Corelation Between Job Satisfaction and Job Compensation

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


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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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


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%


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


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


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


16


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32

4

Satisfied

6

12

5

Highly Satisfied

4

8

Total

50

100


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


No of respondent

Percentage

1

Highly Dissatisfied


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0

0

2

Dissatisfied

5

10

3


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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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


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.

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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


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


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


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


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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42/68

Under Graduate

7

14

2

Graduate

31

62

3

Post Graduate

12

24

Total

50

100


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


22

44.00%

4

Satisfied

16

32.00%

5

Highly Satisfied

6

12.00%

Total


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50

100.00%


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


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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47/68

No of respondent

Percentage

1

Highly Dissatisfied

6

12

2

Dissatisfied

18

36

3


16

32

4

Satisfied

6

12

5

Highly Satisfied

4

8

Total


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50

100


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


No of respondent

Percentage

1

Highly Dissatisfied

0

0

2

Dissatisfied

5

10

3


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


50/68

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


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


Kaiser- Meyer-Olkin Measure of Sampling Adequacy

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.836


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


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


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


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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Corelation Between Job Satisfaction and Job Compensation

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