This report analyzes employee attrition data from FermaLogis Inc. It finds that the different types of employee turnover - retirement, voluntary resignation, involuntary resignation, and termination - should generally be treated separately in the analysis, as they have different underlying causes. One exception is that involuntary resignation and termination can be analyzed together. Several variables are found to impact turnover hazards non-proportionally, such as education, job satisfaction, business travel, income, tenure, and manager tenure. The bonus variable is found to only impact retirement hazards. The analysis aims to help FermaLogis better understand and reduce attrition.

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FermaLogis Inc – Analysis on
Employee Attrition
TEAM 13 | PROJECT 2
OPIM 5894: Survival Analysis using SAS | July 7, 2017
Ajay Muthukrishnan
Ashish Doke
Hansini Homma
Meghana Kasula
Rikdev Bhattacharya

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CONTENTS
1. Executive Summary
2. Problem Statement
3. Objectives
4. Methodology
4.1 Data Description
4.2 Data Exploration
4.3 Data Pre-processing
5. Data Analysis and Insights
5.1 Can I combine different event types together? Or do all need to be
handled separately?
5.2 What attributes increase/decrease the hazard rates for certain event
types?
5.3 Does bonus affect employee turnover? If yes, how?
5.4 Are there any variables which affect hazards non-proportionally?
6. Business Recommendations and Conclusion
7. References

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1. Executive Summary:
This report is a further investigation of the previously analyzed Fermalogis’
employee’s attrition data. The reason for this extended study was stated as a
misalignment between the previous results with the business insights. The HR
Director has also informed that the dataset presented previously was not an
attrition dataset, rather a turnover dataset. These insights lead to the extension to
determine new information breakthroughs to help reduce attrition.
The main change in the dataset, compared to that previously investigated, is that
the ‘Attrition’ column is replaced by the ‘Turnover’ column. The ‘Turnover’ column
has 4 different events occurring which are categories for employee attrition.
Though the words attrition and turnover might sound the same to the business, a
retirement and resignation have two different properties entirely though it leads
to a common effect of employee reduction. Analyzing the turnover types for
similarity in survival functions leads us to the conclusion that most of the types are
to be treated separately. One pair of event types – Involuntary Resignation &
Termination were analyzed together based on the outcome from LogLog Survival
(LLS) plots. The bonus variable was found to have impact only on retirement.
We found the following 8 variables to affect the hazard non-proportionally, -
Education Field, Job Satisfaction, Business Travel, Monthly Income, Total Working
Year, Years in Current Role, Daily rate and Years with Current Manager.
These are the new findings proposed in this extended study to help align the
business insights with the information presented in this report and the previous
one.

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2. Problem Statement:
Fermalogis, a renowned pharmaceutical company is reliant on employees as it main
resource. It has been investing into various employee development programs. New
recruits are provided extensive professional training to lift them to desired
productivity levels. Experienced employees are put through an intensive Executive
Development Program by bringing in external consultants and professors.
Employees contribute with improved competencies over a period of a time and the
company thus enjoys the benefits of its investments.
It is now facing the problem of attrition and it is an implied understanding that the
employees are being poached by competitors to benefit from their enhanced skill
levels. The company needs to identify the employee sub groups that are most
prone to attrition and understand the main reasons leading to their exits. This
knowledge will help the organization in implementing measures to retain its highly
prized employees.
Preliminary attrition analysis was conducted and output was found to have gaps in
terms available insights. Perhaps deeper analysis after identifying different types
of exits and looking for independent reasons would provide a clearer picture.
3. Objective:
This project requires us to use Survival Analysis techniques using SAS to -
1. Determine if turnover-types are to be treated separately using tools
available in SAS.
2. Identify attributes that affect the hazard rates for certain turnover types.
3. Analyze the impact of Bonus on employee turnover.
4. Look for variables that may impact hazards non-proportionally.

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4. Methodology:
a. Data Description:
Initially, Fermalogis’ employee’s attrition consisted of 76 variables with 1470
observations. In the new dataset, we have an additional variable describing the
specific type of attrition.
The new dataset contains the variable “Turnover” which indicates whether an
employee has churned, but due to different event types. We have chosen “TYPE”
as our target variable.
The following are the different types:
• Retirement - Retirement is the point in time when an employee chooses to
leave his or her employment permanently.
• Voluntary Resignation - When an employee leaves the company of her own
volition, it's called voluntary termination.
• Involuntary Resignation – Employment decision to terminate the employees
because of health problems or family problems etc.
• Job termination - When an employee fired from the company because of
poor performance.
b. Data Pre-Processing:
The categorical variables have been modified and recoded in the dataset as per the
approach followed in Project 1. Additionally, a new variable named Turnover Type
is created as follows:
• If type=”0” then Turnover Type=No turnover
• If type=”1” then Turnover Type=Retirement
• If type=”2” then Turnover Type=Voluntary Resignation

