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8/2/2019 Corelation Between Job Satisfaction and Job Compensation
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Corelation Between Job Satisfaction And Job Compensation
Job Staisfaction
Human life has become very complex and completed in now-a-days. In modern society the needs
and requirements of the people are ever increasing and ever changing. When the people are everincreasing and ever changing when the peoples needs are not fulfilled they become dissatisfied.
Dissatisfied people are likely to contribute very little for any purpose. Job satisfaction ofindustrial workers us very important for the industry to function successfully. Apart from
managerial and technical aspects, employers can be considered as backbone of any industrial
development. To utilize their contribution they should be provided with good working conditions
to boost their job satisfaction..
Job satisfaction is important technique used to motivate the employees to work harder. It is often
said that A HAPPY EMPLOYEE IS A PRODUCTIVE EMPLOYEE. A happy employee isgenerally that employee who is satisfied with his job.
Job satisfaction is very important because most of the people spend a major portion of their lifeat working place. Moreover, job satisfaction has its impact on the general life of the employees
also, because a satisfied employee is a contented and happy human being. A highly satisfied
worker has better physical and mental well being.
Definitions:
In simple words , job satisfaction can defined as extent of positive feelings or attitudes that
individuals have towards their jobs. When a person says that he has high job satisfaction , it
means that he really likes his job, feels good about it and value his job dignity.
-P. Robbins
Job satisfaction is a general attitude towards ones job: the difference
between the amount of reward workers receive and the amount they believe
they should receive.
Fieldman and Arnold
Job satisfaction will be defined as amount of overall positive affect that individuals have towardstheir jobs.
Maslows hierarchy of needs theory, a motivation theory, laid the foundation for job satisfaction
theory. This theory explains that people seek to satisfy five specific needs in lifephysiologicalneeds, safety needs, social needs, self-esteem needs, and self-actualization. This model served as
a good basis from which early researchers could develop job satisfaction theories.
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Salary
Relationship w/Peers
Achievement
Recognition
Work itself
Responsibility
Advancement
Growth
Herzberg and Money: It is often wrongly assumed that Herzberg did not value money, in thesense that he did not consider it a motivator. This is misleading, as Herzberg argues that theabsence of good hygiene factors including money, will lead to dissatisfaction and thus potentially
block any attempt to motivate the worker. Herzberg prefers us to think of money as a force
which will move an individual to perform a task, but not generate any internal desire to do thetask well. In fact to get an individual to perform the task again, he argues, we will need to offer
more money. Although the original studies have been repeated with different types of workers,
and results have proved consistent with the original research, Herzberg's theory has beencriticised. Critics point out that a single factor may be a satisfier for one person, but cause job
dissatisfaction for another. For example increased responsibility may be welcomed by some,
whilst dreaded by others. Whatever the criticisms, Herzberg has drawn our attention to the
importance of job design in order to bring about job enrichment, emphasized in the phrase'Quality of Working Life'.
Data Analysis and Interpretation
Table 1: To know the department in which the employees belong to
Sl No.
Department
No of respondents
Percentage
1
ISO
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20
40
2
D2C
7
14
3
Brocking
11
22
4
Support & IT
5
10
5
Telestar
7
14
Total
50
100
From the above data we can see that
40% employee belong to ISO Channel
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14% belong to D2c channel and another 14% belong to Telestar Channel
22% employee belong to Brocking channel
Only 10% employee belong to Support Channel.
Inferance: majority of the respondents belong to the ISO Channel.
Table 2: To know the Marital Status of the employee
Sl No
Status
No of respondent
Percentage
1
Married
10
20
2
Single
40
80
Total
50
100
From the above data we can see that
80% of the respondents are Unmarried
Only 20% of the respondents are Married
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Inferance: Majority of the respondents are unmarried which signifies that the workforcw is
mainly young in this company
Table 3: To know the Gender distribution pof the respondents
Sl No.
Gender
No of respondents
Percentage
1
Male
30
60
2
Female
20
40
Total
50
100
From the above data we van see that
40% of the respondents are Female
60% of the respondents are Male
Inferance: Here the male and female ration is 3:2 which signifies that the gender discrimination
is absent here
Table 4: To know the Age of the respondents
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Sl No
Age
No of respondents
Percentage
1
19-24
27
54
2
25-30
21
42
3
31-36
1
2
4
37 & above
1
2
Total
50
100
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From the above data we can see that
54% of the respondents fall under the age group of 19 to 24 years
42% of the respondents fall under the age group of 25 to30 years
It shows that majority of the employee are very young.
