Obedience and Trust in the Brazilian Police from a Youth’s Perspective: The Preliminary Results

Nikolaos Stamatakis

doi:10.16993/iberoamericana.6

Obedience and Trust in the Brazilian Police from a Youth’s Perspective: The Preliminary Results

Iberoamericana – Nordic Journal of Latin American and Caribbean Studies

Volume 45 (2016): Issue 1

By: Nikolaos Stamatakis

Open Access

|Oct 2016

Figures & Tables

Table 1

Logistic regressions explaining the influence of selected variables on perceptions of law, willingness to obey and actual obedience*.

Obedience to orders^{a b}		B	Sig.	Exp(B)	Lower Bound	Upper Bound
No	Intercept	–2.754	.000
	Equality_police (ref.cat. is ‘Always’)
	Never	–.057	.879	.945	.455	1.962
	Almost never	.213	.531	1.237	.635	2.410
	Sometimes	.166	.620	1.180	.613	2.274
	Almost always	.057	.891	1.058	.468	2.392
	Evaluation_police (ref. cat. is ‘Efficient’)
	Inefficient	.823	.153	2.276	.737	7.034
	Almost inefficient	.568	.291	1.764	.615	5.059
	Neither efficient nor inefficient	1.050	.044	2.856	1.028	7.939
	Almost efficient	.674	.195	1.963	.708	5.443
	Trust_police (ref. cat. is ‘Yes’)
	No	1.467	.010	4.338	1.422	13.229
	More or less	.979	.075	2.662	.906	7.820
	Age (ref.cat. is ‘16’)
	13	–.911	.004	.402	.217	.745
	14	–.685	.007	.504	.307	.828
	15	–.523	.035	.593	.364	.965
	Gender (ref.cat. is ‘Female’)
	Male	.298	.129	1.347	.917	1.979

Frequency of obedience to laws^{c d}		B	Sig.	Exp(B)	Lower Bound	Upper Bound
Never	Intercept	–3.304	.002
	Equality_police (ref.cat. is ‘Always’)
	Never	.811	.213	2.249	.628	8.059
	Almost never	.307	.632	1.359	.388	4.761
	Sometimes	.260	.690	1.297	.361	4.656
	Almost always	.081	.922	1.085	.211	5.565
	Evaluation_police (ref. cat. is ‘Efficient’)
	Inefficient	1.288	.112	3.625	.741	17.741
	Almost inefficient	.086	.915	1.090	.223	5.317
	Neither efficient nor inefficient	.022	.977	1.023	.216	4.838
	Almost efficient	–.201	.799	.818	.174	3.837
	Trust_police (ref. cat. is ‘Yes’)
	No	.659	.419	1.933	.390	9.574
	More or less	.211	.791	1.234	.261	5.838
	Age (ref.cat. is ‘16’)
	13	.288	.569	1.334	.495	3.596
	14	.113	.811	1.120	.444	2.822
	15	.281	.535	1.324	.545	3.213
	Gender (ref.cat. is ‘Female’)
	Male	.106	.760	1.111	.565	2.186

Sometimes	Intercept	–1.331	.010
	Equality_police (ref.cat. is ‘Always’)
	Never	.393	.236	1.481	.774	2.836
	Almost never	–.011	.970	.989	.545	1.794
	Sometimes	–.073	.807	.930	.519	1.665
	Almost always	.126	.726	1.135	.561	2.296
	Evaluation_police (ref. cat. is ‘Efficient’)
	Inefficient	.645	.179	1.906	.744	4.878
	Almost inefficient	.773	.064	2.167	.956	4.912
	Neither efficient nor inefficient	.619	.129	1.856	.836	4.122
	Almost efficient	.343	.391	1.409	.643	3.084
	Trust_police (ref. cat. is ‘Yes’)
	No	.191	.637	1.211	.547	2.679
	More or less	.204	.587	1.226	.588	2.557
	Age (ref.cat. is ‘16’)
	13	–.303	.271	.739	.431	1.266
	14	–.273	.251	.761	.477	1.213
	15	.103	.658	1.109	.702	1.753
	Gender (ref.cat. is ‘Female’)
	Male	.189	.294	1.208	.849	1.717

[i] ^aThe reference category is: Yes

^bNagelkerke R² = .171

^cThe reference category is: Always

^dNagelkerke R² = .032

*Results appear in odd-ratios.

Frequency of equal treatment by the police.

Table 2

Logistic regressions explaining the influence of selected variables on the evaluation of police*.

