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Johnston, William R.

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Johnston

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William R.

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Johnston, William R.
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    The Roots of Opting Out: Family, School, and Neighborhood Characteristics Associated With Non-Local School Choices
    (2015-05-08) Johnston, William R.; Hill, Nancy E.; Winship, Christopher; Leventhal, Tama
    Intra-district open enrollment policies are increasingly implemented as a means of expanding children’s educational opportunities and promoting greater racial integration in urban schools. However, racial segregation continues to endure in many choice-oriented urban school districts, to the extent that schools are often more segregated than their surrounding communities. I investigate the interplay between family, school, and neighborhood racial characteristics as they relate to pre-k and kindergarten school choice patterns in Boston, Massachusetts. Findings suggest school choice is a function of a variety of factors, with a school’s racial composition remaining salient even after accounting for academic achievement, discipline records, and distance from home. Furthermore, racial background moderates school choices such that White and Asian families displayed similar behavior, as they tended to choose schools with higher proportions of White and Asian students and lower proportions of Black students and students receiving free and reduced-price lunch subsidies. Neighborhood racial composition was not found to be a significant factor in families’ choices, but the average racial profile of the neighborhood schools did shape White and Asian families’ decisions to stay local or not. Finally, I found that families from neighborhoods with higher levels of ethnic heterogeneity and lower levels of socioeconomic advantage were more willing to travel longer distances for schools. The results underscore the importance of acknowledging the persistent salience of race in school choice processes, even when also accounting for various aspects of schools’ academic achievement, discipline, and location.
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    Modeling contextual effects using individual-level data and without aggregation: an illustration of multilevel factor analysis (MLFA) with collective efficacy
    (BioMed Central, 2015) Dunn, Erin; Masyn, Katherine E; Johnston, William R.; Subramanian, SV
    Population health scientists increasingly study how contextual-level attributes affect individual health. A major challenge in this domain relates to measurement, i.e., how best to measure and create variables that capture characteristics of individuals and their embedded contexts. This paper presents an illustration of multilevel factor analysis (MLFA), an analytic method that enables researchers to model contextual effects using individual-level data without using derived variables. MLFA uses the shared variance in sets of observed items among individuals within the same context to estimate a measurement model for latent constructs; it does this by decomposing the total sample variance-covariance matrix into within-group (e.g., individual-level) and between-group (e.g., contextual-level) matrices and simultaneously modeling distinct latent factor structures at each level. We illustrate the MLFA method using items capturing collective efficacy, which were self-reported by 2,599 adults in 65 census tracts from the Los Angeles Family and Neighborhood Survey (LAFANS). MLFA identified two latent factors at the individual level and one factor at the neighborhood level. Indicators of collective efficacy performed differently at each level. The ability of MLFA to identify different latent factor structures at each level underscores the utility of this analytic tool to model and identify attributes of contexts relevant to health. Electronic supplementary material The online version of this article (doi:10.1186/s12963-015-0045-1) contains supplementary material, which is available to authorized users.