Table 5: Educational Qualification of the respondents
Sl No
Educational Qualification
No of respondents
Percentage
1
Under Graduate
7
14
2
Graduate
31
62
3
Post Graduate
12
24
Total
50
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100
From the above data we can see that
62% of the respondents are Graduate
24% of the respondents are Post graduate by qualification among them some have done MBA,some have done M.Com, some have specialized in other fields
Only 24% of the respondents are Under Graduate, and these employees are mainly working as
T.M.E.
Inferance: Here majority of the employee are Graduate, which shows that the educational level is
high
Table 6: To know the relationship with the management and colleagues
Sl No
Relationship with management & colleagues
No of respondent
Percentage(%)
1
Highly Dissatisfied
2
4.00%
2
Dissatisfied
4
8.00%
3
Neither Satisfied nor dissatisfied
22
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44.00%
4
Satisfied
16
32.00%
5
Highly Satisfied
6
12.00%
Total
50
100.00%
From the above data we can see that
A major proportion of the respondent have a good relationship with the management and
colleagues (32%)
Very few people are dissatisfied with the relationship(4%)
Inferance: It can be said that the majority of the employee are satisfied with the management and
their collegues. Management maintain a clear and regular communication with the employee.
Table 7: Monthly Salary of the respondents
Sl No.
CTC(in Rs.)
No of respondents
Percentage
1
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5000-14000
40
80
2
15000-24000
6
12
3
25000-34000
2
4
4
35000 & above
2
4
Total
50
100
From the above data we can see that
80% of the respondemts draw a monthly salary between Rs. 5000 to Rs.14000
Inferance: Here the maximum employee draw a salary between Rs. 5000- Rs.14000. So theoverall salary scale is quite low.
Table 8: To know the level of satisfaction with the compensation paid to the employees
Sl No
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Incremental Benefits offered to employee
No of respondent
Percentage
1
Highly Dissatisfied
5
10
2
Dissatisfied
11
22
3
Neither Satisfied nor dissatisfied
23
46
4
Satisfied
7
14
5
Highly Satisfied
4
8
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Total
50
100
From the above data we can see that only 14% respondents are satisfied with the compensationpiad and 8% are highly satisfied. Whereas 10% respondents are highly dissatisfied with the
compensation paid and 22% are dissatisfied with the compensation paid.
Inferance: Here almost half of the respondents are neither satisfied nor dissatisfied with the
compensation paid, 1/5th of the respondents are dissatisfied with the compensation structure.Which signify that the overall impression of the employee towards the company's compensation
scheme is negative.
Table 9: To know the level of satisfaction with the leave structure
Sl No
Level of satisfaction with the leave structure
No of respondent
Percentage
1
Highly Dissatisfied
6
12
2
Dissatisfied
18
36
3
Neither Satisfied nor dissatisfied
16
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32
4
Satisfied
6
12
5
Highly Satisfied
4
8
Total
50
100
From the above data we can see that
36% of the respondents are dissatiesfied with the paid leave structure and only 12% of the
respondents are satissified with the leave structure.
Inferance: Most of the employss are dissatiesfied with the paid leave structure of the company,
which once again shows the employee's dissatiesfaction with the compensation allowances (aspaid leave is a part of the compensation)
Table 10: To know the level of satisfaction with other facilities provided by the company
Sl No
Level of satisfaction with the leave structure
No of respondent
Percentage
1
Highly Dissatisfied
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0
0
2
Dissatisfied
5
10
3
Neither Satisfied nor dissatisfied
9
18
4
Satisfied
17
34
5
Highly Satisfied
19
38
Total
50
100
From the above data we can see that most of the respondents are satisfied with other facilitiesprovoded by the company and only 10% of the respondents are dissatiesfied with the facilities
provided by the company.
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Inferance: Here only 34% of the employee's are getting the benefit from the compsny's other
benefit scheme like travelling allowances, telephone bill reimbursement facilities etc. So thecompany need to increase the number and make sure that the maximum number of employee
should coverd under these facilities.
