In-Depth Report: Employee Satisfaction, Workplace Dynamics Analysis

This report provides a comprehensive analysis of employee satisfaction and various workplace dynamics. It examines the relationship between gender and life happiness, revealing no significant correlation, but identifies a gender pay bias within the company. The study also explores the impact of geographical region on pay, finding no significant dependency. Furthermore, the report highlights the positive correlation between training programs and job satisfaction levels, demonstrating a substantial improvement in employee satisfaction post-training. An assessment of promotion practices indicates no gender bias in promotions. The analysis extends to the dependency of salary on age, confirming a positive relationship, and investigates the interrelationships among several numerical variables related to employee satisfaction, teamwork, information access, job passion, and work-life balance. Desklib offers a range of similar solved assignments and resources for students.

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Table of Contents
1. Relationship between gender and life happiness score............................................................8
2. Pay is gender biased.................................................................................................................9
3. Pay is region dependent.........................................................................................................11
4. Relationship between training and job satisfaction...............................................................12
5. Promotion is gender biased....................................................................................................17
6. Dependency of salary on age.................................................................................................20
7. Relationship among many numerical variables.....................................................................21
REFERENCES..............................................................................................................................25
1

Marital status of employees
Statistics
Marital Status
N Valid 300
Missing 0
Marital Status
Frequency Percent Valid Percent Cumulative
Percent
Valid
Married 252 84.0 84.0 84.0
Single 48 16.0 16.0 100.0
Total 300 100.0 100.0
2

Number of employees within each department
Statistics
Departments
N Valid 300
Missing 0
Mean 2.2933
Median 2.0000
Mode 2.00
Departments
Frequency Percent Valid Percent Cumulative
Percent
3

Valid
IT 101 33.7 33.7 33.7
Marketing 109 36.3 36.3 70.0
Sales 32 10.7 10.7 80.7
HR 30 10.0 10.0 90.7
Finance 15 5.0 5.0 95.7
Innovation 13 4.3 4.3 100.0
Total 300 100.0 100.0
Satisfaction level of employees before training
Statistics
Job Satisfaction Score before
training(1-5)
N Valid 300
4

Missing 0
Mean 2.1000
Median 2.0000
Mode 2.00
Job Satisfaction Score before training(1-5)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Extremely dissatisfied 93 31.0 31.0 31.0
2.00 112 37.3 37.3 68.3
3.00 69 23.0 23.0 91.3
4.00 24 8.0 8.0 99.3
Extremely satisfied 2 .7 .7 100.0
Total 300 100.0 100.0
5

Life happiness
Statistics
Life Happiness Score (1-10)
N Valid 300
Missing 0
Mean 5.6100
Median 6.0000
Mode 6.00
Life Happiness Score (1-10)
Frequency Percent Valid Percent Cumulative
Percent
6

Valid
extremely unhappy 4 1.3 1.3 1.3
2.00 11 3.7 3.7 5.0
3.00 22 7.3 7.3 12.3
4.00 41 13.7 13.7 26.0
extremely happy 63 21.0 21.0 47.0
6.00 67 22.3 22.3 69.3
7.00 43 14.3 14.3 83.7
8.00 35 11.7 11.7 95.3
9.00 11 3.7 3.7 99.0
10.00 3 1.0 1.0 100.0
Total 300 100.0 100.0
7

1. Relationship between gender and life happiness score
Descriptive Statistics
Mean Std. Deviation N
Life Happiness Score (1-10) 5.6100 1.81537 300
Gender 1.6200 .48620 300
Correlations
Life Happiness
Score (1-10)
Gender
Pearson Correlation Life Happiness Score (1-10) 1.000 .010
Gender .010 1.000
Sig. (1-tailed) Life Happiness Score (1-10) . .434
Gender .434 .
N Life Happiness Score (1-10) 300 300
Gender 300 300
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Genderb . Enter
a. Dependent Variable: Life Happiness Score (1-10)
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .010a .000 -.003 1.81832
a. Predictors: (Constant), Gender
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .091 1 .091 .028 .868b
8