Evaluation of police function^{a b}		B	Sig.	Exp(B)	Lower Bound	Upper Bound
Inefficient	Intercept	1.295	.131
	Equality_police (ref.cat. is ‘Always’)
	Never	3.108	.000	22.385	4.772	105.009
	Almost never	1.589	.011	4.900	1.432	16.758
	Sometimes	.549	.466	1.732	.395	7.589
	Almost always	–1.028	.264	.358	.059	2.176
	Experience_police (ref.cat. is ‘Yes’)
	No	–1.178	.053	.308	.093	1.017
	Age (ref.cat. is ‘16’)
	13	–1.713	.005	.180	.055	.594
	14	–1.242	.048	.289	.084	.989
	15	–1.199	.031	.302	.102	.895
	Gender (ref.cat. is ‘Female’)
	Male	–.210	.605	.810	.365	1.798

Almost inefficient	Intercept	.840	.292
	Equality_police (ref.cat. is ‘Always’)
	Never	3.037	.000	20.834	4.929	88.070
	Almost never	1.994	.000	7.346	2.522	21.394
	Sometimes	1.859	.002	6.418	2.007	20.519
	Almost always	–.218	.735	.804	.228	2.838
	Experience_police (ref.cat. is ‘Yes’)
	No	–.784	.177	.457	.146	1.424
	Age (ref.cat. is ‘16’)
	13	–.1.222	.025	.295	.101	.860
	14	–.198	.715	.820	.283	2.374
	15	–.844	.099	.430	.158	1.173
	Gender (ref.cat. is ‘Female’)
	Male	.141	.695	1.152	.569	2.330
Neither inefficient nor efficient	Intercept	1.783	.017
	Equality_police (ref.cat. is ‘Always’)
	Never	1.563	.028	4.773	1.187	19.194
	Almost never	.907	.066	2.477	.940	6.522
	Sometimes	1.761	.001	5.821	2.076	16.324
	Almost always	–.341	.519	.711	.252	2.004
	Experience_police (ref.cat. is ‘Yes’)
	No	–.876	.127	.416	.135	1.281
	Age (ref.cat. is ‘16’)
	13	–1.263	.019	.283	.098	.811
	14	.086	.871	1.090	.388	3.058
	15	–.725	.146	.484	.182	1.287
	Gender (ref.cat. is ‘Female’)
	Male	–.041	.907	.960	.487	1.894

Almost efficient	Intercept	1.951	.008
	Equality_police (ref.cat. is ‘Always’)
	Never	1.072	.132	2.921	.723	11.793
	Almost never	.467	.343	1.596	.607	4.196
	Sometimes	1.762	.001	5.826	2.120	16.015
	Almost always	.392	.418	1.480	.574	3.816
	Experience_police (ref.cat. is ‘Yes’)
	No	–1.061	.061	.346	.114	1.049
	Age (ref.cat. is ‘16’)
	13	–.635	.219	.530	.193	1.458
	14	–.015	.977	.985	.353	2.751
	15	–.714	.149	.490	.186	1.291
	Gender (ref.cat. is ‘Female’)
	Male	.046	.893	1.047	.537	2.042

[i] ^aThe reference category is: Efficient

^bNagelkerke R² = .276

*Results appear in odd-ratios.

Explaining the level of confidence in the police.

Table 3

Logistic regressions explaining the influence of selected variables on confidence in the police*.

Confidence in police^{a b}		B	Sig.	Exp(B)	Lower Bound	Upper Bound
No	Intercept	–.745	.331
	Referral_police	–.604	.024	.547	.324	.924
	Equality_police (ref.cat. is ‘Always’)
	Never	1.786	.006	5.968	1.683	21.158
	Almost never	1.073	.050	2.923	1.002	8.527
	Sometimes	.532	.306	1.703	.615	4.718
	Almost always	.502	.420	1.652	.487	5.606
	Evaluation_police (ref. cat. is ‘Efficient’)
	Inefficient	2.795	.000	16.362	3.608	74.200
	Almost inefficient	2.705	.000	14.957	3.752	59.615
	Neither efficient nor inefficient	2.380	.000	10.808	2.987	39.105
	Almost efficient	1.457	.014	4.295	1.341	13.760
	Age (ref.cat. is ‘16’)
	13	–.450	.380	.638	.234	1.742
	14	.337	.472	1.401	.559	3.511
	15	.588	.238	1.801	.677	4.788
	Gender (ref.cat. is ‘Female’)
	Male	.167	.634	1.182	.594	2.354

More or less	Intercept	–.271	.681
	Referral_police	–.062	.809	.940	.569	1.553
	Equality_police (ref.cat. is ‘Always’)
	Never	.781	.201	2.184	.660	7.222
	Almost never	.853	.082	2.346	.896	6.139
	Sometimes	.683	.127	1.980	.823	4.761
	Almost always	.630	.231	1.878	.669	5.266
	Evaluation_police (ref. cat. is ‘Efficient’)
	Inefficient	.896	.205	2.450	.613	9.787
	Almost inefficient	2.017	.001	7.517	2.307	24.489
	Neither efficient nor inefficient	2.096	.000	8.131	2.844	23.247
	Almost efficient	1.401	.002	4.058	1.691	9.736
	Age (ref.cat. is ‘16’)
	13	–.025	.956	.975	.402	2.369
	14	.183	.675	1.201	.511	2.821
	15	.662	.157	1.938	.775	4.848
	Gender (ref.cat. is ‘Female’)
	Male	.455	.160	1.575	.836	2.969