2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 303
services would fail to produce the types of engagement and participation
called for by the theory. Organizers of such place-based interventions
would undoubtedly anticipate impact on individuals (e.g., increased
knowledge or skills), but they would also be interested in showing effects
on the community as a whole (e.g., increased collective efficacy).
Given the multilevel structure of cluster randomized trials, various
important statistical considerations must be taken into account when
planning these studies. One of the most crucial is the question of the
statistical power to detect impact at the cluster and individual levels.
The power is dependent on several factors. The number of clusters and
the number of cases per cluster are key elements of statistical power.
Covariates can be added at either level to reduce variance due to preexisting
differences, which also contributes to power calculations. Finally,
a consideration that is particular to cluster randomized designs is the
role played by the degree of within- and between-cluster heterogeneity
(Raudenbush 1997). Although a detailed discussion of statistical and
design principles is beyond the scope of this chapter, the W. T. Grant
Foundation has undertaken to provide practical tools for researchers
interested in implementing cluster randomized trials (see http://www.
wtgrantfoundation.org/resources/research-tools).
The evaluation of the Jobs-Plus employment program for public housing
residents is an example of a randomized trial applied to evaluation
of a neighborhood-level intervention (Bloom and Riccio 2005).The goal
of Jobs-Plus was to demonstrate that a place-based and comprehensive
employment-focused intervention could raise employment rates among
public housing residents. The initiative rested on the premise that focusing
financial incentives, employment programs, and resident engagement
in a place would be an effective way to address the employment problems
of public housing residents. Public housing developments in five cities
were randomly assigned to be in an experimental or control group. Jobs-
Plus used a comparative interrupted time series design to create a strong
counterfactual (Bloom 2005). The researchers were able to construct a
multiyear baseline trend and a postintervention trend on employment
rates by using data on all adults living in both the experimental and
control sites. An additional feature of the Jobs-Plus evaluation was that
residents who were exposed to the intervention were tracked even if they
left the public housing development. This design allowed the estimation
of the causal impact of the place-based Jobs-Plus model on individuals
regardless of whether they stayed in public housing the entire time or