Quantitative Data Analysis Report Using SPSS

Background and Introduction

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 IPVAS-r 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 Marlowe-Crowne 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:

1. To what extent do young people agree with the use of violence, abuse and control in relationships?
2. What kind of relationships are young people engaging in?
3. Find the descriptive statistics, totals, percentages, range and mean results of Gender, Age, Ethnicity Relationship status and Sexual orientation.
4. What are the cross function summary to explore the association between Sexual orientation with Gender, Age, Ethnicity and Relationship status?
5. Do the participant group responses indicate high levels of agreement with any of the following IPVAS-r subscales; abuse, violence and/or control?
6. What is the result of independent t-tests Mean scores of control, violence, abuse and Marlowe-Crowne social desirability score.

Crosstabs1

(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

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	114.121a	24	.000
Likelihood Ratio	104.060	24	.000
Linear-by-Linear 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 chi-square. We found that the value of Pearson chi-square 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 Chi-Square. The value Pearson’s chi-square is 114.121 with degrees of freedom 3. χ2 (24) = 114.121 and p-value is 0.0.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided 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

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	194.121a	27	.000
Likelihood Ratio	176.881	27	.000
Linear-by-Linear 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 chi-square. We found that the value of Pearson chi-square 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 Chi-Square. The value Pearson’s chi-square is 194.121 with degrees of freedom 3. χ2 (27) = 194.121 and p-value is 0.0.

Research Questions

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided 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

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	215.909a	24	.000
Likelihood Ratio	156.998	24	.000
Linear-by-Linear 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 chi-square. We found that the value of Pearson chi-square 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 Chi-Square. The value Pearson’s chi-square is 215.909 with degrees of freedom 3. χ2 (24) = 215.909 and p-value is 0.0.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between Age and Mean score of violence. 

Descriptive of IPVAS-r 

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 IPVAS-r 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) 

Findings, Calculation, and Discussion

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 cross-value 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.

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	3.969a	3	.265
Likelihood Ratio	5.094	3	.165
Linear-by-Linear 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. Errora	Approx. Tb	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.179			.265
Interval by Interval	Pearson’s R	.105	.090	1.150	.253c
Ordinal by Ordinal	Spearman Correlation	.082	.091	.898	.371c
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 chi-square. We found that the value of Pearson chi-square of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson Chi-Square. The value Pearson’s chi-square is 3.969 with degrees of freedom 3. χ2 (3) = 3.969 and p-value is 0.265.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided significance is less than 0.05. Hence, we reject the null hypothesis of strong association between gender and sexual orientation. 

Sexual Orientation * Age

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.

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	27.566a	9	.001
Likelihood Ratio	28.390	9	.001
Linear-by-Linear 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. Errora	Approx. Tb	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.432			.001
Interval by Interval	Pearson’s R	-.029	.092	-.319	.751c
Ordinal by Ordinal	Spearman Correlation	-.068	.096	-.736	.463c
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 chi-square. We found that the value of Pearson chi-square of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson Chi-Square. The value Pearson’s chi-square is 27.566 with degrees of freedom 3. χ2 (9) = 27.566 and p-value is 0.001.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided significance is less than 0.05. Hence, we accept the null hypothesis of strong association between age and sexual orientation. 

Sexual Orientation * Ethnicity

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.

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	18.144a	12	.111
Likelihood Ratio	19.817	12	.071
Linear-by-Linear 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. Errora	Approx. Tb	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.362			.111
Interval by Interval	Pearson’s R	.057	.089	.621	.536c
Ordinal by Ordinal	Spearman Correlation	.039	.094	.421	.675c
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 chi-square. We found that the value of Pearson chi-square of the cross processing summary of crosstabs between Gender and sexual orientation. We can rely on our significance test for which we use Pearson Chi-Square. The value Pearson’s chi-square is 18.144 with degrees of freedom 3. χ2 (12) = 18.144 and p-value is 0.111.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided significance is less than 0.05. Hence, we reject the null hypothesis of strong association between Ethnicity and sexual orientation. 

Sexual Orientation * Relationship Status

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.  

