job satisfaction among faculty members

Job Satisfaction Among

Faculty Members: A Study of

Engineering Colleges Under

BPUT

A B.Tech. Project Reportsubmitted in partial fulfillment of

the requirements for theDegree of Bachelor of Technology

Under Biju Patnaik University of Technology

By

Toshalika Ray Roll # EIE200750152

Abhishek Ranjan Roll # EIE200720449

2010 - 2011

Under the guidance of

Mr. Bhanu Prasad Behera

NATIONAL INSTITUTE OF SCIENCE &TECHNOLOGYPalur Hills, Berhampur, Orissa – 761008, India

ABSTRACT

This report provides an analytical overview of job satisfaction among the faculty

members of BPUT, Rourkela based on various college contributions to a

questionnaire.

This Questionnaire postulates that job satisfaction depends on the balance between

work-role inputs - such as education, working time, effort - and work-role outputs -

wages, fringe benefits, status, working conditions, intrinsic aspects of the job.

If work-role outputs (‘pleasures’) increase relative to work-role inputs(‘pains’), then

job satisfaction will increase. The report then examines survey results on levels of

general or overall job satisfaction among faculty members, as well as identifying the

relationship between specific factors relating to work and job satisfaction. Taking into

consideration the work force, this report draws a conspicuous conclusion of the BPUT

work prospects.

i

ACKNOWLEDGEMENT

Completing a task is never one man's effort and this dissertation is no exception. Here

we would like to take this opportunity to thank all those individuals whose invaluable

contribution in a direct or indirect manner has gone into the making of this

dissertation.

First and foremost we express our deep sense of gratitude to our advisor, Mr. Bhanu

Prasad Behera for having been a constant source of encouragement and also for his

valuable guidance in each and every aspect of this dissertation.

We give our sincere thanks to our project co-advisors Prof. Sushanta Tripathy and

Mr. Sarat Kumar Jena for their valuable guidance and constant unfailing

encouragement.

We give our sincere thanks to Mr. Nihar Ranjan Sahu, B. Tech Project

Coordinator, for giving us the opportunity and motivating us to complete the project

within stipulated period of time and providing a helping environment.

Our sincere thanks to Prof. (Dr.) A. K. Panda, Dean, N.I.S.T, who has given us

opportunity to do this project.

We thank to Prof. Sangram Mudali, for his immense effort to provide a better

quality at NIST.

Finally we thank our parents, friends and all those people who are related to this

dissertation at any stage of its making, for their readiness to help us out whenever

required

Toshalika Ray

ii

Abhishek Ranjan

iii

TABLE OF CONTENTS

ABSTRACT...................................................................................................................i

ACKNOWLEDGEMENT...........................................................................................ii

TABLE OF CONTENTS...........................................................................................iii

LIST OF FIGURE......................................................................................................vi

CHAPTER - 1

INTRODUCTION........................................................................................................1

1.1 Objective..............................................................................................................1

1.2 Scope....................................................................................................................1

1.3 LITERATURE SURVEY....................................................................................2

1.3.1 Models of Job Satisfaction............................................................................2

1.4 Methodology........................................................................................................4

CHAPTER - 2

FACTORS THAT INFLUENCE JOB SATISFACTION........................................5

CHAPTER - 3

EMPLOYEE ATTITUDE AND JOB SATISFACTION..........................................8

3.1 The Causes of Employee Attitudes......................................................................8

3.1.1 Dispositional Influences................................................................................8

3.1.2 Cultural Influences........................................................................................9

3.1.3 Work Situation Influences.............................................................................9

3.2 The Results of Positive or Negative Job Satisfaction........................................10

3.3 Job Satisfaction and Job Performance................................................................10

3.4 Job Satisfaction and Life Satisfaction................................................................11

3.5 Job Satisfaction and Withdrawal Behaviors.......................................................12

3.6 Measure and Influence Employee Attitudes......................................................12

3.6.1 Employee Attitude Surveys.........................................................................12

3.6.2 The Use of Norms.......................................................................................14

iv

3.6.3 Comparisons and Numerical Accuracy.......................................................14

3.6.4 Survey Feedback and Action.......................................................................15

CHAPTER - 4

MINITAB....................................................................................................................16

4.1 Minitab Windows...............................................................................................16

4.2 Data Types.........................................................................................................16

4.3 Entering Data.....................................................................................................17

4.4 Saving Data........................................................................................................18

CHAPTER - 5

STATISTICAL ANALYSIS......................................................................................19

5.1 Descriptive Statistics..........................................................................................19

5.2 Mean...................................................................................................................20

5.2.1 Arithmetic mean (AM)................................................................................21

5.2.2 Geometric mean (GM)................................................................................22

5.2.3 Harmonic Mean (HM).................................................................................22

5.2.4 Weighted Arithmetic Mean.........................................................................23

5.2.5 Truncated Mean...........................................................................................23

5.3 Median................................................................................................................24

5.3.1 Notation.......................................................................................................24

5.3.2 Medians in Descriptive Statistics................................................................25

5.4 Standard Deviation.............................................................................................25

5.5 Correlation..........................................................................................................26

5.6 Regression..........................................................................................................27

CHAPTER - 6

RESULTS....................................................................................................................29

6.1 Basic Analysis....................................................................................................29

6.2 Descriptive Analysis..........................................................................................30

v

6.3 Correlation..........................................................................................................31

6.4 Regression..........................................................................................................37

vi

CHAPTER - 7

CONCLUSIONS........................................................................................................39

REFERENCES...........................................................................................................40

APPENDIX..................................................................................................................41

vii

LIST OF FIGURE

Figure 4.1 Worksheet.............................................................................................17

Figure 5.1 A menu of the statistics categories and the subcategories for Basic

Statistics from Student Version 12 .....................................................19

Figure 5.2 Cumulative Probability of a normal distribution with expected value 0

and standard deviation 1......................................................................26

Figure 5.3 A data set with a mean of 50 (shown in blue) and a standard deviation

() of 20...............................................................................................26

Figure 5.4 Positive Correlation..............................................................................27

Figure 6.1 Colleges participating and their contribution towards the project.......29

Figure 6.2 Number of male and female faculty involved in the survey................29

viii

JOB SATISFACTION AMONG FACULTY MEMBERS: A STUDY OF ENGINEERING COLLEGES UNDER BPUT

INTRODUCTION

OVERVIEW

Job satisfaction has been defined as a pleasurable emotional state resulting from the

appraisal of one’s job; an affective reaction to one’s job; and an attitude towards one’s

job.

