SIGNIFICANT PREDICTORS OF

FORMING POLICE-CITIZEN

COLLABORATIVE PARTNERSHIPS: A SECONDARY DATA

ANALYSISDR. ERIC KEITH

INTRODUCTION

• Community policing has a foundation in partnering with the community.• Collaboration with the community has been shown to

be effective in mitigating crime and social disorder.• Community policing activities, or community policing

orientation, have long been the featured resources utilized to create collaboration through partnerships.

INTRODUCTION• Past research in community policing has pointed out the importance of, and

emphasis on, community policing activities, crime and social disorder as influential factors in forming collaborative based partnerships.

• This same research indicates there are weaknesses in the ability to form and sustain true collaborative partnerships between police and citizens; collaboration has been identified as the open and free exchange of information and ideas by, and between, both groups resulting in positive progress.

• Parallel research in public administration sectors involving communication and operations introduced a relatively newer factor into community relations with the goal of improving engagement with citizens known as e-government technology, or e-technology.

• The majority of municipal police departments have incorporated some aspect of e-technology in the last few years to enhance communication, information flow, and transparency with hopes to build more trust, legitimacy and collaborative partnerships with citizens as demonstrated by public administration.

STATEMENT OF THE PROBLEM• Research supports the use collaborative partnerships

in community policing when addressing crime and disorder.• Research supports the use of e-technology to

enhance communication, trust, relationships and collaboration between government and communities.• Conversely, research has revealed major difficulties in

forming productive collaboration partnerships between police and communities, as well as sustaining them long term.

STATEMENT OF THE PROBLEM• E-technology research supports the supposition and inferences of

potentially improving collaboration between police and community, particularly in highly disordered communities.• To date, until this study, there has been no quantitative statistical

data analysis to further examine the contention that e-technology can contribute to, enhance and sustain collaborative partnerships.

( Collaborative communication and information exchanges are the only variables examined through the lens of e-technology)• The existing problem centers on the likelihood or probability that

the use of e-technology in municipal police departments, along with levels of community policing orientation, crime, and social disorder, form a significantly predictive model for forming and sustaining true collaborative partnerships with the community.

METHODOLOGY• This study utilized 801 randomly sampled municipal police

departments that took part in the 2007 LEMAS survey.• The original 3,095 sampled agencies from the 2007 LEMAS were

reduced based on the type of questionnaire they completed.• Agencies that completed the short version did not answer the

relevant questions pertaining to the variables analyzed in the study and were dropped (2,145 agencies dropped).

• The remaining 950 were reduced to 801 based on the type of agency (49 state police agencies were not included) and based on not answering all relevant questions pertaining to the variables in the study (100 did not answer all questions completely).

METHODOLOGY• The 2007 LEMAS datasets and codebooks

were downloaded from the ICPSR website.• The 2000 Census information was

downloaded from the Missouri Census Data Center and Census.gov.• The 2006 UCR information was

downloaded from the ICPSR website and FBI.gov.

METHODOLOGY• The 2007 LEMAS provided secondary data regarding

the community policing orientation, e-technology usage, and collaborative partnerships variables.• The 2006 UCR provided secondary data regarding

the crime rate variable.• The 2000 Census provided secondary data

regarding the social disorder variable (percentage of black population demographics, percent unemployed, percent below poverty level, and percent of single female parent head of household)

METHODOLOGY – DATA SCREENING• The data screening in SPSS indicated no

missing data and accurate data was present within acceptable ranges of SD, ranges, and M.• The data sample was determined to have

adequate numbers of cases to variables ratio (80 minimum).• The data sample was determined to have

adequate number of cases to reach a statistical power of .80 (360 minimum).

METHODOLOGY – DATA ASSUMPTIONS• Linearity of the logit was tested via the Box Tidwell approach and no

variable was significant indicating no violation of the linear relationship between predictor variables and the logit of the outcome variable.

• Hosmer-Lemeshow score is not significant at p = .855, p < .05 with a chi square of x2(4, N = 801) = 4.026, p < .001 indicating linearity of the logit.

• Goodness of fit was tested and all frequencies between cases and variables were above 1 and none lower than 5 indicating no issue.

• Multicollinearity was tested via a correlation matrix and no correlation was significant at .700 or higher; VIF indicated no score above 10 and Tolerance revealed no score below .10.

