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Advances in Analytic Methods for Neighborhood Data 285
which to think about analytic needs: the purpose of the analysis and the
main concepts of interest.
Purpose of Analysis
The primary purpose of a study determines what elements of research
design and statistical analysis need to be emphasized. Or put another
way, depending on the main purpose of an analysis, there are particular
challenges to obtaining precise, unbiased, and dependable findings. The
methodological advances discussed in this chapter produce findings that
are as valid as possible with respect to the purpose of the analysis. This is
not to say that other issues are irrelevant, but since perfection in research
is seldom possible to achieve, practical considerations often dictate giving
priority to some concerns over others. Consistent with social science
research in general, the purposes of neighborhood indicators studies
include description, classification, and explanation and prediction.
Description is a common purpose of neighborhood indicators analysis.
Sometimes description is an end in itself, but accurate description
can also be the first step in building a more advanced model for explanation
or prediction. In descriptive studies there is an emphasis on making
precise and unbiased estimates of the level or range of neighborhood
attributes or conditions. Neighborhood studies can be plagued in this
regard by problems of data sparseness, incomplete data, or ambiguity of
neighborhood definitions and boundaries. These challenges and some
promising approaches to address them are covered in this chapter.
A second purpose of neighborhood data analysis is classification of
communities or conceptual domains. Classification may involve grouping
places into categories or types based on similarities and differences
among cases along a variety of dimensions. In addition to requiring the
precision of measurement mentioned above for descriptive studies, classification
requires a method for uncovering patterns of similarities and
differences to be applied to the data. A valid classification is one in which
cases can be assigned to a category with the least ambiguity along reliable
dimensions of interest and in which the classification is meaningful or
predictive according to some external criterion. Some examples of techniques
related to classification are discussed in this chapter.
Many analyses of community indicators have the goal of explanation
or prediction, which includes discovering why communities differ