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• If type=”3” then Turnover Type=Involuntary Resignation
• If type=”4” then Turnover Type=Job Termination
(Note: If Type=0 then no employee is leaving the organization.)
Attrition variable has been modified to 1 for Yes values and 0 for No values
Business-Travel variable has been recoded to:
0 for the Non-Travel
1 for Travel-Rarely
2 for Travel-Frequently
Over Time variable has been recoded to 1 for employees who have worked
overtime and 0 for those who have not.
Gender variable is recoded as:
1 for Male employees
0 for Female employees.
Job-Role variable has been recoded to as follows:
0 for Sales Executive
1 for Research Scientist
2 for Laboratory Technician
3 for Manufacturing Director
4 for Healthcare Representative
5 for Manager
6 for Sales Representative
7 for Research Director

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8 for Human Resources
Marital Status variable has been recoded to:
0 for Single
1 for Married
2 for Divorced
Department variable has been recoded as:
0 for Human Resources
1 for Research & Development
2 for Sales.
Education Field variable has been recoded as
0 for Human Resource
1 for Life Sciences
2 for Marketing
3 for Medical
4 for Technical Degree
5 for Others

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c. Data Exploration:
Since retirement, resignation (voluntary and involuntary) and termination can have
different properties, we must find out why those employees were leaving and
whom the company was firing. We performed a few basic exploratory data analysis
to understand the data better.
❖ Job Satisfaction:
• A bar plot using SGPLOT was plotted for the variable job satisfaction.
• It can be observed that employees whose satisfaction levels are the
least (i.e. Job satisfaction =1) voluntarily leaving the organization.
However, in all the other three types majority of people are satisfied
with their jobs though they are leaving the company.

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❖ Overtime Vs Turnover Type
• A bar plot using SGPLOT was plotted for the variable overtime.
• From the bar plot it can be observed that employees whose overtime
frequency is more in number are voluntarily resigning the company. in
three types (voluntary, Involuntary, Retirement)
• The number of employees who are not doing overtime are getting
terminated from the company. This can be due to performance issues.
❖ Business Travel Vs Turnover Types:
The graph below establishes a relation between variables turnover types and
Business Travel and found that in all the four event types, employees who travel
frequently tend to leave the company.

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❖ Education Field Vs Turnover Type
It can be interpreted that the employees belonging to Life Sciences and medical
field are more prone to leave the company in comparison to other education fields.
Involuntary Resignation Job Termination Retirement Voluntary Resignation
turnoverType
0
20
40
60
80
100
Frequency
Technical DegOtherMedicalMarketingLife SciencesHuman Resourc
EducationField
SGPLOT :Education Vs Turnover types

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❖ Frequency distribution of the event type:
Since it is important to know the attrition type of the employees who are leaving,
a frequency distribution was made to identify the event type corresponding to
maximum number of employees leaving the company and it was found maximum
number of employees belong to Voluntary resignation event type.
5. Data Analysis and Insights
a. Can I combine different event types together? Or do all need to be
handled separately?
The turnover types are:
1. Retirement
2. Voluntary Resignation
3. Involuntary Resignation (due to factors such as health, family contingency
etc.)
4. Termination (fired)
Business logic is that we should be focusing on “Voluntary Resignation” as a
separate event as that is the source of the attrition problem at FermaLogis.

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Retirement is an event that is purely a function of an employee serving long enough
to reach retirement age. Involuntary resignation is driven by factors outside of the
organization. Termination is usually because of indiscipline, incompetence or
incompatibility. Thus, we can presume that the events have separate nature and
have to be analyzed accordingly. But, we can also study the event types using
“LogLog-Survival (LLS)” plot in the PROC LIFETEST function. In that we separate each
event type and create a dataset by censoring all other event types. Before plotting
LLS we combine all the separate datasets so that the graphs can combined in a
single plot and compared visually.

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The LLS plots show that there is no proportionality maintained i.e. the curves are
neither similar nor parallel to each other. Further, looking at the Chi square values
(in the below figure) from Wilcoxon test for each pair of events, we can see that
most of them are dissimilar. One pair of events, Involuntary Retirement-
Termination is such that we cannot reject our null hypothesis of them being equal.
Hence this pair of events may be studied together.

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b. What attributes increase/decrease the hazard rates for certain event
types?
From the analysis of the variables which affect the hazards non-proportionally
we observed that the three variables which affect the model non-proportionally
are Total working years of the employee, Years in the current role of the
employee and Number of companies worked the employee worked for in the
past.
These variables are used to create new interaction variables with the dependent
variable Years at the company of the employee. The interaction variables are -
1. TotalWorkingYears_I = YearsAtCompany x TotalWorkingYears
2. CurrentRole_I = YearsAtCompany x YearsInCurrentRole
3. NumCompaniesWorked_I = YearsAtCompany x NumCompaniesWorked
Modeling and Results:
• We have modeled the data with respect to each event type initially and a
nested modeled to confirm the Wilcoxon test results whether the event
types can be combined.
• As said above we have modelled all the four-independent hypothesis and a
nested hypothesis, by a hypothesis test using degrees of freedom and
determined that independent models are better explaining the covariates
and hazard rate than a nested model. We have discussed all the significant
covariates and hazard rates on individual event types in the below section.
(Note: Significant covariates are highlighted with pink color and named as
significant. When the hazard rate>1, hazard is increasing with unit increase in the

