Demographic Information of the Research Participants This analysis is provide the information of 150 participants regarding their gender

Demographic Information of the Research Participants
This analysis is provide the information of 150 participants regarding their gender, age, educational background, occupation and family system.
Gender

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
male
75
50.0
50.0
50.0

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female
75
50.0
50.0
100.0

Total
150
100.0
100.0

Age_group

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
11-20
15
10.0
10.0
10.0

21-30
58
38.7
38.7
48.7

31-40
59
39.3
39.3
88.0

41-50
18
12.0
12.0
100.0

Total
150
100.0
100.0

Educational_background

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
below metric
2
1.3
1.3
1.3

metric
13
8.7
8.7
10.0

intermediate
12
8.0
8.0
18.0

bachelor
68
45.3
45.3
63.3

master
50
33.3
33.3
96.7

master above
5
3.3
3.3
100.0

Total
150
100.0
100.0

Occupation

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
unemployed
80
53.3
53.3
53.3

teacher
24
16.0
16.0
69.3

engineer
2
1.3
1.3
70.7

doctor
5
3.3
3.3
74.0

other
39
26.0
26.0
100.0

Total
150
100.0
100.0

Family_system

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
nuclear
77
51.3
51.3
51.3

joint
73
48.7
48.7
100.0

Total
150
100.0
100.0

Bar charts of Demographic Data

Mean and Standard Deviation
Statistics

Gender
Age_group
Educational_background
Occupation
Family_system
N
Valid
150
150
150
150
150

Missing
0
0
0
0
0
Mean
1.50
2.53
4.11
2.33
1.49
Std. Deviation
.502
.833
1.004
1.713
.501
Analysis of Relationship between Dependent and Independent variation
Correlations

resilience
Narcissism_
authority
self-sufficiency
superiority
Exhibitionism
exploitativeness
vanity
entitlement
Resilience
Pearson Correlation
1
-.044
-.073
.037
-.014
-.119
.014
.039
.073

Sig. (2-tailed)

.589
.374
.651
.865
.147
.868
.632
.377

N
150
150
150
150
150
150
150
150
150
Narcissism_
Pearson Correlation
-.044
1
.459
.354
.464
.493
.381
.231
.428

Sig. (2-tailed)
.589

.000
.000
.000
.000
.000
.004
.000

N
150
150
150
150
150
150
150
150
150
Authority
Pearson Correlation
-.073
.459
1
-.102
.188
-.044
.045
.102
.024

Sig. (2-tailed)
.374
.000

.213
.021
.597
.581
.214
.767

N
150
150
150
150
150
150
150
150
150
self_sufficiency
Pearson Correlation
.037
.354
-.102
1
.091
.015
.014
-.004
.069

Sig. (2-tailed)
.651
.000
.213

.271
.859
.862
.963
.400

N
150
150
150
150
150
150
150
150
150
Superiority
Pearson Correlation
-.014
.464
.188
.091
1
.006
.076
.072
.040

Sig. (2-tailed)
.865
.000
.021
.271

.946
.352
.383
.627

N
150
150
150
150
150
150
150
150
150
Exhibitionism
Pearson Correlation
-.119
.493
-.044
.015
.006
1
-.009
-.036
.066

Sig. (2-tailed)
.147
.000
.597
.859
.946

.912
.659
.423

N
150
150
150
150
150
150
150
150
150
Exploitativeness
Pearson Correlation
.014
.381
.045
.014
.076
-.009
1
-.037
.092

Sig. (2-tailed)
.868
.000
.581
.862
.352
.912

.653
.263

N
150
150
150
150
150
150
150
150
150
Vanity
Pearson Correlation
.039
.231
.102
-.004
.072
-.036
-.037
1
-.031

Sig. (2-tailed)
.632
.004
.214
.963
.383
.659
.653

.708

N
150
150
150
150
150
150
150
150
150
Entitlement
Pearson Correlation
.073
.428
.024
.069
.040
.066
.092
-.031
1

Sig. (2-tailed)
.377
.000
.767
.400
.627
.423
.263
.708

N
150
150
150
150
150
150
150
150
150
age_groups
Pearson Correlation
.264
-.108
-.079
.042
-.175
-.128
.004
.024
.063