• Outliers were revealed within the social disorder and crime rate variables with extreme z scores above 3.29, p <.001. 6 scores in the social disorder variable and 3 in the crime rate variable were changed to the next highest raw score within the sample distribution not considered an outlier.

METHODOLOGY – STATISTICAL ANALYSES• Descriptive analysis ran to determine M, SD, and

percentages of all variables.• Point-Biserial correlation run to determine general

relationships between variables prior to logistic regression.• Multiple logistic regression run to test all hypotheses as well

as to determine the significance of the predictive model.• Logistic regression run on e-technology and community

policing orientation individually within the model to address the overall significant predictive value of e-technology.

RESULTS – DESCRIPTIVE ANALYSES• Descriptive statistics revealed trends in community

policing (22% or 2 out of 9 measured activities above 50% of agencies) and e-technology (20% or 3 out of 15 measured activities above 50% of agencies) that are utilized for collaborative purposes.• The majority of agencies utilize e-technology for

crime analysis and reporting. • The majority have community policing orientation

focused more on organizational, internal accountability measures rather than collaborative actions with the public.

RESULTS – POINT-BISERIAL CORRELATIONS• Point-biserial correlations on social disorder and crime rates were

positively and significantly correlated, rpb = .586, p < .01. • Crime rates were positively correlated to community policing orientation, rpb = .100, p < .01, levels of technology used, rpb = .158, p < .01, and collaborative partnerships, rpb = .123, p < .01.

• Significant negative relationships exist between levels of technology usage and single female parent households rpb = -.085, p < .05, and levels of unemployment, rpb = -.089, p < .05;

• Collaborative partnerships were positively, significantly, correlated to e-technology usage,  rpb = .369, p < .01, and community policing orientation,  rpb = .485, p < .01.

RESULTS –MULTIPLE LOGISTIC REGRESSION• Multiple logistic regression analysis revealed that the full model with all of

the predictor variables including e-technology, community policing orientation, social disorder and crime rate was significantly different than the constant only model, (4, N =801) = 218.122, p < .001. This indicates the predictors as a set, or at least one predictor variable, significantly predicted the likelihood of having formed a collaborative partnerships with the citizens they serve.

• Based on the Hosmer-Lemeshow Test, the full model is further supported as a good fit for the data at the (8) = 4.011, p > .05 predicting the outcome variable 83% of the time.

• The Nagelkerke score of R2 = .365, p < .05 indicates the predictor variables account for about 37 % of the outcome.

RESULTS – RESEARCH QUESTION 1• To what extent does e-government technology, utilized for collaboration with the

public, impact the likelihood, or probability, that a municipal police department forms collaborative partnerships with the community?

• H1: Police agencies utilizing higher levels of e-government technology are more likely, or more probable, to form collaborative partnerships with the community, as opposed to decreasing the likelihood of forming collaborative partnerships.

• Ho1: E-government technology does not significantly contribute to the likelihood or odds that a municipal police department will form collaborative partnerships.

• Technology usage was significant at (1, N = 801) = 16.636, p < .001, odds ratio = 1.164, 95% CI [1.082, 1.252]. The odds were 1.2 times as likely.

• The model without CP was x2 (3, N = 801) = 117.382, p < .001. However, the overall accuracy of the model decreased to 79.4% and according to the Nagelkerke score, R2 = 0.210, p < .05, only 21% of the variance was accounted for. The odds increased to 1.4 times as likely.

RESULTS –RESEARCH QUESTION 2• To what extent does the level of community policing orientation impact the

likelihood, or the probability, that collaborative partnerships are formed between local police and the community?

• H2: Police agencies that exhibit higher levels of community policing orientation are more likely, or more probable, to form collaborative partnerships with community citizens, as opposed to decreasing this likelihood.

• Ho2: Community policing orientation does not significantly contribute to the likelihood or odds that municipal police departments will form collaborative partnerships.

• The community policing ordination variable is significant at (1, N = 801) = 84.158, p < .001, odds ratio = 1.646, 95% CI [1.480, 1.831] within the full model.

• The model without technology remained significant at x2(3, N = 801) = 201.089, p < .001, and the overall accuracy of the model dropped slightly to 82.3% with a Nagelkerke score, R2 = 0.340, p < .05, indicating that 34% of the variance was explained by the model without technology. The odds increased to 1.8 times.