Residual 985.279 298 3.306
Total 985.370 299
a. Dependent Variable: Life Happiness Score (1-10)
b. Predictors: (Constant), Gender
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 5.552 .366 15.178 .000
Gender .036 .216 .010 .166 .868
a. Dependent Variable: Life Happiness Score (1-10)
H0=Null hypothesis. There is no significant relationship between two variables. P>0.05
H1=Alternative hypothesis There is significant relationship between two variables. P<0.05
Interpretation: On the basis of above data, it can be said that value of P is: 0.434 that is more
than 0.05. It satisfied null hypothesis which means, there is no relationship between gender and
life happiness. It also shows that company is not biased with their male and female employees
because everyone is happy at workplace, there is no link between life happiness and gender
Vieira, (2017).
2. Pay is gender biased
Descriptive Statistics
Mean Std. Deviation N
Salary(000) 52.4233 9.12588 300
9

Gender 1.6200 .48620 300
Correlations
Salary(000) Gender
Pearson Correlation Salary(000) 1.000 -.124
Gender -.124 1.000
Sig. (1-tailed) Salary(000) . .016
Gender .016 .
N Salary(000) 300 300
Gender 300 300
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Genderb . Enter
a. Dependent Variable: Salary(000)
b. All requested variables entered.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 383.974 1 383.974 4.667 .032b
Residual 24517.263 298 82.273
Total 24901.237 299
a. Dependent Variable: Salary(000)
b. Predictors: (Constant), Gender
H0=Null hypothesis. There is no significant relationship between two variables. P>0.05
H1=Alternative hypothesis There is significant relationship between two variables. P<0.05
Interpretation: Value of P is less than 0.05 and it is 0.016 that means it follows alternative
hypothesis. It shows that there is relationship between gender and pay. It also shows that
10

employees are being paid as per their gender roles. It means this company is gender pay biased
Antunes & et.al. (2021).
3. Pay is region dependent
ANOVA
salary
Sum of Squares df Mean Square F Sig.
Between Groups 48.056 3 16.019 .191 .903
Within Groups 24853.181 296 83.963
Total 24901.237 299
Multiple Comparisons
Dependent Variable: salary
Tukey HSD
(I) region (J) region Mean Difference
(I-J)
Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
east
west -.94937 1.35768 .897 -4.4572 2.5584
north -.66389 1.74127 .981 -5.1628 3.8350
south -.99270 1.60912 .927 -5.1502 3.1648
west
east .94937 1.35768 .897 -2.5584 4.4572
north .28548 1.59467 .998 -3.8346 4.4056
south -.04333 1.44922 1.000 -3.7877 3.7010
north
east .66389 1.74127 .981 -3.8350 5.1628
west -.28548 1.59467 .998 -4.4056 3.8346
south -.32881 1.81355 .998 -5.0145 4.3568
south
east .99270 1.60912 .927 -3.1648 5.1502
west .04333 1.44922 1.000 -3.7010 3.7877
north .32881 1.81355 .998 -4.3568 5.0145
salary
Tukey HSD
region N Subset for alpha
= 0.05
1
east 72 51.7361
11

north 45 52.4000
west 124 52.6855
south 59 52.7288
Sig. .926
Means for groups in homogeneous
subsets are displayed.
a. Uses Harmonic Mean Sample Size =
65.440.
b. The group sizes are unequal. The
harmonic mean of the group sizes is
used. Type I error levels are not
guaranteed.
Interpretation: There are 51 employees from East, 52 from West, North and South. It
means this company employs from different region and does not bias them on the basis of
region. Pay is being given to all Giri & Paul, (2020). value is 0.903 that means there is no
relationship between region and pay. All employees of different region are being paid equally.
4. Relationship between training and job satisfaction
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1
Job Satisfaction Score
before training(1-5) 2.1000 300 .95553 .05517
Job Satisfaction Score after
training(1-5) 3.5433 300 1.08260 .06250
Paired Samples Correlations
N Correlation Sig.
12

