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In this report, we have done quantitative data analysis report using SPSS in order to answer specific research questions. This report will include a total number of 40 data variables of 120 frequencies. We have would write the background section, an account of the process of analysis and discussion of the findings. These are supported by tables and charts as per needed. We have calculated the appropriate descriptive statistics including dispersion and central tendency to describe participants and the relationships they are engaging in. We have selected visual observations of tables and graphs to display the information. The mean results for the IPVASr subscales (control, abuse, violence) are labeled as “MeanAbuse”, “MeanViolence” and “MeanControl”. The summary statistics of MCSDS and IPVASr are calculated. The regression analysis between predictor identifier and Mean scores including MarloweCrowne score of desirability are calculated and their results are interpreted to test the association.
Gender, Age, Ethnicity, Relationship Status and Sexual Orientation are nominal data. Rests of data are categorical. We have scaled the data by Likert scale. Both of the qualitative and quantitative data are analyzed simultaneously.
The overall research questions of the report are:
(Age vs. Mean score of control)
Case Processing Summary 


Cases 

Valid 
Missing 
Total 

N 
Percent 
N 
Percent 
N 
Percent 

Age * Mean score of the control subscale 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Age * Mean score of the control subscale Crosstabulation 

Count 


Mean score of the control subscale 
Total 

2.25 
2.50 
2.75 
3.25 
3.50 
3.75 
4.00 
4.25 
4.50 

Age 
16 
6 
6 
0 
0 
0 
0 
0 
0 
0 
12 
17 
18 
12 
6 
6 
12 
6 
0 
6 
0 
66 

18 
6 
0 
6 
0 
6 
6 
6 
0 
6 
36 

19 
0 
0 
0 
0 
0 
0 
6 
0 
0 
6 

Total 
30 
18 
12 
6 
18 
12 
12 
6 
6 
120 
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
114.121^{a} 
24 
.000 
Likelihood Ratio 
104.060 
24 
.000 
LinearbyLinear Association 
29.578 
1 
.000 
N of Valid Cases 
120 


a. 27 cells (75.0%) have expected count less than 5. The minimum expected count is .30. 
Symmetric Measures 


Value 
Approx. Sig. 

Nominal by Nominal 
Phi 
.975 
.000 
Cramer's V 
.563 
.000 

N of Valid Cases 
120 


a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 
The graphs and tables indicate that age and mean score of control of has significant relation in case of 17 years old young people.
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Age and Mean score of control. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 114.121 with degrees of freedom 3. χ2 (24) = 114.121 and pvalue is 0.0.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between Age and Mean score of control.
(Age vs Mean score of Abuse)
Case Processing Summary 


Cases 

Valid 
Missing 
Total 

N 
Percent 
N 
Percent 
N 
Percent 

Age * Mean score of the abuse subscale 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Age * Mean score of the abuse subscale Crosstabulation 

Count 


Mean score of the abuse subscale 
Total 

1.13 
1.25 
1.50 
1.63 
1.75 
1.88 
2.00 
2.13 
2.25 
2.88 

Age 
16 
0 
6 
0 
6 
0 
0 
0 
0 
0 
0 
12 
17 
6 
0 
18 
6 
6 
0 
6 
0 
12 
12 
66 

18 
0 
0 
0 
0 
0 
6 
0 
12 
18 
0 
36 

19 
0 
0 
0 
0 
0 
0 
0 
6 
0 
0 
6 

Total 
6 
6 
18 
12 
6 
6 
6 
18 
30 
12 
120 
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
194.121^{a} 
27 
.000 
Likelihood Ratio 
176.881 
27 
.000 
LinearbyLinear Association 
17.168 
1 
.000 
N of Valid Cases 
120 


a. 32 cells (80.0%) have expected count less than 5. The minimum expected count is .30. 
Symmetric Measures 


Value 
Approx. Sig. 