Sig. (2-tailed)
.001
.190
.336
.611
.032
.118
.963
.768
.444

N
150
150
150
150
150
150
150
150
150
Interpretation of Correlation Analysis of all Independent and Dependent Variables
According to the correlation test the relationship between independent variable i.e, age groups and all dependent variables are interpreted as follows:
• The independent variable (age groups) has a positive and mild relationship with resilience (r=.264).
• The independent variable (age groups) has a mild negative relationship with narcissism (r=-.108).
• The independent variable (age groups) has a mild negative relationship with authority (r=-.079).
• The independent variable (age groups) has a low positive relationship with self-sufficiency (r=.042).
• The independent variable (age groups) has a mild negative relationship with superiority (r=-.175).
• The independent variable (age groups) has a mild negative relationship with exhibitionism (r=–.128).
• The independent variable (age groups) has almost no relationship with exploitativeness (r=.004).
• The independent variable (age groups) has a very low positive relationship with vanity (r=.024).
• The independent variable (age groups) has a low positive relationship with entitlement (r=.063).
3.2 Analysis of variance of the impact of Independent variable on Dependent variable (Regression Model)
Regression analysis is carried out for estimating the relationship between dependent variable and predictor or predictors. It helps us to understand how the value of dependent variable changes when the independent variables are varied, while the other independent variables are held fixed. This model is most commonly used for forecasting and prediction about the relation between dependent and independent variable and on the basis of t value we accept or reject hypothesis.

Model Summary
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
.264a
.069
.063
14.26570
.069
11.053
1
148
.001
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
2249.299
1
2249.299
11.053
.001b

Residual
30119.521
148
203.510

Total
32368.820
149

a. Dependent Variable: resilience
b. Predictors: (Constant), age_groups

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

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
75.715
2.565

29.522
.000
70.647
80.783

age_groups
1.393
.419
.264
3.325
.001
.565
2.221
a. Dependent Variable: resilience

The conclusion from the above regression analysis shows that there is a positive and mild relationship (R=.264) between predictor age group and dependent variable resilience, R square value shows that 6.90% variability or change in resilience is explained by independent variable age group, the value of adjusted R squared (.063) shows that there is inconsiderable variation which in succession tells us that the results can be generalized beyond the sample to the population. In Analysis of variance (ANOVA) table, the F value (11.053) and the value of P i.e, .001 which is less than 95% (.05) level of significance reveals that the overall fitness of the model is logically good. In Coefficient table (Beta=.264, t=3.325, p=0.001) which reveals that pragmatic effect of predictor on outcome (dependent variable) that’s why this hypothesis is accepted.
Analysis of Variance between Age groups and Narcissism
Model Summary
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
.108a
.012
.005
.09660
.012
1.732
1
148
.190
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.016
1
.016
1.732
.190b

Residual
1.381
148
.009

Total
1.397
149

a. Dependent Variable: Narcissism_
b. Predictors: (Constant), age_groups

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
95.0% Confidence Interval for B

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.525
.017

87.825
.000
1.491
1.560

age_groups
-.004
.003
-.108
-1.316
.190
-.009
.002
a. Dependent Variable: Narcissism_

The conclusion we derive from the above Regression analysis table that there is a mild positive relationship (R=.108) between independent variable age group and dependent variable narcissism, R square value (.012) shows that 1.20% change in narcissism is explained by independent variable age group, the Adjusted R square value (.005) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (1.732) and the value of P i.e, .190 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (-.108) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-1.316) and p (.190) is greater than level of significance so we accept our null hypothesis.
Analysis of Variance between age groups and authority

Model Summary
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
.079a
.006
.000
.20065
.006
.931
1
148
.336
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.037
1
.037
.931
.336b

Residual
5.959
148
.040

Total
5.996
149

a. Dependent Variable: authority
b. Predictors: (Constant), age_groups

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

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.536
.036

42.579
.000
1.465
1.607

age_groups
-.006
.006
-.079
-.965
.336
-.017
.006
a. Dependent Variable: authority

The conclusion we derive from the above Regression analysis table that there is a low positive relationship (R=.079) between independent variable age group and dependent variable authority, R square value (.006) shows that .06% change in authority is explained by independent variable age group, the Adjusted R square value (.000) shows that there is no variation. In Analysis of variance (ANOVA) table, the F value (.931) and the value of P i.e, .336 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (-.079) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-.965) and p (.336) is greater than significant value so the null hypothesis become accepted.
Analysis of Variance between age groups and Self-sufficiency

Model Summary
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
.042a
.002
-.005
.21729
.002
.259
1
148
.611
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.012
1
.012
.259
.611b

Residual
6.988
148
.047

Total
7.000
149

a. Dependent Variable: self_sufficiency
b. Predictors: (Constant), age_groups

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

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.449
.039

37.091
.000
1.372
1.526

age_groups
.003
.006
.042
.509
.611
-.009
.016
a. Dependent Variable: self_sufficiency

