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Justice made to measure: NSW legal needs survey in disadvantaged areas

, 2006 Six disadvantaged areas were surveyed by telephone interviews: three suburban areas within Sydney (Campbelltown, Fairfield, South Sydney), one major provincial centre (Newcastle) and two rural/remote areas (Nambucca and Walgett)...

Ch 3. The incidence of legal events

This chapter describes the number and type of legal events reported by survey participants across the six LGAs. It also examines the sociodemographic factors associated with the incidence of different types of legal events.

Number of legal events reported

The participants were asked about legal events they had experienced in the 12 months prior to the survey. Overall, the 2431 participants across the six LGAs reported experiencing a total of 5776 legal events in the 12-month period. The maximum number of legal events reported by any individual was 33.

Almost one-third of participants (752 or 30.9%) reported that they did not experience any legal events during the reference period (see Figure 3.1). The remaining two-thirds (1679 or 69.1%) reported experiencing at least one legal event during the reference period, with approximately one-third of all participants reporting either one or two legal events, and one-third reporting three or more legal events. The average number of legal events reported across all 2431 participants was 2.4, while the median number was 1.0. The average number of legal events reported by the 1679 participants who reported at least one event was 3.4.

Figure 3.1: Number of events reported per participant, all six LGAs, 2003

Note: N=2431 participants.

Table 3.1 presents a cumulative frequency distribution of legal events. It can be seen that a minority of participants accounted for a disproportionate number of the legal events reported. For example, the third of participants who reported three or more legal events accounted for more than three-quarters (79.0%) of the 5776 legal events reported. Less than one-quarter of the sample (23.9%) accounted for two-thirds of the events (67.5%) and about one-sixth of the sample (16.4%) accounted for over half the events (54.9%).

Table 3.1: Cumulative frequency distribution of legal events, all six LGAs, 2003

No. of events reported per participant	*Participants*		*Events*
No. of events reported per participant	*No.*	Cumulative %	*No.*	Cumulative %
16+	16	0.7	308	5.3
15	6	0.9	90	6.9
14	7	1.2	98	8.6
13	10	1.6	130	10.8
12	14	2.2	168	13.7
11	17	2.9	187	17
10	29	4.1	290	22
9	30	5.3	270	26.7
8	41	7	328	32.4
7	45	8.8	315	37.8
6	71	11.8	426	45.2
5	112	16.4	560	54.9
4	182	23.9	728	67.5
3	221	32.9	663	79
2	337	46.8	674	90.6
1	541	69.1	541	100
0	752	100	0
Total	2431		5776

Types of legal events reported

The survey measured the incidence of 101 different legal events. These events were categorised under three broad areas of law, namely civil, criminal and family law, and then further categorised into 15 legal event groups (see Table B1, Appendix B).¹ The categorisation resulted in 76 civil law events, 16 criminal law events and nine family law events.

Table 3.2 presents the reported incidence of legal events during the 12-month reference period, broken down by broad area of law and legal event group. Table C1 in Appendix C presents the reported incidence of each of the 101 different legal events.²

Table 3.2: Incidence of legal events by broad area of law andlegal event group, all six LGAs, 2003

*Area of law*	*Level event group*	*Participants*		*Events*
*Area of law*	*Level event group*	*No.*	%	*No.*	%
Civil	Accident/injury	466	19.2	554	9.6
	Business	122	5.0^a	125	2.2
	Consumer	536	22	690	11.9
	Credit/debt	292	12	384	6.6
	Education	181	7.4^b	223	3.9
	Employment	293	12.1^c	426	7.4
	Government	474	19.5	631	10.9
	Health	77	3.2^d	90	1.6
	Housing	550	22.6	673	11.7
	Human rights	141	5.8	196	3.4
	Wills/estates	356	14.6	417	7.2
	Total civil	1518	62.4	4409	76.3
Criminal	Domestic violence	96	3.9	109	1.9
	General crime	646	26.6	872	15.1
	Traffic offences	78	3.2	83	1.4
	Total crime	733	30.2	1064	18.4
Family	Family	206	8.5	292	5.1
Unclassified		11	0.5	11	0.2
Total		1679	69.1	5776	100