Nominal by Nominal 
Phi 
1.272 
.000 
Cramer's V 
.734 
.000 

N of Valid Cases 
120 


a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 
The graphs and tables indicate that Age and Mean score of abuse of has significant relation in case of 17 and 18 years old young people.
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Age and Mean score of control. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 194.121 with degrees of freedom 3. χ2 (27) = 194.121 and pvalue is 0.0.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between Age and Mean score of abuse.
(Age vs. mean score of violence)
Case Processing Summary 


Cases 

Valid 
Missing 
Total 

N 
Percent 
N 
Percent 
N 
Percent 

Age * Mean score of the violence subscale 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Age * Mean score of the violence subscale Crosstabulation 

Count 


Mean score of the violence subscale 
Total 

1.00 
1.20 
1.40 
1.60 
1.80 
2.00 
2.20 
2.40 
2.60 

Age 
16 
0 
6 
0 
0 
0 
6 
0 
0 
0 
12 
17 
0 
6 
12 
18 
0 
12 
0 
6 
12 
66 

18 
0 
12 
0 
0 
12 
6 
6 
0 
0 
36 

19 
6 
0 
0 
0 
0 
0 
0 
0 
0 
6 

Total 
6 
24 
12 
18 
12 
24 
6 
6 
12 
120 
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
215.909^{a} 
24 
.000 
Likelihood Ratio 
156.998 
24 
.000 
LinearbyLinear Association 
5.488 
1 
.019 
N of Valid Cases 
120 


a. 27 cells (75.0%) have expected count less than 5. The minimum expected count is .30. 
Symmetric Measures 


Value 
Approx. Sig. 

Nominal by Nominal 
Phi 
1.341 
.000 
Cramer's V 
.774 
.000 

N of Valid Cases 
120 


a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 
The graphs and tables indicate that Age and Mean score of violence of has significant relation in case of 17 and 18 years old young people.
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Age and Mean score of control. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 215.909 with degrees of freedom 3. χ2 (24) = 215.909 and pvalue is 0.0.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between Age and Mean score of violence.
Descriptive of IPVASr
Descriptive Statistics 


N 
Range 
Minimum 
Maximum 
Mean 
Std. Deviation 
Variance 
Skewness 
Kurtosis 

Statistic 
Statistic 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 

IPVASr1 
120 
2 
1 
3 
1.50 
.054 
.594 
.353 
.734 
.221 
.417 
.438 
IPVASr2 
120 
3 
1 
4 
1.80 
.080 
.875 
.766 
1.321 
.221 
1.450 
.438 
IPVASr5 
120 
3 
1 
4 
1.80 
.074 
.816 
.666 
.952 
.221 
.618 
.438 
IPVASr8 
120 
2 
1 
3 
2.10 
.064 
.703 
.494 
.142 
.221 
.950 
.438 
IPVASr11 
120 
2 
1 
3 
1.50 
.054 
.594 
.353 
.734 
.221 
.417 
.438 
IPVASr12 
120 
4 
1 
5 
3.10 
.136 
1.486 
2.208 
.081 
.221 
1.431 
.438 
IPVASr13 
120 
4 
1 
5 
3.30 
.077 
.846 
.716 
.620 
.221 
1.159 
.438 
IPVASr14 
120 
3 
1 
4 
3.25 
.076 
.833 
.693 
1.033 
.221 
.607 
.438 
IPVASr17 
120 
4 
1 
5 
2.80 
.099 
1.082 
1.170 
.078 
.221 
.563 
.438 
IPVASr3 
120 
3 
1 
4 
1.75 
.070 
.770 
.592 
1.139 
.221 
1.552 
.438 
IPVASr4 
120 
1 
1 
2 
1.50 
.046 
.502 
.252 
.000 
.221 
2.034 
.438 
IPVASr6 
120 
3 
1 
4 
1.70 
.072 
.784 
.615 
1.224 
.221 
1.558 
.438 
IPVASr7 
120 
3 
1 
4 
2.40 
.084 
.920 
.847 
.300 
.221 
.707 
.438 
IPVASr9 
120 
3 
1 
4 
1.75 
.070 
.770 
.592 
1.139 
.221 
1.552 
.438 
IPVASr10 
120 
3 
1 
4 
1.80 
.080 
.875 
.766 
.862 
.221 
.067 
.438 
IPVASr15 
120 
3 
1 
4 
2.20 
.090 
.984 
.968 
.556 
.221 
.638 
.438 
IPVASr16 
120 
3 
1 
4 
2.55 
.079 
.868 
.754 
.079 
.221 
.667 
.438 
Valid N (listwise) 
120 











