Abstract
Objectives
To examine whether group capacity for problem solving and partnership building could be enhanced at police–community meetings by providing the results from community surveys and training for group facilitators.
Methods
A randomized control trial was conducted in 51 police beats in Chicago’s community policing program, CAPS. Unlike control beats, results from web-based community surveys were provided at beat meetings in the feedback and training beats, with facilitators in training beats also receiving training and exercises to guide problem solving about survey results. Analysis included OLS and logistic regression of data from questionnaires administered to police and resident participants, as well as observations at beat meetings, which measured resident capacity, attitudes about the police–community partnership, and problem-solving activities.
Results
Support for hypothesized effects was found with greater resident confidence in their ability to achieve outcomes and solve local problems, as well as officers viewing their relationships with residents at beat meetings more favorably. Effects, however, were inconsistent and limited to the feedback group. While additional training and support provided in training beats indicated fuller engagement in problem solving, possible negative effects on attitudes were observed.
Conclusions
Failure to find more effects is discussed in terms of implementation and resistance. Officer resistance to and a shift in organizational priorities away from community policing worked against achieving full program implementation. The beat meeting context provided a traditional framework for police–resident interactions that precluded more comprehensive use of community data and possibly heightened dissatisfaction with the level of problem solving that occurred.
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Using formulas established by the American Association for Public Opinion Research (1998), the outcomes for the telephone survey of the random sample of residents were deemed acceptable: 11 % response rate, 24 % cooperation rate, 34 % refusal rate, and 57 % contact rate. Data generated from this sample was then used to create feedback for the feedback and training beats. On average, this sample exhibited a response rate for completing online surveys of 34.5 %, compared to an average response rate of 10.6 % for CAPS resident participants. Sample sizes and response rates were not included in the feedback materials given to the beat meeting participants. For a variety reasons, including low response rate, these samples are not used for any analyses presented here.
Modeling procedures outlined by Bryk and Raudenbush (1992) were employed to estimate within and between beat equations at the same time. While the example shown below uses general partnership, the same individual and beat level models were used for general interaction. The individual-level model used was:
$$ \mathrm{General}\ \mathrm{Partnershi}{{\mathrm{p}}_{\mathrm{ij}}} = {\upbeta_{0\mathrm{j}}}+\mathop{\sum}\limits_{{\mathrm{q}=1}}^{11}{\upbeta_{\mathrm{q}}}{{\mathrm{X}}_{\mathrm{q}\mathrm{ij}}}+{{\mathrm{r}}_{\mathrm{ij}}} $$In this model, β0j is the intercept; Xqij is the value for covariate q as related to respondent i in beat j; and βq is the partial effect of the covariate on the dependent variable. The error term is rij and represents the individual contribution of each respondent, with the assumption that it is both independently and normally distributed and has constant variance σ2. The beat-level model was:
$$ {{\mathrm{b}}_{{0\mathrm{j}}}}={{\mathrm{\gamma}}_{00 }}+{{\mathrm{\gamma}}_{01 }}\left( {\mathrm{Feedback}} \right) + {{\mathrm{\gamma}}_{02 }}\left( {\mathrm{Training}} \right) + {{\mathrm{U}}_{{0\mathrm{j}}}} $$For this model, γ00 is the overall score for attitudes about the general partnership, while γ01 and γ02 are regression coefficients of the effects of feedback and training. It has been argued that the pooled-within-neighborhood relationship (βw) is the individual-level coefficient of interest (Bryk and Raudenbush 1992):
$$ \begin{array}{*{20}c} {{\mathrm{Y}}_{\mathrm{ij}}}={\upbeta_{0\mathrm{j}}}+{\upbeta_{1\mathrm{j}}}({{\mathrm{X}}_{\mathrm{ij}}}-\mathop{{{{{\bar{\mathrm{X}}}}_{.\mathrm{j}}})+{{\mathrm{r}}_{\mathrm{ij}}}}}\limits \hfill \\ {\upbeta_{0\mathrm{j}}}={\upgamma_{00}}+{{\mathrm{u}}_{0\mathrm{j}}} \hfill \\ {\upbeta_{1\mathrm{j}}}={\upgamma_{10}} \hfill \\ \mathrm{where}\begin{array}{*{20}c} {} & {{\upgamma_{10}}={\upbeta_{\mathrm{w}}}} \\ \end{array} \hfill \\\end{array} $$For this reason, tests of these dependent variables used level-1 predictors that were group-mean centered.
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Graziano, L.M., Rosenbaum, D.P. & Schuck, A.M. Building group capacity for problem solving and police–community partnerships through survey feedback and training: a randomized control trial within Chicago’s community policing program. J Exp Criminol 10, 79–103 (2014). https://doi.org/10.1007/s11292-012-9171-y
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DOI: https://doi.org/10.1007/s11292-012-9171-y