Table 11: To know the reason for what the employee enjoy late sitting in the office
Sl No
Parameters
CTC(monthly)
For completion of work
provides extra money
provides food/snacks
provides compensation allowance
Total
1
5000-14000
2
20
0
15
37
2
15000-24000
0
3
2
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3
8
3
25000-34000
0
1
1
1
3
4
35000 & above
1
0
0
1
2
50
Table 12: To know the level of job satisfaction of the respondents based on their salary
Sl No.
Monthly Salary
Overall Job satisfaction
Highly Dissatisfied
Dissatisfied
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Neither Satisfied nor dissatisfied
Satisfied
Highly Satisfied
Total
1
5000-14000
3
16
6
15
0
40
2
15000-24000
0
4
1
1
0
6
3
25000-34000
0
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RELATIONSHIP BETWEEN THE JOB SATISFACTION AND JOB COMPENSATION:
Null Hypothesis (Ho):
There is no significant relation between job satisfaction and job compensation
Alternate Hypothesis (H1):
There is a significant relation between job satisfaction and job compensation
Observed Frequencies:
Expected Frequencies:
fe= row total* column total/total frequency
Expected Frequencies
Column Variable
Monthly Salary
Highly Dissatisfied
Dissatisfied
Neither Satisfied nor dissatisfied
Satisfied
Highly Satisfied
Total
5000-14000
2.4
16.8
5.6
14.4
0.8
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40
15000-24000
0.36
2.52
0.84
2.16
0.12
6
25000-34000
0.12
0.84
0.28
0.72
0.04
2
35000 & above
0.12
0.84
0.28
0.72
0.04
2
Total
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3
21
7
18
1
50
Calculation:
Degree of freedom = (no of row-1)*(no of column-1)
Level of significance = 0.05 or 0.01
Here we have taken it 0.05
The above result of the chi-square test shows that the Critical Value or Tabulated Value = 21.03
and
the Calculate Value = 28.01.
Here the calculated value is greater than the critical value. It means that the null hypothesis is
rejected and the alternate hypothesis is accepted.
After analysing the above result we can reject the null hypothesis(H0) i.e. There is no significant
relation between job satisfaction and job compensation and accept the alternate hypothesis i.e.
There is a significant relation between job satisfaction and job compensation.
It means that the level of job satisfaction is highly related with salary paid to the employee. More
the salary the more is the job satisfaction and vice-versa.
Factor Analysis:
Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of
correlations within a set of observed variables. Factor analysis is often used in data reduction toidentify a small number of factors that explain most of the variance observed in a much largernumber of manifest variables. Factor analysis can also be used to generate hypotheses regarding
causal mechanisms or to screen variables for subsequent analysis (for example, to identify
collinearity prior to performing a linear regression analysis).
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In this survey I have used the factor analysis methodology to find out which is the most
important factor that attracts the employee most. Whether it is the compensation schemes or theincentives or the gratuity policy or any other factor.
The variable taken are as follows:
The salary is commensurate to the responsibilities shouldered, denoted as variable15
The review system is a regular phenomenon, denoted as Variable 16
Availability of paid leave, denoted as Variable 17
Availability of the insuarance scheme, denoted as Variable 18
Effectiveness of the welfare schemes of the employee,denoted as Variable 19
Level of satisfaction with the gratuity policy of the company, denoted as Variable 20
Correlation Matrixa
var15
var16
var17
var18
var19
var20
Correlation
var15
1.000
.716
.662
.575
.405
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.528
var16
.716
1.000
.563
.516
.439
.561
var17
.662
.563
1.000
.596
.431
.440
var18
.575
.516
.596
1.000
.644
.615
var19
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.405
.439
.431
.644
1.000
.695
var20
.528
.561
.440
.615
.695
1.000
a. Determinant = .036
This correlation matrix is used to find out that how the common variance exist amongthe
variables.
The next step is to determine the factoability of the correlation matrix, for that we I have
conducted two tests
Bartlett's Test of Sphericity
Kaiser- Meyer-Olkin Measure of Sampling Adequacy
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.836
Bartlett's Test of Sphericity
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Approx. Chi-Square
153.795
df
15
Sig.
.000
Bartlett's test of sphericity
Calculates the determinate of the matrix of the sums of products and cross-products (S) fromwhich the intercorrelation matrix is derived. The determinant of the matrix is converted to a chi-
square statistic and tested for significance. The null hypothesis is that the correlation matrixcomes from a factor in which the variables are noncollinear (i.e. an identity matrix) And that thenon-zero correlations in the sample matrix are due to sampling error.
