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SUBMITTED BY: GROUP 1
UM14004 Abinash Mallick
UM14001 Abhijeet Dash
UM14024 Esha Epsita
UM14025 Tarakshewar Rao
UM14029 Krishna Kumari
UM14032 Kundan Mohapatra
UM14036 Nitika Baralia
UM14038 Padmalaya Mallick
UM14037 Goutam Prasad Rao
UM14056 Sukanya Dash
Employee Satisfaction
OBJECTIVE
OBJECTIVE
 To gather information on employee satisfaction in an organization.
 This survey focuses on how employees feel about their job description, position
within the company, relationships with colleagues and superiors, advancement
opportunities and overall satisfaction.
METHODOLOGY
• Problem Identification
• A population and sample were identified.
• A set of questionnaire was prepared to get the responses of the sample population.
• An online survey was conducted using Qualtrics application, which formed the basis for the
primary research. Social networking sites and email were used as medium for creating
awareness about the survey.
• Convenient sampling technique was used for sampling the population data.
• Analysis of the collected data using SPSS.
• Interpretation/inferences of the result obtained.
A priori Reasoning
An employee’s job satisfaction quotient depends on factors
such as job description, position within the company,
relationships with colleagues and superiors, future
prospects and overall satisfaction.
All these variables which contribute to the overall
satisfaction of an employee can define one particular
construct.
HYPOTHESIS
Ho: Income level and employee satisfaction are not
dependent
H1: Income level and employee satisfaction are dependent
ANALYSIS
The following analysis was carried out on the data collected using SPSS
package.
Univariate Analysis on the demographic variables
Bivariate Analysis – Linear Regression
Multivariate Analysis
Multiple Regression
Factor Analysis of the Likert Scale data
Cluster Analysis of the demographic variables
UNIVARIATE ANALYSIS
GENDER FREQUEN
CY
PERCENT
AGE
Male 81 66.94 %
Female 40 33.06 %
44
77
MARITAL STATUS
MARRIED
SINGLE
MARITAL
STATUS
FREQUEN
CY
PERCENT
AGE
Married 44 36.36 %
Single 77 63.64 %
MARITAL STATUS
Mean 1.636364
Standard Error 0.043913
Median 2
Mode 2
Standard
Deviation
0.483046
Sample Variance 0.233333
Kurtosis -1.69878
Skewness -0.57409
Range 1
Minimum 1
Maximum 2
Sum 198
Count 121
85
21
10
5
AGE GROUP
20 - 30
30 - 40
40 - 50
50 plus
AGE
GROUP
FREQUEN
CY
PERCENT
AGE
20 – 30 85 70.25 %
30 – 40 21 17.35 %
40 – 50 10 8.26 %
50 Plus 05 4.13%
AGE GROUP
Mean 1.46281
Standard Error 0.074265
Median 1
Mode 1
Standard
Deviation
0.816918
Sample Variance 0.667355
Kurtosis 2.208654
Skewness 1.753446
Range 3
Minimum 1
Maximum 4
Sum 177
Count 121
31
45
28
10
7
INCOME LEVEL
< 5 Lakhs
5 - 10 Lakhs
10 - 15 Lakhs
15 - 20 Lakhs
> 20 Lakhs
INCOME
LEVEL
FREQUENC
Y
PERCENTA
GE
< 5 lakhs 31 25.62 %
< 5 – 10
Lakhs
45 37.19 %
< 10 – 15
Lakhs
28 23.14 %
< 15 – 20
Lakhs
10 8.26 %
> 20 Lakhs 7 5.78 %
INCOME LEVEL
Mean 2.31405
Standard Error 0.101662
Median 2
Mode 2
Standard Deviation 1.11828
Sample Variance 1.250551
Kurtosis -0.02803
Skewness 0.732344
Range 4
Minimum 1
Maximum 5
Sum 280
Count 121
5
23
37
15
