This study examined factors that predict whether police departments form collaborative partnerships with citizens. It analyzed survey data from 801 municipal police departments. Logistic regression models found that higher levels of community policing orientation and use of e-government technology significantly increased the likelihood of partnerships. Crime rates and social disorder did not significantly impact partnerships. The models accurately predicted partnership formation 83% of the time overall. E-technology and community policing were the most important predictors individually and together in the models.

1. SIGNIFICANT
PREDICTORS OF
FORMING POLICE-
CITIZEN
COLLABORATIVE
PARTNERSHIPS: A
SECONDARY DATA
ANALYSIS
DR. ERIC KEITH

2. 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.

3. 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

4. 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.

5. 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

6. 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).

7. 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.

8. 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)

9. 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).

10. 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

11. 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.

12. 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.

13. 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.

14. 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, 𝑥2
(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 𝑥2
(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.

15. 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 𝑥2
(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

16. 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 𝑥2
(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.

17. 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, 𝑥2
(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.

18. 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

19. 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.

20. 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).

21. 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

22. 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.