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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, Y10 +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.

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.647-674.

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 crosstab-based statistical method for effective fault localization. In Software Testing, Verification, and Validation, 2008 1st International Conference on (pp. 42-51). IEEE. 

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[Accessed 28 March 2024].

My Assignment Help. 'Quantitative Data Analysis Essay Using SPSS.' (My Assignment Help, 2022) <https://myassignmenthelp.com/free-samples/crm4207-research-methods-and-strategies-in-criminology-and-psychology/data-analysis-with-spss-file-A9F44E.html> accessed 28 March 2024.

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