18
14
9
Position in the Organization
Management Trainee
Junior engineer
Senior Engineer
Entry-level manager
Mid-level manager
Executive
Other
POSITION FREQUENCY
PERCENTA
GE
Management
Trainee 5 4.13%
Junior engineer 23 19.01%
Senior Engineer 37 30.58%
Entry-level
manager 15 12.40%
Mid-level
manager 18 14.88%
Executive 14 11.57%
Other 9 7.44%
29
14
3
7
86
54
CURRENT DEPARTMENT
Operations
Finance
Sales
Marketing
Strategy
Procurement
Other
CURRENT
DEPARTMENT
FREQUENC
Y PERCENTAGE
Operations 29 23.97%
Finance 14 11.57%
Sales 3 2.48%
Marketing 7 5.79%
Strategy 8 6.61%
Procurement 6 4.96%
Other 54 44.63%
0
5
10
15
20
25
30
35
40
45
50
Primary Work Location
Primary Work Location 0
2
4
6
8
10
12
Pune
Kota
Rourkela
Sweden
Bhubaneswar
Talcher
Vishakhapatnam
Hyderabad
Durgapur
Mysore
Bhillai
Noida
PhoenixUSA
Jamshedpur
Odisha
Ahmedabad
Other Cities
Other Cities
PRIMARY WORK
LOCATION FREQUENCY PERCENTAGE
Delhi 17 14.05%
Chennai 7 5.79%
Mumbai 13 10.74%
Bangalore 29 23.97%
Kolkata 9 7.44%
Others 46 38.02%
16
64
24
17
NO. OF YEARS IN THE
COMPANY
Less than a
year
1 - 3 years
4 - 6 years
More than six
years
NO. OF
YEARS IN
THE
ORGANIZ
ATION
FREQUEN
CY
PERCENT
AGE
Less than a
year 16 13.22%
1 - 3 years 64 52.89%
4 - 6 years 24 19.83%
More than
six years 17 14.05%
Less than a
year 16 13.22%
11
24
21
47
18
Overall Employee Satisfaction
Level
Extremely Dissatisfied
Somewhat Dissatisfied
Neutral
Somewhat Satisfied
Extremely Satisfied
OVERALL
SATISFAC
TION
LEVEL
FREQUEN
CY
PERCENT
AGE
Extremely
Dissatisfied 11 9.09%
Somewhat
Dissatisfied 24 19.83%
Neutral 21 17.36%
Somewhat
Satisfied 47 38.84%
Extremely
Satisfied 18 14.88%
BIVARIATE ANALYSIS
Objective(to find
relationship b/w)
Dependent
variable
Independent
Variable
A Priori Reasoning
Income &
satisfaction
Overall how
satisfied you are
with the company
Income level Higher income level
generates higher
satisfaction
No of years in the
organization &
satisfaction
Overall how
satisfied you are
with the company
How long have you
been working in the
organization
A satisfied
employees stays
longer in the
organization
Position & Income Income level Your current
position in the
company
Higher position
leads to higher
income
BIVARIATE ANALYSIS
HYPOTHESIS
Null Hypothesis: There is no
relationship between income level
and satisfaction.
Alternative Hypothesis: There is a
relationship between income level
and satisfaction.
Income & satisfaction
BIVARIATE ANALYSIS
HYPOTHESIS
Null Hypothesis: There is no
relationship between number of years
in an organization and satisfaction.
Alternative Hypothesis: There is a
relationship between number of years
in an organization and satisfaction.
No of years & satisfaction
BIVARIATE ANALYSIS
HYPOTHESIS
Null Hypothesis: There is no
relationship between income level
and position.
Alternative Hypothesis: There is a
relationship between income level
and position.
Income & position
Position vs. satisfied
Age Vs satisfaction
BIVARIATE ANALYSIS (income vs. satisfaction)
Conclusion
Income level is statistically significant in explaining the satisfaction
level of the employee, but due to the smaller value of R² , we cannot
generalize our hypothesis.