The descriptive statistics table of IPVASr indicates that mean of IPVASr13 is maximum (3.30) and the mean of IPVASr1, IPVASr4 and IPVASr11 is minimum (1.50). It means that people generally disagree with the question regarding IPVASr13 and agrees with IPVASr1, IPVASr4 and IPVAS11. The standard deviation of the responses is least for IPVASr4 (0.502) and maximum for IPVASr12 (1.486). It interprets that the variability of responses regarding the question is maximum for IPVASr12 and minimum for IPVASr4. The standard error for Skewness and Kurtosis respectively for IPVASr are 0.221 and 0.438.
Descriptive Statistics 


N 
Range 
Minimum 
Maximum 
Mean 
Std. Deviation 
Variance 
Skewness 
Kurtosis 

Statistic 
Statistic 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 

MC1 
120 
1 
1 
2 
1.35 
.044 
.479 
.229 
.637 
.221 
1.622 
.438 
MC2 
120 
1 
1 
2 
1.40 
.045 
.492 
.242 
.413 
.221 
1.860 
.438 
MC3 
120 
1 
1 
2 
1.50 
.046 
.502 
.252 
.000 
.221 
2.034 
.438 
MC4 
120 
1 
1 
2 
1.45 
.046 
.500 
.250 
.204 
.221 
1.992 
.438 
MC5 
120 
1 
1 
2 
1.45 
.046 
.500 
.250 
.204 
.221 
1.992 
.438 
MC6 
120 
1 
1 
2 
1.20 
.037 
.402 
.161 
1.519 
.221 
.312 
.438 
MC7 
120 
1 
1 
2 
1.45 
.046 
.500 
.250 
.204 
.221 
1.992 
.438 
MC8 
120 
1 
1 
2 
1.55 
.046 
.500 
.250 
.204 
.221 
1.992 
.438 
MC9 
120 
1 
1 
2 
1.20 
.037 
.402 
.161 
1.519 
.221 
.312 
.438 
MC10 
120 
1 
1 
2 
1.20 
.037 
.402 
.161 
1.519 
.221 
.312 
.438 
MC11 
120 
1 
1 
2 
1.20 
.037 
.402 
.161 
1.519 
.221 
.312 
.438 
MC12 
120 
1 
1 
2 
1.80 
.037 
.402 
.161 
1.519 
.221 
.312 
.438 
MC13 
120 
1 
1 
2 
1.15 
.033 
.359 
.129 
1.985 
.221 
1.974 
.438 
Valid N (listwise) 
120 











The descriptive statistics table of MCSDS indicates that mean of MC12 is maximum (1.80) and the mean of MC13 is minimum (1.15). It means that people generally disagree with the question regarding MC12 and agrees with MC13. The standard deviation of the responses is least for MC13 (0.359) and maximum for MC4, MC5, MC7 and MC8 (0.500). It interprets that the variability of responses regarding the question is maximum for MC4, MC5, MC7, MC8 and minimum for MC13.
Surprisingly, the standard error for Skewness and Kurtosis are respectively for MCSDS are 0.221 and 0.438, which is same as IPVASr.
(Totals, percentages, range and mean results of Gender, Age, Ethnicity, Relationship status, Sexual orientation)
Statistics 


Gender 
Age 
Ethnicity 
Relationship Status 
Sexual Orientation 

N 
Valid 
120 
120 
120 
120 
120 
Missing 
0 
0 
0 
0 
0 
The table shows that there are 120 variables are present here and no missing values are present here.
Gender 


Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid 
Male 
66 
55.0 
55.0 
55.0 
Female 
54 
45.0 
45.0 
100.0 

Total 
120 
100.0 
100.0 

The male frequency is 66 (55%) and female frequency is 54 (45%) among all total 120 population.
Age 


Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid 
16 
12 
10.0 
10.0 
10.0 
17 
66 
55.0 
55.0 
65.0 

18 
36 
30.0 
30.0 
95.0 

19 
6 
5.0 
5.0 
100.0 

Total 
120 
100.0 
100.0 

The frequency of age 17 is maximum (66) that is 55% of total population. The frequency of age 19 is minimum (6) that is 5% of the population.
Ethnicity 


Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid 
White 
66 
55.0 
55.0 
55.0 
Asian 
6 
5.0 
5.0 
60.0 

Black 
30 
25.0 
25.0 
85.0 

Mixed 
12 
10.0 
10.0 
95.0 

ChineseOther 
6 
5.0 
5.0 
100.0 

Total 
120 
100.0 
100.0 

The frequency of white people is maximum (66) with 55% of total population. The frequency of Asian and Chinese people is minimum (6 people for each category) with 5% of total population.
Relationship Status 


Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid 
Single 
42 
35.0 
35.0 
35.0 
OnOff 
18 
15.0 
15.0 
50.0 

New 
24 
20.0 
20.0 
70.0 

Long Term 
36 
30.0 
30.0 
100.0 

Total 
120 
100.0 
100.0 

The relationship status of “Single” is maximum (42) with 35% of the total population. The status “OnOff” is minimum (18) with 15% of the total population.
Sexual Orientation 


Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid 
Straight 
80 
66.7 
66.7 
66.7 
Bisexual 
23 
19.2 
19.2 
85.8 

Gay 
14 
11.7 
11.7 
97.5 

Don't Know 
3 
2.5 
2.5 
100.0 

Total 
120 
100.0 
100.0 

The Sexual orientation of “Straight” is maximum (80) with 66.7% of the total population. The people who denied giving their responses were tabulated in “Don’t Know” category. The frequency of “Don’t Know” category is lowest in number that is 3 and 2.5% of the total population.
Descriptive Statistics 


N 
Range 
Minimum 
Maximum 
Mean 
Std. Deviation 
Variance 
Skewness 
Kurtosis 

Statistic 
Statistic 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 

Gender 
120 
1 
1 
2 
1.45 
.046 
.500 
.250 
.204 
.221 
1.992 
.438 
Age 
120 
3 
1 
4 
2.30 
.065 
.717 
.514 
.317 
.221 
.049 
.438 
Ethnicity 
120 
4 
1 
5 
2.05 
.118 
1.289 
1.661 
.768 
.221 
.731 
.438 
Relationship Status 
120 
3 
1 
4 
2.45 
.114 
1.249 
1.561 
.037 
.221 
1.639 
.438 
Sexual Orientation 
120 
3 
1 
4 
1.50 
.073 
.799 
.639 
1.457 
.221 
1.137 
.438 
Valid N (listwise) 
120 











The descriptive statistic table indicates that as mean of gender is 1.45, therefore, number of female is less than the number of males. The mean of the age is 2.3; it indicates that total number of young people of ages 17 and 18 are maximum in number. Average of Ethnicity is 2.05 and Relationship status is 2.45. The mean of Sexual orientation interprets that most people are straight in nature. The standard deviation of the “Ethnicity” (1.289) and “Relationship Status” (1.249) is high significantly than other three factors that are gender, age and sexual orientation.
(Crosstabs function to explore relation: Gender, Age, Ethnicity, Relationship status and sexual orientation)
Case Processing Summary 


Cases 

Valid 
Missing 
Total 

N 
Percent 
N 
Percent 
N 
Percent 

Sexual Orientation * Gender 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Sexual Orientation * Age 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Sexual Orientation * Ethnicity 
120 
100.0% 
0 
0.0% 
120 
100.0% 
Sexual Orientation * Relationship Status 
120 
100.0% 
0 
0.0% 
120 
100.0% 
In the following tables, we are finding the crossvalue summary of the factor Sexual Orientation with respect to Gender, Age, Ethnicity and Relationship Status.
Crosstab 

Count 


Gender 
Total 

Male 
Female 

Sexual Orientation 
Straight 
46 
34 
80 
Bisexual 
12 
11 
23 

Gay 
8 
6 
14 

Don't Know 
0 
3 
3 

Total 
66 
54 
120 
The table indicates that most of the male are straight (46) in nature. No male denied giving his responses. Most of the females are straight (34) in nature. Very few female refused to deliver their responses.
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
3.969^{a} 
3 
.265 
Likelihood Ratio 
5.094 
3 
.165 
LinearbyLinear Association 
1.318 
1 
.251 
N of Valid Cases 
120 


a. 2 cells (25.0%) have expected count less than 5. The minimum expected count is 1.35. 
Symmetric Measures 


Value 
Asymp. Std. Error^{a} 
Approx. T^{b} 
Approx. Sig. 

