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Research Report: Legal Australia-Wide Survey: Legal need in Australia
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Legal Australia-Wide Survey: Legal need in Australia  ( 2012 )  Cite this report

8. Outcome of legal problems



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Predicting favourability of outcome of legal problems


This section describes the problem and demographic characteristics associated with achieving favourable outcomes for legal problems. A binary multilevel logistic regression model was fitted to the Australian data to examine the independent predictors of achieving favourable outcomes. The regression compared finalised legal problems that had favourable outcomes to finalised legal problems that had unfavourable outcomes on the following variables: gender, age, Indigenous status, disability status, education, employment status, family status, housing type, main income, main language, remoteness of residential area, legal problem group and strategy used in response to legal problems.(3)

Thus, the regression reveals the types of problems, strategies and demographic groups with lower levels of favourable outcomes. Although regression analysis can be used to show where relationships exist, it cannot explain any relationships. Nonetheless, the regression on favourability of outcome helps to signal the types of problems and demographic groups that may benefit most from initiatives that aim to improve outcomes, and it also helps to identify the strategies to be encouraged. For example, problems with worse outcomes may be more serious, complex or intractable. As a result, improved pathways for resolving these types of problems may be warranted. In addition, the demographic groups that achieve worse outcomes may have a reduced capacity for resolving problems and may require additional encouragement, support or assistance to resolve their problems more favourably.

Table 8.5 provides a summary of the regression results on favourability of outcome for Australia. Legal problem group was the strongest significant predictor of achieving favourable outcomes for legal problems, with the next strongest predictor being the strategy used in response to legal problems. However, only a few demographic variables — namely, age, employment status and remoteness of residential area — were significant predictors of the favourability of the outcomes achieved. Although significant, these demographic predictors were relatively weak. Gender, Indigenous status, disability status, education, family status, housing type, main income and main language were not significant predictors. The regression results are further described in the sections below, with reference to the relevant unprocessed (or descriptive) statistics.(4)

Table 8.5: Regression summary – favourability of outcome of legal problems, Australia

SIGNIFICANT VARIABLES
Variable Categories compared
Odds ratioa
Problem group Accidents | mean
1.9
Consumer | mean
1.2
Credit/debt | mean
0.7
Crime | mean
0.6
Employment | mean
0.7
Family | mean
1.3
Government | mean
0.7
Health | mean
0.7
Housing | mean
1.5
Money | mean
-
Personal injury | mean
1.7
Rights | mean
0.8
Strategy Sought advice | took no action
1.5
Handled without advice | took no action
1.7
Age 15–17 | 65+
1.3
18–24 | 65+
-
25–34 | 65+
-
35–44 | 65+
-
45–54 | 65+
-
55–64 | 65+
-
Employment status Unemployed | other
0.8
Remoteness Remote | major city
-
Regional | major city
1.1
NON-SIGNIFICANT VARIABLES gender, Indigenous status, disability status, education, family status, housing type, main income, main language

a An odds ratio (OR)>1.0 indicates that the first category had significantly higher odds of a favourable outcome than the second category. OR<1.0 indicates that the first category had significantly lower odds. The size of the OR indicates the strength of the relationship. E.g. OR=2.0 means that the odds for the first category were twice those for the second category. OR=0.5 means that the odds for the first category were half those for the second category, or, in other words, that the odds for the second category were twice those (i.e. 1/0.5=2.0) for the first category. See Appendix A2, ‘Data analysis: Significance and strength of predictors’ section for further details. ‘-’ indicates that the comparison was not significant.
Note: N=11 800 finalised problems. Data were missing for 527 problems.

Problem group

The regression results indicated that problem group was the strongest significant predictor of whether or not respondents achieved favourable outcomes for their legal problems (see Table 8.5). Significantly higher odds of favourable outcomes than average were found for the accidents (1.9), consumer (1.2), family (1.3), housing (1.5) and personal injury (1.7) problem groups. The percentage of favourable outcomes for these problem groups ranged between 72.7 and 78.4 per cent, while the corresponding percentage for all problems on average was 66.6 per cent (see Figure 8.4).

Figure 8.4: Favourability of outcome of legal problems by problem group, Australia

R Reference category for problem group in the regression was mean of all problems.
* Significant difference (p<0.05) between this problem group and the mean of all problems in the regression.
Note: N=11 853 finalised problems. Data were missing for 475 problems.

Significantly lower odds of favourable outcomes than average were found for credit/debt (0.7), crime (0.6), employment (0.7), government (0.7), health (0.7) and rights (0.8) problems (see Table 8.5). The percentage of favourable outcomes for these problem groups ranged from 55.7 to 59.3 per cent (see Figure 8.4).

Strategy

Compared to taking no action, both seeking advice (1.5) and handling the problem without advice (1.7) resulted in higher odds of a favourable outcome (see Table 8.5). Favourable outcomes were achieved for 67.7 per cent of the problems involving advice and for 70.8 per cent of the problems handled without advice, but for only 58.1 per cent of the problems resulting in taking no action (see Figure 8.5).