Pair 1
Job Satisfaction Score
before training(1-5) & Job
Satisfaction Score after
training(1-5)
300 -.059 .307
Paired Samples Test
Paired Differences
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the
Difference
Lower Upper
Pair 1
Job Satisfaction Score before
training(1-5) - Job Satisfaction
Score after training(1-5)
-1.44333 1.48576 .08578 -1.61214 -1.2745
Correlations
Gender Salary(000)
Gender
Pearson Correlation 1 -.124*
Sig. (2-tailed) .032
N 300 300
Salary(000)
Pearson Correlation -.124* 1
Sig. (2-tailed) .032
N 300 300
*. Correlation is significant at the 0.05 level (2-tailed).
H0=Null hypothesis. There is no significant relationship between two variables. P>0.05
H1=Alternative hypothesis There is significant relationship between two variables. P<0.05
Satisfaction level of employees before training
Statistics
13

Job Satisfaction Score before
training(1-5)
N Valid 300
Missing 0
Mean 2.1000
Median 2.0000
Mode 2.00
Job Satisfaction Score before training(1-5)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Extremely dissatisfied 93 31.0 31.0 31.0
2.00 112 37.3 37.3 68.3
3.00 69 23.0 23.0 91.3
4.00 24 8.0 8.0 99.3
Extremely satisfied 2 .7 .7 100.0
Total 300 100.0 100.0
14

Satisfaction level of employees after training
Statistics
Job Satisfaction Score after
training(1-5)
N Valid 300
Missing 0
Mean 3.5433
Median 3.0000
15

Mode 3.00
Job Satisfaction Score after training(1-5)
Frequency Percent Valid Percent Cumulative
Percent
Valid
extremely dissatisfied 8 2.7 2.7 2.7
2.00 31 10.3 10.3 13.0
3.00 134 44.7 44.7 57.7
4.00 44 14.7 14.7 72.3
extremely satisfied 83 27.7 27.7 100.0
Total 300 100.0 100.0
16

Interpretation: On the basis of above data, it can be said that before providing training,
only 2 employees were extremely satisfied and 93 were extremely dissatisfied. After providing
training, satisfaction level of employees changed. 83 employees were extremely satisfied after
getting proper training. So, on this basis, it can be said that there is relationship between training
and satisfaction level. P value is negative that means it is less than and it shows there is
relationship between training and job satisfaction Zhao & Liu, (2018).
5. Promotion is gender biased
Number of males and females within organization
Statistics
Gender
N Valid 300
Missing 0
Mean 1.6200
Median 2.0000
Mode 2.00
Gender
Frequency Percent Valid Percent Cumulative
Percent
Valid
male 114 38.0 38.0 38.0
female 186 62.0 62.0 100.0
Total 300 100.0 100.0
17

Number of employees who got promoted
Statistics
Promoted
N Valid 300
Missing 0
Promoted
Frequency Percent Valid Percent Cumulative
Percent
18

Valid
No 110 36.7 36.7 36.7
Yes 190 63.3 63.3 100.0
Total 300 100.0 100.0
Interpretation: Above data shows that this company has female employees more than male
employees. It means it does not bias with gender. Out of 300 employees, 190 employees got
promoted in which majority of employees were female. So, on this basis, it can be said that
promotion is not gender biased.
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6. Dependency of salary on age
Descriptive Statistics
Mean Std. Deviation N
Salary(000) 52.4233 9.12588 300
Age 41.8800 10.17895 300
Correlations
Salary(000) Age
Pearson Correlation Salary(000) 1.000 .755
Age .755 1.000
Sig. (1-tailed) Salary(000) . .000
Age .000 .
N Salary(000) 300 300
Age 300 300
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .755a .570 .569 5.99282 .570 395.358 1 29
a. Predictors: (Constant), Age
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 14198.881 1 14198.881 395.358 .000b
Residual 10702.355 298 35.914
Total 24901.237 299
a. Dependent Variable: Salary(000)
b. Predictors: (Constant), Age
Coefficientsa
20

Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 24.071 1.467 16.405 .000
Age .677 .034 .755 19.884 .000
a. Dependent Variable: Salary(000)
Interpretation: Value of P is 0.000 that is less than 0.05 so, on this basis it can be said that
it satisfies alternative hypothesis which means there is relationship between salary and age.
Salary is paid as per the age of people Schober, Boer & Schwarte, (2018).
7. Relationship among many numerical variables
Descriptive Statistics
Mean Std. Deviation N
Job Satisfaction Score after
training(1-5) 3.5433 1.08260 300
Teamwork and employee
satisfaction survey 3.0567 .97466 300
Information and employee
satisfaction survey 3.8533 .93169 300
Job passion and self-
evaluation employee
satisfaction survey
3.3900 .98036 300
Work/Life balance and
employee satisfaction survey 3.0233 1.10462 300
Correlations
Job Satisfaction
Score after
training(1-5)
Teamwork and e
mployee
satisfaction
survey
Information and e
mployee
satisfaction
survey
Job passion and
self-evaluation
employee
satisfaction
survey
Pearson Correlation Job Satisfaction Score after
training(1-5)
1.000 .294 .905 .433
21

