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A methodological framework for neigh...

Graham, Elliott Thomas.

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A methodological framework for neighborhood indicators: Using spatial probability techniques and multi-attribute utility analysis to evaluate the effectiveness of a neighborhood-based child maltreatment prevention program.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A methodological framework for neighborhood indicators: Using spatial probability techniques and multi-attribute utility analysis to evaluate the effectiveness of a neighborhood-based child maltreatment prevention program./
Author:	Graham, Elliott Thomas.
Description:	291 p.
Notes:	Source: Dissertation Abstracts International, Volume: 62-10, Section: A, page: 3593.
Contained By:	Dissertation Abstracts International62-10A.
Subject:	Sociology, Theory and Methods. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3029093
ISBN:	0493413871

A methodological framework for neighborhood indicators: Using spatial probability techniques and multi-attribute utility analysis to evaluate the effectiveness of a neighborhood-based child maltreatment prevention program.
Graham, Elliott Thomas.

A methodological framework for neighborhood indicators: Using spatial probability techniques and multi-attribute utility analysis to evaluate the effectiveness of a neighborhood-based child maltreatment prevention program. - 291 p.

Source: Dissertation Abstracts International, Volume: 62-10, Section: A, page: 3593.

Thesis (Ph.D.)--University of Minnesota, 2001.

This research project examined the application of research methods from the disciplines of urban geography and evaluation research to measure the effectiveness of a neighborhood-based child maltreatment prevention program implemented in Saint Paul, Minnesota. Specifically, the study had two primary research objectives: (1) the establishment of a physical definition of an urban neighborhood that is conceptually relevant to the study of child maltreatment, and (2) the development of an indicator system to assess longitudinal shifts in child maltreatment risk within the confines of that neighborhood. In addressing the first objective, the author employed spatial probability techniques in order to identify a geographic area within the city with a higher than expected density of phenomena relevant to the study of child maltreatment risk, namely (1) the presence of minor children, (2) the prevalence of poverty, (3) housing instability, and (4) exposure to neighborhood-based child maltreatment prevention services. Following the delineation of a geographic area with a high density of these phenomena, the study author employed multi-attribute utility analysis (MAU), a form of structured decision making that involves the weighting and assignment of utility values to sets of criteria and attributes identified by program stakeholders, to evaluate changes in child maltreatment risk within that geographic area. Survey data collected at three-month intervals and analyzed using a modified MAU model suggested a moderate decrease in child maltreatment risk within the geographic area targeted during the first phase of the study, a change that may have been partially attributable to the effects of the child maltreatment prevention program under investigation. Although a sensitivity analysis conducted subsequently by the author confirmed the overall robustness of the MAU model as a tool for building a neighborhood indicator system, several implementation problems, namely small sample sizes and high levels of attrition among study subjects, necessitate a cautious interpretation of the study's results. From the standpoint of neighborhood indicator research, this study highlights the potential role that knowledge from the fields of urban geography and evaluation research may play in fostering the development of more accurate and conceptually meaningful indicators of social change within urban neighborhoods.

ISBN: 0493413871Subjects--Topical Terms:

626625
Sociology, Theory and Methods.

A methodological framework for neighborhood indicators: Using spatial probability techniques and multi-attribute utility analysis to evaluate the effectiveness of a neighborhood-based child maltreatment prevention program.
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520 $a This research project examined the application of research methods from the disciplines of urban geography and evaluation research to measure the effectiveness of a neighborhood-based child maltreatment prevention program implemented in Saint Paul, Minnesota. Specifically, the study had two primary research objectives: (1) the establishment of a physical definition of an urban neighborhood that is conceptually relevant to the study of child maltreatment, and (2) the development of an indicator system to assess longitudinal shifts in child maltreatment risk within the confines of that neighborhood. In addressing the first objective, the author employed spatial probability techniques in order to identify a geographic area within the city with a higher than expected density of phenomena relevant to the study of child maltreatment risk, namely (1) the presence of minor children, (2) the prevalence of poverty, (3) housing instability, and (4) exposure to neighborhood-based child maltreatment prevention services. Following the delineation of a geographic area with a high density of these phenomena, the study author employed multi-attribute utility analysis (MAU), a form of structured decision making that involves the weighting and assignment of utility values to sets of criteria and attributes identified by program stakeholders, to evaluate changes in child maltreatment risk within that geographic area. Survey data collected at three-month intervals and analyzed using a modified MAU model suggested a moderate decrease in child maltreatment risk within the geographic area targeted during the first phase of the study, a change that may have been partially attributable to the effects of the child maltreatment prevention program under investigation. Although a sensitivity analysis conducted subsequently by the author confirmed the overall robustness of the MAU model as a tool for building a neighborhood indicator system, several implementation problems, namely small sample sizes and high levels of attrition among study subjects, necessitate a cautious interpretation of the study's results. From the standpoint of neighborhood indicator research, this study highlights the potential role that knowledge from the fields of urban geography and evaluation research may play in fostering the development of more accurate and conceptually meaningful indicators of social change within urban neighborhoods.
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