Job satisfaction describes how content an individual is with his or her job. The

happier people are within their job, the more satisfied they are said to be. Job

satisfaction is not the same as motivation, although it is clearly linked. Job design

aims to enhance job satisfaction and performance, methods include job rotation, job

enlargement and job enrichment. Other influences on satisfaction include the

management style and culture, employee involvement, empowerment and

autonomous work groups. Job satisfaction is a very important attribute which is

frequently measured by organizations. The most common way of measurement is the

use of rating scales where employees report their reactions to their jobs. Questions

relate to rate of pay, work responsibilities, variety of tasks, promotional opportunities

the work itself and co-workers.

1.2 Objectives

1. To identify critical factors leading to job satisfaction among faculty members

of different colleges under BPUT after proper analysis.

2. To find out relationship between the critical factors

3. To find out the most important factor that affects job satisfaction by factor

analysis.

1.2 Scope This project is limited to the job satisfaction of faculty members of engineering colleges under BPUT ,in finding out the critical factors .

0


LITERATURE REVIEW

Models of Job Satisfaction

1. Affect Theory

Edwin A. Locke’s Range of Affect Theory (1976) is arguably the most famous job

satisfaction model. The main premise of this theory is that satisfaction is determined

by a discrepancy between what one wants in a job and what one has in a job. Further,

the theory states that how much one values a given facet of work (e.g. the degree of

autonomy in a position) moderates how satisfied/dissatisfied one becomes when

expectations are/aren’t met. When a person values a particular facet of a job, his

satisfaction is more greatly impacted both positively (when expectations are met) and

negatively (when expectations are not met), compared to one who doesn’t value that

facet. To illustrate, if Employee A values autonomy in the workplace and Employee B

is indifferent about autonomy, then Employee A would be more satisfied in a position

that offers a high degree of autonomy and less satisfied in a position with little or no

autonomy compared to Employee B. This theory also states that too much of a

particular facet will produce stronger feelings of dissatisfaction the more a worker

values that facet.

2. Dispositional Theory

Another well-known job satisfaction theory is the Dispositional Theory Template:

Jackson April 2007. It is a very general theory that suggests that people have innate

dispositions that cause them to have tendencies toward a certain level of satisfaction,

regardless of one’s job. This approach became a notable explanation of job

satisfaction in light of evidence that job satisfaction tends to be stable over time and

across careers and jobs. Research also indicates that identical twins have similar

levels of job satisfaction.

A significant model that narrowed the scope of the Dispositional Theory was the Core

Self-evaluations Model, proposed by Timothy A. Judge in 1998. Judge argued that

1

http://en.wikipedia.org/w/index.php?title=Template:JacksonApril_2007&action=edit&redlink=1

http://en.wikipedia.org/w/index.php?title=Template:JacksonApril_2007&action=edit&redlink=1


there are four Core Self-evaluations that determine one’s disposition towards job

satisfaction: self-esteem, general self-efficacy, locus of control, and neuroticism. This

model states that higher levels of self-esteem (the value one places on his/her self) and

general self-efficacy (the belief in one’s own competence) lead to higher work

satisfaction. Having an internal locus of control (believing one has control over her\

his own life, as opposed to outside forces having control) leads to higher job

satisfaction. Finally, lower levels of neuroticism lead to higher job satisfaction.

3. Two-Factor Theory (Motivator-Hygiene Theory)

Frederick Herzberg’s Two factor theory (also known as Motivator Hygiene Theory)

attempts to explain satisfaction and motivation in the workplace. This theory states

that satisfaction and dissatisfaction are driven by different factors – motivation and

hygiene factors, respectively. An employee’s motivation to work is continually related

to job satisfaction of a subordinate. Motivation can be seen as an inner force that

drives individuals to attain personal and organizational goals . Motivating factors are

those aspects of the job that make people want to perform, and provide people with

satisfaction, for example achievement in work, recognition, promotion opportunities.

These motivating factors are considered to be intrinsic to the job, or the work carried

out. Hygiene factors include aspects of the working environment such as pay,

company policies, supervisory practices, and other working conditions.

While Hertzberg's model has stimulated much research, researchers have been unable

to reliably empirically prove the model, with Hackman & Oldham suggesting that

Hertzberg's original formulation of the model may have been a methodological

artifact. Furthermore, the theory does not consider individual differences, conversely

predicting all employees will react in an identical manner to changes in

motivating/hygiene factors. Finally, the model has been criticised in that it does not

specify how motivating/hygiene factors are to be measured.

4. Job Characteristics Model

Hackman & Oldham proposed the Job Characteristics Model, which is widely used as

a framework to study how particular job characteristics impact on job outcomes,

2

http://en.wikipedia.org/w/index.php?title=Job_Characteristics_Model&action=edit&redlink=1

http://en.wikipedia.org/wiki/Two_factor_theory

http://en.wikipedia.org/wiki/Frederick_Herzberg

http://en.wikipedia.org/wiki/Neuroticism

http://en.wikipedia.org/wiki/Neuroticism

http://en.wikipedia.org/wiki/Locus_of_control

http://en.wikipedia.org/wiki/Self-efficacy

http://en.wikipedia.org/wiki/Self-esteem


including job satisfaction. The model states that there are five core job characteristics

(skill variety, task identity, task significance, autonomy, and feedback) which impact

three critical psychological states (experienced meaningfulness, experienced

responsibility for outcomes, and knowledge of the actual results), in turn influencing

work outcomes (job satisfaction, absenteeism, work motivation, etc.). The five core

job characteristics can be combined to form a motivating potential score (MPS) for a

job, which can be used as an index of how likely a job is to affect an employee's

attitudes and behaviors----. A meta-analysis of studies that assess the framework of

the model provides some support for the validity of the JCM.

5. Communication Overload and Communication Underload

One of the most important aspects of an individual’s work in a modern organization

concerns the management of communication demands that he or she encounters on

the job. Demands can be characterized as a communication load, which refers to “the

rate and complexity of communication inputs an individual must process in a

particular time frame .Individuals in an organization can experience communication

over-load and communication underload which can affect their level of job

satisfaction. Communication overload can occur when “an individual receives too

many messages in a short period of time which can result in unprocessed information

or when an individual faces more complex messages that are more difficult to

process.” Due to this process, “given an individual’s style of work and motivation to

complete a task, when more inputs exist than outputs, the individual perceives a

condition of overload which can be positively or negatively related to job satisfaction.