Figure 8.5: Favourability of outcome of legal problems by strategy in response to legal problems, Australia

R Reference category for strategy in the regression.
* Significant difference (p<0.05) between this strategy and took no action in the regression.
Note: N=11 842 finalised problems. Data were missing for 485 problems.


Demographic variables

The regression revealed that only a few demographic variables were significantly related to the favourability of outcomes. As already noted, these demographic variables were weaker predictors than both problem group and strategy. Age, employment status and remoteness of residential area were the only significant demographic predictors, and these predictors had similar strengths of association to the favourability of outcomes (see Table 8.5). Compared to their counterparts, the following demographic groups had significantly lower odds of achieving favourable outcomes:
    • people aged 65 years or over (versus 15–17 year olds)
    • people who had been unemployed
    • people living in major city areas (versus those living in regional areas).

Table 8.6 presents the descriptive statistics for the demographic variables examined in the regression. Although both the youngest and the oldest age groups reported that 68.9 per cent of their problems resulted in favourable outcomes (see Table 8.6), the youngest group had significantly higher odds of favourable outcomes (1.3; see Table 8.5). Thus, the higher level of favourable outcomes for the youngest group becomes evident once the influences of other demographic and problem characteristics are also appropriately taken into account.

Table 8.6: Favourability of outcome of legal problems by each demographic variable, Australia

Demographic variable Category
Favourability of outcome
All finalised problems
Favourable
Unfavourable
%
%
%
N
Gender Female
67.8
32.2
100.0
5 574
MaleR
65.5
34.5
100.0
6 278
Total
66.6
33.4
100.0
11 853
Age 15–17
68.9
31.1
100.0
639*
18–24
64.7
35.3
100.0
1 987
25–34
66.5
33.5
100.0
2 462
35–44
66.5
33.5
100.0
2 499
45–54
68.1
31.9
100.0
1 985
55–64
65.0
35.0
100.0
1 372
65+R
68.9
31.1
100.0
910
Total
66.6
33.4
100.0
11 853
Indigenous status Indigenous
67.4
32.6
100.0
230
OtherR
66.6
33.4
100.0
11 623
Total
66.6
33.4
100.0
11 853
Disability status Disability
65.0
35.0
100.0
2 802
No disabilityR
67.1
32.9
100.0
9 051
Total
66.6
33.4
100.0
11 853
Education <Year 12
65.7
34.3
100.0
3 023
Year 12
65.9
34.1
100.0
2 463
Post-schoolR
67.4
32.6
100.0
6 325
Total
66.7
33.3
100.0
11 811
Employment status Unemployed
62.0
38.0
100.0
1 820*
OtherR
67.4
32.6
100.0
10 033
Total
66.6
33.4
100.0
11 853
Family status Single parent
68.7
31.3
100.0
1 109
OtherR
66.4
33.6
100.0
10 743
Total
66.6
33.4
100.0
11 853
Housing type Disadvantaged
63.5
36.5
100.0
874
OtherR
66.9
33.1
100.0
10 979
Total
66.6
33.4
100.0
11 853
Main income Government payment
66.7
33.3
100.0
2 681
OtherR
66.6
33.4
100.0
9 172
Total
66.6
33.4
100.0
11 853
Main language Non-English
67.4
32.6
100.0
634
EnglishR
66.6
33.4
100.0
11 219
Total
66.6
33.4
100.0
11 853
Remoteness Remote
66.6
33.4
100.0
310
Regional
68.5
31.5
100.0
3 380*
Major cityR
65.8
34.2
100.0
8 163
Total
66.6
33.4
100.0
11 853

R Reference category for this demographic variable in the regression.
* Significant difference (p<0.05) between this category and the reference category for this demographic variable in the regression.
Note: N=11 811 finalised problems for education and N=11 853 finalised problems for other demographic variables. Data were missing where totals are less than 12 327.

Respondents who had been unemployed had significantly lower odds of favourable outcomes (0.8) than other respondents (see Table 8.5). Respondents who had been unemployed reported favourable outcomes for 62.0 per cent of problems compared to 67.4 per cent for other respondents (see Table 8.6).

Respondents living in regional areas had higher odds of favourable outcomes (1.1) than respondents living in major city areas (see Table 8.5). Whereas favourable outcomes were reported for 68.5 per cent of the problems experienced by residents of regional areas, the corresponding percentage for residents of major city areas was 65.8 (see Table 8.6).

3. See Chapter 2, ‘Method: Multivariate analyses’ section, and Appendix Tables A2.8 and A2.9 (model 8a) for further details.

4. See Appendix Table A8.1 for the full results of this regression.



  


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Coumarelos, C, Macourt, D, People, J, MacDonald, HM, Wei, Z, Iriana, R & Ramsey, S 2012, Legal Australia-Wide Survey: legal need in Australia, Law and Justice Foundation of NSW, Sydney