Analysis of the participants' data incorporated by SPSS

Contents Background and Introduction:- 3 Research Questions:- 3 Findings, Calculation and Discussion:- 3 Crosstabs1 3 Descriptive1 6 Frequency Tables 7 Descriptive2 8 Crosstabs2 8 Linear Regression 12 Independent-T test:- 16 Conclusion:- 17 Annotated Bibliography:- 18 Background and Introduction:- In this report, we have done quantitative data analysis report using SPSS in order to answer specific research questions. Pearson 6 12.000 Descriptive1 6 Frequency Tables 7

Running head: DATA ANALYSIS WITH SPSS
Data Analysis with SPSS
Name of the Student:
Name of the University:
Author’s Note:

DATA ANALYSIS WITH SPSS 1
Executive Summary
The report elaborates the data analysis of the participants’ data incorporated by SPSS. The data is analyzed and properly interpreted.
The SPSS analyzed data is reported with tables and graphs with valid interpretation. The relationship between 40 variables (4 nominal
and 36 ordinal) of 120 frequencies are reported and verified. We applied cross value summary with cross tab function, multiple R-
square by linear regression and summary by descriptive statistics. The conclusions and discussions are delivered along with
calculation.

DATA ANALYSIS WITH SPSS 2
Contents
Background and Introduction:-....................................................................................................................................................................3
Research Questions:-...................................................................................................................................................................................3
Findings, Calculation and Discussion:-.......................................................................................................................................................3
Crosstabs1................................................................................................................................................................................................3
Descriptive1.............................................................................................................................................................................................6
Frequency Tables.....................................................................................................................................................................................7
Descriptive2.............................................................................................................................................................................................8
Crosstabs2................................................................................................................................................................................................8
Linear Regression..................................................................................................................................................................................12
Independent-T test:-...............................................................................................................................................................................16
Conclusion:-...............................................................................................................................................................................................17
Annotated Bibliography:-..........................................................................................................................................................................18

DATA ANALYSIS WITH SPSS 3
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.
Research Questions:-
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.
Findings, Calculation and Discussion:-
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

DATA ANALYSIS WITH SPSS 4
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.

DATA ANALYSIS WITH SPSS 5
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.
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

DATA ANALYSIS WITH SPSS 6
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.
Descriptive1
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

DATA ANALYSIS WITH SPSS 7
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 of MCSDS
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.
Frequency Tables
(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

DATA ANALYSIS WITH SPSS 8
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.
Descriptive2
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.
Crosstabs2
(Crosstabs function to explore relation: Gender, Age, Ethnicity, Relationship status and sexual orientation)

DATA ANALYSIS WITH SPSS 9
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.
Sexual Orientation * Gender
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

DATA ANALYSIS WITH SPSS 10
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

DATA ANALYSIS WITH SPSS 11
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.

DATA ANALYSIS WITH SPSS 12
Linear Regression
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 Std. Error of the Change Statistics

DATA ANALYSIS WITH SPSS 13
Square Estimate 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

DATA ANALYSIS WITH SPSS 14
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

DATA ANALYSIS WITH SPSS 15
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

DATA ANALYSIS WITH SPSS 16
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.
Independent-T test:-
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.

DATA ANALYSIS WITH SPSS 17
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.

DATA ANALYSIS WITH SPSS 18
Annotated Bibliography:-
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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
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