Using geostatistical models to study neighborhood effects: An alternative to using hierarchical linear models

USING GEOSTATISTICAL MODELS TO STUDY NEIGHBORHOOD EFFECTS: AN ALTERNATIVE TO USING HIERARCHICAL LINEAR MODELS By Steven James Pierce Neighborhoods are important ecological contexts that influence the development, behavior, health, and welfare of their residents. Community psychologists studying neighborhood effects usually turn to hierarchical linear modeling (HLM) to test multilevel theories that explain neighborhood effects by examining the links between neighborhood characteristics and resident outcomes. Geostatistical modeling (GSM) can also test such theories, but it relies on a different way of conceptualizing neighborhoods than used in HLM and few social scientists have ever applied this method. This study developed an argument for why GSM may be a valuable alternative to HLM, then applied both methods to study the effects of neighborhood crime and neighborhood socioeconomic status (NSES) on residents' perceptions of neighborhood problems. Applying them to the same data allowed the study to examine the effect of varying the neighborhood boundaries used to measure crime and NSES and to explore whether the conceptual and statistical differences between HLM and GSM led to different scientific inferences about crime and NSES effects on residents' perceptions. While HLM and GSM models detected similar amounts of neighborhood-level variance and autocorrelation in perceived neighborhood problems, GSM provided a better description of the data from this sample because crucial HLM assumptions about the independence of the residuals were violated. The specific neighborhood boundaries used to measure crime and NSES in this study had important implications for the size and statistical significance of their effects. For this sample, GSM showed that circular buffers centered on residents' homes provided better operational definitions of the neighborhoods than the fixed cluster boundaries required by HLM. The HLM models overestimated the size and significance of the NSES effect on perceived neighborhood problems due to inaccurate assumptions about the residuals at both levels of analysis. The GSM models did not suffer problems with their residuals and showed that while a cluster-based NSES measure did not affect residents' perceptions in these data, NSES measured in 0.2 km radius buffers around residents' homes did (but not as strongly as indicated by the HLM models). The GSM models showed that residents' perceptions of neighborhood problems were more sensitive to crime occurring inside 1.1 km radius buffers around their homes than they were to the level of crime occurring inside the much smaller neighborhood cluster boundaries used in the HLM models. Thus, HLM underestimated how strongly crime affected residents' perceptions in this study because crime was not measured on the right spatial scale, despite following "best-practice" advice from the HLM literature to choose the smallest neighborhood units that are feasible. The study concludes by discussing the implications of the findings for conceptualizing and operationally defining neighborhoods, measuring neighborhoodlevel constructs, and applying research findings to inform community intervention efforts. Future directions for research are suggested, as are some ways of dealing with the practical issues of using GSM. Copyright by STEVEN JAMES PIERCE 2010 DEDICATION This work is dedicated to my parents, Rex and Lynn Pierce. My achievements rest on the foundation that they laid. Because they wrought well, I can reach the sky.