Equation Alpha Beta1 Beta2 Beta3 R Squared
Simple
Linear
2.22222
(0.0000)
0.462
(0.000)
0.113
Log Linear 0.900
(0.000)
0.287
(0.001)
0.096
Quadratic 2.332
(0.000)
0.456
(0.249)
-0.012
(0.861)
0.13
Cubic 2.664
(0.016)
-0.24
(0.987)
0.183
(0.754)
-0.023
(0.736)
0.130
CROSS Tabulation (income vs. satisfaction)
CROSS Tabulation (Years in organization vs. Satisfaction )
CROSS Tabulation (Position in organization vs. Income )
MULTIPLE REGRESSION
OBJECTIVE
To find a relationship of the overall employee satisfaction with gender,
age group, income level, no. of years working in the organization,
appraisal process involvement, and degree of freedom
A PRIORI REASONING
•As income level increases ,employee satisfaction level increases.
•People working for more number of years in an organization having
transparent appraisal process and when given optimum degree of
freedom, tend to be more satisfied.
MULTIPLE REGRESSION
HYPOTHESIS
Null Hypothesis: There is no relationship of the overall employee
satisfaction with gender, age group, income level, no. of years working in
the organization, appraisal process involvement, and degree of freedom
Alternative Hypothesis: There is a relationship of the overall employee
satisfaction with gender, age group, income level, no. of years working in
the organization, appraisal process involvement, and degree of freedom
Variables Used
• Variables-
• Dependent variable
• Y = Satisfaction Level in Company
• Independent Variables
• Q1 = Gender
• Q3 = Age group
• Q4 = Income level
• Q7 = No. of years working in the organization
• Q9 = Appraisal process involvement
• Q10 = Degree of freedom
Analysis By Multiplicative model
Iteration Log(
Constant
)
Log
Q1
(β1)
Log
Q3
(β2)
Log
Q4
(β3)
Log
Q7
(β4)
Log Q9
(β5)
Log
Q10
(β6)
Adj
R^2
1 Log Y 1.173 (0.160) 0.865 0.118 0.077 0.327 1.383 0.343
Significance 0.002 0.283 0.297 0.246 0.283 0.213 0.295
2 Log Y 0.870 (0.161) 0.233 0.468 0.344 1.349 0.300
Significance 0.019 0.583 0.297 0.072 0.121 0.000
3 Log Y 0.824 0.262 0.457 0.336 1.353 0.305
Significance 0.022 0.224 0.077 0.127 0.000
4 Log Y 0.778 0.568 0.382 1.438 0.302
Significance 0.030 0.019 0.079 0.000
Analysis By Additive model
Iteration Constant Q1
(β1)
Q3
(β2)
Q4
(β3)
Q7
(β4)
Q9
(β5)
Q10
(β6)
Adj
R
Sqr
1 Coefficient 0.640 -0.094 0.445 -0.086 0.067 0.125 0.527 0.372
Significanc
e
(0.195) (0.632) (0.013) (0.468) (0.631) (0.130) (0.000)
2 Coefficient 0.499 0.444 (0.76) 0.065 0.122 0.530 0.371
Significanc
e
(0.206) (0.013) (0.514) (0.639) (0.136) (0.000)
3 Coefficient 0.570 0.487 (.072) 0.120 0.535 0.369
Significanc
e
(0.116) (0.001) (0.535) (0.142) (0.000)
4 Coefficient 0.570 0.427 0.112 0.519 0.367
Significanc
e
(0.115) (0.000) (0.163) (0.000)
MULTIPLE REGRESSION
CONCLUSION
Income level and degree of freedom provided by superiors in idea
generation are statistically significant in explaining the overall
satisfaction level of employees.
However, since the R2 value is not that high there are various other
factors which help in explaining the overall satisfaction level of
employees.
FACTOR ANALYSIS
• To analyze the different factors that influence the
satisfaction level of an employee in an organization
• To use the factor analysis as a data reduction technique to
group the different manifest variables into constructs and
thus find out the “Factors responsible for satisfaction
level of an employee working in an organization ”
A PRIORI REASONING
• Factor analysis is used to measure various latent variables
that can be measured by multiple observable variables
• The observable variables are
• Proper communication in the company
• Opportunities in the job
• Relations with the supervisor
• Overall facilities available in the company
• These variables are used to measure the overall
satisfaction of the employee
DATA COLLECTION
• Data was collected with the help of a questionnaire where
the responses from the respondents were recorded in a
likert scale (from 1-5)
• Total of 120 responses collected have been tabulated in
excel along with their coded values.