In comparison, communication under load can occur when messages or inputs are

sent below the individual’s ability to process them .” According to the ideas of

communication over-load and under-load, if an individual does not receive enough

input on the job or is unsuccessful in processing these inputs, the individual is more

likely to become dissatisfied, aggravated, and unhappy with their work which leads to

a low level of job satisfaction.

3

http://en.wikipedia.org/wiki/Meta-analysis


FACTORS THAT INFLUENCE JOB

SATISFACTION

1. Opportunity

Employees are more satisfied when they have challenging opportunities at work. This

includes chances to participate in interesting projects, jobs with a satisfying degree of

challenge, and opportunities for increased responsibility. Important: this is not simply

"promotional opportunity." As organizations have become flatter, promotions can be

rare. People have found challenge through projects, team leadership, special

assignment as well as promotions.

Actions:

Promote from within when possible.

Reward promising employees with roles on interesting projects.

Divide jobs into levels of increasing leadership and responsibility.

It may be possible to create job titles that demonstrate increasing levels of expertise

which are not limited by availability of positions. They simply demonstrate

achievement.

2. Stress

When negative stress is continuously high, job satisfaction is low. Jobs are more

stressful if they interfere with employees' personal lives or are a continuing source of

worry or concern.

Actions: Promote a balance of work and personal lives. Make sure that senior managers

model this behavior.

Distribute work evenly (fairly) within workteams.

Review work procedures to remove unnecessary "red tape" or bureaucracy.

4


Manage the number of interruptions employees have to endure while trying to

do their jobs.

Some organizations utilize exercise or "fun" breaks at work.

3. Leadership

Data from employee satisfaction surveys has shown employees are more satisfied

when their managers are good leaders. This includes motivating employees to do a

good job, striving for excellence, or just taking action.

Actions:

Make sure your managers are well trained. Leadership combines attitudes and

behavior. It can be learned.

People respond to managers that they can trust and who inspire them to

achieve meaningful goals.

4. Work Standards

Employees are more satisfied when their entire workgroup takes pride in the quality

of its work.

Actions:

Encourage communication between employees and customers. Quality gains

importance when employees see its impact on customers.

Develop meaningful measures of quality. Celebrate achievements in quality.

5. Fair Rewards

Employees are more satisfied when they feel they are rewarded fairly for the work

they do. Consider employee responsibilities, the effort they have put forth, the work

they have done well, and the demands of their jobs.

Actions:

Make sure rewards are for genuine contributions to the organization.

Be consistent in your reward policies.

5


If your wages are competitive, make sure employees know this.

Rewards can include a variety of benefits and perks other than money.

As an added benefit, employees who are rewarded fairly, experience less stress.

6. Adequate Authority

Employees are more satisfied when they have adequate freedom and authority to do

their jobs.

Actions:

When reasonable:

Let employees make decisions.

Allow employees to have input on decisions that will affect them.

Establish work goals, but let employees determine how they will achieve those

goals. Later reviews may identify innovative "best practices."

Ask, "If there were just one or two decisions that you could make, which ones

would make the biggest difference in your job?"

When these six factors are high, job satisfaction is high. When the six factors are

low, job satisfaction is low.

6


EMPLOYEE ATTITUDE AND JOB

SATISFACTION

“Happy employees are productive employees.”

“Happy employees are not productive employees.”

We hear these conflicting statements made by HR professionals and managers in

organizations.

This article identifies three major gaps between HR practice and the scientific

research in the area of employee attitudes in general and the most focal employee

attitude in particular—job satisfaction:

(1) the causes of employee attitudes,

(2) the results of positive or negative job satisfaction,

and (3) how to measure and influence employee attitudes

The most-used research definition of job satisfaction is by Locke (1976), who defined

it as “. . . a pleasurable or positive emotional state resulting from the appraisal of

one’s job or job experiences” . Implicit in Locke’s definition is the importance of both

affect, or feeling, and cognition, or thinking. When we think, we have feelings about

what we think. Conversely, when we have feelings, we think about what we feel.

Cognition and affect are thus inextricably linked, in our psychology and even in our

biology. Thus, when evaluating our jobs, as when we assess anything important to us,

both thinking and feeling are involved.

3.1 The Causes of Employee Attitudes

3.1.1 Dispositional Influences

7


Several innovative studies have shown the influences of a person’s disposition on job

satisfaction. One of the first studies in this area demonstrated that a person’s job

satisfaction scores have stability over time, even when he or she changes jobs or

companies. In a related study, childhood temperament was found to be statistically

related to adult job satisfaction up to 40 years later . Evidence even indicates that the

job satisfaction of identical twins reared apart is statistically similar. Although this

literature has had its critics, an accumulating body of evidence indicates that

differences in job satisfaction across employees can be traced, in part, to differences

in their disposition or temperament. Despite its contributions to our understanding of

the causes of job satisfaction, one of the limitations in this literature is that it is not yet

informative as to how exactly dispositions affect job satisfaction.

3.1.2 Cultural Influences

In terms of other influences on employee attitudes, there is also a small, but growing

body of research on the influences of culture or country on employee attitudes and job

satisfaction.

The continued globalization of organizations poses new challenges for HR

practitioners, and the available research on cross-cultural organizational and human

resources issues can help them better understand and guide. The four cross-cultural

dimensions are:

(1) individualism-collectivism;

(2) uncertainty avoidance versus risk taking;

(3) power distance, or the extent to which power is unequally distributed; and

(4) masculinity/femininity, more recently called achievement orientation.

3.1.3 Work Situation Influences

As discussed earlier, the work situation also matters in terms of job satisfaction and

organization impact . Contrary to some commonly held practitioner beliefs, the most

notable situational influence on job satisfaction is the nature of the work itself—often

8


called “intrinsic job characteristics.” Research studies across many years,

organizations, and types of jobs show that when employees are asked to evaluate

different facets of their job such as supervision, pay, promotion opportunities,

coworkers, and so forth, the nature of the work itself generally emerges as the most

important job facet . This is not to say that well-designed compensation programs or

effective supervision are unimportant; rather, it is that much can be done to influence

job satisfaction by ensuring work is as interesting and challenging as possible.

Unfortunately, some managers think employees are most desirous of pay to the

exclusion of other job attributes such as interesting work. For example, in a study

examining the importance of job attributes, employees ranked interesting work as the

most important job attribute and good wages ranked fifth, whereas when it came to

what managers thought employees wanted, good wages ranked first while interesting

work ranked fifth. Of all the major job satisfaction areas, satisfaction with the nature

of the work itself— which includes job challenge, autonomy,variety, and scope—best

predicts overall job satisfaction, as well as other important outcomes like employee

retention. Thus, to understand what causes people to be satisfied with their jobs, the

nature of the work itself is one of the first places for practitioners to focus on.

