## Using the crime survey of England and Wales, examine how experience of crime affects citizen's opinions of the criminal justice system. What demographic factors influence the relationship between experience of crime and rating of the criminal justice system?

### Introduction:

In order to answer the question posed, the following analysis is split in to three sections. Firstly, Section 1 presents an initial inspection of the variables in the dataset. A statistical modelling procedure is then proposed in Section 2 in order to address which variables affect citizen’s opinions of the criminal justice system. Appropriate conclusions are then drawn in Section 3.### Section 1: Description of the data

#### 1.1: Variables in the dataset

The crime survey of England and Wales provided data for 35371 individuals. There is a clear problem with missing data in the dataset, which will be investigated in due course and discussed in detail in Section 2. The variables in the dataset can be grouped in to three types for this analysis:**(1) Demographic factors:**Sex: categorical variable Age: continuous variable Marital status: categorical variable Respondent Social Class: categorical variable Type of area: categorical variable

**(2) Variables relating to citizen’s opinions of the criminal justice system, such as:**How confident are you that the Criminal Justice System as a whole is effective? (4 level ordinal categorical variable ranging from “very confident” to “not at all confident”)

**(3) Variable relating to citizen’s experience of crime:**Experience of any crime in the previous 12 months? Categorical variable

#### 1.2: Inspection of the data

As a starting point, some initial inspections of the data were conducted by assessing variables on an individual basis. Of the 35371 individuals, there were 16176 males and 19195 females, as shown in Table 1. Hence there were no missing values for the sex variable.**Table 1: Gender frequencies in the crime survey of England and Wales**

Valid | Frequency | Percent | Valid Percent | Cumulative Percent |

Male | 16176 | 45.7 | 45.7 | 45.7 |

Female | 19195 | 54.3 | 54.3 | 100.0 |

Total | 35371 | 100.0 | 100.0 |

**Table 2: Marital status frequencies in the crime survey of England and Wales**

Valid | Frequency | Percent | Valid Percent | Cumulative Percent |

Single | 10513 | 29.7 | 29.8 | 29.8 |

Married and living with husband/wife | 15657 | 44.3 | 44.3 | 74.1 |

In a same-sex civil partnership and living with partner | 90 | .3 | .3 | 74.4 |

Separated | 1273 | 3.6 | 3.6 | 78.0 |

Divorced | 3936 | 11.1 | 11.1 | 89.1 |

Widowed | 3818 | 10.8 | 10.8 | 99.9 |

Separated but legally in same-sex civil partnership | 18 | .1 | .1 | 100.0 |

Surviving civil partner | 5 | .0 | .0 | 100.0 |

Total | 35310 | 99.8 | 100.0 | |

Missing | System | 61 | 0.2 | |

Total | 35371 | 100.0 |

**Table 3: Marital status frequencies (with combined categories) in the crime survey of England and Wales**

Valid | Frequency | Percent | Valid Percent | Cumulative Percent |

Single | 10513 | 29.7 | 29.8 | 29.8 |

Married | 15747 | 44.5 | 44.6 | 74.4 |

Separated | 1291 | 3.6 | 3.7 | 78.0 |

Divorced | 3936 | 11.1 | 11.1 | 89.2 |

Widowed | 3823 | 10.8 | 10.8 | 100.0 |

Total | 35310 | 99.8 | 100.0 | |

Missing | System | 61 | .2 | |

Total | 35371 | 100.0 |

**Table 4: Frequencies for citizen’s opinions of the criminal justice system**

How confident are you that the police are effective at catching criminals? | How confident are you that the Crown Prosecution Service is effective at prosecuting people accused of committing a crime? | How confident are you that prisons are effective at rehabilitating offenders who have been convicted of a crime? | How confident are you that the probation service is effective at preventing criminals from re-offending? | How confident are you that the Criminal Justice System as a whole is effective? | |

