Intelligence led policing: how the use of crime intelligence analysis translates in to the decision-making
International Journal of Security and Terrorism •
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- 3.2. Findings
30 International Journal of Security and Terrorism • Volume: 4 (1) (OLR) model. This model is “estimated by a method called Maximum Likelihood Estimation (MLE)” (Aldrich & Nelson, 1984: 49) that deals with “picking parameter estimates that imply the highest probability or likelihood of having obtained the observed sample Y” (p. 51; see also Agresti, 2002). An ordered response model can be developed as a linear probability model with the use of a continuous latent 5 variable (Long & Freese, 2006). One assumption of the ordered response model is that an unmeasured (latent) variable, y * , that ranges from – ∞ to + ∞ exists, and is “mapped to an observed variable y” (Long, 1997: 116), where the mapping from the latent variable is done with the response categories of not utilized, utilized some, and highly utilized in the current study. This division of y* into three “values of the observed y” (Long, 1997: 117) is done by thresholds, or cut points, that are denoted as τ . Here a linear equation model is created by using log odds, where standardized coefficients are used the same way that the coefficients are used in a linear regression model (LRM). Another way of interpreting the results of ordinal-level outcomes is to use a non- linear model—specifically, odds ratios—rather than standardized coefficients. In that regard, one assumption of the ordinal regression model is the “parallel regression assumption [italics original] and, for the ordinal logit [logistic regression] model, the proportional odds assumption” [italics original] (Long & Freese, 2006: 197) where the intercepts may change, but the coefficients for the independent variables are unchanged for each equation (see also Powers & Xie, 1999). When “the assumption of parallel regressions is rejected, alternative models should be considered that do not impose the constraint of parallel regressions” (Long, 1997: 145). All of the three models in this study (i.e., command, patrol, and detective models) were tested with the likelihood-ratio test, particularly with a model 6 command in Stata (Long & Freese, 2006; Wolfe & Gould, 1998). It is “an omnibus test that the coefficients for all variables are simultaneously equal” (Long & Freese, 2006: 199), and it evaluates “how the log likelihood of the ORM would change if the constraint…was removed” (Long, 1997: 143). The test results also showed that all three models in the current study are appropriate for the data.
3.2.1.Descriptive Statistics The descriptive statistics of all variables (i.e., dependent, explanatory independent, and control) used in the models are presented in the Table 2. 5 In this section, the “latent” concept does not represent the same thing as it represented in the factor analysis section. 6 This is not an official Stata command. 31 Intelligence - Led Policing: How the Use Of Crime Intelligence Analysis Translates in to the Decision-Making
3.2.2. Multivariate Analysis As indicated in the previous sections, the results from nonlinear models are not easy to interpret. The ordered logit is mostly “interpreted in terms of odds ratios for cumulative probabilities” (Long, 1997: 138). In the current study, the results of ordered logit models are presented in terms of the percent change in odds. In other words, the percent change of the metric values in the odds of “higher versus lower outcomes” (Long & Freese, 2006: 218) in the dependent variable will be provided in this section. Variable N Measure Min. Max.
Mean SD Dependent Command-level use CAa efforts 519
Ordinal 0 2 1.28 0.621
0 = not utilized 1 = utilized some 2 = highly utilized Detectives use CAa efforts 521 Ordinal
0 2 1.25 0.627 0 = not utilized 1 = utilized some 2 = highly utilized Patrol officers use CAa efforts 518
Ordinal 0 2 1.05 0.607
0 = not utilized 1 = utilized some 2 = highly utilized Explanatory Factor 1: Statistical analysis 423
Continuous -2.02
3.94 0 1 Factor 2: Crime analysis 423
Continuous -2.29
3.3 0 1 Factor 3: Intelligence analysis 423
Continuous -2.21
3.64 0 1 Factor 4: Survey analysis 423
Continuous -1.94
4.21 0 1 Factor 5: Patrol strategy analysis 423
Continuous -2.56
3.12 0 1 Factor 6: Displacement analysis 423
Continuous -3.06
3.74 0 1 Control Crime analysis unit 517 Dummy
0 1 0.65 0.479 (yes = 1, no = 0) Unions in the agency 535
Dummy 0 1 0.61 0.489
(yes = 1, no = 0) Agency size 493 Continuous 0.09 6.32
1.637 0.983
(# of sworn x 1,000 ⁄ population) Total operating budget 535
Continuous 4,554
1,492,567 153,366
112,401 (dollars in 12-month period) Organizational hierarchy b 535
Continuous 0.19
4.65 1.33
0.627 [(min - max salary) ⁄ min salary] Crime rates 500 Continuous 0 421.47
61.27 39.17
(# of crimes x 1,000 ⁄ population) a CA = crime analysis. b Unit in dollars. Yüklə 273,3 Kb. Dostları ilə paylaş:
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