%)
    • 16. e xp (X β ) i j Multinomial Logit Model P r(y = j) = i 1 + ∑ J e x p ( X β )' j i j Multinomial logit model is one of the most common models within the family of choice models that attempts to capture the underlying rational decision process by which individuals choose among different options. Allows the effects of the independent variables to differ for each distinct outcome category Estimates simultaneously binary logits for all possible comparisons among the outcome categories The final MLM was specified using survey design features (primary sample unit and sampling weight)
    • 17. Results Multinomial Logit Model N = 9,289 cases PSU = 652 Population size: 2.220.358 McFadden R2 = 0.274 F (68, 584) = 19.97 (Prob > F = 0.0000) Adjusted Count R2 = 0.390
    • 18. Main Findings  Analyses support the hypothesis that postsecondary education enrollment is affected by social capital  Variables measuring the four social capital constructs—attainment norms enforcement, access to information, support, and social networks—were significant for both outcomes, enrollment in a two- year institution and enrollment in a four-year institution
    • 19. Main Findings  Resources available through social networks increase the probability of enrollment in postsecondary education beyond what would be expected given a student’s socioeconomic background as measured by family income and parents’ education  As expected, family positively affects enrollment in both four-year and two-year institutions compared to no enrollment in postsecondary education  However, family income does not have significant effect on enrollment in four-year compared to enrollment two-year institution
    • 20. Main Findings  Among school control variables only percentage of graduating class going to either a two-year institution or a four-year institution (in the year a student enters high school) has significant effects on postsecondary education enrollment  None of the academic variables is a significant predictor of enrollment in a two-year institution compared to not enrolling in postsecondary education, and all of them are positive predictors of enrollment in a four-year institution.
    • 21. Main Findings Analyses support the hypothesis that postsecondary education enrollment is affected by students’ social networks. Variables measuring community involvement (changing school due to family moves while in high school and feeling involved in the neighborhood) and social networks (volunteering, friends to attend a two-year institution, and friends to attend a four- year institution) are among the strongest predictors for enrollment in both two-year and four-year institutions.
    • 22. Main Findings  Enforcement of high attainment norms—by high school peer models and significant others’ aspirations--increases the probability of enrollment in postsecondary education beyond what would be expected given a student’s socioeconomic background.  Significant others’ aspirations increases likelihood of enrollment beyond what would be expected according to the family income, although is not enough to overcome inequalities due to socioeconomic status.
    • 23. Main Findings  Information asymmetries are related to inequalities in access to postsecondary education  Positive effect from the number of information sources about financial aid handled by parents hold consistently across all four income groups  The effect on enrollment by the number of sources of information about financial aid decreases as mother’ education increases  Results add evidence to support the claim that information asymmetries regarding postsecondary education are related to parental education rather than related to household income.
    • 24. Main Findings  Concerns about variables that may restrict access to postsecondary education (which includes taking into account college expenses, availability of financial aid, and admission standards) prevent some students from choosing to enroll in a four- year institution and make it more likely that they choose a two-year institution.
    • 25. Main Findings  Results suggest that high school counselors may be focusing on helping students who want to apply for selective four-year colleges, while overlooking the support needs of students looking for postsecondary education opportunities in two-year institutions  The negative effect of not having taken a rigorous college preparatory curriculum may be reduced by the positive effect of high school support, but such support is not enough to overcome the detrimental effects of low grades.
    • 26. Policy Recommendations  Policies aimed to increase enrollment in a four- year institution must target middle school students.  All programs intended to inform about postsecondary education opportunities, to boost academic preparation, and to facilitate access to financial aid should be directed to students in grades 7th through 9th.  High schools, local community colleges and state universities should work on partnerships in order to set mentoring programs for 9th grade students.
    • 27. Policy Recommendations  District and state level educational policies should aim to increase opportunities for community involvement and volunteer work for middle school and high school students.  Special programs should target students that are new in their neighborhoods, students who face family disruption—due to death, divorce, or unemployment—and students of immigrant families.  Community colleges should improve their informational outreach toward high school counselors, students and their parents.
    • 28. Impact of Social Capital, Academic Preparation and Financial Factors on Postsecondary Education Enrollment
    • 29. Further Research  Continuity of this research agenda will entail gathering original data from a sample of middle school and high school students  Data collection should comprise at least three waves (7th grade, 10th grade, and a year after scheduled high school completion)  Data collected at the district or state level would facilitate the incorporation of field or social structure variables to the dataset by linking to district level Census data
    • 30. Further Research Instruments for data collection:  Social network position generator  Resources inventory  Questionnaire on actual resource's usage
    • 31. Further Research Analytical Techniques:  Confirmatory factor analysis  Cluster analysis  Alternative specific conditional logit
    • 32. Missing Data Imputation Original sample: 11,705 cases Sample after listwise deletion for missing data: 1,216 cases Sample after imputation for regressors and, then, listwise deletion for those with missing outcome variable: 9,261 cases
    • 33. Missing Data Imputation • The imputation of missing values was performed using a sequential regression imputation method • A missing indicator was generated by using the Imputation and Variance Estimation Software (IVEware) • That missing indicator is the outcome variable in a logistic regression model • The resulting regression equation is used for calculating the value for that missing indicator

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