Valid | 17727 | 16892 | 16145 | 15193 | 17452 |

Missing | 17644 | 18479 | 19226 | 20178 | 17919 |

**Table 5: How confident are you that the Criminal Justice System as a whole is effective (CJSopinion)?**

Frequency | Percent | Valid Percent | Cumulative Percent | |

Very confident | 573 | 1.6 | 3.3 | 3.3 |

Fairly confident | 7556 | 21.4 | 43.3 | 46.6 |

Not very confident | 7164 | 20.3 | 41.0 | 87.6 |

Not at all confident | 2159 | 6.1 | 12.4 | 100.0 |

Total | 17452 | 49.3 | 100.0 | |

Refusal | 2 | .0 | ||

Don't know | 779 | 2.2 | ||

System | 17138 | 48.5 | ||

Total | 17919 | 50.7 | ||

Total | 35371 | 100.0 |

### Section 2: Modelling the data

#### 2.1: Potential approaches

Based on Section 1.2, CJSopinion is chosen as the dependent variable with sex, age, MaritalStatusNew, type of area and experience of crime as the independent variables. The continuous variable age is mean centered to aid interpretability. There are a number of modelling based methods that one may consider in order to determine which of the independent variables significantly affect citizen’s opinions of the criminal justice system. For example: (1) Linear regression with CJSopinion as the dependent variable and sex, age, MaritalStatusNew, type of area and experience of crime as the independent variables. (2) Multinomial logistic regression with CJSopinion as the dependent variable and the same independent variables as (1). (3) Ordinal logistic regression with CJSopinion as the dependent variable and the same independent variables as (1). Approach (1) relies upon the assumption that the dependent variable is truly continuous and the intervals between consecutive values are equal, both of which are questionable for this case. Approach (2) is an acceptable approach but it does not exploit the fact that the dependent variable in this case is ordinal. Approach (3) is the preferred approach since it exploits the fact that the dependent variable is truly ordinal. In contrast to traditional logistic regression approaches, the ordinal approach in SPSS is based upon the logit of the cumulative probabilities. SPSS uses the proportional odds form of this model. The reader is referred to Agresti (2013, chapter 8.2) for more details.#### 2.2 Missing data and checking the adequacy of the model

As detailed in Section 1.2, the dataset contains missing values on both the dependent and the marital status independent variable. The problem is of more concern for the dependent variable since 50.66% of the values were missing. The ordinal regression procedure in SPSS only allows for the listwise deletion method of dealing with missing data. The listwise deletion method deletes all the observations for any individual who has any missing values on either the independent or dependent variables. Despite this, listwise deletion still leaves 17427 individuals who have no missing values on any of the dependent or independent variables. However, listwise deletion is a strong assumption that relies on the missingness being random. The assumption was deemed to be acceptable for this dataset, the details of which are given in Section. More details on missing data are given in Agresti (2013, p. 471) and Little and Rubin (2002). Prior to running the ordinal regression model in SPSS it is important to make sure that there are no low cell counts for combinations of the dependent variable with each of the categorical independent variables in the dataset (for the individuals with no missing values). Crosstabs of the dependent variable against each of sex, MaritalStatusNew, type of area and experience of crime were assessed. All the counts were sufficiently large, thus the model was deemed acceptable to be run in SPSS. The proportional odds model also assumes a proportional odds assumption. This means that the model assumes the same regression effects for each cumulative logit (Agresti, 2013). This can be assessed in SPPS, as detailed in Section 2.3.#### 2.3 Ordinal logistic regression in SPSS

In order to address the first part of the research question: “Examine how experience of crime affects citizen's opinions of the criminal justice system”, an ordinal logistic regression was run in SPSS with the dependent variable CJSopinion and independent variable experience of crime. For the dependent variable, “not at all confident” is treated as the baseline and for experience of crime, “not a victim of crime” is treated as the baseline. Ordinal logistic regression can be performed in SPSS by selecting the Analyze tab then Regression and then ordinal, as shown below. CJSopinion is then entered as the dependent variable and experience of crime as the single independent variable in the factor(s) box. In order to test the proportional odds assumption, the test of parallel lines tick box should be checked on the output tab, as shown below: The key part of the SPSS output is the parameter estimates. These are shown in the table below.**Table 6: Parameter estimates for the proportional odds model (no demographic variables)**