^a 562 participants owned a small business. Of these, 122 (21.7%) reported at least one business event.
^b 1076 participants were full- or part-time students, or were responsible for a student. Of these, 181 (16.8%) reported at least one education event.
^c 1417 participants were employed full- or part-time at some time during the reference period. Of these, 293 (20.7%) reported at least one employment event.
^d 768 participants had chronic conditions or mental/physical disabilities or were responsible for a person with a disability or an elderly person. Of these, 77 (10.0%) reported at least one health event.

Notes: Participants sometimes reported multiple legal events (within or across legal event groups). ‘Unclassified’ legal events consist of events that were unclearly described by participants.

Table 3.2 shows that 62.4 per cent of participants reported experiencing one or more civil law events during the 12-month period, compared with only 30.2 per cent for criminal law events and 8.5 per cent for family law events. It is worth noting that this distribution may partly reflect the survey’s greater focus on civil law events than on criminal or family law events.

Within civil law, the legal event groups reported by the highest proportions of participants were housing (22.6% of all participants), consumer (22.0%), government (19.5%), accident/injury (19.2%), wills/estates (14.6%), employment (12.1%) and credit/debt (12.0%).

As detailed in Appendix Table C1, the most frequently reported housing events involved buying or selling a home (9.0% of all participants), disputes with neighbours (6.3%), tenancy problems (5.0%) and homelessness (3.9%).

The most common consumer events involved problems related to goods and services (10.6%), disputes with financial institutions (9.8%) and problems with insurance (4.8%). Problems related to goods/services and disputes with financial institutions had the third and fourth highest incidence rates among the 101 different legal events examined.

Relatively frequently reported government events included local council problems (6.5%), non-traffic-related fines (5.0%), problems with pensions or benefits (4.6%), and disputes related to taxation or debt (3.8%).

Eight per cent of respondents reported a car accident involving property damage, 6.5 per cent reported a work injury and 7.2 per cent reported a personal injury not related to work or a car accident.

Making or altering a will had the second highest incidence rate among the 101 legal events examined, with 11.1 per cent of respondents reporting this event.

Respondents reported that employment events included disputes related to employment conditions (7.3%), workplace harassment or mistreatment (5.1%) and workplace discrimination (3.0%).

The most frequently reported credit/debt events involved problems concerning money owed to the respondent (6.2%) and problems paying bills or debts (6.0%).

In terms of the broad area of criminal law, events within the general crime legal event group were reportedly experienced by over one-quarter (26.6%) of all participants, whereas domestic violence events (3.9%) and traffic offence events (3.2%) were only reported by relatively small proportions of participants.

The most commonly reported event within the general crime legal event group was having one’s property stolen or vandalised, with 18.9 per cent of all survey participants reporting being victims of stolen or vandalised property. It is worth noting that stolen/vandalised property was the most frequently reported of all the 101 legal events examined. Nine per cent of participants reported being victims of assault and 4.4 per cent reported that the police failed to investigate a crime. It is also worth noting that only five participants reported being in an adult prison or juvenile detention centre at some time during the reference period, and as a result, only a small number of legal events were related to imprisonment.

Within family law, the most frequently reported events included experiencing divorce or separation (3.3%), problems with child support payments (3.2%), and problems with residence or contact arrangements for children (2.9%).

Reporting multiple legal events

Some participants reported more than one event of a particular type (i.e. within the same legal event group). Table 3.3 presents the number of participants who reported multiple events within a particular legal event group.³ It can be seen that the event groups with the highest percentages of participants reporting multiple events were employment, family, human rights, general crime, credit/debt and government.