RESULTS –RESEARCH QUESTION 3• Research Question 3: To what extent does the level of social disorder impact

the likelihood, or probability, that municipal police departments with form collaborative partnerships with community citizen groups?

• H3: Police agencies that serve communities with higher levels of community social disorder are less likely, or less probable, to form collaborative partnerships with community citizens, as opposed to increasing this likelihood.

• Ho3: Social disorder does not significantly contribute to the likelihood, or odds, that municipal police departments will form collaborative partnerships.

• This predictor variable was not significant in the model, (1, N = 801) = .003, p < .001, odds ratio = .999, 95% CI [.960, 1.039], so the expected odds ratio of less than 1 indicating that an increase in social disorder predicts a decrease in collaborative partnerships is not supported. Thus the Ho3 is accepted that social disorder does not significantly predict the likelihood that municipal police departments will form collaborative partnerships.

RESULTS –RESEARCH QUESTION 4• To what extent do crime rates impact the likelihood, or

probability, that municipal police departments will form collaborative partnerships with community citizen groups?

• H4: Police agencies that serve communities with higher the overall crime rates are less likely, or less probable, to form collaborative partnerships with community citizen groups, as opposed to increasing this likelihood.

• Ho4: Crime rates do not significantly contribute to the likelihood, or odds, that municipal police departments will form collaborative partnerships.

• The predictor variable of crime rate was not a significant variable, x2 (1, N = 801) = .965, p < .001, odds ratio = 1.000, 95% CI [1.000, 1.000], in the overall predictive model, thus this hypothesis (H4) is not supported. Meaning that the expected odds ratio factor of less than one pointing towards the odds that higher crime rates predict a lower likelihood of forming collaborative partnerships, is not supported. The null hypothesis (Ho4) is accepted that crime rates has no significant predictive impact on whether municipal police departments form and maintain collaborative partnerships.

RESULTS – MULTIPLE LOGISTIC REGRESSION VARIABLE ANALYSIS• The logistic regression run for high crime areas,

the model with all of the variables was significantly better than just the model with the constant, at x2 (4, N = 333) = 79.427, p < .001. This is further supported with the non-significant Hosmer-Lemeshow score of x2(8) = 11.095, p < .05. This indicates the model for the police departments forming collaborative partnerships in high crime areas was a significantly predictive model, with the model predicting the desired outcome 86.8% of the time. The Nagelkerke score, R2 = .352, p < .05, illustrates the variables within the model account for about 35% of the variance.

RESULTS – MULTIPLE LOGISTIC REGRESSION VARIABLE ANALYSIS• The logistic regression run for low crime areas, again the

model with all variables was a significantly better predictive model than that of just the constant, x2 (4, N = 468) = 134.501, p < .001. The Hosmer-Lemeshow score again supports the fit of the model at x2(8) = 6.876, p < .05. This particular model of police departments forming collaborative police departments in low crime areas correctly predicted the desired outcome 79.7% of the time. Within Table 6, the Nagelkerke score, R2 = .367, p < .05, illustrates that about 37% of the variance is accounted for by the variables in the model (Tabachnick & Fidell, 2013).

CONCLUSIONS• The main predictor variables of community policing orientation, e-

technology usage, social disorder, and crime rates combined to be an efficient, effective predictive model for the likelihood of municipal police departments forming collaborative partnerships.

• Community policing orientation and e-technology were the two significant variables in both models.

• This study accounted for the gaps in research discerning the formation of collaborative partnerships as to the use of e-technology as an important element in predicting the likelihood of municipal police departments forming these partnerships.

• E-technology and community policing individually and significantly predict the likelihood of forming collaborative partnerships; however the accuracy of the model is best when combined.

• Crime rates and social disorder were not significant, however they may not hinder formation regardless of their levels in communities.

• The model tested in high crime areas is more predictive and accurate than in low crime areas. This may be a product of the need for these partnerships in these areas, or e-technology and community policing can be effective in the most disordered of communities.

RECOMMENDATIONS

• Examining this model and/or the variables with more contemporary data representing more forms of social media.• Examine this model and/or the variables from the

viewpoint of citizens as to the overall effectiveness.• Isolating the e-technology variable on its own and

examining potential impacts on crime and disorder alone.