3.2 The Results of Positive or Negative Job Satisfaction

A second major practitioner knowledge gap is in the area of understanding the

consequences of job satisfaction. We hear debates and confusion about whether

satisfied employees are productive employees, and HR practitioners rightfully

struggle as they must reduce costs and are concerned about the effects on job

satisfaction and, in turn, the impact on performance and other outcomes. The focus of

our discussion in this section is on job satisfaction, because this is the employee

attitude that is most often related to organizational outcomes. Other employee

attitudes, such as organizational commitment, have been studied as well, although

they have similar relationships to outcomes as job satisfaction.

9


3.3 Job Satisfaction and Job Performance

The study of the relationship between job satisfaction and job performance has a

controversial history. The Hawthorne studies, conducted in the 1930s, are often

credited with making researchers aware of the effects of employee attitudes on

performance. Shortly after the Hawthorne studies, researchers began taking a critical

look at the notion that a “happy worker is a productive worker.” Most of the earlier

reviews of the literature suggested a weak and somewhat inconsistent relationship

between job satisfaction and performance. A review of the literature in 1985

suggested that the statistical correlation between job satisfaction and performance

was about 17.

Thus, these authors concluded that the presumed relationship between job satisfaction

and performance was a “management fad” and “illusory.” This study had an

important impact on researchers, and in some cases on organizations, with some

managers and HR practitioners concluding that the relationship between job

satisfaction and performance was trivial.

However, further research does not agree with this conclusion. Organ (1988) suggests

that the failure to find a strong relationship between job satisfaction and performance

is due to the narrow means often used to define job performance. Organ argued that

when performance is defined to include important behaviors not generally reflected in

performance appraisals, such as organizational citizenship behaviors, its relationship

with job satisfaction improves. Research tends to support Organ’s proposition in that

job satisfaction correlates with organizational citizenship behaviors .

In addition, in a more recent and comprehensive review it was found that when the

correlations are appropriately corrected (for sampling and measurement errors), the

average correlation between job satisfaction and job performance is a higher.30. In

addition, the relationship between job satisfaction and performance was found to be

even higher for complex (e.g., professional) jobs than for less complex jobs. Thus,

10


contrary to earlier reviews, it does appear that job satisfaction is, in fact, predictive of

performance, and the relationship is even stronger for professional jobs.

3.4 Job Satisfaction and Life Satisfaction

An emerging area of study is the interplay between job and life satisfaction.

Researchers have speculated that there are three possible forms of the relationship

between job satisfaction and life satisfaction:

(1) spillover- where job experiences spill over into nonwork life and vice versa;

(2) segmentation-where job and life experiences are separated and have little to do

with one another; and

(3) compensation-

where an individual seeks to compensate for a dissatisfying job by seeking fulfillment

and happiness in his or her nonwork life and vice versa.

3.5 Job Satisfaction and Withdrawal Behaviors

Numerous studies have shown that dissatisfied employees are more likely to quit their

jobs or be absent than satisfied employees. Job dissatisfaction also appears to be

related to other withdrawal behaviors, including lateness, unionization, grievances,

drug abuse, and decision to retire.” Because the occurrence of most single withdrawal

behaviors is quite low, looking at a variety of these behaviors improves the ability for

showing the relationship between job attitudes and withdrawal behaviors. Rather than

predicting isolated behaviors, withdrawal research and applied practice would do

better, as this model suggests, to study patterns in withdrawal behaviors—such as

turnover, absenteeism ,lateness, decision to retire, etc.— together. Several studies

have supported this, showing that when various withdrawal behaviors are grouped

together, job satisfaction better predicts these behavioral groupings than the individual

behaviors. Based on the research that shows job satisfaction predicts withdrawal

behaviors like turnover and absenteeism, researchers have been able to statistically

measure the financial impact of employee attitudes on organizations. Using these

11


methods can be a powerful way for practitioners to reveal the costs of low job

satisfaction and the value of improved employee attitudes on such outcomes as

absenteeism and retention.

3.6 Measure and Influence Employee Attitudes

There are a number of possible methods for measuring employee attitudes, such as

conducting focus groups, interviewing employees, or carrying out employee surveys.

3.6.1 Employee Attitude Surveys

Two major research areas on employee attitude surveys are discussed below:

employee attitude measures used in research and facet versus global measures. In the

research literature, the two most extensively validated employee attitude survey

measures are the Job Descriptive Index and the Minnesota Satisfaction Questionnaire.

The JDI assesses satisfaction with five different job areas: pay, promotion, coworkers,

supervision, and the work itself. The JDI is reliable and has an impressive array of

validation evidence. The MSQ has the advantage of versatility—long and short forms

are available, as well as faceted and overall measures. Another measure used in job

satisfaction research is an updated and reliable five-item version of an earlier scale by

Brayfield and Rothe (1951). All of these measures have led to greater scientific

understanding of employee attitudes, and their greatest value may be for research

purposes, yet these measures may be useful for practitioners as well.. There are two

additional issues with measuring employee attitudes that have been researched and

provide potentially useful knowledge for practitioners. First, measures of job

satisfaction can be faceted (such as the JDI)—whereby they measure various

dimensions of the job—while others are global—or measure a single, overall feeling

toward the job. An example of a global measure is “Overall, how satisfied are you

with your job?” If a measure is facet-based, overall job satisfaction is typically

defined as a sum of the facets. Scarpello and Campbell (1983) found that individual

questions about various aspects of the job did not correlate well with a global measure

12


of overall job satisfaction. However, if one uses job satisfaction facet scores—based

on groups of questions on the same facet or dimension rather than individual

questions—to predict an independent measure of overall job satisfaction, the

relationship is considerably higher. As has been noted elsewhere job satisfaction

facets are sufficiently related to suggest that they are measuring a common construct

—overall job satisfaction. Second, while most job satisfaction researchers have

assumed that overall, single item measures are unreliable and therefore should not be

used, this view has not gone unchallenged. Therefore, respectable levels of reliability

can be obtained with an overall measure of job satisfaction, although these levels are

somewhat lower than most multiple-item measures of job satisfaction. Based on the

research reviewed, there is support for measuring job satisfaction with either a global

satisfaction question or by summing scores on various aspects of the job. Therefore,

in terms of practice, by measuring facets of job satisfaction, organizations can obtain

a complete picture of their specific strengths and weaknesses related to employee job

satisfaction and use those facet scores for an overall satisfaction measure, or they can

reliably use overall satisfaction questions for that purpose.