Nominal by Nominal 
Contingency Coefficient 
.179 


.265 
Interval by Interval 
Pearson's R 
.105 
.090 
1.150 
.253^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.082 
.091 
.898 
.371^{c} 
N of Valid Cases 
120 




a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 3.969 with degrees of freedom 3. χ2 (3) = 3.969 and pvalue is 0.265.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we reject the null hypothesis of strong association between gender and sexual orientation.
Crosstab 

Count 


Age 
Total 

16 
17 
18 
19 

Sexual Orientation 
Straight 
7 
42 
31 
0 
80 
Bisexual 
2 
15 
1 
5 
23 

Gay 
3 
7 
3 
1 
14 

Don't Know 
0 
2 
1 
0 
3 

Total 
12 
66 
36 
6 
120 
The table indicates that most of the straight people (42) have age 17. Age 16 people are majorly straight (7) and age 18 people are also straight (31). However, majorly of the people is Bisexual (5) in case of 19 years old.
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
27.566^{a} 
9 
.001 
Likelihood Ratio 
28.390 
9 
.001 
LinearbyLinear Association 
.102 
1 
.749 
N of Valid Cases 
120 


a. 10 cells (62.5%) have expected count less than 5. The minimum expected count is .15. 
Symmetric Measures 


Value 
Asymp. Std. Error^{a} 
Approx. T^{b} 
Approx. Sig. 

Nominal by Nominal 
Contingency Coefficient 
.432 


.001 
Interval by Interval 
Pearson's R 
.029 
.092 
.319 
.751^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.068 
.096 
.736 
.463^{c} 
N of Valid Cases 
120 




a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 27.566 with degrees of freedom 3. χ2 (9) = 27.566 and pvalue is 0.001.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between age and sexual orientation.
Crosstab 

Count 


Ethnicity 
Total 

White 
Asian 
Black 
Mixed 
ChineseOther 

Sexual Orientation 
Straight 
43 
5 
24 
4 
4 
80 
Bisexual 
15 
0 
1 
6 
1 
23 

Gay 
7 
1 
3 
2 
1 
14 

Don't Know 
1 
0 
2 
0 
0 
3 

Total 
66 
6 
30 
12 
6 
120 
The table interprets that according to the ethnicity, majorly white people are straight (43) in nature followed by bisexual (15). Asian peoples are mainly straight (5). Black (24) and Chinese (4) people are too straight in nature. Surprisingly, mixed people (6) category is majorly “Bisexual” in nature.
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
18.144^{a} 
12 
.111 
Likelihood Ratio 
19.817 
12 
.071 
LinearbyLinear Association 
.388 
1 
.533 
N of Valid Cases 
120 


a. 14 cells (70.0%) have expected count less than 5. The minimum expected count is .15. 
Symmetric Measures 


Value 
Asymp. Std. Error^{a} 
Approx. T^{b} 
Approx. Sig. 

Nominal by Nominal 
Contingency Coefficient 
.362 


.111 
Interval by Interval 
Pearson's R 
.057 
.089 
.621 
.536^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.039 
.094 
.421 
.675^{c} 
N of Valid Cases 
120 




a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 18.144 with degrees of freedom 3. χ2 (12) = 18.144 and pvalue is 0.111.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we reject the null hypothesis of strong association between Ethnicity and sexual orientation.
Crosstab 

Count 


Relationship Status 
Total 

Single 
OnOff 
New 
Long Term 

Sexual Orientation 
Straight 
30 
14 
17 
19 
80 
Bisexual 
8 
1 
2 
12 
23 

Gay 
3 
3 
4 
4 
14 

Don't Know 
1 
0 
1 
1 
3 

Total 
42 
18 
24 
36 
120 
The sexual orientation according to the relationship status interprets that the entire Single, OnOff, New and Long Term status persons are straight in nature. Long Term relationship status has tendency of Bisexuality (12) with significance.
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
10.936^{a} 
9 
.280 
Likelihood Ratio 
11.807 
9 
.224 
LinearbyLinear Association 
1.897 
1 
.168 
N of Valid Cases 
120 


a. 10 cells (62.5%) have expected count less than 5. The minimum expected count is .45. 
Symmetric Measures 


Value 
Asymp. Std. Error^{a} 
Approx. T^{b} 
Approx. Sig. 