Table 3.3: Incidence of multiple legal events by broad area of law and legal event group, all six LGAs, 2003

*Area of Law*	*Legal event group*	*No. of participants with*		*% of participants with multiple events*
*Area of Law*	*Legal event group*	*1+ events*	*Multiple events*	*% of participants with multiple events*
Civil	Accident/injury	466	74	15.9
	Business	122	3	2.5
	Consumer	536	125	23.3
	Credit/debt	292	69	23.6
	Education	181	40	22.1
	Employment	293	100	34.1
	Government	474	112	23.6
	Health	77	10	13
	Housing	550	107	19.5
	Human rights	141	39	27.7
	Wills/estates	356	50	14
Criminal	Domestic violence	96	12	12.5
	General crime	646	161	24.9
	Traffic offences	78	5	6.4
Family	Family	206	62	30.1
Total		1679	1138	67.8

Some participants reported experiencing events across more than one legal event group during the reference period. To examine whether different types of events tended to co-occur, that is, tended to be experienced by the same participants, a hierarchical cluster analysis and an exploratory factor analysis were conducted on the 15 legal event groups. The cluster analysis placed legal event groups that tended to be experienced together in the same cluster, and event groups that tended to be unrelated in different clusters. The factor analysis also examined the pattern of relationships between legal event groups, with related event groups contributing to, or loading on, the same underlying dimension or factor.⁴

Figure 3.2 summarises the results of the cluster analysis in the form of a tree diagram or dendrogram. The branches of the dendrogram join together legal event groups that tended to be related (or co-occurred), with shorter branches representing greater similarity (or co-occurrence) between legal event groups than longer branches. The dendrogram reveals three main clusters, with two of these clusters consisting of further, smaller clusters.⁵

Figure 3.2: Dendrogram of legal event groups

Notes: N=2431 participants.
The centroid method of clustering was used.

The first cluster includes a broad range of legal event groups, comprising general crime, consumer, government, housing, accident/injury, employment and wills/estates events. This broad cluster consists of three more defined sub-clusters, namely (a) general crime and consumer events, (b) government and housing events, and (c) accident/injury and employment events.

The second cluster comprises family, domestic violence, human rights and education events, with family and domestic violence events forming one sub-cluster, and human rights and education events forming a second sub-cluster.

The third cluster is an economic cluster comprising business and credit/debt events. Health and traffic offence events do not fit neatly into any of the main clusters identified.⁶

The factor analysis revealed a similar pattern, also resulting in three main groupings or factors, with health and traffic offence events again not cohering with any of these groupings (see Table C2 in Appendix C for a summary of the factor solution).⁷ The first factor was a broad factor which included five of the seven legal event groups evident in the broad grouping according to the cluster analysis—general crime, consumer, government, accident/injury and employment. The factor analysis suggested, however, that housing and wills/estates events did not significantly contribute to this grouping. It also suggested that human rights events formed an additional element of this broad grouping rather than cohering with the family grouping as suggested by the cluster analysis.

The second factor, like the second cluster, was dominated by family and domestic violence events, but human rights and education events did not significantly contribute to this factor.

The factor analysis, like the cluster analysis, also revealed a third grouping dominated by business and credit/debt events. The factor analysis also suggested that consumer events significantly contributed to this grouping, although not as strongly as they contributed to the broad factor.⁸

Demographic factors related to reporting legal events of any type

A standard binary logistic regression was conducted to examine the relationship between sociodemographic factors and experiencing legal events. The regression compared participants who reported one or more legal events of any type with participants who did not report any legal event on the following sociodemographic characteristics: gender, age, Indigenous Australian status, country of birth, disability status, personal income and education level.⁹ The regression was used to determine which of the sociodemographic factors were statistically independent predictors of reporting legal events of any type, after taking into account the interrelationships between these factors and their combined effect on reporting legal events.

Table 3.4 provides a summary of the regression results while Table C3 in Appendix C provides the full results. Table 3.5 presents the corresponding descriptive statistics.

The regression revealed that age, country of birth, disability status, personal income and education level were statistically independent predictors of reporting legal events (of any type). Gender and Indigenous status were not significant predictors of reporting legal events (see Table 3.4).