13


3.6.2 The Use of Norms

Ratings made by employees on survey questions can systematically vary—and vary

widely—no matter what company they work for. For example, ratings of pay are

typically low and ratings of workgroup cooperation are typically rated very high.

Similar systematic variations are found when comparing survey data . Survey norms

are descriptive statistics that are compiled from data on the same survey questions.. If

survey norms are not an option, unit results can serve as internal norms, although they

encourage an inward focus and potentially internal competition. Actions determined

through normed-based comparisons can be strong drivers of change and help focus a

institute externally to other competitors.

3.6.3 Comparisons and Numerical Accuracy

Comparing data is one of the most useful survey analysis techniques, such as

described above for using norms to compare a organization’s survey results to that of

other organization’s. Comparisons for the same organization or unit over time with a

trended survey are also valuable to measure progress. At the same time, comparisons

must be done with professional care, taking into account measurement issues . This is

one of the major areas of practitioner misinterpretation in experience.

In general, the lower the number, the greater the effects of random error on data, like

the differences between flipping a coin 10 times versus 1,000 times. Thus,

comparisons of groups with small numbers generally should not be done, especially

when the survey is a sample survey and designed to provide data only at higher

levels.To avoid these measurement issues, it is helpful to have a lower limit on the

organization size and/or number of respondents needed to create reports for

comparisons .Numerical accuracy and appropriate comparisons are especially

important when using survey data for performance targets and employment-related

decisions.

14


3.6.4 Survey Feedback and Action

Employee surveys, used effectively, can be catalysts for improving employee attitudes

and producing organizational change. This statement is based on two important

assumptions:

first, that employee attitudes affect behavior and

second, that employee attitudes are important levers of organizational performance.

Methodology

A two member team was formed by us to carry out this project. At first we went

through a lot of books and e-books for a thorough literature survey. After the

literature survey, taking into account the various factors that affect job satisfaction we

prepared the questionnaire and distributed the questionnaire in various engineering

colleges under BPUT. After collecting the questionnaire from the colleges we ended

up in having a sample size of 70. We found out the correlations and regressions

between various factors as part of our analysis which helped us in reaching our

objective.

Data source: Primary data

Data collection method: Questionaires

Sample size: 70

Data analysis: MINITAB

MINITAB

Minitab is statistical analysis software. It can be used for learning about statistics as

well as statistical research. Statistical analysis computer applications have the

advantage of being accurate, reliable, and generally faster than computing statistics

and drawing graphs by hand.

4.1 Minitab Windows

15


When you first open Minitab, you will see two windows, a Session window and a

Worksheet window.

Session Window: The area that displays the statistical results of your data

analysis and can also be used to enter commands.

Worksheet Window: A grid of rows and columns used to enter and manipulate

the data. Note: This area looks like a spreadsheet but will not automatically

update the columns when entries are changed.

Other windows include

Graph Window: When you generate graphs, each graph is opened in its own

window.

Report Window: Version 13 has a report manager that helps you organize your

results in a report

4.2 Data Types

Numerical: Numerical data is the only type Minitab will use for statistical

calculations. Numerical data is aligned on the right side of the column.

Minitab will not recognize numbers with commas as numbers but will

consider them text.

Text: Text cannot be used for computations. Though “text” generally means

words or characters, numbers can be classified as text. If column 1 has text in

it, the column label will change from C1 to C1-T. Data types can be changed.

4.3 Entering Data

You can enter your data going down or across. In the top left corner of the Worksheet

window, there is a cell with an arrow in it. Click this cell to change the action of the

Enter key.

If the arrow is pointing down, then the cursor will go down the column when

you press Enter.

16


If the arrow is pointing to the right, then the cursor will go across the row, to

the next column when you press Enter

Figure 4.1 Worksheet

Minitab can change data types within limits. You cannot make a simple switch of

people’s names to numeric values, but if you have a column of numbers that was

accidentally entered as text, then you can change those numbers to numeric values.

Minitab makes the following types of transformations.

numeric to text

text to numeric

date/time to text

date/time to numeric

numeric to date/time

text to date/time

To make these changes in Minitab, from the main menu select MANIP > CHANGE

DATA TYPE. Then, select the option that you want and fill in the dialog box.

4.4 Saving Data

In Minitab, you can save data in two different formats. You can save the worksheet by

itself or the entire project. Saving the worksheet as a separate file is a good habit.

17


Then you will always have access to the data, even if the project you are working with

becomes corrupted. To save the data in a worksheet by itself

1. Select FILE > SAVE CURRENT WORKSHEET AS.

2. Use the arrow beside the Save in: field to select the location of your diskette or

USB device.

3. In the File Name field, type the name of the worksheet. Minitab will

automatically add the extension MTW for Minitab worksheet.

4. Click Save.

The worksheet with the data will be saved automatically.

18


STATISTICAL ANALYSIS

Minitab will conduct a variety of statistical calculations. These are found under the

main menu option of STAT. Each category also has subcategories.

Figure 5.1 A menu of the statistics categories and the subcategories for

Basic Statistics from Student Version 12 .

5.1 Descriptive Statistics

Terms in the output and some definitions

N = number of data items in the sample

N* = number of items in the sample that have missing values (N* does not

show up when all the items in the sample have values)

Mean = average

19


Median = 50th percentile

TrMean= the 5% trimmed mean

StDev = standard deviation

SE Mean = standard error of the mean = standard deviation divided by the

square root of the sample size

Minimum = smallest data value

Maximum = largest data value

Q1 = 25th percentile = first quartile

Q3 = 75th percentile = third quartile

5.2 Mean

In statistics, mean has two related meanings:

the arithmetic mean (and is distinguished from the geometric mean or

harmonic mean).

the expected value of a random variable, which is also called the population

mean.

There are other statistical measures that use samples that some people confuse with

averages - including 'median' and 'mode'. Other simple statistical analyses use

measures of spread, such as range, interquartile range, or standard deviation. For a

real-valued random variable X, the mean is the expectation of X. Note that not every

probability distribution has a defined mean (or variance); see the Cauchy distribution

for an example.

For a data set, the mean is the sum of the values divided by the number of values. The

mean of a set of numbers x1, x2, ..., xn is typically denoted by , pronounced "x bar".