ANALYSIS
• All factor analysis techniques try to clump subgroups of variables together based upon their
correlations and often you can get a feel for what factors are going just by looking at the correlation
matrix and spotting clusters of high correlations between groups of variables
• Here we have four likert table data
• In the first case we have the latent variable of proper communication in the company
This relation is better verified by KMO and Bartlett Test
• Factor loading, communalities and total variance explained
Component Matrix and Rotated Component Matrix
• Here we have four likert table data
• In the second case we have the latent variable of relations with the supervisor
This relation is better verified by KMO and Bartlett Test
• Factor loading, communalities and total variance explained
Component Matrix and Rotated Component Matrix
• Here we have four likert table data
• In the third case we have the latent variable of overall facilities available in the company
This relation is better verified by KMO and Bartlett Test
• Factor loading, communalities and total variance explained
Component Matrix and Rotated Component Matrix
• Here we have four likert table data
• In the fourth case we have the latent variable of opportunities in the job
This relation is better verified by KMO and Bartlett Test
• Factor loading, communalities and total variance explained
Component Matrix and Rotated Component Matrix
FACTOR ANALYSIS
Factor No. Factor Name Variables
1 communication
Employee-manager (0.880)
Manager-employee (0.813)
2 Clarity of mission
Clarity to employees (0.798)
Clarity to company (0.826)
3 Relation with superiors
Superior does Good job (0.777)
Superior enables my best performance (0.798)
4 Evaluation & suggestion
Regularity of work evaluation (0.811)
provides actionable solution(0.820)
5 Facilities in the company
Appraisal process (0.809)
Promotion process (0.820)
Health care benefits(0.919)
6 Opportunities in job
Recognition for work (0.822)
Talent utilization(0.790)
CLUSTER ANALYSIS
Demographic variables used for cluster analysis
• Gender
• Marital Status
• Age
• Income Level
• Current Position
• Department
• Number of years
• Primary work Location
• Involvement in Appraisal
• Degree of freedom for Idea generation
• Satisfaction in Company
• Satisfaction in Department
• Recommendation to work in the company
Cluster Centers
Srm group1 sec_a_ppt
CONCLUSION
As observed from different analysis, the satisfaction level of employees
changes is very much influenced by their income and degree of freedom
provided for idea generation.
Apart from that age group, position, the function they perform also play a
vital role.
THANK YOU

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Srm group1 sec_a_ppt

  • 1. SUBMITTED BY: GROUP 1 UM14004 Abinash Mallick UM14001 Abhijeet Dash UM14024 Esha Epsita UM14025 Tarakshewar Rao UM14029 Krishna Kumari UM14032 Kundan Mohapatra UM14036 Nitika Baralia UM14038 Padmalaya Mallick UM14037 Goutam Prasad Rao UM14056 Sukanya Dash Employee Satisfaction
  • 2. OBJECTIVE OBJECTIVE  To gather information on employee satisfaction in an organization.  This survey focuses on how employees feel about their job description, position within the company, relationships with colleagues and superiors, advancement opportunities and overall satisfaction.
  • 3. METHODOLOGY • Problem Identification • A population and sample were identified. • A set of questionnaire was prepared to get the responses of the sample population. • An online survey was conducted using Qualtrics application, which formed the basis for the primary research. Social networking sites and email were used as medium for creating awareness about the survey. • Convenient sampling technique was used for sampling the population data. • Analysis of the collected data using SPSS. • Interpretation/inferences of the result obtained.
  • 4. A priori Reasoning An employee’s job satisfaction quotient depends on factors such as job description, position within the company, relationships with colleagues and superiors, future prospects and overall satisfaction. All these variables which contribute to the overall satisfaction of an employee can define one particular construct.