Test Result:
2 = 153.795
df = 15
Significance = 0.000
When the significance is 0.000, then it means that factor analysis is very much relevant for thethe data set.
Interpretation of the KMO as characterized by Kaiser, Meyer, and Olkin
KMO Value
Degree of Common Variance
0.90 to 1.00
Marvelous
0.80 to 0.89
Meritorious
0.70 to 0.79
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Middling
0.60 to 0.69
Mediocre
0.50 to 0.59
Miserable
0.00 to 0.49
Don't Factor
The KMO = 0.836
Interpretation:
The degree of common variance among the six variables is "meritorious" bordering on
"mervelous"
If the factor analysis is conducted then the factors extracted will account for a significant
amonut.
Variety of methods have been developed to extract factors from an intercorrelation matrix.
Among them the Principal Component Analysis is the most commonly used methodology.
In the initial solution, each variable is standardized to have a mean of 0.0 and a standarddeviation of 1.0.
Thus
The variance of each variable = 1.0
And the total variance to be explained is 6,
i.e. 6 variables, each with a variance = 1.0
Since a single variable can account for 1.0 unit of variance
A useful factor must account for more than 1.0 unit of variance, or have an eigenvalue l > 1.0
Otherwise the factor extracted explains no more variance than a single variable.
Total Variance Explained
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Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.799
63.315
63.315
3.799
63.315
63.315
2
.846
14.101
77.416
3
.515
8.579
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85.995
4
.316
5.261
91.256
5
.281
4.688
95.944
6
.243
4.056
100.000
Extraction Method: Principal Component Analysis.
6 factors (components) were extracted, the same as the number of variables factored.
Factor I: The 1st factor has an eigenvalue = 3.799. Since this is greater than 1.0, it explains more
variance than a single variable, in fact 3.799 times as much.
The percent a variance explained
(3.799 / 6 units of variance) (100) = 63.316%
Factors 2 through 6 have eigenvalues less that 1, and therefore explain less variance that a single
variable.
The cumulative variance explained should be atleast 60% for the appropriateness odf the factoranalysis. Here the first factor account for 63.315% of the variance, so all the remaining factor are
not significant.
The initial solution suggest that the final solution should not extract more than one factor.
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Cattell's Scree Plot
Another way to determine the number of factors to extract in the final solution is Cattell's scree
plot. This is a plot of the eigenvalues associated with each of the factors extracted, against each
factor.
In the above diagram the graph is very stiff and we can also see that the first factor is verystrongly influencing the level of satisfaction.
Component Matrix
The component matrix indicates the correlation of each variable with each factor.
Component Matrixa
Component
1
var15
.817
var16
.796
var17
.772
var18
.829
var19
.752
var20
.804
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
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The variable sentence
Correlates 0.817 with Factor I
The total proportion of the variance in sentence explained by one factor is simply the sum of its
squared factor loadings.
(0.817)^2 = 0.668
This is called the communality of the variables
The communalities of the 6 variables are as follows:
Communalities
Initial
Extraction
var15
1.000
.668
var16
1.000
.634
var17
1.000
.597
var18
1.000
.687
var19
1.000
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.566
var20
1.000
.647
Extraction Method: Principal Component Analysis.
Reproduced Correlations
var15
var16
var17
var18
var19
var20
Reproduced Correlation
var15
.668a
.651
.631
.678
.615
.657
var16
.651
.634a
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.615
.660
.599
.640
var17
.631
.615
.597a
.640
.581
.621
var18
.678
.660
.640
.687a
.624
.667
var19
.615
.599
.581
.624
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.031
-.052
-.044
-.150
-.181
var18
-.102
Factors Affecting Job Attitudes
Leading to Dissatisfaction
Leading to Satisfaction
Company policy
Supervision
Relationship w/Boss
Work conditions
Salary
Relationship w/Peers
Achievement
Recognition
Work itself
Responsibility
Advancement
Growth
Herzberg and Money: It is often wrongly assumed that Herzberg did not value money, in the
sense that he did not consider it a motivator. This is misleading, as Herzberg argues that the
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absence of good hygiene factors including money, will lead to dissatisfaction and thus potentially
block any attempt to motivate the worker. Herzberg prefers us to think of money as a forcewhich will move an individual to perform a task, but not generate any internal desire to do the
task well. In fact to get an individual to perform the task again, he argues, we will need to offer
more money. Although the original studies have been repeated with different types of workers,
and results have proved consistent with the original research, Herzberg's theory has beencriticised. Critics point out that a single factor may be a satisfier for one person, but cause job
dissatisfaction for another. For example increased responsibility may be welcomed by some,
whilst dreaded by others. Whatever the criticisms, Herzberg has drawn our attention to theimportance of job design in order to bring about job enrichment, emphasized in the phrase
'Quality of Working Life'.