Table 3.4 shows the categories of each predictor that were compared in the regression (see column headed 'Comparison'). For age, people aged 65 or over were compared with each other age group. Table 3.4 presents the odds ratios for significant comparisons. It can be seen that all the age comparisons tested were significant. As noted in the Method section in Chapter 2, an odds ratio that is significantly greater than 1.0 indicates the first category in the comparison had higher odds than the second, whereas an odds ratio that is significantly less than 1.0 indicates the reverse. Thus, Table 3.4 shows that, compared with participants aged 65 years or over, all other age groups had higher odds of reporting legal events. Interestingly, the likelihood of reporting legal events tended to decrease with increasing age. More specifically, the odds of reporting legal events were approximately:

twice as high for 55 to 64 year olds (2.1) compared with the oldest age group
three times as high for 45 to 54 year olds (3.1) and 35 to 44 year olds (3.6) compared with the oldest age group
four times as high for 25 to 34 year olds (4.5) and 15 to 24 year olds (4.3) compared with the oldest age group (see Table 3.4).

Table 3.4: Summary of standard binary logistic regression for reporting legal events of any type

SIGNIFICANT VARIABLES
*Variable*	*Comparison*	Odds ratio^a
Age (years)	15–24 versus 65+ 25–34 versus 65+ 35–44 versus 65+ 45–54 versus 65+ 55–64 versus 65+	4.3 4.5 3.6 3.1 2.1
Country of birth	English speaking versus non-English speaking	1.5
Disability status	Disability versus no disability	1.7
Personal income ($/week)	0–199 versus 1000+ 200–499 versus 1000+ 500–999 versus 1000+	0.5 0.6 0.7
Education level	Didn't finish/at school versus university degree Year 10/equivalent versus university degree Year 12/equivalent versus university degree Certificate/diploma versus university degree	ns 0.7 ns ns
NON-SIGNIFICANT VARIABLES: Gender, Indigenous status

^a An odds ratio greater than 1.0 indicates the first category in the comparison had higher odds than the second.
An odds ratio less than 1.0 indicates the first category in the comparison had lower odds than the second.
Notes: N=1988 participants. Data on one or more potential predictor variables were missing for 443 participants.
'ns' indicates the odds ratio was not statistically significant, that is, the odds for the first category in the comparison were not statistically different from the odds for the second category (even though the overall variable was significant).

Table 3.5 shows that, whereas only 44.6 per cent of the oldest age group reported experiencing one or more legal events, over three-fifths of the other age groups reported experiencing one or more legal events.

Table 3.5: Reporting legal events of any type by each sociodemographic factor, all six LGAs, 2003

*Sociodemographic factor*		*Participants reporting 1+ events*		*All participants*
*Sociodemographic factor*		*No.*	%	*No.*
Gender	Female	840	69.7	1205
	Male	839	68.4	1226
	Total	1679	69.1	2431
Age (years)	15–24	295	73.2	403
	25–34	364	78.6	463
	35–44	362	75.3	481
	45–54	322	71.6	450
	55–64	187	62.5	299
	65+	148	44.6	332
	Total	1678	69.1	2428
Indigenous status	Indigenous	59	73.8	80
	Non-Indigenous	1444	68.6	2106
	Total	1503	68.8	2186
Country of birth	English speaking	1448	70.2	2062
	Non-English speaking	228	62.3	366
	Total	1676	69	2428
Disability status	Disability	370	72.8	508
	No disability	1305	68.1	1917
	Total	1675	69.1	2425
Personal income	0–199	307	62.7	490
($/week)	200–499	549	67	820
	500–999	511	74.3	688
	1000+	190	79.2	240
	Total	1557	69.6	2238
Education level	Didn't finish/at school	164	60.7	270
	Year 10/equivalent	421	63.3	665
	Year 12/equivalent	340	67.3	505
	Certificate/diploma	316	77.3	409
	University degree	431	76.1	566
	Total	1672	69.2	2415

Note: Where the total for a given sociodemographic factor is less than 2431, data were missing on that factor.