  • 5. HYPOTHESIS Ho: Income level and employee satisfaction are not dependent H1: Income level and employee satisfaction are dependent
  • 6. ANALYSIS The following analysis was carried out on the data collected using SPSS package. Univariate Analysis on the demographic variables Bivariate Analysis – Linear Regression Multivariate Analysis Multiple Regression Factor Analysis of the Likert Scale data Cluster Analysis of the demographic variables
  • 8. 44 77 MARITAL STATUS MARRIED SINGLE MARITAL STATUS FREQUEN CY PERCENT AGE Married 44 36.36 % Single 77 63.64 % MARITAL STATUS Mean 1.636364 Standard Error 0.043913 Median 2 Mode 2 Standard Deviation 0.483046 Sample Variance 0.233333 Kurtosis -1.69878 Skewness -0.57409 Range 1 Minimum 1 Maximum 2 Sum 198 Count 121
  • 9. 85 21 10 5 AGE GROUP 20 - 30 30 - 40 40 - 50 50 plus AGE GROUP FREQUEN CY PERCENT AGE 20 – 30 85 70.25 % 30 – 40 21 17.35 % 40 – 50 10 8.26 % 50 Plus 05 4.13% AGE GROUP Mean 1.46281 Standard Error 0.074265 Median 1 Mode 1 Standard Deviation 0.816918 Sample Variance 0.667355 Kurtosis 2.208654 Skewness 1.753446 Range 3 Minimum 1 Maximum 4 Sum 177 Count 121
  • 10. 31 45 28 10 7 INCOME LEVEL < 5 Lakhs 5 - 10 Lakhs 10 - 15 Lakhs 15 - 20 Lakhs > 20 Lakhs INCOME LEVEL FREQUENC Y PERCENTA GE < 5 lakhs 31 25.62 % < 5 – 10 Lakhs 45 37.19 % < 10 – 15 Lakhs 28 23.14 % < 15 – 20 Lakhs 10 8.26 % > 20 Lakhs 7 5.78 % INCOME LEVEL Mean 2.31405 Standard Error 0.101662 Median 2 Mode 2 Standard Deviation 1.11828 Sample Variance 1.250551 Kurtosis -0.02803 Skewness 0.732344 Range 4 Minimum 1 Maximum 5 Sum 280 Count 121
  • 11. 5 23 37 15 18 14 9 Position in the Organization Management Trainee Junior engineer Senior Engineer Entry-level manager Mid-level manager Executive Other POSITION FREQUENCY PERCENTA GE Management Trainee 5 4.13% Junior engineer 23 19.01% Senior Engineer 37 30.58% Entry-level manager 15 12.40% Mid-level manager 18 14.88% Executive 14 11.57% Other 9 7.44%
  • 12. 29 14 3 7 86 54 CURRENT DEPARTMENT Operations Finance Sales Marketing Strategy Procurement Other CURRENT DEPARTMENT FREQUENC Y PERCENTAGE Operations 29 23.97% Finance 14 11.57% Sales 3 2.48% Marketing 7 5.79% Strategy 8 6.61% Procurement 6 4.96% Other 54 44.63%
  • 13. 0 5 10 15 20 25 30 35 40 45 50 Primary Work Location Primary Work Location 0 2 4 6 8 10 12 Pune Kota Rourkela Sweden Bhubaneswar Talcher Vishakhapatnam Hyderabad Durgapur Mysore Bhillai Noida PhoenixUSA Jamshedpur Odisha Ahmedabad Other Cities Other Cities PRIMARY WORK LOCATION FREQUENCY PERCENTAGE Delhi 17 14.05% Chennai 7 5.79% Mumbai 13 10.74% Bangalore 29 23.97% Kolkata 9 7.44% Others 46 38.02%
  • 14. 16 64 24 17 NO. OF YEARS IN THE COMPANY Less than a year 1 - 3 years 4 - 6 years More than six years NO. OF YEARS IN THE ORGANIZ ATION FREQUEN CY PERCENT AGE Less than a year 16 13.22% 1 - 3 years 64 52.89% 4 - 6 years 24 19.83% More than six years 17 14.05% Less than a year 16 13.22%
  • 15. 11 24 21 47 18 Overall Employee Satisfaction Level Extremely Dissatisfied Somewhat Dissatisfied Neutral Somewhat Satisfied Extremely Satisfied OVERALL SATISFAC TION LEVEL FREQUEN CY PERCENT AGE Extremely Dissatisfied 11 9.09% Somewhat Dissatisfied 24 19.83% Neutral 21 17.36% Somewhat Satisfied 47 38.84% Extremely Satisfied 18 14.88%
  • 16. BIVARIATE ANALYSIS Objective(to find relationship b/w) Dependent variable Independent Variable A Priori Reasoning Income & satisfaction Overall how satisfied you are with the company Income level Higher income level generates higher satisfaction No of years in the organization & satisfaction Overall how satisfied you are with the company How long have you been working in the organization A satisfied employees stays longer in the organization Position & Income Income level Your current position in the company Higher position leads to higher income
