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Categorical latent variable modeling...
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Sweetman, Heidi.
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Categorical latent variable modeling approaches to the study of neighborhood poverty, social capital, and their relationship to academic achievement.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Categorical latent variable modeling approaches to the study of neighborhood poverty, social capital, and their relationship to academic achievement./
作者:
Sweetman, Heidi.
面頁冊數:
179 p.
附註:
Adviser: David Kaplan.
Contained By:
Dissertation Abstracts International67-06A.
標題:
Education, Sociology of. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3221132
ISBN:
9780542726309
Categorical latent variable modeling approaches to the study of neighborhood poverty, social capital, and their relationship to academic achievement.
Sweetman, Heidi.
Categorical latent variable modeling approaches to the study of neighborhood poverty, social capital, and their relationship to academic achievement.
- 179 p.
Adviser: David Kaplan.
Thesis (Ph.D.)--University of Delaware, 2006.
Using latent class analysis and growth mixture modeling to analyze data from the Early Childhood Longitudinal Study-Kindergarten (ECLS-K), this dissertation examines the relationship between family advantage, neighborhood advantage, social capital and reading and mathematics achievement. First, using variables available in ECLS-K and latent class analysis, two latent classes of social capital (higher and lower social capital) were identified. Growth mixture models were then used to investigate the relationship between family advantage, neighborhood advantage, social capital and both reading and mathematics achievement. Several aspects of family advantage, such as race, gender, and father's employment status were found to be significantly associated with reading achievement trajectories. Race, gender and mother and father's occupational prestige were significantly related to mathematics achievement growth trajectories. Income as a percent of the poverty threshold was significant in both the reading and math models, but the direction of the relationship was opposite to that which was anticipated. Neighborhood advantage was not significantly associated with reading or math achievement trajectories in the majority of the models. Finally, while social capital was not significantly associated with reading achievement and growth, it was significantly related to mathematics achievement and growth. The results have implications for both future research and policy. Further research into how to refine the measurement of neighborhood advantage and how to incorporate social networks into the measurement of social capital is needed. If, as the results indicate, social capital has an impact on mathematics achievement over and above other measures of family and neighborhood advantage, educational policies targeted at enhancing social capital are needed.
ISBN: 9780542726309Subjects--Topical Terms:
626654
Education, Sociology of.
Categorical latent variable modeling approaches to the study of neighborhood poverty, social capital, and their relationship to academic achievement.
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Using latent class analysis and growth mixture modeling to analyze data from the Early Childhood Longitudinal Study-Kindergarten (ECLS-K), this dissertation examines the relationship between family advantage, neighborhood advantage, social capital and reading and mathematics achievement. First, using variables available in ECLS-K and latent class analysis, two latent classes of social capital (higher and lower social capital) were identified. Growth mixture models were then used to investigate the relationship between family advantage, neighborhood advantage, social capital and both reading and mathematics achievement. Several aspects of family advantage, such as race, gender, and father's employment status were found to be significantly associated with reading achievement trajectories. Race, gender and mother and father's occupational prestige were significantly related to mathematics achievement growth trajectories. Income as a percent of the poverty threshold was significant in both the reading and math models, but the direction of the relationship was opposite to that which was anticipated. Neighborhood advantage was not significantly associated with reading or math achievement trajectories in the majority of the models. Finally, while social capital was not significantly associated with reading achievement and growth, it was significantly related to mathematics achievement and growth. The results have implications for both future research and policy. Further research into how to refine the measurement of neighborhood advantage and how to incorporate social networks into the measurement of social capital is needed. If, as the results indicate, social capital has an impact on mathematics achievement over and above other measures of family and neighborhood advantage, educational policies targeted at enhancing social capital are needed.
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