Nominal by Nominal 
Contingency Coefficient 
.289 


.280 
Interval by Interval 
Pearson's R 
.126 
.087 
1.383 
.169^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.147 
.089 
1.611 
.110^{c} 
N of Valid Cases 
120 




a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
Now, we are interested to test the hypothesis related to assumption related to independent chisquare. We found that the value of Pearson chisquare of the cross processing summary of crosstabs between relationship status and sexual orientation. We can rely on our significance test for which we use Pearson ChiSquare. The value Pearson’s chisquare is 10.936 with degrees of freedom 9. χ2 (9) = 10.936 and pvalue is 0.280.
We usually say that the association between two variables is statistically significant if and only if asymptotic 2sided significance is less than 0.05. Hence, we reject the null hypothesis of strong association between relationship status and sexual orientation.
The regression analysis was employed in order to empirically identify whether the Sexual orientation is a statistically important to all other factors or not. The equation is, Y_{1}=β_{0} +β_{1}*X_{1 }+ µ, where Y_{1} refers to Sexual orientation, β_{0} refers to the constant or the intercept, X_{1} refers mean score of abuse or mean score of violence or mean score of control or Total score of MarloweCrowne desirability, β_{1} refers to the change of coefficient for the different predictors, while µ refers to the error term. The regression result shows the goodness of fit for the regression between the Predictors and response.
Linear regression model is a commonly used generalized form of regression model where the response factor linearly relates with the parameters of explanatory variables. In linear regression model, the response variable should be continuous and dependent with explanatory variables (Faraway 2016). The high value (near to 1) gives the signal of strong linear relationship, the lowest value (near to 1) shows strong negative linear relationship and the value near to zero gives the signal to weakest linear relationship with response and predictors. Multiple regression equation also can calculate the regression value if all the parameters of simple linear regression taken together in case of dichotomous (continuous or discrete) response parameter (Darlington and Hayes 2016).
(Response: Participant Identifier, predictor: Mean abuse score)
Model Summary^{b} 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

1 
.013^{a} 
.000 
.009 
12.339 
.000 
.020 
1 
112 
.888 
a. Predictors: (Constant), Mean score of the abuse subscale 

b. Dependent Variable: Participant Identifier 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
3.060 
1 
3.060 
.020 
.888^{b} 
Residual 
17052.098 
112 
152.251 



Total 
17055.158 
113 




a. Dependent Variable: Participant Identifier 

b. Predictors: (Constant), Mean score of the abuse subscale 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
95.0% Confidence Interval for B 
Correlations 

B 
Std. Error 
Beta 
Lower Bound 
Upper Bound 
Zeroorder 
Partial 
Part 

1 
(Constant) 
20.141 
5.077 

3.967 
.000 
10.081 
30.201 



Mean score of the abuse subscale 
.354 
2.497 
.013 
.142 
.888 
4.593 
5.301 
.013 
.013 
.013 

a. Dependent Variable: Participant Identifier 
As the value of multiple R^{2} is 0.0, we can tell that there is no significant association between participant identifier and Mean score of abuse subscales. It also interprets 0% of the variations in the participant identifier could be explained by the Mean scores of abuse subscales. The Value of adjusted R^{2} (0.009) indicates a very bad (0 to 0.3 or 0 to 0.3) fitting as per the rules of goodness of fit.
The significant pvalue of predictor identifier and Mean score of the abuse subscale (0.888) has pvalue more than 0.05. Therefore, we cannot accept the null hypothesis of linear relationship of these two factors at 95% confidence limit.
The fitting of regression graph between Participant identifier and Mean score of abuse is not at all good.
(Response: Participant Identifier, predictor: Mean control score)
Model Summary^{b} 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

1 
.267^{a} 
.071 
.063 
11.894 
.071 
8.568 
1 
112 
.004 
a. Predictors: (Constant), Mean score of the control subscale 

b. Dependent Variable: Participant Identifier 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
1211.968 
1 
1211.968 
8.568 
.004^{b} 
Residual 
15843.190 
112 
141.457 



Total 
17055.158 
113 




a. Dependent Variable: Participant Identifier 

b. Predictors: (Constant), Mean score of the control subscale 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
95.0% Confidence Interval for B 
Correlations 

B 
Std. Error 
Beta 
Lower Bound 
Upper Bound 
Zeroorder 
Partial 
Part 

1 
(Constant) 
7.235 
4.780 

1.514 
.133 
2.236 
16.707 



Mean score of the control subscale 
4.327 
1.478 
.267 
2.927 
.004 
1.398 
7.256 
.267 
.267 
.267 

a. Dependent Variable: Participant Identifier 
As the value of multiple R^{2} is 0.071, we can tell that there is very weak significant association between participant identifier and Mean score of control subscales. It also interprets 7.1% of the variations in the participant identifier could be explained by the Mean score of the control. The Value of adjusted R^{2} (0.063) indicates a very bad (0 to 0.3 or 0 to 0.3) fitting as per the rules of goodness of fit.
The insignificant pvalue of participant identifier and Mean score of control subscales (0.004) has pvalue less than 0.05. Therefore, we reject the null hypothesis of linear relationship of these two factors at 95% confidence limit.
The fitting of regression graph between Participant identifier and Mean score of control is not good.
(Response: Participant Identifier, predictor: Mean violence score)
Model Summary^{b} 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

