Sex and Race Differences in Law’s Application

Abstract

This chapter presents the results of a statistical analysis of court and police records for Indigenous and non-Indigenous men and women who had been charged with at least one breach of a domestic violence order. The research sites cover discrete Indigenous communities in northern Queensland, Australia. The analysis exposes sex and race differences in the application of the law and highlights the scope of the problem identified in the introduction. It also reveals that more than half of all those in the sample of respondents charged with breaching domestic violence orders had also been the subject of orders naming them as the aggrieved—that is, a victim of violence.

Appendices

Appendix 1: Research Sites, Data Management and Analysis

Selection of Research Sites

I selected Cairns and Mount Isa, regional Queensland cities, as research sites. These sites ensure inclusion of matters involving people who live in remote parts of the state, as well as in urban areas, providing access to populations not included in any prior analysis of matched court and police data. Further, each city’s population has a relatively high proportion of Aboriginal and Torres Strait Islander people, which was important in attaining the desired sample. The population in each site is large enough, however, to avoid the risk of identifying individual people in reporting the results.

The empirical research underpinning the book addresses three broad questions. The data, the data management, and methods of analyse for each is discussed below.

Data

Question 1: What Was the Intention of Queensland’s Civil Domestic Violence Law; That Is, What Kind of Violence Did It Seek to Address?

Question 1 is the subject of Chapter 3: Gendered Aspirations in Domestic Violence Law . The data analysed to answer this question were key historical documents that informed the drafting of the Domestic Violence (Family Protection) Bill 1989 (Qld), and the official parliamentary records on the introduction and debates on the Bill in Parliament (Hansard). The primary source of data for analysis of the policy context was the report of the Queensland Domestic Violence Task Force established by the Queensland Government in 1987. The Task Force report included a review of national and international literature, and the results of extensive consultation with government and non-government agencies and a state-wide Domestic Violence Phone-In survey of women with lived experience of domestic violence. Reports of similar domestic violence investigations conducted in other Australian states and explanatory notes, which accompany the introduction of a Bill, were secondary sources of data for the analysis.

I searched digital records of parliamentary proceedings (Hansard) on the Queensland Parliament website to collect data for the thematic analysis of the introductory speech and parliamentary debates on the Domestic Violence (Family Protection) Bill 1989. Key amendments to the legislation in the years 1992, 1999 and 2002 were relevant for the research since they occurred prior to the end of the period of time (2004–2012) relevant for inclusion in the sample of 185 cases for the quantitative analysis addressing Question 2. Therefore, I included these in my search of the digital records.

Question 2: What Is the Impact of Queensland’s Domestic Violence Laws on Indigenous People, in Particular Women?

Following approval from the Griffith University Human Research Ethics Committee on 24 August 2011, I sought administrative court and police data to answer Question 2.

Access to court data was subject to approval from the Department of Justice and Attorney-General and access to police data was subject to approval from the Queensland Police Service. Both granted approval in October 2011, following negotiations related to security and access to data, and formal agreements.

Court Data

Under my guidance, the Queensland Department of Justice and Attorney-General randomly selected records from its electronic records database, within the sample parameters: people who had been charged with breaching a domestic violence protection order related to spousal or interpersonal relationships, and for whom the charge had been dealt with in the Cairns or the Mount Isa Magistrates’ Court. The records included the complete domestic violence history for each person in the sample. The period of time for the sampling was from the most current data available at the time (between October 2011 and January 2012), to as far back in time as was required to meet the sample size target. I requested the sample include 25 cases in each of four groups (Indigenous and non-Indigenous men and women), for each of the two research sites (Cairns and Mount Isa).

There was a shortfall of seven in the group of non-Indigenous women for Mount Isa. Only 18 non-Indigenous women had been charged with a breach of DVO and dealt with in the Mount Isa Court since 1 January 2004, the date from which the Department began systematically collecting data on Indigenous identity. Therefore, a sample of 193 people was possible and provided to me by the Department of Justice and Attorney-General. The overall sample size was reduced by a further eight, however, because of data entry errors. Data had been entered twice for some cases and not at all for others. This reduced the sample size to 185 cases, with some differences in group size.

The Department of Justice and Attorney-General provided the court data in three files.