The odds of reporting legal events were 1.5 times higher for participants born in an English speaking country than for participants born in a non-English speaking country (see Table 3.4). Whereas 70.2 per cent of participants born in an English speaking country reported experiencing legal events, only 62.3 per cent of those born in a non-English speaking country reported experiencing legal events (see Table 3.5).

The odds of reporting legal events were 1.7 times higher for people with a chronic illness or disability than for other people (see Table 3.4).

When compared with the highest personal income group ($1000 or more per week), each of the other income groups had lower odds of reporting legal events (see Table 3.4). The likelihood of reporting legal events tended to increase with increasing income, with the lowest income earners (under $200 per week) having the lowest incidence rate (62.7%) and the highest income earners ($1000 or more per week) having the highest incidence rate (79.2%, see Table 3.5).

The odds of reporting legal events were lower for people who had completed schooling only as far as Year 10 than for university graduates (see Table 3.4). Whereas 63.3 per cent of those who had completed schooling only as far as Year 10 reported a legal event, 76.1 per cent of university graduates reported a legal event (see Table 3.5).

Demographic factors related to reporting different types of legal events

To assess whether the types of events experienced were related to the characteristics of participants, a series of standard binary logistic regressions were performed. Each regression examined whether sociodemographic factors were associated with whether or not participants reported experiencing one or more events from a particular legal event group.¹⁰ Given that the frequency of reporting some types of events was low, there were insufficient numbers to conduct a separate regression for some legal event groups. As a result, regressions were performed for the 10 most frequently occurring legal event groups. These event groups comprised eight civil legal event groups (i.e. accident/injury, consumer, credit/debt, education, employment, government, housing and will/estates), one criminal legal event group (i.e. general crime) and the family legal event group.

The full results of these 10 logistic regression models are presented in Tables C4 to C13 in Appendix C, and the corresponding descriptive statistics are presented in Tables C14 to C23 in Appendix C. The results of these regressions are discussed in turn below.

Although the relationships of sociodemographic factors with reporting the five least frequent types of legal events¹¹ were not examined via regression analyses, they were examined via chi-square analyses. It is worth noting that, unlike regression analyses, chi-square analyses only examine the bivariate relationship of each sociodemographic factor to reporting each type of event. That is, chi-square analyses do not take into account the interrelationships between sociodemographic factors and their combined effect on reporting each type of event. The chi-square results and the relevant cross-tabulations for the five least frequent legal event groups are presented in Tables C24 to C28 in Appendix C.

Accident/injury events

The logistic regression results revealed that gender, age, country of birth, disability status and personal income were statistically independent predictors of reporting one or more accident/injury legal events. Indigenous status and education were not significant predictors of reporting accident/injury events (see Appendix Table C4).

More specifically the odds of reporting at least one accident/injury event were:

lower (0.7) for females than for males
higher for 15 to 24 year olds (5.9), 25 to 34 year olds (3.4), 35 to 44 year olds (3.0) and 45 to 54 year olds (2.3), when compared with oldest age group
1.5 times higher for people born in an English speaking country than for people born elsewhere
1.4 times higher for people with a chronic illness or disability than for other people
lower for people earning under $200 per week (0.5) and people earning $200 to $499 per week (0.5) than for people in the highest income bracket ($1000 or more per week).

Consumer events

According to the logistic regression model, age, disability status and personal income were statistically independent predictors of reporting at least one consumer event. Gender, Indigenous status, country of birth and education were not significant predictors of reporting consumer events (see Appendix Table C5).

The odds of reporting at least one consumer event were:

higher for all age groups when compared with the group aged 65 or over, being 7.1 times higher for 15 to 24 year olds, 5.4 times higher for 25 to 34 year olds, 5.0 times higher for 35 to 44 year olds, 3.6 times higher for 45 to 54 year olds, and 2.4 times higher for 54 to 64 year olds
2.1 times higher for persons with a chronic illness or disability when compared with other individuals
lower (0.5) for the lowest income group than for the highest income group.