1 
.223^{a} 
.050 
.041 
12.028 
.050 
5.888 
1 
112 
.017 
a. Predictors: (Constant), Mean score of the violence subscale 

b. Dependent Variable: Participant Identifier 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
851.850 
1 
851.850 
5.888 
.017^{b} 
Residual 
16203.308 
112 
144.672 



Total 
17055.158 
113 




a. Dependent Variable: Participant Identifier 

b. Predictors: (Constant), Mean score of the violence subscale 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
95.0% Confidence Interval for B 
Correlations 

B 
Std. Error 
Beta 
Lower Bound 
Upper Bound 
Zeroorder 
Partial 
Part 

1 
(Constant) 
30.782 
4.248 

7.245 
.000 
22.364 
39.200 



Mean score of the violence subscale 
5.689 
2.344 
.223 
2.427 
.017 
10.334 
1.044 
.223 
.223 
.223 

a. Dependent Variable: Participant Identifier 
As the value of multiple R^{2} is 0.05, we can tell that there is very weak significant association between participant identifier and Mean score of violence subscales. It also interprets 5.0% of the variations in the participant identifier could be explained by the Mean score of the violence. The Value of adjusted R^{2} (0.041) indicates a very bad (0 to 0.3 or 0 to 0.3) fitting as per the rules of goodness of fit.
The insignificant pvalue of participant identifier and Mean score of violence subscales (0.017) has pvalue less than 0.05. Therefore, we reject the null hypothesis of linear relationship of these two factors at 95% confidence limit.
The fitting of regression graph between Participant identifier and Mean score of violence is not good.
(Response: Participant Identifier, predictor: Total MC score)
Model Summary^{b} 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

1 
.008^{a} 
.000 
.009 
12.340 
.000 
.008 
1 
112 
.929 
a. Predictors: (Constant), Total score of the MarloweCrowne Social Desireability Scale 

b. Dependent Variable: Participant Identifier 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
1.208 
1 
1.208 
.008 
.929^{b} 
Residual 
17053.950 
112 
152.267 



Total 
17055.158 
113 




a. Dependent Variable: Participant Identifier 

b. Predictors: (Constant), Total score of the MarloweCrowne Social Desireability Scale 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
95.0% Confidence Interval for B 
Correlations 

B 
Std. Error 
Beta 
Lower Bound 
Upper Bound 
Zeroorder 
Partial 
Part 

1 
(Constant) 
22.350 
16.970 

1.317 
.191 
11.273 
55.973 



Total score of the MarloweCrowne Social Desireability Scale 
.075 
.842 
.008 
.089 
.929 
1.743 
1.593 
.008 
.008 
.008 

a. Dependent Variable: Participant Identifier 
As the value of multiple R^{2} is 0.0, we can tell that there is very weak significant association between participant identifier and Total score of MarloweCrowne Social Desirability subscales. It also interprets 0.0% of the variations in the participant identifier could be explained by the Total score of MarloweCrowne Social Desirability Scale. The Value of adjusted R^{2} (0.009) indicates a very bad (0 to 0.3 or 0 to 0.3) fitting as per the rules of goodness of fit.
The insignificant pvalue of participant identifier and Total score of MarloweCrowne Social Desirability subscales (0.929) has pvalue higher than 0.05. Therefore, we accept the null hypothesis of linear relationship of these two factors at 95% confidence limit.
The fitting of regression graph between Participant identifier and Total score of the MarloweCrowne Social Desirability Scale is not good.
Group Statistics 


Participant Identifier 
N 
Mean 
Std. Deviation 
Std. Error Mean 
Mean score of the control subscale 
>= 20 
54 
3.3889 
.83365 
.11345 
< 20 
60 
2.9250 
.60768 
.07845 