Teamwork and employee
satisfaction survey .294 1.000 .260 .092
Information and employee
satisfaction survey .905 .260 1.000 .407
Job passion and self-
evaluation employee
satisfaction survey
.433 .092 .407 1.000
Work/Life balance and
employee satisfaction survey .289 -.029 .286 .183
Sig. (1-tailed)
Job Satisfaction Score after
training(1-5) . .000 .000 .000
Teamwork and employee
satisfaction survey .000 . .000 .055
Information and employee
satisfaction survey .000 .000 . .000
Job passion and self-
evaluation employee
satisfaction survey
.000 .055 .000
Work/Life balance and
employee satisfaction survey .000 .307 .000 .001
N
Job Satisfaction Score after
training(1-5) 300 300 300 300
Teamwork and employee
satisfaction survey 300 300 300 300
Information and employee
satisfaction survey 300 300 300 300
Job passion and self-
evaluation employee
satisfaction survey
300 300 300 300
Work/Life balance and
employee satisfaction survey 300 300 300 300
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
22

1
Work/Life
balance and
employee
satisfaction
survey,
Teamwork and e
mployee
satisfaction
survey, Job
passion and
self-evaluation
employee
satisfaction
survey,
Information and
employee
satisfaction
surveyb
. Enter
a. Dependent Variable: Job Satisfaction Score after
training(1-5)
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .910a .829 .827 .45093
a. Predictors: (Constant), Work/Life balance and employee satisfaction
survey, Teamwork and employee satisfaction survey, Job passion and
self-evaluation employee satisfaction survey,
Information and employee satisfaction survey
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 290.452 4 72.613 357.106 .000b
Residual 59.985 295 .203
Total 350.437 299
a. Dependent Variable: Job Satisfaction Score after training(1-5)
23

b. Predictors: (Constant), Work/Life balance and employee satisfaction survey,
Teamwork and employee satisfaction survey, Job passion and self-evaluation employee
satisfaction survey, Information and employee satisfaction survey
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -.865 .142 -6.101 .000
Teamwork and employee
satisfaction survey .076 .028 .068 2.720 .007
Information and employee
satisfaction survey .983 .033 .846 30.093 .000
Job passion and self-
evaluation employee
satisfaction survey
.084 .029 .076 2.871 .004
Work/Life balance and
employee satisfaction survey .034 .025 .035 1.364 .174
a. Dependent Variable: Job Satisfaction Score after training(1-5)
Interpretation: On the basis of above data, it can be said that job satisfaction is related
with all other variables such as: work life balance, job passion, employees’ satisfaction and team
working. It can be said that if employees are being encouraged to work in a team, promoted work
life balance then it can increase their job satisfaction after training Liu & Wang, (2021).
24

REFERENCES
Books and journals
Vieira, E. T. (2017). Introduction to real world statistics: With step-by-step SPSS instructions.
Routledge.
Giri, A., & Paul, P. (2020). APPLIED MARKETING ANALYTICS USING SPSS: MODELER,
STATISTICS AND AMOS GRAPHICS. PHI Learning Pvt. Ltd..
Zhao, J., & Liu, X. (2018). A hybrid method of dynamic cooling and heating load forecasting for
office buildings based on artificial intelligence and regression analysis. Energy and
Buildings. 174. 293-308.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and
interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
Liu, Q., & Wang, L. (2021). t-Test and ANOVA for data with ceiling and/or floor
effects. Behavior Research Methods. 53(1). 264-277.
Antunes, A. R. & et.al. (2021, September). A Multivariate Analysis Approach to Diamonds’
Pricing Using Dummy Variables in SPSS. In International Conference on Computational
Science and Its Applications (pp. 609-623). Springer, Cham.
25