This mean is a type of arithmetic mean. If the data set were based on a series of

observations obtained by sampling a statistical population, this mean is termed the

"sample mean" to distinguish it from the "population mean". The mean is often

quoted along with the standard deviation: the mean describes the central location of

20


the data, and the standard deviation describes the spread. An alternative measure of

dispersion is the mean deviation, equivalent to the average absolute deviation from

the mean. It is less sensitive to outliers, but less mathematically tractable.

21


If a series of observations is sampled from a larger population (measuring the heights

of a sample of adults drawn from the entire world population, for example), or from a

probability distribution which gives the probabilities of each possible result, then the

larger population or probability distribution can be used to construct a "population

mean", which is also the expected value for a sample drawn from this population or

probability distribution. For a finite population, this would simply be the arithmetic

mean of the given property for every member of the population. For a probability

distribution, this would be a sum or integral over every possible value weighted by the

adding probability of that value. It is a universal convention to represent the

population mean by the symbol µ. In the case of a discrete probability distribution, the

mean of a discrete random variable x is given by taking the product of each possible

value of x and its probability P(x), and then all these products together, giving

The sample mean may differ from the population mean, especially for small samples,

but the law of large numbers dictates that the larger the size of the sample, the more

likely it is that the sample mean will be close to the population mean.

As well as statistics, means are often used in geometry and analysis; a wide range of

means have been developed for these purposes, which are not much used in statistics.

These are listed below.

Equality holds only when all the elements of the given sample are equal.

5.3 Median

In probability theory and statistics, a median is described as the numeric value

separating the higher half of a sample, a population, or a probability distribution, from

the lower half. The median of a finite list of numbers can be found by arranging all

the observations from lowest value to highest value and picking the middle one. If

22


there is an even number of observations, then there is no single middle value; the

median is then usually defined to be the mean of the two middle values.

In a sample of data, or a finite population, there may be no member of the sample

whose value is identical to the median (in the case of an even sample size), and, if

there is such a member, there may be more than one so that the median may not

uniquely identify a sample member. Nonetheless, the value of the median is uniquely

determined with the usual definition. A related concept, in which the outcome is

forced to correspond to a member of the sample, is the medoid. At most, half the

population have values less than the median, and, at most, half have values greater

than the median. If both groups contain less than half the population, then some of the

population is exactly equal to the median. For example, if a < b < c, then the median

of the list {a, b, c} is b, and, if a < b < c < d, then the median of the list {a, b, c, d} is

the mean of b and c; i.e., it is (b + c)/2. The median can be used as a measure of

location when a distribution is skewed, when end-values are not known, or when one

requires reduced importance to be attached to outliers, e.g., because they may be

measurement errors. A disadvantage of the median is the difficulty of handling it

theoretically.

5.3.1 Notation

The median of some variable x is denoted either as

23


5.3.2 Medians in Descriptive Statistics

The median is used primarily for skewed distributions, which it summarizes

differently than the arithmetic mean.

Consider the multiset { 1, 2, 2, 2, 3, 14 }. The median is 2 in this case, as is the mode,

and it might be seen as a better indication of central tendency than the arithmetic

mean of 4.

Calculation of medians is a popular technique in summary statistics and summarizing

statistical data, since it is simple to understand and easy to calculate, while also giving

a measure that is more robust in the presence of outlier values than is the mean.

5.4 Standard Deviation

Standard deviation is a widely used measurement of variability or diversity used in

statistics and probability theory. It shows how much variation or "dispersion" there is

from the average (mean, or expected value). A low standard deviation indicates that

the data points tend to be very close to the mean, whereas high standard deviation

indicates that the data are spread out over a large range of values. Technically, the

standard deviation of a statistical population, data set, or probability distribution is the

square root of its variance. It is algebraically simpler though practically less robust

than the average absolute deviation. A useful property of standard deviation is that,

unlike variance, it is expressed in the same units as the data. In addition to expressing

the variability of a population, standard deviation is commonly used to measure

confidence in statistical conclusions. For example, the margin of error in polling data

is determined by calculating the expected standard deviation in the results if the same

poll were to be conducted multiple times. The reported margin of error is typically

about twice the standard deviation – the radius of a 95 percent confidence interval. In

science, researchers commonly report the standard deviation of experimental data, and

only effects that fall far outside the range of standard deviation are considered

24


statistically significant – normal random error or variation in the measurements is in

this way distinguished from causal variation. Standard deviation is also important in

finance, where the standard deviation on the rate of return on an investment is a

measure of the volatility of the investment.

Figure 5.2 Cumulative Probability of a normal distribution with expected

value 0 and standard deviation 1

Figure 5.3 A data set with a mean of 50 (shown in blue) and a standard

deviation () of 20

25


5.5 Correlation

In statistics and probability theory, correlation means how closely related two sets of

data are. Correlation does not always mean that one causes the other. It is very

possible that there is a third factor involved. Correlation usually has one of two

directions. These are positive or negative. If it is positive, then the two sets go up

together. If it is negative, then one goes up while the other goes down. Lots of

different measurements of correlation are used for different situations. For example on

a scatter graph, people draw a line of best fit to show the direction of the correlation.

Figure 5.4 Positive Correlation

Explaining Correlation

Strong and weak are words used to describe correlation. If there is strong correlation,

then the points are all close together. If there is weak correlation, then the points are

all spread apart. There are ways of making numbers show how strong the correlation

is. These measurements are called correlation coefficients. The best known is the

Pearson product-moment correlation coefficient. You put in data into a formula and it

gives you a number. If the number is 1 or -1, then there is strong correlation. If the

answer is 0, then there is no correlation. Another kind of correlation coefficient is

Spearman's rank correlation coefficient.

26


5.6 Regression

In statistics, regression analysis includes any techniques for modeling and analyzing

several variables, when the focus is on the relationship between a dependent variable

and one or more independent variables. More specifically, regression analysis helps

one understand how the typical value of the dependent variable changes when any one

of the independent variables is varied, while the other independent variables are held

fixed. Most commonly, regression analysis estimates the conditional expectation of

the dependent variable given the independent variables — that is, the average value of

the dependent variable when the independent variables are held fixed. Less

commonly, the focus is on a quantile, or other location parameter of the conditional

distribution of the dependent variable given the independent variables. In all cases, the

estimation target is a function of the independent variables called the regression

function. In regression analysis, it is also of interest to characterize the variation of the

dependent variable around the regression function, which can be described by a

probability distribution. Regression analysis is widely used for prediction and

forecasting, where its use has substantial overlap with the field of machine learning.

Regression analysis is also used to understand which among the independent variables

are related to the dependent variable, and to explore the forms of these relationships.