The conclusion we derive from the above Regression analysis table that there is a low positive relationship (R=.042) between independent variable age group and dependent variable self-sufficiency, R square value (.002) shows that .02% change in authority is explained by independent variable age group, the Adjusted R square value (-.005) shows that there is no variation. In Analysis of variance (ANOVA) table, the F value (.259) and the value of P i.e, .611 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (.042) which is close to 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (.509) and p (.661) is greater than the significance value so the null hypothesis become accepted.
Analysis of Variance between age groups and Superiority

Model Summary
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
.175a
.031
.024
.22980
.031
4.689
1
148
.032
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.248
1
.248
4.689
.032b

Residual
7.815
148
.053

Total
8.063
149

a. Dependent Variable: superiority
b. Predictors: (Constant), age_groups

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
95.0% Confidence Interval for B

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.602
.041

38.786
.000
1.521
1.684

age_groups
-.015
.007
-.175
-2.165
.032
-.028
-.001
a. Dependent Variable: superiority

The conclusion we derive from the above Regression analysis table that there is a mild positive relationship (R=.175) between independent variable age group and dependent variable superiority, R square value (.031) shows that 3.10% change in superiority is explained by independent variable age group, the Adjusted R square value (.024) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (4.689) and the value of P i.e, .032 which is less than 95% (.05) level of significance reveals that the overall fitness of the model is logically good. In Coefficient table the Beta value (-.175) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-2.165) and p (.032) indicates that this hypothesis become accepted.
Analysis of Variance between age groups and Exhibitionism

Model Summary
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
.128a
.016
.010
.27149
.016
2.468
1
148
.118
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.182
1
.182
2.468
.118b

Residual
10.909
148
.074

Total
11.091
149

a. Dependent Variable: exhibitionism
b. Predictors: (Constant), age_groups

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
95.0% Confidence Interval for B

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.565
.049

32.073
.000
1.469
1.662

age_groups
-.013
.008
-.128
-1.571
.118
-.028
.003
a. Dependent Variable: exhibitionism

The conclusion we derive from the above Regression analysis table that there is a mild positive relation (R=.128) between independent variable age group and dependent variable exhibitionism, R square value (.016) shows that 1.60% change in authority is explained by independent variable age exhibitionism, the Adjusted R square value (.010) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (2.468) and the value of P i.e, .118 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (-.128) which is less than 0 indicates that for every 1-unit increase in the independent variable the dependent variable or outcome will be decreased, the value of t (-.128) and p (.118) indicates that the null hypothesis becomes accepted.
Analysis of Variance between age groups and Exploitativeness

Model Summary
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
.004a
.000
-.007
.24591
.000
.002
1
148
.963
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.000
1
.000
.002
.963b

Residual
8.949
148
.060

Total
8.950
149

a. Dependent Variable: exploitativeness
b. Predictors: (Constant), age_groups

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

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.554
.044

35.154
.000
1.467
1.642

age_groups
.000
.007
.004
.047
.963
-.014
.015
a. Dependent Variable: exploitativeness

The conclusion we derive from the above Regression analysis table that there is a almost no relationship (R=.004) between independent variable age group and dependent variable exploitativeness, R square value (.000) shows that 0% change in exploitativeness is explained by independent variable age group, the Adjusted R square value (-.007) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (.002) and the value of P i.e, .963 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (.004) which is almost 0 indicates, the value of t (.047) and p (.963) p indicates that the null hypothesis becomes accepted.
Analysis of Variance between age groups and vanity

Model Summary
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
.024a
.001
-.006
.27025
.001
.087
1
148
.768
a. Predictors: (Constant), age_groups

ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
.006
1
.006
.087
.768b

Residual
10.809
148
.073

Total
10.816
149

a. Dependent Variable: vanity
b. Predictors: (Constant), age_groups

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
95.0% Confidence Interval for B

B
Std. Error
Beta

Lower Bound
Upper Bound
1
(Constant)
1.447
.049

29.786
.000
1.351
1.543

age_groups
.002
.008
.024
.296
.768
-.013
.018
a. Dependent Variable: vanity

The conclusion we derive from the above Regression analysis table that there is a low positive relationship (R=.024) between independent variable age group and dependent variable vanity, R square value (.001) shows that .1% change in vanity is explained by independent variable age group, the Adjusted R square value (-.006) shows that there is almost no variation. In Analysis of variance (ANOVA) table, the F value (.087) and the value of P i.e, .786 which is greater than 95% (.05) level of significance reveals that the overall fitness of the model is logically not good. In Coefficient table the Beta value (.024) which is almost 0, the value of t (.296) and p (.786) indicates that the null hypothesis becomes accepted.
Analysis of Variance between age groups and E

x

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