• 1. Domestic violence protection order applications— respondent . This file includes age, sex and cultural identity; the type of relationship involved; the lodging authority (police or private application); conditions on the order; and whether the respondent was present in court (ascertained from the provision of the Act under which the charge was laid). This file also includes personal details for the person or persons named as the aggrieved on any protection order related to the respondent, such as their name, date of birth, cultural identity and relationship to the respondent.

• 2. Details of all breaches for the sample. The breach file contains details of the summary offence charges available in the breach provisions of the Domestic and Family Violence Protection Act 1989 (Qld), and the outcomes at court, for each respondent. The court data do not include details for any charges, such as assault or rape, under the Criminal Code Act 1899 (Qld) (Criminal Code).

• 3. Domestic violence applications— aggrieved . The aggrieved file provides a list of those in the respondent file (the sample), who were also named as the aggrieved on other order/s; and lists, for example, dates, lodging authority and the respondent/s of those other order/s. This makes it possible to identify cross-applications, as well as respondents who had been victimised by others not included in the sample.

In the sample of 185 people charged with breaching a DVO (the respondent/perpetrator), 110 had also been the subject of a DVO naming them as the aggrieved/victim. Some of these cases relate to cross-DVOs (shown in Fig. 1.3 in Chapter 1), and others involve a third party (Fig. 4.2).

In some cross-DVO cases in the sample, I had police reports of breaches for both parties. In other cases (such as that of Doreen and Sam, discussed in Chapter 6), only one party to a cross-DVO is included in the sample, so police reports of breaches are available for that party only. However, in Doreen and Sam’s case, and some others, the police reports refer to cross-DVOs and some refer to an event resulting in breach charges against both parties. If one of those parties (like Sam) is not in the sample, however, there is no information about their history of DVO applications and breaches. That is, histories of DVOs and police reports of breaches are only available for both parties to a breach of a DVO, if the pair is included in the sample.

Court data were provided in electronic format on a password protected compact disc. Importantly, the data included full names, birth dates and cultural identity of the respondent and the aggrieved so that cross-referencing between the complete set of court data was possible. This enabled an examination of cases where a person convicted of breaching a DVO had previously, or subsequently, been named as the aggrieved (victim) on one or more DVOs, the relationship between the parties, and the occurrence of cross-applications and cross-orders. It also enabled matching court files with police files to obtain the relevant police reports on breaches of DVOs. A condition of access to this court data was that I must not use the information to recruit people for interviews.

Police Data

Using identifying information from the court files, a Queensland Police Service representative located the relevant police reports of all breaches of orders for each of the respondents in the sample. Copies of the reports describing each event that led to a respondent being charged for breach of a DVO under section 80(1) of the Domestic and Family Violence Protection Act 1989, on one or more occasions, were provided in an excel spreadsheet. Further, the excel spreadsheet included, where relevant, information on additional charges made under the provisions of the Criminal Code. Police reports were also provided on an encrypted compact disc. I coded information from police reports as categorical data and used these in the quantitative data analysis for Question 2.

Question 3: What Explains the Greater Frequency of Indigenous People as Victims and Perpetrators of Domestic Violence?

I used two sources of data to answer Question 3: interviews with a purposive sample of domestic violence service providers and police prosecutors in Cairns and Mount Isa, and the police reports of breach offences and criminal charges. I was able to carry out two field trips to Cairns and one to Mount Isa, so there was a narrow time frame to recruit and refer interviewees.

I conducted the interviews in December 2011 and January 2012. I audio-recorded interviews with 15 professionals: 12 service providers who worked in services responding to domestic and family violence; and three police prosecutors , with at least 2 years’ experience with DVO breaches. Five Indigenous people (including one man) were among the 12 service providers. The service providers were managers, counsellors and court support workers who assisted victims and/or perpetrators of domestic violence with matters related to domestic violence and the law. Of the three police prosecutors , two were female. The service providers and the police prosecutors had a minimum of 18 months’ experience in their respective roles, with most having more than 5 years’ experience and some more than 10 years’ experience.

Interviews ranged in length from approximately 45 minutes to 2 hours. I kept interview questions to a minimum, adopting instead a conversational style. Among the key questions I asked were these:

• 1. What are your observations about violence involving Indigenous men and women?