In restricted circumstances, regression analysis can be used to infer causal

relationships between the independent and dependent variables. A large body of

techniques for carrying out regression analysis has been developed. Familiar methods

such as linear regression and ordinary least squares regression are parametric, in that

the regression function is defined in terms of a finite number of unknown parameters

that are estimated from the data. Nonparametric regression refers to techniques that

allow the regression function to lie in a specified set of functions, which may be

infinite-dimensional.

The performance of regression analysis methods in practice depends on the form of

the data-generating process, and how it relates to the regression approach being used.

27


Since the true form of the data-generating process is in general not known, regression

analysis often depends to some extent on making assumptions about this process.

These assumptions are sometimes (but not always) testable if a large amount of data is

available. Regression models for prediction are often useful even when the

assumptions are moderately violated, although they may not perform optimally.

However, in many applications, especially with small effects or questions of causality

based on observational data, regression methods give misleading results.

28


DATA ANALYSIS

CONTRIBUTION OF VARIOUS ENGINEERING COLLEGES IN THE SURVEY

6.1 Basic Analysis of the data

Figure 6.1 Colleges participating and their contribution towards the

project

29


Figure 6.2 Number of male and female faculty involved in the survey

6.2 DESCRIPTIVE ANALYSIS

Variable N Mean Median Minimum Maximum

Ideas 70 3.9714 4.0000 2.0000 5.000

Variety 70 4.0714 4.0000 2.0000 5.000

bst wrk 70 4.171 4.0000 2.0000 5.000

job sec 70 4.000 4.0000 1.000 5.000

Pay 70 3.514 4.0000 1.000 5.000

knw-hw 70 3.914 4.0000 2.0000 5.000

copertn 70 4.143 4.0000 1.000 5.000

result 70 4.2286 4.0000 2.000 5.000

Wrkabilt 70 4.086 4.0000 1.000 5.000

Surndngs 70 4.014 4.0000 2.0000 5.000

getn ahe 70 3.8143 4.0000 2.0000 5.000

pride 70 4.1143 4.0000 2.0000 5.000

Routine 70 4.0571 4.0000 2.0000 5.000

rub elbo 70 3.729 4.0000 2.0000 5.000

bks up 70 3.986 4.0000 1.000 5.000

Promotns 70 3.714 4.0000 1.000 5.000

wrk div 70 3.900 4.0000 2.0000 5.000

Cmplnts 70 3.800 4.0000 1.000 5.000

Helps 70 4.071 4.0000 1.000 5.000

Frdm 70 3.700 4.0000 1.000 5.000

Apprcntn 70 4.0857 4.0000 1.000 5.000

servce 70 3.8857 4.0000 2.0000 5.000

trnsfrs 70 3.7000 4.0000 2.0000 5.000

Advncmnt 70 4.1286 4.0000 2.0000 5.000

30


RESULTS AND INTERPRETATION

SCREE PLOT:

24222018161412108642

9

8

7

6

5

4

3

2

1

0

Factor Number

Eigenvalu

e

Scree Plot of ideas, ..., advncmnt

It shows that after four factors the curve becomes more or less as a straight line signifying that we can extract four factors from the twentyfive factors

31


FOUR MAJOR GROUPS AFTER ANALYSIS AND THE VARIABLES

INCLUDED IN IT

Group1 Group2 Group3 Group4

helps trnsfrs variety pride

wrk div ideas Getn ahed Freedom

bks up rub elbows Job security friendship

cmplnts servce appreciation

copertn knw-hw

promotns routine

bst wrk

wrkabilty

result

advncmnt

pay

surndngs

REGRESSION ANALYSIS

32


Regression Analysis: Avg versus gr1, gr2, gr3, gr4

The regression equation is

Avg = - 0.0120 + 0.474 gr1 + 0.240 gr2 + 0.170 gr3 + 0.118 gr4

Predictor Coef SE Coef T P

Constant -0.01203 0.01857 -0.65 0.519

gr1 0.473904 0.005549 85.40 0.000

gr2 0.240368 0.005504 43.67 0.000

gr3 0.170153 0.005034 33.80 0.000

gr4 0.118036 0.004105 28.76 0.000

S = 0.0180904 R-Sq = 99.9% R-Sq(adj) = 99.9%

Analysis of Variance

Source DF SS MS F P

Regression 4 18.8496 4.7124 14399.46 0.000

Residual Error 65 0.0213 0.0003

Total 69 18.8709

Source DF Seq SS

gr1 1 17.2138

gr2 1 0.9229

gr3 1 0.4422

gr4 1 0.2706

33


Source - indicates the source of variation, either from the factor, the interaction, or the

error. The total is a sum of all the sources.

DF - degrees of freedom from each source. the degrees of freedom for sample size

70 is 70 (n - 1).

SS - sum of squares between groups (factor) and the sum of squares within groups

(error)

MS - mean squares are found by dividing the sum of squares by the degrees of

freedom.

F - calculate by dividing the factor MS by the error MS; you can compare this ratio

against a critical F found in a table or you can use the p-value to determine whether a

factor is significant.

P - use to determine whether a factor is significant; typically compare against an

alpha value of 0.05. If the p-value is lower than 0.05, then the factor is significant.

For our Variance table we have p-value= (0.000), indicating that the relationship

is statistically significant.

For our Regression analysis we have R-Sq or Percentage of response variable

variation that is explained by its relationship with one or more predictor

variables is 99.99%.. R2 is always between 0 and 100%. It is also known as the

coefficient of determination or multiple determination (in multiple regression).

Since in our case the R-Sq is 99.99% R-Sq (adj) is also 99.99%, it signifies that

model fits the data well.

MAJOR CORREALTIONS:

Correlations: Avg, gr1

Pearson correlation of Avg and gr1 = 0.955

P-Value = 0.000

34




P-Value = 0.000



P-Value = 0.000



P-Value = 0.000

Correlations: gr1, gr2

Pearson correlation of gr1 and gr2 = 0.612

P-Value = 0.000



P-Value = 0.000



P-Value = 0.000

35




P-Value = 0.000



P-Value = 0.000



P-Value = 0.000

From the above analysis we have found that the correlation coefficient between

average and each group is very high, the values being 0.955, 0.759,0.766,0.733

Whereas the correlation coefficient among the groups itself is relatively low.

