Table of Contents

 

Neighbourhood Factors and Children: Hierarchical Linear Models and Small Area Statistics

 

CASE STUDY

Last modified 2003-05-20 23:07

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Overview

The data for this study are taken from the synthetic file released for cycle three of the National Longitudinal Survey of Children and Youth (NLSCY). The data provided represent only a subset of the data available. The provided data represent children aged 4, 5 or 6, living in one of 24 major metropolitan areas.  1,016 records are provided. In addition to studying the relationship between child outcomes and determinants, you will learn about hierarchical methods and small area statistics.  

 

Dafna Kohen - dafna.kohen@statcan.ca, Sander Post - sander.post@statcan.ca,

Karla Nobrega - karla.nobrega@statcan.ca, Patricia Whitridge - patricia.whitridge@statcan.ca

 

Introduction 

Neighbourhood factors such as poverty and residential instability have been identified as being important in explaining neighbourhood problems such as delinquency and crime encountered in many poor urban neighbourhoods (Sampson, 1992; Sampson & Groves, 1989; Sampson & Morenoff, 1997). Neighbourhood conditions of poverty and instability impede the establishment of formal and informal institutions of neighbourhood organization which are believed to maintain and foster strong community relations as well as public order within a community. For example, neighbourhood safety and cohesion or a sense of trust and belonging are seen to strengthen the community and have positive effects on its members. Often these factors are spatially based so that poverty conditions co-occur in similar areas (Massey, 1990; 1996; Massey & Denton, 1993). The geographic or spatial associations may be due in part to housing policies, housing affordability, as well as to conditions of ethnic and economic segregation (Wilson, 1987). For example, public housing is often found in predominantly low socio-economic neighbourhoods leading to areas of isolated and concentrated poverty as well as other separate areas of concentrated affluence. These differences as well as the conditions of neighbourhoods children reside in may be important for child health and well-being. When discussing the associations of neighbourhood characteristics with child outcomes it is important to note that both risk and protective factors occur at multiple levels, individual, family, and neighbourhood and it is not just a single protective or risk factor but the accumulation of factors that result in negative or positive child and family outcomes.

 

The emerging literature on the effects of neighbourhood factors on children and youth has focused on structural characteristics of the neighbourhood such as income/socio-economic conditions and residential instability yet most of the literature is based on studies conducted in the United States. Most studies have focused on outcomes in early childhood or late adolescence (see Leventhal & Brooks-Gunn, 2002 for review). Some consistent findings have been reported. For example, neighbourhood effects for socio-economic factors are more common than effects of residential instability across all child outcomes, and neighbourhood effects are generally small (explaining 5-10% of the variability in outcomes). As would be expected, family level factors tend to be more strongly associated with individual child outcomes than neighbourhood level factors but neighbourhood effects are consistently reported even after controlling for family level factors, for outcomes of children, youth, and adolescents.

 

Data Description 

National Longitudinal Survey of Children and Youth
The National Longitudinal Survey of Children and Youth (NLSCY) is a long-term survey designed to measure child development and well-being. The first cycle of the survey was conducted by Statistics Canada in 1994-1995 on behalf of Human Resources Development Canada. The requirement for the NLSCY design was to select a representative sample of children in Canada and to follow and monitor these children over time into adulthood. All of the information for the household collection was collected in a face-to-face or telephone interview using computer-assisted interviewing (CAI). Questions were asked to the respondent in the home or by telephone and directly entered into a computer by the interviewer.

Before the NLSCY was undertaken there were few statistical studies describing a broad range of characteristics of children in Canada. Measures of health, well-being and life opportunities are needed, however, if governments and researchers hope to learn more about the ongoing life conditions of Canadian children and youth, and their developmental experiences. Longitudinal data are central to discovering developmental changes occurring in children over time, and studying the impacts of the social environment of the child and various family-related factors.

The primary objective of the NLSCY is to develop a national database on the characteristics and life experiences of children and youth in Canada as they grow from infancy to adulthood. The more specific objectives of the NLSCY are:
 

* To determine the prevalence of various biological, social and economic characteristics and risk factors of

   children and youth in Canada;
 * To monitor the impact of such risk factors, life events and protective factors on the development of these

   children; and
 * To provide this information to policy and program officials for use in developing effective policies and

   strategies to help young people live healthy, active and rewarding lives.

Underlying these objectives is the need to:

 
 *  Fill an existing information gap regarding the characteristics and experiences of children in Canada,

    particularly in their early years;
 *  Focus on all aspects of the child in a holistic manner (i.e., the child, his/her family, school, and community);
*  Provide national, and as far as possible, provincial-level data; and
*  Explore subject areas that are amenable to policy intervention and which affect a significant segment of the

    population.

Data are available at http://dissemination.statcan.ca/english/IPS/Data/89M0015XCB1999001.htm
 

Background: Survey Weights

 

Background: Geo-Codes

 

Census Metropolitan Area (CMA)

A very large urban area, together with adjacent urban and rural areas that have a high degree of economic and social integration with that urban area. A CMA is comprised of one or more contiguous census subdivisions (CSD). CMA's are defined by Statistics Canada.

A CMA is delineated around an urban area (called the urbanized core and having a population of at least 100,000, based on the previous census). Census subdivisions are included in the CMA on the basis of decennial place-of-work commuting data. Once an area becomes a CMA, it is retained in the program even if its population subsequently declines.

Census Metropolitan Area (CMA) codes are listed in the data documentation.

Background: Linking data files
If you choose to include the CMA level variables, you must first merge this data by CMA onto the NLSCY synthetic file. In addition to this file you are free to add on other macro level variables from other sources.

 

Macro Level Data (Excel) - CMA Summary Indicators

Micro Level Data (Text, Excel, SAS) - Individual data from the synthetic NLSCY

 

Appendix A

 

Appendix B

 

Appendix C

 

Objectives

For this case study, a survey example will be used to:

 
a) Study hierarchies (Problem 1) in survey data - understand micro and macro levels.
        

         i) Study the relationship between the child outcomes (Child chronic health problems (Count of the 

            conditions a child has had; 3 categories), Child Injury (binary), or Cognitive Competence (continuous))

            and micro and macro level dependent variables using a hierarchical linear model.
        ii) Compare the hierarchical model to traditional regression models.
 

b) Study the small area (Problem 2) issues with this data set - understand issues.
 

         i) Decide on a method to estimate outcomes in areas with sparse individual level data.
        ii) Compare results with methods that do not take into account the small area problem.

 
Frequently Asked Questions

Please check this section regularly for updates.

References