Academic journal article Social Work Research

Analyzing the Relationship between Poverty and Child Maltreatment: Investigating the Relative Performance of Four Levels of Geographic Aggregation

Academic journal article Social Work Research

Analyzing the Relationship between Poverty and Child Maltreatment: Investigating the Relative Performance of Four Levels of Geographic Aggregation

Article excerpt

The purpose of this article is to compare four different levels of aggregation to assess their utility as areal units in child maltreatment research. The units examined are county, zip code, tract, and block group levels. Each of the four levels is analyzed to determine which show the strongest effects in modeling the correlation between poverty and child maltreatment report rates. Tract-level aggregation appears to be the most generally robust level, with other levels of aggregation being more vulnerable to different kinds of threats. Some zip codes contain very few people, raising reliability issues, but if weighting or minimum population cutoffs are used, this problem is minimized, and zip codes become an attractive choice. County-level data are less homogeneous than other levels, introducing validity concerns. The smaller populations commonly present in block groups also invoke reliability problems, reducing their utility, especially when rare events are examined.

KEY WORDS: child maltreatment; geography; neighborhood; poverty


Neighborhood effects on child maltreatment are an important area of study. Many studies of child maltreatment use geographically aggregated data to represent neighborhood-level constructs in statistical models. Levels of aggregation can range from the state or county level to zip codes, neighborhoods, tracts, block groups, or even smaller geographically defined areas (Sampson, Morenoff, & Gannon-Rowley, 2002). Contextual measures of poverty are also often used as control variables in studies using individual-level data. For the current research, aggregate data is defined as means, percentages, or similar values derived from and representing a geographic area.

The modifiable areal unit problem (MAUP) is well known in many fields (King, 1997) and has recently received attention in the child maltreatment literature (Lery, 2008, 2009).The MAUP is an issue encountered when arbitrary geographic areas are established. Should the area be small, events within the area may be rare, and reliability can suffer because of poor signal-to-noise ratios. Should the area be large and not homogeneous with regard to key factors, then any aggregate measure of those factors will not represent the area well (Nakaya, 2000). An ideal areal unit would be one in which the area is large enough to create stable (reliable) counts of the variables of interest and also sufficiently homogenous on all key variables to minimize error due to conflation of dissimilar subareas. For child welfare research, it would be best to use geographic boundaries that are large enough to provide stable counts of maltreatment but small enough to encompass families that are generally similar to each other on key factors, especially poverty, a key construct relative to child maltreatment (Drake & Zuravin, 1998).

An obvious practical question confronting child welfare researchers and agencies involves which levels of aggregation to use for different purposes. This article presents data to assist academic and agency researchers in answering that question. For each of four geographic levels, data are presented showing observed correlations between poverty and child maltreatment reporting rates. Correlations obtained at each level of analysis are presented side by side so that the relative strengths and significance of the correlations can be observed. To the degree that each level of analysis fosters reliability (stability) and validity (homogeneity within each unit of analysis), error will be reduced, and the observed correlations will be correspondingly higher (Nakaya, 2000). This is, perhaps, the most straightforward way of demonstrating the relative utility of different levels of aggregation, at least relative to poverty and its relationship to maltreatment. To maximize utility and generalizability, a simple and important construct (poverty), a very basic statistical operation (correlation), and the four most universally available levels of aggregation (county, zip code, tract, and block group) are used. …

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