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	10.936a	9	.280
Likelihood Ratio	11.807	9	.224
Linear-by-Linear 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. Errora	Approx. Tb	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.289			.280
Interval by Interval	Pearson’s R	.126	.087	1.383	.169c
Ordinal by Ordinal	Spearman Correlation	.147	.089	1.611	.110c
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 chi-square. We found that the value of Pearson chi-square 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 Chi-Square. The value Pearson’s chi-square is 10.936 with degrees of freedom 9. χ2 (9) = 10.936 and p-value is 0.280.

We usually say that the association between two variables is statistically significant if and only if asymptotic 2-sided 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, Y₁=β₀ +β₁*X₁ + µ, where Y₁ refers to Sexual orientation, β₀ refers to the constant or the intercept, X₁ refers mean score of abuse or mean score of violence or mean score of control or Total score of Marlowe-Crowne desirability, β₁ 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 Summaryb
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	.013a	.000	-.009	12.339	.000	.020	1	112	.888
a. Predictors: (Constant), Mean score of the abuse subscale
b. Dependent Variable: Participant Identifier

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.060	1	3.060	.020	.888b
Residual	17052.098	112	152.251
Total	17055.158	113
a. Dependent Variable: Participant Identifier
b. Predictors: (Constant), Mean score of the abuse subscale

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B	Correlations
B	Std. Error	Beta	Lower Bound	Upper Bound	Zero-order	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 R² 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 R² (-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 p-value of predictor identifier and Mean score of the abuse subscale (0.888) has p-value 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 Summaryb
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	.267a	.071	.063	11.894	.071	8.568	1	112	.004
a. Predictors: (Constant), Mean score of the control subscale
b. Dependent Variable: Participant Identifier

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1211.968	1	1211.968	8.568	.004b
Residual	15843.190	112	141.457
Total	17055.158	113
a. Dependent Variable: Participant Identifier
b. Predictors: (Constant), Mean score of the control subscale

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B	Correlations
B	Std. Error	Beta	Lower Bound	Upper Bound	Zero-order	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 R² 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 R² (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 p-value of participant identifier and Mean score of control subscales (0.004) has p-value 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 Summaryb
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	.223a	.050	.041	12.028	.050	5.888	1	112	.017
a. Predictors: (Constant), Mean score of the violence subscale
b. Dependent Variable: Participant Identifier

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	851.850	1	851.850	5.888	.017b
Residual	16203.308	112	144.672
Total	17055.158	113
a. Dependent Variable: Participant Identifier
b. Predictors: (Constant), Mean score of the violence subscale

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B	Correlations
B	Std. Error	Beta	Lower Bound	Upper Bound	Zero-order	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 R² 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 R² (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 p-value of participant identifier and Mean score of violence subscales (0.017) has p-value 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 Summaryb
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	.008a	.000	-.009	12.340	.000	.008	1	112	.929
a. Predictors: (Constant), Total score of the Marlowe-Crowne Social Desireability Scale
b. Dependent Variable: Participant Identifier

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1.208	1	1.208	.008	.929b
Residual	17053.950	112	152.267
Total	17055.158	113
a. Dependent Variable: Participant Identifier
b. Predictors: (Constant), Total score of the Marlowe-Crowne Social Desireability Scale

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B	Correlations
B	Std. Error	Beta	Lower Bound	Upper Bound	Zero-order	Partial	Part
1	(Constant)	22.350	16.970		1.317	.191	-11.273	55.973
Total score of the Marlowe-Crowne 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 R² is 0.0, we can tell that there is very weak significant association between participant identifier and Total score of Marlowe-Crowne Social Desirability subscales. It also interprets 0.0% of the variations in the participant identifier could be explained by the Total score of Marlowe-Crowne Social Desirability Scale. The Value of adjusted R² (-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 p-value of participant identifier and Total score of Marlowe-Crowne Social Desirability subscales (0.929) has p-value 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 Marlowe-Crowne 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 Marlowe-Crowne 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 Marlowe-Crowne 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	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
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 Marlowe-Crowne 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 t-tests (one-sample) of mean score of control subscale, abuse subscale, violence subscale, Marlowe-Crowne Social Desirability Scale indicates the t-values. 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 p-values 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 p-values related to the t-tests 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. P-values of t-tests of Mean score of abuse subscale and Total score of the Marlowe-Crowne 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.  

Conclusion:- 

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.

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