Citation

Neighborhood Social Capital, Neighborhood Disadvantage and Change of Neighborhood as Predictors of School Readiness

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Abstract:

This study shows how economic and social capital aspects of neighborhoods have separate impacts on children’s school readiness. Measures of social capital are derived from aggregated judgments of neighborhood collective efficacy while economic advantage/disadvantage is measured by census variables from neighborhood and community profiles from census subdivisions and census tracts. These neighborhood-level variables are merged with nationally representative data from a prospective longitudinal study of children (National Longitudinal Survey of Children and Youth) in order to explore how neighborhood-level characteristics influence children’s school readiness. Findings show that economic and social capital characteristics of neighborhoods each have significant impacts on children’s vocabulary-related test performance but that such neighborhood effects seem not to be involved in other cognitive dimensions of children’s school readiness, namely, those abilities that are less related to vocabulary-dependent tests, such as copying, symbol manipulation and mathematics. Balanced randomized replication (BRR) estimations and bootstrap weights are adopted, in order to adjust for stratification and clustering in the sample design and also incorporate a post-stratification element to correct for biases resulting from longitudinal-data attrition.

Most Common Document Word Stems:

neighborhood (175), children (123), social (61), incom (51), level (50), low (46), child (42), household (41), collect (37), effect (36), canada (36), use (33), capit (31), school (31), disadvantag (31), j (30), al (30), score (28), et (28), poverti (27), efficaci (26),
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Association:
Name: American Sociological Association Annual Meeting
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http://www.asanet.org


Citation:
URL: http://citation.allacademic.com/meta/p411231_index.html
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MLA Citation:

Jones, Charles. and Shen, Jing. "Neighborhood Social Capital, Neighborhood Disadvantage and Change of Neighborhood as Predictors of School Readiness" Paper presented at the annual meeting of the American Sociological Association Annual Meeting, Hilton Atlanta and Atlanta Marriott Marquis, Atlanta, GA, Aug 13, 2010 <Not Available>. 2014-11-27 <http://citation.allacademic.com/meta/p411231_index.html>

APA Citation:

Jones, C. L. and Shen, J. , 2010-08-13 "Neighborhood Social Capital, Neighborhood Disadvantage and Change of Neighborhood as Predictors of School Readiness" Paper presented at the annual meeting of the American Sociological Association Annual Meeting, Hilton Atlanta and Atlanta Marriott Marquis, Atlanta, GA Online <APPLICATION/PDF>. 2014-11-27 from http://citation.allacademic.com/meta/p411231_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: This study shows how economic and social capital aspects of neighborhoods have separate impacts on children’s school readiness. Measures of social capital are derived from aggregated judgments of neighborhood collective efficacy while economic advantage/disadvantage is measured by census variables from neighborhood and community profiles from census subdivisions and census tracts. These neighborhood-level variables are merged with nationally representative data from a prospective longitudinal study of children (National Longitudinal Survey of Children and Youth) in order to explore how neighborhood-level characteristics influence children’s school readiness. Findings show that economic and social capital characteristics of neighborhoods each have significant impacts on children’s vocabulary-related test performance but that such neighborhood effects seem not to be involved in other cognitive dimensions of children’s school readiness, namely, those abilities that are less related to vocabulary-dependent tests, such as copying, symbol manipulation and mathematics. Balanced randomized replication (BRR) estimations and bootstrap weights are adopted, in order to adjust for stratification and clustering in the sample design and also incorporate a post-stratification element to correct for biases resulting from longitudinal-data attrition.


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