Describing Socioeconomic Characteristics Of Respondents

State Variable

The first variable considered here is State. The variable indicates in which state the individual resides in. For the variables how many years of education and age, mean is the most appropriate measure of central tendency. This measure gives the average number of years of education and the average age of the individuals. The variable State is a categorical variable. Thus, no measure of descriptive statistics can be appropriate in measuring this variable.

Most people have studied for 10 years, 12 years and 15 years. Most of the people are from New South Wales and most of the people fall in the age group of 51-60 years. These data can be clearly visible from the histograms given below. The variables in this part are all discrete variables. The grouping of ages is also taken in such a way that it is a discrete variable. Thus, the data can be represented most appropriately by a bar graph. Bar graph is the most appropriate chart to represent discrete data.

Statistics
	R: How many years of education have you completed?	State Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	R: Age (10yr categories)
N	Valid	2705	2775	2737
Missing	76	6	44
Mean	13.57	2.58	3.52
Median	14.00	2.00	4.00
Mode	12	1	4
Std. Deviation	3.640	1.597	1.632
Variance	13.246	2.551	2.663
Skewness	.330	1.011	.131
Std. Error of Skewness	.047	.046	.047
Kurtosis	1.389	.441	-.819
Std. Error of Kurtosis	.094	.093	.094
Range	40	7	6
Minimum	0	1	1
Maximum	40	8	7
Percentiles	25	11.00	1.00	2.00
50	14.00	2.00	4.00
75	16.00	3.00	5.00

R: How many years of education have you completed?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	0	2	.1	.1	.1
2	6	.2	.2	.3
3	4	.1	.1	.4
4	2	.1	.1	.5
5	10	.4	.4	.9
6	23	.8	.9	1.7
7	36	1.3	1.3	3.1
8	88	3.2	3.3	6.3
9	117	4.2	4.3	10.6
10	309	11.1	11.4	22.1
11	208	7.5	7.7	29.8
12	322	11.6	11.9	41.7
13	210	7.6	7.8	49.4
14	263	9.5	9.7	59.1
15	304	10.9	11.2	70.4
16	257	9.2	9.5	79.9
17	194	7.0	7.2	87.1
18	141	5.1	5.2	92.3
19	63	2.3	2.3	94.6
20	76	2.7	2.8	97.4
21	20	.7	.7	98.2
22	23	.8	.9	99.0
23	11	.4	.4	99.4
24	2	.1	.1	99.5
25	5	.2	.2	99.7
26	3	.1	.1	99.8
27	1	.0	.0	99.8
28	3	.1	.1	99.9
30	1	.0	.0	100.0
40	1	.0	.0	100.0
Total	2705	97.3	100.0
Missing	Missing	76	2.7
Total	2781	100.0

State
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	New South Wales	898	32.3	32.4	32.4
Victoria	687	24.7	24.8	57.1
Queensland	538	19.3	19.4	76.5
South Australia	235	8.5	8.5	85.0
Western Australia	267	9.6	9.6	94.6
Tasmania	81	2.9	2.9	97.5
ACT	51	1.8	1.8	99.4
Northern Territory	18	.6	.6	100.0
Total	2775	99.8	100.0
Missing	Missing	6	.2
Total	2781	100.0

R: Age (10yr categories)
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	18-30	376	13.5	13.7	13.7
31-40	419	15.1	15.3	29.0
41-50	568	20.4	20.8	49.8
51-60	589	21.2	21.5	71.3
61-70	428	15.4	15.6	87.0
71-80	272	9.8	9.9	96.9
Over 80	85	3.1	3.1	100.0
Total	2737	98.4	100.0
Missing	Missing	44	1.6
Total	2781	100.0

In this part the variables were recoded to a new variable by eliminating the missing values, the unknown values and the responses not known. Including these responses will give vague results of the analysis as these responses have no effect with the analysis. Thus, the values coded to them will give wrong interpretation.

In this part, it can be seen that the p-values of the Chi-Square statistics for education in predicting attitude towards government’s performance in controlling crime are less than 0.05 (The level of significance). Thus, it can be said that there is no association between the variables for which the cross tabulation has been conducted.

B8d Recoded * M5 Recoded Crosstabulation
Count
	M5 Recoded	Total
1.00	2.00	3.00	4.00	5.00
B8d Recoded	1.00	14	7	11	9	2	43
2.00	233	114	219	140	105	811
3.00	310	166	294	141	86	997
4.00	188	106	165	60	45	564
5.00	53	39	50	19	20	181
Total	798	432	739	369	258	2596

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	37.461^a	16	.002
Likelihood Ratio	37.388	16	.002
Linear-by-Linear Association	11.927	1	.001
N of Valid Cases	2596
a. 1 cells (4.0%) have expected count less than 5. The minimum expected count is 4.27.