• 2. Have you seen changes over the years in the way the police and courts deal with domestic and family violence and if so, what are those changes and what do you think is behind them?

• 3. In your experience, how helpful is mainstream legal policy in addressing domestic and family violence cases involving Indigenous people?

• 4. Do you think there are differences in the way the law is applied in those cases and if so, how and why?

I used these questions as prompts to guide discussion while encouraging a narrative-based exploration of the experiences of Indigenous men and women charged with breaching DVOs, from each participant’s perspective.

Data Management

SPSS File Creation

First, I allocated a case number and pseudonym for each of the 185 individuals in the sample. I cross-checked names and dates of offences on the police file with the court files to ensure I had consistently assigned pseudonyms and case numbers in the files. Then, using IBM SPSS Statistics 20, I created a single electronic file with more than 100 variables from the three court files. I cross-referenced names and dates of birth to identify the number of distinct victims, and individuals who were named as the respondent and as the aggrieved on DVOs. I also cross-referenced dates of DVO applications on the respondent and the aggrieved file to identify if the individual had been first the respondent or the aggrieved; and names, dates of birth and dates of DVO applications to identify cross-applications, and whether cross-applications were made simultaneously or consecutively. In some cases, I saw that the respondent was also an aggrieved but I was unable to tell if this involved a cross-application because the other party was not included in the sample. As variables were created, I recorded them in a data code book for the electronic management of the combined data to facilitate later data analysis.

I also created two variables from the written police reports : level of violence used and whether or not there had been charges under the Criminal Code Act 1899. Using the case numbers I had allocated to each of the 185 people in the sample, I added coded data from the police files to the SPSS data file and added the variables and coding details to the code book. The police reports varied in the detail provided, and I could not use some variables I created from the reports for the statistical analysis (e.g. jealousy and presence of alcohol or other drugs), but they are included in the analysis of the police reports in Chapter 6.

Data Cleaning

I identified and eliminated the eight cases involving data entry errors at the point of collection (i.e. cases entered twice, and those with no data entered). As data for each case were entered, I checked for accuracy and I checked again when I had entered all the data for all cases. I then ran frequencies for all data and, through a variety of procedures, I identified and corrected errors.

Data Recoding

Some of the variables in the data set had a very large number of attributes. For example, the court data file identified 43 court locations in which a magistrate made any DVO related to the sample as respondents, and 28 court locations where a breached DVO related to the sample as respondents had been made. This level of detail may be useful for understanding the mobility of those in the sample, but it is less helpful in comparing four groups. I recoded data for these variables as remote or other court locations, creating a dichotomous variable for analysis.

Some of the variables I created from the police reports were also re-coded. For example, I created a variable from the reports on the level of violence used in breach offences with the following five attributes:

• 1. No actual violence—usually a breach of a no contact condition or entering prohibited premises, but a small number of cases involved a threat of violence.

• 2. Low level violence—includes damage to property; a slap or shove without injury.

• 3. Medium level violence– physical abuse of a limited nature (e.g. one punch to the shoulder; a slap) not causing injury. It may also include significant property damage or other behaviour that would likely cause considerable fear.

• 4. High level violence—more substantial violence or exacerbating circumstances including pregnancy and violence towards children; and in a few cases the respondent’s violence towards self.

• 5. Extremely high level violence—stabbing/multiple blows/kicks causing injury; assault with a weapon; strangulation, rape/attempted rape; suffocation/assault while victim restrained/trapped; risk of death or permanent injury.

There were few cases with no actual violence and few cases with extremely high level violence. Thus, I created three categories: I combined categories one and two as low level violence, I retained the category medium level violence, and I combined categories four and five into high level violence. I later re-coded this variable as a dichotomous variable for conducting statistical tests to compare the four groups. I also dichotomised other variables such as type of penalty and number of distinct DVO breaches .

Police Reports and Interviews

I copied the police reports from the encrypted compact disc to an encrypted electronic excel spreadsheet file on my password protected laptop computer. I had assigned case numbers and pseudonyms to link the police reports with the court files, which was also important for the content analysis to follow.