The p-value in the Analysis of Variance table (0.000), indicates that the relationship is

statistically significant

36


FUTURE WORKINCREASE

CONCLUSIONS

From the literature survey we came to the conclusion that there are 25 factors which

affect job satisfaction among faculty members of various engineering colleges under

BPUT .After determining these factors we did the factor analysis and found out that

these factors can be subdivided into four major groups.Group1 comprising of pay,

result, advancement, workability, surroundings, promotion, cooperation, complaints

taken care of, back up, work division, chance to help and the work one is best at,

while Group2 includes six factors namely ideas, know-how, routine, transfers,

service they can provide and chances to rub-elbows with important people. Group3

and Group4 includes variety, getting ahead, job security , appreciation and

pride ,freedom ,friendship respectively. Then from regressions and correlations we

found the p-value to be 0.000 and R-Sq value to be 99.99% which signifies the

significance of these groups and from pearson correlation coefficient we determined

that Group1 is the most important of all having value of 0.955.

37


REFERENCES

[1] http://en.wikipedia.org/wiki/Job_satisfaction

[2] http://www.management.org/Free Employee Job Satisfaction

Questionnaire.mht

[3] http://www.ieee.org/Case Study Six Factors that Influence Job

Satisfaction.mht

[4] http://www.wikipedia.org/whitepapers/abstract/details/Jobsatisfaction.pdf

[5] http://www.NBRI.inc/

[6] Minnesota Satisfaction Questionnaire

[7] http://en.wikipedia.org/wiki/Mean

[8] http://en.wikipedia.org/wiki/Median

[9] http://en.wikipedia.org/wiki/standard deviation

[10] http://en.wikipedia.org/wiki/correlation

[11] http://en.wikipedia.org/wiki/ Regression

[12] http://en.wikipedia.org/wiki/Minitab

38

http://en.wikipedia.org/wiki/standard

http://en.wikipedia.org/wiki/Median

http://en.wikipedia.org/wiki/Mean

http://www.management.org/Free


APPENDIX

QUESTIONNAIRE

On my present job, this is how I feel about

1. The change to try out some of my own ideas

2. The variety in my work

3. The chance to do the kind of work that I do best

4. My job security

5. The amount of pay for the work I do

6. The technical “know-how” of my supervisor

7. The spirit of cooperation among my co-workers

8. Being able to see the result of the work I do

9. The chance to do work that is well suited to my abilities

10. The physical surroundings where I work

11. The chance of getting ahead on this job

12. The chance to develop close friendships with my co-workers

13. Being able to take pride in a job well done

14. The routine in my work

15. The chance to “rub elbows” with important people

16. The way my boss backs up his/her employees (with top management)

17. The way promotions are given out on this job

18. The way my boss delegates work to others

19. The way my boss takes care of the complaints of his/her employees

20. The way my boss provides help on hard problems

21. The freedom to use my own judgment

22. The way they usually tell me when I do my job well

23. The chance to be of some small service to other people

24. The way layoffs and transfers are avoided in my job

25. My chances for advancements

39


6.3 Factor Analysis

Sorted Rotated Factor Loadings and Communalities

Variable Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7

helps 0.797 -0.212 -0.079 0.108 0.227 -0.210 -0.029

wrk div 0.778 -0.165 -0.251 0.160 -0.076 -0.017 -0.069

bks up 0.749 -0.214 0.123 0.046 0.345 -0.082 -0.023

cmplnts 0.738 -0.053 -0.149 0.196 0.419 -0.114 -0.153

copertn 0.690 -0.212 0.095 0.127 0.004 -0.371 0.186

promotns 0.622 -0.240 -0.282 0.252 0.106 -0.012 -0.238

bst wrk 0.338 -0.767 -0.071 0.143 0.048 -0.115 -0.131

pride -0.050 -0.738 -0.158 -0.054 0.217 -0.224 -0.091

wrkabilty 0.304 -0.707 -0.114 0.149 0.099 0.095 0.046

result 0.226 -0.677 -0.125 0.118 0.179 -0.042 -0.200

variety 0.237 -0.403 -0.338 0.266 0.393 -0.005 -0.036

getn ahed 0.016 -0.156 -0.804 -0.000 0.189 -0.056 0.145

advncmnt 0.294 -0.136 -0.754 -0.124 0.066 -0.063 -0.304

trnsfrs 0.031 -0.082 -0.547 0.342 0.160 -0.031 -0.306

ideas 0.275 -0.097 0.019 0.741 -0.026 -0.033 -0.162

rub elbows 0.193 -0.111 0.110 0.613 0.258 -0.110 -0.268

servce -0.055 -0.368 -0.493 0.566 0.073 -0.212 0.263

job sec 0.170 -0.199 -0.191 0.121 0.787 -0.137 0.027

apprcntn 0.372 -0.288 -0.156 -0.064 0.601 -0.045 -0.119

pay 0.195 -0.149 -0.156 -0.066 -0.016 -0.774 -0.251

knw-hw 0.203 -0.003 -0.011 0.341 0.293 -0.694 0.050

routine 0.065 -0.203 -0.059 0.213 0.031 -0.132 -0.842

frndshp 0.376 -0.185 0.092 0.092 0.159 -0.256 0.132

frdm 0.473 -0.090 -0.297 -0.047 0.036 0.050 -0.194

surndngs 0.293 -0.262 -0.118 0.042 0.117 -0.223 0.028

40


Variance 4.4324 2.9826 2.3373 1.8636 1.8542 1.5781 1.4098

% Var 0.177 0.119 0.093 0.075 0.074 0.063 0.056

Variable Factor8 Factor9 Communality

helps 0.122 0.077 0.816

wrk div 0.176 -0.188 0.799

bks up 0.060 -0.230 0.808

cmplnts 0.201 -0.020 0.860

copertn 0.216 -0.041 0.766

promotns -0.001 -0.269 0.728

bst wrk -0.174 -0.020 0.792

pride 0.288 -0.116 0.778

wrkabilty 0.067 -0.225 0.703

result 0.175 -0.076 0.649

variety -0.261 0.237 0.684

getn ahed 0.051 -0.200 0.773

advncmnt -0.015 0.093 0.798

trnsfrs 0.091 -0.504 0.807

ideas -0.084 0.013 0.670

rub elbows 0.192 -0.297 0.714

servce 0.096 0.030 0.831

job sec 0.130 -0.055 0.778

apprcntn 0.016 -0.253 0.691

pay 0.006 -0.212 0.797

knw-hw 0.181 -0.010 0.760

routine -0.001 -0.030 0.823

frndshp 0.738 -0.026 0.847

frdm 0.618 -0.035 0.747

surndngs 0.014 -0.747 0.793

41


Variance 1.3941 1.3608 19.2129

% Var 0.056 0.054 0.769

42

job satisfaction among faculty members

Documents