  • 17. BIVARIATE ANALYSIS HYPOTHESIS Null Hypothesis: There is no relationship between income level and satisfaction. Alternative Hypothesis: There is a relationship between income level and satisfaction. Income & satisfaction
  • 18. BIVARIATE ANALYSIS HYPOTHESIS Null Hypothesis: There is no relationship between number of years in an organization and satisfaction. Alternative Hypothesis: There is a relationship between number of years in an organization and satisfaction. No of years & satisfaction
  • 19. BIVARIATE ANALYSIS HYPOTHESIS Null Hypothesis: There is no relationship between income level and position. Alternative Hypothesis: There is a relationship between income level and position. Income & position
  • 22. BIVARIATE ANALYSIS (income vs. satisfaction) Conclusion Income level is statistically significant in explaining the satisfaction level of the employee, but due to the smaller value of R² , we cannot generalize our hypothesis. Equation Alpha Beta1 Beta2 Beta3 R Squared Simple Linear 2.22222 (0.0000) 0.462 (0.000) 0.113 Log Linear 0.900 (0.000) 0.287 (0.001) 0.096 Quadratic 2.332 (0.000) 0.456 (0.249) -0.012 (0.861) 0.13 Cubic 2.664 (0.016) -0.24 (0.987) 0.183 (0.754) -0.023 (0.736) 0.130
  • 23. CROSS Tabulation (income vs. satisfaction)
  • 24. CROSS Tabulation (Years in organization vs. Satisfaction )
  • 25. CROSS Tabulation (Position in organization vs. Income )
  • 26. MULTIPLE REGRESSION OBJECTIVE To find a relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom A PRIORI REASONING •As income level increases ,employee satisfaction level increases. •People working for more number of years in an organization having transparent appraisal process and when given optimum degree of freedom, tend to be more satisfied.
  • 27. MULTIPLE REGRESSION HYPOTHESIS Null Hypothesis: There is no relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom Alternative Hypothesis: There is a relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom
  • 28. Variables Used • Variables- • Dependent variable • Y = Satisfaction Level in Company • Independent Variables • Q1 = Gender • Q3 = Age group • Q4 = Income level • Q7 = No. of years working in the organization • Q9 = Appraisal process involvement • Q10 = Degree of freedom
  • 29. Analysis By Multiplicative model Iteration Log( Constant ) Log Q1 (β1) Log Q3 (β2) Log Q4 (β3) Log Q7 (β4) Log Q9 (β5) Log Q10 (β6) Adj R^2 1 Log Y 1.173 (0.160) 0.865 0.118 0.077 0.327 1.383 0.343 Significance 0.002 0.283 0.297 0.246 0.283 0.213 0.295 2 Log Y 0.870 (0.161) 0.233 0.468 0.344 1.349 0.300 Significance 0.019 0.583 0.297 0.072 0.121 0.000 3 Log Y 0.824 0.262 0.457 0.336 1.353 0.305 Significance 0.022 0.224 0.077 0.127 0.000 4 Log Y 0.778 0.568 0.382 1.438 0.302 Significance 0.030 0.019 0.079 0.000
  • 30. Analysis By Additive model Iteration Constant Q1 (β1) Q3 (β2) Q4 (β3) Q7 (β4) Q9 (β5) Q10 (β6) Adj R Sqr 1 Coefficient 0.640 -0.094 0.445 -0.086 0.067 0.125 0.527 0.372 Significanc e (0.195) (0.632) (0.013) (0.468) (0.631) (0.130) (0.000) 2 Coefficient 0.499 0.444 (0.76) 0.065 0.122 0.530 0.371 Significanc e (0.206) (0.013) (0.514) (0.639) (0.136) (0.000) 3 Coefficient 0.570 0.487 (.072) 0.120 0.535 0.369 Significanc e (0.116) (0.001) (0.535) (0.142) (0.000) 4 Coefficient 0.570 0.427 0.112 0.519 0.367 Significanc e (0.115) (0.000) (0.163) (0.000)
  • 31. MULTIPLE REGRESSION CONCLUSION Income level and degree of freedom provided by superiors in idea generation are statistically significant in explaining the overall satisfaction level of employees. However, since the R2 value is not that high there are various other factors which help in explaining the overall satisfaction level of employees.