I transferred the audio-recorded interviews to encrypted files on my laptop and deleted the digital recordings. In the following weeks I had the recorded interviews transcribed and I assigned each participant a coded identity. The coding protocol first identifies the participant as a service provider (SP) or a police prosecutor (PP), followed by a number representing the order in which they were interviewed to distinguish them from their counterparts. For example, PP3 is the third police prosecutor to have been interviewed. Five of the service providers I interviewed identified as Indigenous. To add further context to the reflections from service providers, the coding for them includes an additional element to represent Indigenous (I) or non-Indigenous (NI) status. SP/11/I, for example, is an Indigenous person and the eleventh service provider interviewed. For the sake of anonymity, I do not identify whether the participant was male or female, nor their location (Cairns or Mount Isa), except for the case of Thelma in the opening paragraph of Chapter 1, where I disclose the location as the Cairns Esplanade. This is a very large public area, where many Indigenous people gather, so anonymity is not at risk.

Methods of Analysis

Thematic Content Analyses

The approach to analysing the data included both deductive (beginning with an existing theoretical framework to test hypotheses) and inductive (building a theoretical framework from the data) processes.

Policy Documents and Parliamentary Debates

My analysis of the policy documents and parliamentary debates focused on the intent of the legislation as set out in the report of the Queensland Domestic Violence Task Force (QDVTF, 1988), which gave rise to the Domestic Violence (Family Protection) Act 1989. I conducted a line-by-line analysis of the text of relevant introductory speeches and subsequent debates recorded in Hansard to extract evidence of how the intent was to be given effect in the legislation. I also looked for evidence in the Hansard records of expressions of intent that were different to that described in the policy documents. Effectively, I treated the key policy documents and the Hansard records as a set of textual data from which I extracted key concepts (the intent of the legislation) and the themes (expressions of the intent in the parliamentary debates).

Interview Data

Reflecting the flexible, semi-structured nature of the interviews, my analysis of the interview data is consistent with constructivist grounded theory, although I have not used this method to inform further data collection. Grounded theory is an evolving and contested method with different positions on coding procedures. First, I identified concepts and categories from the data, using colour coding to sort them (open coding). I then identified the relationship between them (axial coding), with attention to context and the research questions, to create core themes.

Police Reports

I used a deductive approach when analysing the police reports . Drawing on the extant literature I developed a list of elements indicative of coercive control, violent resistance and fights and looked for evidence of each of these types of violence in the data. Based on the results of my analysis of interview data I identified elements indicative of chaos context violence , and from the work of anthropologists (Burbank, 1994; Langton, 1988; and Williams, 1987), I also developed a list of elements indicative of contemporary expressions of traditional Aboriginal dispute resolution. I looked for evidence of these subsets of fights in the police reports.

Statistical Data Analysis

My analysis began with diagnostic tests to confirm there were no significant differences in results for the two research sites that would prevent aggregating the data for analysis. I then ran frequencies and cross-tabulations for each of the four groups, followed by a sex and race comparison to answer Question 2. Using Pearson’s chi-square test of independence with a 2 × 2 contingency table, I analysed the relationship between sex and culture (race) and four sets of variables: demographics, respondent histories, aggrieved histories, and case complexity. Chi-square analysis calculates the difference between observed and expected values displayed in a contingency table. If the chi-square coefficient is zero, there is no association between the variables. The strength of an association between variables is reflected in the size of the difference between the observed and expected values in the contingency table, with a larger difference showing stronger association. For a 2 × 2 contingency table (df = 1), a coefficient of 3.84, or greater, indicates a statistically significant relationship between the variables. This means I can be 95% (alpha level .05) confident that the association is not a product of the sampling, and can reject the null hypothesis. Chi-square cannot be used if the expected count is less than 5 in any cell, so in those cases I used Fisher’s exact test.

To create the 2 × 2 contingency tables, I first split the data file by sex, selected the chi-square statistic, expected count, percentages and standardised residual for the output, and ran cross-tabulations for culture and relevant dichotomous variables. I then split the file by culture and ran cross-tabulations for sex and each of the relevant dichotomous variables.