L2 Recoded * M7 Recoded Crosstabulation
Count
	M7 Recoded	Total
1.00	2.00	3.00	4.00	5.00	6.00	7.00	8.00
L2 Recoded	1.00	7	4	4	3	5	3	2	3	31
2.00	11	19	3	4	4	3	0	1	45
3.00	75	109	23	21	35	21	7	6	297
4.00	114	159	67	45	102	46	16	33	582
5.00	80	97	82	46	98	41	26	41	511
6.00	58	91	93	63	108	50	32	54	549
7.00	10	17	34	24	34	18	14	37	188
8.00	3	4	11	13	10	8	8	10	67
9.00	0	2	5	7	4	0	0	5	23
10.00	1	1	3	0	3	1	1	4	14
Total	359	503	325	226	403	191	106	194	2307

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	293.463^a	63	.000
Likelihood Ratio	300.098	63	.000
Linear-by-Linear Association	151.438	1	.000
N of Valid Cases	2307
a. 26 cells (32.5%) have expected count less than 5. The minimum expected count is .64.

In this part, it can be seen that the p-values of the Chi-Square statistics for occupation in predicting identification of social position are less than 0.05 (The level of significance). Thus, it can be said that there is no association between the variables for which the cross tabulation has been conducted.

B23 Recoded * M15 Recoded Crosstabulation
Count
	M15 Recoded	Total
1.00	2.00
B23 Recoded	1.00	196	91	287
2.00	450	157	607
3.00	542	148	690
4.00	512	122	634
5.00	340	58	398
Total	2040	576	2616

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	36.746^a	4	.000
Likelihood Ratio	36.596	4	.000
Linear-by-Linear Association	35.722	1	.000
N of Valid Cases	2616
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 63.19.

In this part, it can be seen that the p-values of the Chi-Square statistics for birth place in predicting size of social contact are less than 0.05 (The level of significance). Thus, it can be said that there is no association between the variables for which the cross tabulation has been conducted.

To test whether there is any difference between the incomes of males and females, independent sample t-test has been done in SPSS. From the results, it can be seen that the p-value or the Sig (2-tailed) value is 0.000, which is less than the level of significance (0.05). Thus, there is significant difference between the individual annual incomes of males and females in the society. Moreover, it is clear from the Group statistics table that the income of males is comparatively higher than that of females. Thus, there is a socio economic difference in the income of the individuals with respect to gender.

Group Statistics
	R: Gender	N	Mean	Std. Deviation	Std. Error Mean
Individual income – gross annual (4 categories)	Female	1311	2.06	.932	.026
Male	1220	2.65	1.006	.029

Independent Samples Test for Individual Income with respect to gender.
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
Individual income – gross annual (4 categories)	Equal variances assumed	23.842	.000	-15.320	2529	.000	-.590	.039	-.666	-.515
Equal variances not assumed			-15.278	2474.476	.000	-.590	.039	-.666	-.515

To test whether there is any difference in the highest degree of education perused by a male and a female, again, independent sample t-test has been conducted. According to the results of the t-test, it can be seen that the p-value is 0.342, which is greater than the level of significance. Thus, there is no significant difference in the highest degree of education perused by a male and a female.

Independent Samples Test
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
R: Highest level of education completed since leaving school	Equal variances assumed	10.744	.001	.950	2696	.342	.048	.051	-.052	.149
Equal variances not assumed			.953	2690.430	.341	.048	.051	-.051	.148

Thus, it can be said the Australian society still considers women downtrodden and do not consider them fit for work. This is why the women earn less than the men even though they have almost the same level of qualification.

The variables that have been considered to affect the socio economic status of the people are highest level of education and individual income. From the cross tabulation table of Individual income and gender, it can be clearly seen that more women earn the lesser annual income than men. Men are more likely to earn the higher annual incomes. Thus, men are given much more priority in the in the country than the women.

Individual income – gross annual (4 categories) * R: Gender Crosstabulation
Count
	R: Gender	Total
Female	Male
Individual income – gross annual (4 categories)	$0 to $15,599 per annum	446	216	662
$15,600 to $36,399 per annum	433	261	694
$36,400 to $77,999 per annum	344	481	825
$78,000 and over per annum	88	262	350
Total	1311	1220	2531

Again, from the cross tabulation table for the highest level of education, it can be seen that women are advance than men in most of the cases. Only in trade or apprenticeship, more men have studied than women. Thus, any trade related studies are perused more by men than women. Women just get high education. They do not get the proper education that is necessary work. Thus, this explains their low annual income than men. Thus, these indicate that men are more prioritized than women in the socio economic status.

R: Highest level of education (5 categories) * R: Gender Crosstabulation
Count
	R: Gender	Total
Female	Male
R: Highest level of education (5 categories)	Less than Year 12	351	219	570
Year 12	164	128	292
Trade/Apprenticeship	88	359	447
Certificate/Diploma	481	284	765
Bachelor degree and above	349	302	651
Total	1433	1292	2725

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