Data Analysis and Interpretation
Table 1: To know the department in which the employees belong to
Sl No.
Department
No of respondents
Percentage
1
ISO
20
40
2
D2C
7
14
3
Brocking
11
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22
4
Support & IT
5
10
5
Telestar
7
14
Total
50
100
From the above data we can see that
40% employee belong to ISO Channel
14% belong to D2c channel and another 14% belong to Telestar Channel
22% employee belong to Brocking channel
Only 10% employee belong to Support Channel.
Inferance: majority of the respondents belong to the ISO Channel.
Table 2: To know the Marital Status of the employee
Sl No
Status
No of respondent
Percentage
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1
Married
10
20
2
Single
40
80
Total
50
100
From the above data we can see that
80% of the respondents are Unmarried
Only 20% of the respondents are Married
Inferance: Majority of the respondents are unmarried which signifies that the workforcw is
mainly young in this company
Table 3: To know the Gender distribution pof the respondents
Sl No.
Gender
No of respondents
Percentage
1
Male
30
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60
2
Female
20
40
Total
50
100
From the above data we van see that
40% of the respondents are Female
60% of the respondents are Male
Inferance: Here the male and female ration is 3:2 which signifies that the gender discrimination
is absent here
Table 4: To know the Age of the respondents
Sl No
Age
No of respondents
Percentage
1
19-24
27
54
2
25-30
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Under Graduate
7
14
2
Graduate
31
62
3
Post Graduate
12
24
Total
50
100
From the above data we can see that
62% of the respondents are Graduate
24% of the respondents are Post graduate by qualification among them some have done MBA,
some have done M.Com, some have specialized in other fields
Only 24% of the respondents are Under Graduate, and these employees are mainly working as
T.M.E.
Inferance: Here majority of the employee are Graduate, which shows that the educational level ishigh
Table 6: To know the relationship with the management and colleagues
Sl No
Relationship with management & colleagues
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No of respondent
Percentage(%)
1
Highly Dissatisfied
2
4.00%
2
Dissatisfied
4
8.00%
3
Neither Satisfied nor dissatisfied
22
44.00%
4
Satisfied
16
32.00%
5
Highly Satisfied
6
12.00%
Total
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50
100.00%
From the above data we can see that
A major proportion of the respondent have a good relationship with the management andcolleagues (32%)
Very few people are dissatisfied with the relationship(4%)
Inferance: It can be said that the majority of the employee are satisfied with the management andtheir collegues. Management maintain a clear and regular communication with the employee.
Table 7: Monthly Salary of the respondents
Sl No.
CTC(in Rs.)
No of respondents
Percentage
1
5000-14000
40
80
2
15000-24000
6
12
3
25000-34000
2
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4
4
35000 & above
2
4
Total
50
100
From the above data we can see that
80% of the respondemts draw a monthly salary between Rs. 5000 to Rs.14000
Inferance: Here the maximum employee draw a salary between Rs. 5000- Rs.14000. So the
overall salary scale is quite low.
Table 8: To know the level of satisfaction with the compensation paid to the employees
Sl No
Incremental Benefits offered to employee
No of respondent
Percentage
1
Highly Dissatisfied
5
10
2
Dissatisfied
11
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No of respondent
Percentage
1
Highly Dissatisfied
6
12
2
Dissatisfied
18
36
3
Neither Satisfied nor dissatisfied
16
32
4
Satisfied
6
12
5
Highly Satisfied
4
8
Total
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50
100
From the above data we can see that
36% of the respondents are dissatiesfied with the paid leave structure and only 12% of therespondents are satissified with the leave structure.