Appendix 2: Details of Statistical Analysis Results

Group Comparisons: Overall Results of the Statistical Analysis

Respondents—sex and race differences
Variable	Indig men	Non-Indig men	Indig women	Non-Indig women	No diffs	Sex diffs Indig	Sex diffs non-Indig	Race diffs men	Race diffs women
	N = 50 (%)	N = 47 (%)	N = 46 (%)	N = 42 (%)
Demographics
Aged less than 30 years	34	34	48	57			X
Intimate partner DVO1	94	92	89	91	X
Cross-cultural relationship DVO1	8	19	20	5			X		X
Remote court DVO1	28	0	35	2				X	X
Remote court DVO breach 1	22	2	7	0		X		X
Respondent history
One victim only	60	62	63	79	X
Three or more distinct DVOs	62	62	30	24		X	X
Police applications DVO1	86	94	100	91		X			X
Standard conditions only DVO1	74	55	91	69		X		X	X
No contact condition on DVO1	12	34	0	14		X	X	X	X
Breached DVO1, ex parte	72	72	76	62	X
Breach occasions (3 or more)	70	49	28	12		X	X	X
High-level violence, any breach	72	64	39	12		X	X		X
Pleaded guilty, breach 1	96	94	96	100	X
No penalty, breach 1	14	13	41	38		X	X
Penalty jail, breach 1	32	13	9	0			X	X
Conviction flag	98	77	76	33		X	X	X	X
Criminal Code charge (breach 1)	52	19	17	2		X	X	X	X
Respondent also aggrieved	42	43	89	67		X	X		X

Aggrieved—sex and race differences
Variable	Indig men	Non-Indig men	Indig women	Non-Indig women	No diffs	Sex diffs Indig	Sex diffs non-Indig	Race diffs men	Race diffs women
	N = 21 (%)	N = 20 (%)	N = 41 (%)	N = 28 (%)
Demographics
Intimate partner DVO1	95	90	85	90	X
Cross-cultural relationship DVO1	0	40	17	7		X	X	X
Aggrieved history
Police applications DVO1	100	85	97	79					X
Distinct perpetrators (2 or more)	14	25	49	25		X			X
Distinct perpetrators (3 or more)	0	10	34	4		X			X
Respondent was aggrieved first	10	20	61	36		X			X
Cross-DVOs	95	95	88	86	X

Case character—sex and race differences
Variable	Indig men	Non-Indig men	Indig women	Non-Indig women	No diffs	Sex diffs Indig	Sex diffs non-Indig	Race diffs men	Race diffs women
	N = 50 (%)	N = 47 (%)	N = 46 (%)	N = 42 (%)
Complex case	36	34	50	24					X

Group Comparisons: Technical Results—Tests of Independence

Respondents (185)	Sex differences		Race differences
	Indigenous (N = 96)	Non-Indigenous (N = 89)	Men (N = 97)	Women (N = 88)
Demographics
Aged less than 30 years	–	X2 (1, 89) = 4.783, p < .05	–	–
Intimate partner DVO1	–	–	–	–
X-cultural relationship DVO1	–	X2 (1, 89) = 4.238, p < .05	–	X2 (1, 88) = 4.399, p < .05
Remote court DVO1			X2 (1, 97) = 15.380, p < .01	X2 (1, 88) = 14.788, p < .01
Remote court DVO breach 1	X2 (1, 96) = 4.608, p < .05	–	X2 (1, 97) = 8.825, p < .01	–
History as respondent
Distinct victims (1 only)	–	–	–	–
Distinct DVOs (3 or more)	X2 (1, 96) = 9.586, p < .01	X2 (1, 89) = 12.936, p < .05	–	–
Police applications DVO1	P = .008, p < .05*		–	P = .048, p < .05*
Standard conditions only DVO1	X2 (1, 96) = 4.923, p < .05	–	X2 (1, 97) = 3.714, p < .05	X2 (1, 97) = 6.978, p < .05
No contact condition on DVO1	P = .017, p < .05*	X2 (1, 89) = 4.652, p < .05	X2 (1, 97) = 6.712, p < .05	P = .010, p < .05*
Breached DVO1, ex parte	–	–	–	–
Breach occasions (3 or more)	X2 (1, 96) = 16.696, p < .01	X2 (1, 89) = 14.106, p < .01	X2 (1, 97) = 4.471, p < .05	–
High-level violence, any breach	X2 (1, 96) = 10.518, p < .01	X2 (1, 89) = 25.063, p < .01	–	X2 (1, 88) = 8.430, p < .01

1. *Fisher’s exact 2-tailed test is used where cell counts are less than 5, so requirements for Pearson’s chi-square test of independence are not met