Mean score of the abuse subscale 
>= 20 
54 
1.9583 
.30906 
.04206 
< 20 
60 
2.0000 
.57213 
.07386 

Mean score of the violence subscale 
>= 20 
54 
1.6000 
.50357 
.06853 
< 20 
60 
1.8800 
.42498 
.05487 

Total score of the MarloweCrowne Social Desireability Scale 
>= 20 
54 
20.0000 
.82416 
.11215 
< 20 
60 
20.2000 
1.73498 
.22399 
The table indicates that the subdivided part of Mean score of the abuse and Total score of the MarloweCrowne social desirability (greater than equals to 20 and less than 20) have almost equal mean. Oppositely, the Mean score of the control and Mean score of the violence have different mean for different subgroups.
Independent Samples Test 


Levene's Test for Equality of Variances 
ttest for Equality of Means 

F 
Sig. 
t 
df 
Sig. (2tailed) 
Mean Difference 
Std. Error Difference 
95% Confidence Interval of the Difference 

Lower 
Upper 

Mean score of the control subscale 
Equal variances assumed 
9.094 
.003 
3.418 
112 
.001 
.46389 
.13571 
.19501 
.73277 
Equal variances not assumed 


3.363 
96.075 
.001 
.46389 
.13793 
.19010 
.73767 

Mean score of the abuse subscale 
Equal variances assumed 
18.561 
.000 
.476 
112 
.635 
.04167 
.08751 
.21505 
.13172 
Equal variances not assumed 


.490 
92.623 
.625 
.04167 
.08500 
.21046 
.12713 

Mean score of the violence subscale 
Equal variances assumed 
4.015 
.048 
3.218 
112 
.002 
.28000 
.08700 
.45239 
.10761 
Equal variances not assumed 


3.190 
104.246 
.002 
.28000 
.08778 
.45408 
.10592 

Total score of the MarloweCrowne Social Desireability Scale 
Equal variances assumed 
15.974 
.000 
.772 
112 
.442 
.20000 
.25904 
.71326 
.31326 
Equal variances not assumed 


.798 
86.258 
.427 
.20000 
.25050 
.69795 
.29795 
The independent ttests (onesample) of mean score of control subscale, abuse subscale, violence subscale, MarloweCrowne Social Desirability Scale indicates the tvalues. The mean scores of each category are divided in 2 categories that are greater than equals to 20 and less than 20. The two subcategories of each category are compared to each other.
The pvalues for four categories are 0.003, 0.00, 0.48 and 0.00. All the values are less than 0.05. It interprets that we can reject the null hypothesis of unequal variances for each subcategory. According to the pvalues related to the ttests of Mean score of control and Mean score of violence were found to be respectively 0.001 and 0.002. These interpret that we can reject the null hypothesis of unequal variances in these two categories. Pvalues of ttests of Mean score of abuse subscale and Total score of the MarloweCrowne Social Desirability are respectively (0.635, 0.625) and (0.442, 0.427) interpret that we can accept the null hypothesis of unequal variances in these two categories.
No mean value was found to be associated with predictive identifier. Mean score of control and Mean score violence have high significance in unequal variances. No factor is linearly related with predictor identifier such as control, abuse, violence and desirability. The crosstabs function shows the signification of age (17 years), gender (male), ethnicity (white), relationship status (single) and sexual orientation (straight). The tables showed the SPSS generated graphs and tables. Overall, crosstab and simple linear regression is not found to be significantly associated with age or predictor identifier. Therefore, the abuse, control and violence is found to be disagreed by young people at a large scale.
Darlington, R.B. and Hayes, A.F., 2016. Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publications.
Faraway, J.J., 2014. Linear models with R. CRC press.
Gill, J., 1999. The insignificance of null hypothesis significance testing. Political Research Quarterly, 52(3), pp.647674.
Green, S.B. and Salkind, N.J., 2010. Using SPSS for Windows and Macintosh: Analyzing and understanding data. Prentice Hall Press.
Krueger, J., 2001. Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56(1), p.16.
Landau, Sabine. A handbook of statistical analyses using SPSS. CRC, 2004.
Norugis, M. J. (1988). The SPSS Guide to Data Analysis for SPSSX with.
Wong, E., Wei, T., Qi, Y. and Zhao, L., 2008, April. A crosstabbased statistical method for effective fault localization. In Software Testing, Verification, and Validation, 2008 1st International Conference on (pp. 4251). IEEE.
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