Inferance: Most of the employss are dissatiesfied with the paid leave structure of the company,
which once again shows the employee's dissatiesfaction with the compensation allowances (as
paid leave is a part of the compensation)
Table 10: To know the level of satisfaction with other facilities provided by the company
Sl No
Level of satisfaction with the leave structure
No of respondent
Percentage
1
Highly Dissatisfied
0
0
2
Dissatisfied
5
10
3
Neither Satisfied nor dissatisfied
9
18
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4
Satisfied
17
34
5
Highly Satisfied
19
38
Total
50
100
From the above data we can see that most of the respondents are satisfied with other facilities
provoded by the company and only 10% of the respondents are dissatiesfied with the facilities
provided by the company.
Inferance: Here only 34% of the employee's are getting the benefit from the compsny's other
benefit scheme like travelling allowances, telephone bill reimbursement facilities etc. So thecompany need to increase the number and make sure that the maximum number of employeeshould coverd under these facilities.
Table 11: To know the reason for what the employee enjoy late sitting in the office
Sl No
Parameters
CTC(monthly)
For completion of work
provides extra money
provides food/snacks
provides compensation allowance
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Total
1
5000-14000
2
20
0
15
37
2
15000-24000
0
3
2
3
8
3
25000-34000
0
1
1
1
3
4
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35000 & above
1
0
0
1
2
50
Table 12: To know the level of job satisfaction of the respondents based on their salary
Sl No.
Monthly Salary
Overall Job satisfaction
Highly Dissatisfied
Dissatisfied
Neither Satisfied nor dissatisfied
Satisfied
Highly Satisfied
Total
1
5000-14000
3
16
6
15
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0
40
2
15000-24000
0
4
1
1
0
6
3
25000-34000
0
1
0
1
0
2
4
35000 & above
0
0
0
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1
1
2
50
CHI-SQUARE ANALYSIS
The chi square test is one of the simplest and most widely used non-parametric tests in statistical
work. As a non-parametric test it can be used to determine if categorical data shows dependencyor the two classifications are independent. It can also be used to make comparisons between
theoretical population and actual data when categories are used.
n
Chi square, = (fo-fe) / fe
i=1
Where, fo= observed frequency
fe= expected frequency
CHISQUARE TEST IS CONDUCTED TO EXTEND THE
RELATIONSHIP BETWEEN THE JOB SATISFACTION AND JOB COMPENSATION:
Null Hypothesis (Ho):
There is no significant relation between job satisfaction and job compensation
Alternate Hypothesis (H1):
There is a significant relation between job satisfaction and job compensation
Observed Frequencies:
Expected Frequencies:
fe= row total* column total/total frequency
Expected Frequencies
Column Variable
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The above result of the chi-square test shows that the Critical Value or Tabulated Value = 21.03
and
the Calculate Value = 28.01.
Here the calculated value is greater than the critical value. It means that the null hypothesis isrejected and the alternate hypothesis is accepted.
After analysing the above result we can reject the null hypothesis(H0) i.e. There is no significant
relation between job satisfaction and job compensation and accept the alternate hypothesis i.e.There is a significant relation between job satisfaction and job compensation.
It means that the level of job satisfaction is highly related with salary paid to the employee. More
the salary the more is the job satisfaction and vice-versa.
Factor Analysis:
Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of
correlations within a set of observed variables. Factor analysis is often used in data reduction toidentify a small number of factors that explain most of the variance observed in a much larger
number of manifest variables. Factor analysis can also be used to generate hypotheses regarding
causal mechanisms or to screen variables for subsequent analysis (for example, to identifycollinearity prior to performing a linear regression analysis).
In this survey I have used the factor analysis methodology to find out which is the mostimportant factor that attracts the employee most. Whether it is the compensation schemes or the
incentives or the gratuity policy or any other factor.
The variable taken are as follows:
The salary is commensurate to the responsibilities shouldered, denoted as variable15
The review system is a regular phenomenon, denoted as Variable 16
Availability of paid leave, denoted as Variable 17
Availability of the insuarance scheme, denoted as Variable 18
Effectiveness of the welfare schemes of the employee,denoted as Variable 19
Level of satisfaction with the gratuity policy of the company, denoted as Variable 20
Correlation Matrixa
var15
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var16
var17
var18
var19
var20
Correlation
var15
1.000
.716
.662
.575
.405
.528
var16
.716
1.000
.563
.516
.439
.561
var17
.662
.563
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1.000
.596
.431
.440
var18
.575
.516
.596
1.000
.644
.615
var19
.405
.439
.431
.644
1.000
.695
var20
.528
.561
.440
.615
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.695
1.000
a. Determinant = .036
This correlation matrix is used to find out that how the common variance exist amongthevariables.