Credit/debt events

The logistic regression showed that age, Indigenous status and disability status were statistically significant predictors of reporting credit/debt events. The remaining sociodemographic variables were not significant (see Appendix Table C6).

The odds of reporting at least one credit/debt event were:

higher for all age groups when compared with the group aged 65 or over, ranging from 3.5 times higher (for 55 to 64 year olds) to 8.5 times higher (for 25 to 34 year olds)
2.0 times higher for Indigenous people compared with non-Indigenous people
1.7 times higher for those with a chronic illness or disability than for other individuals.

Education events

Age and disability status were the only sociodemographic factors that were statistically significant predictors of reporting at least one legal event related to education (see Appendix Table C7).¹²

Specifically, the odds of reporting at least one education event were:

2.7 times higher for 25 to 34 year olds and 3.0 times higher for 35 to 44 year olds, when compared with people aged 55 years or over
1.8 times higher for people with a chronic illness or disability than for other people.

Employment events

Based on the logistic regression model, age, Indigenous status and disability status were statistically significant predictors of reporting employment events. The remaining sociodemographic variables were not significant (see Appendix Table C8).¹³

The odds of reporting at least one employment event were:

2.0 times higher for Indigenous people compared with non-Indigenous people
1.5 times higher for people with a chronic illness or disability compared with other people.

Although age was also a significant predictor of reporting employment events, none of the specific comparisons tested in the regression were significant.¹⁴ The highest incidence of employment events was reported by 45 to 54 year olds (24.9%), followed by 15 to 24 year olds (22.6%) and 25 to 34 year olds (22.2%, see Appendix Table C18).

Government events

Age, disability status and education level were statistically independent predictors in the logistic regression model for reporting government events. The remaining sociodemographic variables examined were not significant (see Appendix Table C9).

More specifically, the odds of reporting at least one government event were:

about three times higher for 15 to 24 year olds, 25 to 34 year olds, 35 to 44 year olds and 45 to 54 year olds than for people aged 65 years or over
2.0 times higher for people with a chronic illness or disability than for other people
lower (0.7) for people who had completed schooling only as far as Year 10 than for university graduates.

Housing events

The logistic regression revealed that age, disability status and personal income were statistically independent predictors of reporting housing events (see Appendix Table C10). The odds of reporting at least one housing event were:

higher for all age groups when compared with the oldest age group, ranging from 1.9 times higher (for the 55 to 64 year group) to 4.9 times higher (for the 25 to 34 year group)
1.6 times higher for people with a chronic illness or disability than for other people
lower for all of the income brackets (0.4–0.7) compared with the highest income bracket.

Gender, Indigenous status, country of birth, and education level¹⁵ were not significant predictors of reporting housing events.

Wills/estates events

Age, Indigenous status, country of birth, personal income and education level were statistically independent predictors in the logistic regression model for reporting wills/estates events. Gender and disability status were not significant predictors (see Appendix Table C11).

More specifically, the odds of reporting at least one wills/estates event were:

lower for 15 to 24 year olds (0.3) and 25 to 34 year olds (0.6) than for the oldest age group
lower (0.3) for Indigenous people than for non-Indigenous people
2.4 times higher for people born in an English speaking country than for people born in a non-English speaking country
lower (0.6) for individuals earning $200 to $499 per week than for individuals earning $1000 or more per week
lower (0.4–0.5) for the two groups with the lowest education levels than for university graduates.

General crime events

Age, country of birth, disability status and personal income were statistically independent predictors in the regression model (see Appendix Table C12). The odds of reporting at least one general crime event were:

higher for all age groups when compared with the oldest age group, ranging from 2.7 times higher (for 55 to 64 year olds) to 6.0 times higher (for 15 to 24 year olds)
1.6 times higher for persons born in an English speaking country than for persons born in a non-English speaking country
1.7 times higher for persons with a chronic illness or disability than for other persons
lower (0.5–0.7) for all of the income brackets when compared with the highest income bracket.