Estimate | Std. Error | Wald | df | Sig. | |

[CJSopinion = very confident=1] | -3.341 | .043 | 6092.466 | 1 | .000 |

[CJSopinion = fairly confident=2] | -.090 | .016 | 30.468 | 1 | .000 |

[CJSopinion = not very confident=3] | 2.010 | .024 | 6975.842 | 1 | .000 |

[ExperienceOfCrime=victim of crime=1] | .301 | .039 | 59.541 | 1 | .000 |

[ExperienceOfCrime = not a victim of crime=0] | 0^{a} | . | . | 0 | . |

**Table 7: Test of Parallel Lines**

^{a}Model | -2 Log Likelihood | Chi-Square | df | Sig. |

Null Hypothesis | 49.120 | |||

General | 49.032 | .087 | 2 | .957 |

**Table 8: Parameter estimates for the proportional odds model (with demographic variables)**

Estimate | Std. Error | Wald | df | Sig. | |

[cjsovb1 = 1] | -3.323 | .060 | 3099.016 | 1 | .000 |

[cjsovb1 = 2] | -.063 | .045 | 1.967 | 1 | .161 |

[cjsovb1 = 3] | 2.051 | .048 | 1808.076 | 1 | .000 |

AgeMeanCentered | .008 | .001 | 66.790 | 1 | .000 |

[sex=Male] | -.009 | .029 | .102 | 1 | .749 |

[sex=Female] | 0^{a} | . | . | 0 | . |

[Type of area=urban | -.028 | .035 | .667 | 1 | .414 |

[Type of area=rural] | 0^{a} | . | . | 0 | . |

[MaritalStatusNew=Widowed] | -.296 | .065 | 20.602 | 1 | .000 |

[MaritalStatusNew=Divorced] | .230 | .054 | 17.951 | 1 | .000 |

[MaritalStatusNew=Separated] | .080 | .081 | .975 | 1 | .323 |

[MaritalStatusNew=Married] | .110 | .038 | 8.198 | 1 | .004 |

[MaritalStatusNew=Single] | 0^{a} | . | . | 0 | . |

[ExperienceOfCrime=victim of crime=1] | .357 | .040 | 81.390 | 1 | .000 |

[ExperienceOfCrime = not a victim of crime=0] | 0^{a} | . | . | 0 | . |

#### 2.4 Assessing the listwise deletion of missing values

SPSS only allows for listwise deletion of missing values when conducting ordinal regression. However, traditional linear regression techniques in SPSS allow for alternative methods for dealing with missing values. It is acknowledged that the use of traditional regression methods for an ordinal response is more questionable and open to debate. However, the same conclusions with regards to which variables are significant are obtained by treating the response as continuous as opposed to ordinal.**Table 9: Parameter estimates for the linear regression model (with demographic variables)**

Parameter | B | Std. Error | t | Sig. |

Intercept | 2.595 | .017 | 149.591 | .000 |

[sex=Male] | -.003 | .011 | -.258 | .796 |

[sex=Female] | 0^{a} | . | . | . |

[Type of area=urban | -.013 | .014 | -.933 | .351 |

[Type of area=rural] | 0^{a} | . | . | . |

[MaritalStatusNew=Widowed] | -.114 | .025 | -4.499 | .000 |

[MaritalStatusNew=Divorced] | .092 | .021 | 4.304 | .000 |

[MaritalStatusNew=Separated] | .029 | .032 | .920 | .357 |

[MaritalStatusNew=Married] | .044 | .015 | 2.958 | .003 |

[MaritalStatusNew=Single] | 0^{a} | . | . | . |

[ExperienceOfCrime=victim of crime=1] | .139 | .015 | 8.982 | .000 |

AgeMeanCentered | .003 | .000 | 8.191 | .000 |

### 3 Conclusions

To conclude, the results of Section 2.3 indicate that experience of crime significantly affects citizen’s overall opinion of the criminal justice system. This was true before and after accounting for other relevant demographic variables. After accounting for other variables, the odds of being very confident in the criminal justice system were 1.43 times greater for those who have been a victim of crime than those who had not. Age and marital status were also found to significantly affect citizen’s overall opinions of the criminal justice system. Gender and whether the area was urban or rural were not found to affect citizen’s overall opinion of the criminal justice system.### References:

Agresti, A. (2013).*Categorical Data Analysis, 3rd edition*. New Jersey : John Wiley and Sons, Inc, Little, R. J. and Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2

^{nd}ed. Hoboken, NJ: Wiley.