The next step is to determine the factoability of the correlation matrix, for that we I have
conducted two tests
Bartlett's Test of Sphericity
Kaiser- Meyer-Olkin Measure of Sampling Adequacy
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.836
Bartlett's Test of Sphericity
Approx. Chi-Square
153.795
df
15
Sig.
.000
Bartlett's test of sphericity
Calculates the determinate of the matrix of the sums of products and cross-products (S) fromwhich the intercorrelation matrix is derived. The determinant of the matrix is converted to a chi-
square statistic and tested for significance. The null hypothesis is that the correlation matrix
comes from a factor in which the variables are noncollinear (i.e. an identity matrix) And that thenon-zero correlations in the sample matrix are due to sampling error.
Test Result:
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2 = 153.795
df = 15
Significance = 0.000
When the significance is 0.000, then it means that factor analysis is very much relevant for thethe data set.
Interpretation of the KMO as characterized by Kaiser, Meyer, and Olkin
KMO Value
Degree of Common Variance
0.90 to 1.00
Marvelous
0.80 to 0.89
Meritorious
0.70 to 0.79
Middling
0.60 to 0.69
Mediocre
0.50 to 0.59
Miserable
0.00 to 0.49
Don't Factor
The KMO = 0.836
Interpretation:
The degree of common variance among the six variables is "meritorious" bordering on"mervelous"
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If the factor analysis is conducted then the factors extracted will account for a significant
amonut.
Variety of methods have been developed to extract factors from an intercorrelation matrix.
Among them the Principal Component Analysis is the most commonly used methodology.
In the initial solution, each variable is standardized to have a mean of 0.0 and a standard
deviation of 1.0.
Thus
The variance of each variable = 1.0
And the total variance to be explained is 6,
i.e. 6 variables, each with a variance = 1.0
Since a single variable can account for 1.0 unit of variance
A useful factor must account for more than 1.0 unit of variance, or have an eigenvalue l > 1.0
Otherwise the factor extracted explains no more variance than a single variable.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.799
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63.315
63.315
3.799
63.315
63.315
2
.846
14.101
77.416
3
.515
8.579
85.995
4
.316
5.261
91.256
5
.281
4.688
95.944
6
.243
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4.056
100.000
Extraction Method: Principal Component Analysis.
6 factors (components) were extracted, the same as the number of variables factored.
Factor I: The 1st factor has an eigenvalue = 3.799. Since this is greater than 1.0, it explains morevariance than a single variable, in fact 3.799 times as much.
The percent a variance explained
(3.799 / 6 units of variance) (100) = 63.316%
Factors 2 through 6 have eigenvalues less that 1, and therefore explain less variance that a single
variable.
The cumulative variance explained should be atleast 60% for the appropriateness odf the factor
analysis. Here the first factor account for 63.315% of the variance, so all the remaining factor are
not significant.
The initial solution suggest that the final solution should not extract more than one factor.
Cattell's Scree Plot
Another way to determine the number of factors to extract in the final solution is Cattell's scree
plot. This is a plot of the eigenvalues associated with each of the factors extracted, against eachfactor.
In the above diagram the graph is very stiff and we can also see that the first factor is very
strongly influencing the level of satisfaction.
Component Matrix
The component matrix indicates the correlation of each variable with each factor.
Component Matrixa
Component
1
var15
.817
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var16
.796
var17
.772
var18
.829
var19
.752
var20
.804
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
The variable sentence
Correlates 0.817 with Factor I
The total proportion of the variance in sentence explained by one factor is simply the sum of its
squared factor loadings.
(0.817)^2 = 0.668
This is called the communality of the variables
The communalities of the 6 variables are as follows:
Communalities
Initial
Extraction
var15
1.000
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.668
var16
1.000
.634
var17
1.000
.597
var18
1.000
.687
var19
1.000
.566
var20
1.000
.647
Extraction Method: Principal Component Analysis.
Reproduced Correlations
var15
var16
var17
var18
var19
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var20
Reproduced Correlation
var15
.668a
.651
.631
.678
.615
.657
var16
.651
.634a
.615
.660
.599
.640
var17
.631
.615
.597a
.640
.581
.621
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var18
.678
.660
.640
.687a
.624
.667
var19
.615
.599
.581
.624
.566a
.605
var20
.657
.640
.621
.667
.605
.647a
Residualb
var15
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