Gender, Indigenous status and education level were not significant predictors of reporting general crime events.

Family events

Age, Indigenous status, disability status and personal income were statistically independent predictors in the regression model (see Appendix Table C13). The odds of reporting at least one family event were:

higher for all age groups, apart from the 15 to 24 year age group, when compared with the oldest age group, ranging from 2.7 times higher (for 45 to 54 year olds) to 8.3 times higher (for 35 to 44 year olds)
2.1 times higher for Indigenous persons compared with non-Indigenous persons
1.7 times higher for persons with a chronic illness or disability than for other persons.

Although personal income was a significant predictor of reporting family events, none of the specific comparisons tested, using the highest income bracket as the reference category, were significant. The highest rates of family events were reported by the middle two income groups (9.9% and 9.6%) while the lowest rate was reported by the lowest income group (4.9%). The rate for the highest income group (6.3%) fell in between these other rates (see Appendix Table C23).

Gender, country of birth and education level were not significant predictors of reporting family events.

Summary: the incidence of legal events

This chapter focused on the reported incidence of legal events. Some of the major findings were as follows.

About two-thirds of survey respondents reported experiencing one or more legal events in the 12 months prior to the survey. The average number of legal events reported by each participant was 2.4.

A minority of participants accounted for a disproportionate number of the legal events reported, with the one-third of participants who reported experiencing three or more legal events accounting for over three-quarters of all the legal events reported.

Of the 11 civil legal event groups, the accident/injury, consumer, credit/debt, employment, government, housing and wills/estates groups had the highest incidence. The two most common civil law events fell into the consumer group. These events involved problems with goods or services (reported by 10.6% of participants), and disputes with financial institutions (reported by 9.8% of participants).

Of the three criminal legal event groups, the general crime group had the highest incidence. The most frequent criminal law event was the general crime event involving stolen or vandalised property (reported by 18.9% of participants).

Some types of legal events tended to co-occur. Cluster and factor analyses suggested three main groupings of legal event types: a general, broad grouping; a family grouping; and an economic grouping. More specifically:

general crime, consumer, government, accident/injury and employment events tended to co-occur¹⁶
family and domestic violence events tended to co-occur,¹⁷
business and credit/debt events tended to co-occur.¹⁸

Age, country of birth, disability status, personal income and education level were statistically significant independent predictors of reporting any type of legal event according to the logistic regression analysis. The odds of reporting a legal event of any type were higher for:

all age groups compared with people aged 65 years or over
people born in an English speaking country than other people
people with a chronic illness or disability than other people
people in the highest income group compared with every other income group
university graduates than people who completed schooling only to Year 10.

A series of logistic regressions showed that different sociodemographic characteristics were related to experiencing different types of legal events. Table 3.6 summarises the regression results for the 10 most frequent legal event groups (i.e. accident/injury, consumer, credit/debt, education, employment, government, housing, wills/estates, general crime and family). It was found that:

age was a consistent predictor for all 10 event groups
people with a chronic illness or disability had increased vulnerability for nine of the 10 most frequent event groups
Indigenous people, people born in an English speaking country and high-income earners had relatively higher odds of reporting a few or several types of events
university graduates had relatively higher odds of reporting government and wills/estates events
males had relatively higher odds of reporting accident/injury events.

Table 3.6: Summary of significant sociodemographic predictors in the 11 regression models for reporting legal events

*Reporting*	*Gender*	*Age*	*Indigenous status*	Country of birth	*Disability status*	*Personal income*	*Education level*
Legal events of any type		x		x	x	x	x
Accident/injury events	x	x		x	x	x
Consumer events		x			x	x
Credit/debt events		x	x		x
Education events		x			x
Employment events		x	x		x
Government events		x			x		x
Housing events		x			x	x
Wills/estates events		x	x	x		x	x
General crime events		x		x	x	x
Family events		x	x		x	x

¹ In addition to the 101 classified legal events, a further three different types of events were unable to be classified and are excluded from the three broad areas of law and the 15 legal event groups.