  • 32. FACTOR ANALYSIS • To analyze the different factors that influence the satisfaction level of an employee in an organization • To use the factor analysis as a data reduction technique to group the different manifest variables into constructs and thus find out the “Factors responsible for satisfaction level of an employee working in an organization ”
  • 33. A PRIORI REASONING • Factor analysis is used to measure various latent variables that can be measured by multiple observable variables • The observable variables are • Proper communication in the company • Opportunities in the job • Relations with the supervisor • Overall facilities available in the company • These variables are used to measure the overall satisfaction of the employee
  • 34. DATA COLLECTION • Data was collected with the help of a questionnaire where the responses from the respondents were recorded in a likert scale (from 1-5) • Total of 120 responses collected have been tabulated in excel along with their coded values.
  • 35. ANALYSIS • All factor analysis techniques try to clump subgroups of variables together based upon their correlations and often you can get a feel for what factors are going just by looking at the correlation matrix and spotting clusters of high correlations between groups of variables
  • 36. • Here we have four likert table data • In the first case we have the latent variable of proper communication in the company This relation is better verified by KMO and Bartlett Test
  • 37. • Factor loading, communalities and total variance explained
  • 38. Component Matrix and Rotated Component Matrix
  • 39. • Here we have four likert table data • In the second case we have the latent variable of relations with the supervisor This relation is better verified by KMO and Bartlett Test
  • 40. • Factor loading, communalities and total variance explained
  • 41. Component Matrix and Rotated Component Matrix
  • 42. • Here we have four likert table data • In the third case we have the latent variable of overall facilities available in the company This relation is better verified by KMO and Bartlett Test
  • 43. • Factor loading, communalities and total variance explained
  • 44. Component Matrix and Rotated Component Matrix
  • 45. • Here we have four likert table data • In the fourth case we have the latent variable of opportunities in the job This relation is better verified by KMO and Bartlett Test
  • 46. • Factor loading, communalities and total variance explained
  • 47. Component Matrix and Rotated Component Matrix
  • 48. FACTOR ANALYSIS Factor No. Factor Name Variables 1 communication Employee-manager (0.880) Manager-employee (0.813) 2 Clarity of mission Clarity to employees (0.798) Clarity to company (0.826) 3 Relation with superiors Superior does Good job (0.777) Superior enables my best performance (0.798) 4 Evaluation & suggestion Regularity of work evaluation (0.811) provides actionable solution(0.820) 5 Facilities in the company Appraisal process (0.809) Promotion process (0.820) Health care benefits(0.919) 6 Opportunities in job Recognition for work (0.822) Talent utilization(0.790)
  • 49. CLUSTER ANALYSIS Demographic variables used for cluster analysis • Gender • Marital Status • Age • Income Level • Current Position • Department • Number of years • Primary work Location • Involvement in Appraisal • Degree of freedom for Idea generation • Satisfaction in Company • Satisfaction in Department • Recommendation to work in the company
  • 52. CONCLUSION As observed from different analysis, the satisfaction level of employees changes is very much influenced by their income and degree of freedom provided for idea generation. Apart from that age group, position, the function they perform also play a vital role.