² As noted in the Method section in Chapter 2, it is possible that events of a highly personal or sensitive nature (e.g. events involving domestic violence, assault, criminal charges, child protection, discrimination, immigration) were under-reported.

³ Although the survey measured whether participants experienced more than one type of event belonging to the same legal event group, it did not measure the number of times that each specific event was experienced.

⁴ See the Method section in Chapter 2 for further details about the hierarchical cluster analysis and factor analysis.

⁵ The number of clusters was decided by subjective inspection of the dendrogram in conjunction with consideration of large jumps in the fusion coefficient at each stage of the analysis. See the Method section in Chapter 2 for further details and Figure C1 in Appendix C which displays the fusion coefficient at each stage of the analysis.

⁶ Figure C1 in Appendix C reveals large jumps in the fusion coefficient between Stages 1 and 2, between each pair of Stages from 5 to 9, and between Stages 10 and 11. Given that we were interested in the interrelationships between all 15 legal event groups, we examined the clusters formed up to the latest large jump in the fusion coefficient (between Stages 10 and 11). At this point, there were three main clusters consisting of (1) a broad cluster comprising general crime, consumer, government, housing, accident/injury, employment and wills/estates, (2) a family/rights cluster comprising family, domestic violence, human rights and education, and (3) an economic cluster comprising business and credit/debt. Between Stages 5 and 6, the economic cluster was identical to that between Stages 10 and 11. The broad cluster had not yet formed but its three sub-clusters were evident. The family/rights cluster had not yet formed but its two sub-clusters were evident.

⁷ The wills/estates group also did not load significantly on any factor. In the cluster analysis, wills/estates was the weakest contributor to the broad cluster (i.e. the last event group to join onto the broad cluster).

⁸ Note that cluster analysis, unlike factor analysis, does not allow the same legal event group to contribute to more than one cluster, but places each event group in the most relevant cluster.

⁹ A standard logistic regression was appropriate here because there was only one observation for each participant: each individual either reported experiencing at least one legal event or reported not experiencing any legal events.

¹⁰ In each case, a standard rather than mixed-effects logistic regression model was appropriate because there was only one observation for each individual: for example, reporting at least one accident/injury event versus not reporting any accident/injury event.

¹¹ The five least frequent legal event groups in the present study were the business, health, human rights, domestic violence and traffic offence groups.

¹² Of the 2431 respondents, only 1076 were students or were responsible for students. Thus, only these 1076 respondents had the potential to experience an education event. The regression is based on 913 of these 1076 participants who did not have any missing data on the sociodemographic variables. Because only one person aged 65 years or over reported an education event, this age group was combined with the 55 to 64 year age group, and the combined (55 years or over) age group was used as the reference category in the regression.

¹³ Of the 2431 respondents, only 1417 were employed at some time during the 12 months prior to the survey, so only these respondents had the potential to experience an employment event. The regression is based on the 1195 of these 1417 participants who did not have any missing data on the sociodemographic variables. Because only two people aged over 65 years reported an employment event, this age group was combined with the 55 to 64 year age group, and the combined (55 years or over) age group was used as the reference category in the regression.

¹⁴ However, it should be remembered that, for predictors that have three or more categories (such as the age predictor in the present case), regression analyses do not make comparisons between all possible pairs of categories.

¹⁵ Note that even though the overall education variable was not significant in the regression, one of the comparisons for education was significant.

¹⁶ According to the cluster analysis, housing and wills/estates events also tended to co-occur with this grouping of events, while according to the factor analysis, human rights events tended to co-occur with this grouping of events.

¹⁷ According to the cluster analysis, education and human rights events also tended to co-occur with family and domestic violence events.

¹⁸ The factor analysis suggested that consumer events also tended to co-occur with business and credit/debt events.