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covariates whereas Hazard rate<1 shows that hazard is decreasing with unit
increase in the covariates value.)
From the Wilcoxon test results, we infer that the Retirement event type should be
modeled separately.
❖ Retirement
Cox’s Proportional Hazard Regression Model for Retirement Event type:
When modeling only for Retirement event type We know that the various event
types in the data are censored so that we can assess for this event type in detail
and make inferences. We observe that:
Table(iii): Fit statistics
We can observe that from the model fit statistics that the Retirement event type
with covariates is well explained as we can see that difference in the Criterion for
AIC, SBC and -2LOG L they drop from 278.716 to 87.828.

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The attributes which increase the Hazard rates for the Retirement event type are:
Table(iv)
From the table iv we can observe that the Significant variables which increases the
Hazard rate are:
• Age: As the Age of the employee increases by one year the Hazard rates
increase to 1.891.
• Job Satisfaction: When the employee has low job satisfaction (1) the chance
of the event happening is very high with a hazard ratio of 16.013
• cumempBonus: from the variable cumulative employee bonus which tracks
the bonus received by the employee until the current working year we can
see that the amount of the bonus received by the employee received in his
working years is significant and has a hazard rate of 2.373.
• NumCompaniesWorked_I: The interaction attribute Number of companies
worked for by the employee is significant and positively affects the hazard
rate with the hazard ratio increases to 1.162.
The attributes which decrease the Hazard rates for the Retirement event type
are:
• Years in the current role: This variable decreases the hazard rate or the
hazard ratio falls to 0.495 which affects the retirement event to happen.

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• Job involvement and overtime: When the employee’s involvement in the
job and the who don’t work over time decrease the hazard rate or the
probability of the event happening.
❖ Voluntary Resignation
Cox’s Proportional Hazard Regression Model for Voluntary Resignation Event
type:
• When modeling only for Voluntary resignation event type We know that the
various event types in the data are censored so that we can assess for this
event type in detail and make inferences. We observe that:
Table(vi): Fit statistics
• We can observe that from the model fit statistics that the Voluntary
resignation event type with covariates is well explained as we can see that
difference in the Criterion for AIC, SBC and -2LOG L they drop from 876.668
to 512.092.

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The attributes which increase the Hazard rates for the Voluntary resignation
event type are:
From the table, we can observe that the Significant variables which increases the
Hazard rate. From the above tabulation, we can see that:
• Business Travel: When the employee Travels more frequently the chance of
the event happening is very high with a hazard ratio of 2.252.
• Job satisfaction: When the employee is not satisfied with his job or a low job
satisfaction the chance of the event happening is very high with a hazard
ratio of 18.18
• Number of companies worked: when the number of companies worked by
an employee increases by one, the chance of the event happening is very
high with a hazard ratio of 1.25. An increase by 25%.
• Stock option level: We can observe from the significance test that the stock
option variable must be included in the model. Also, if the employee has no
stock option level or level then he is almost 3 times more likely to leave than
the person who has stock options.

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The attributes which decrease the Hazard rates for the Voluntary resignation
event type are:
• Business travel: When an employee doesn’t travel or travels rarely this
decreases the hazard rate or the hazard ratio falls to 0.687 which affects the
voluntary resignation event to happen.
• Over Time: When an employee work over time this attribute decreases the
hazard rate or the hazard ratio falls to 0.20 which affects the voluntary
resignation event to happen.
❖ Involuntary Resignation and Job Termination
Cox’s Proportional Hazard Regression Model for Involuntary Resignation and Job
Termination Event type nested model:
• When modeling for both Involuntary Resignation and Job Termination event
types We know that the other event types in the data are censored so that
we can assess for these events type in detail and make inferences. We
observe that:

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Table(vii): Fit statistics
• We can observe that from the model fit statistics that the Involuntary
Resignation and Job Termination event types with covariates is well
explained as we can see that difference in the Criterion for AIC, SBC and -
2LOG L they drop from 764.794 to 514.556.
The attributes which increase the Hazard rates for the Involuntary Resignation
and Job Termination event types are:
• Business travel: When the employee travels frequently on his job the chance
of the event happening is very high with a hazard ratio of 1.431.

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• Number of companies worked, Years since last promotion, years in current
role: when the number of companies worked by an employee increases by
one the chance of the chance of the event happening is very high with a
hazard ratio of 1.212. An increase by 21% and similar is the case with years
since last promotion (1.12) and years in current role (1.40)
• Job involvement: When the employee’s involvement in the job is low or level
1 this attribute of the employee increases the hazard ratio to 2.379.
The attributes which decrease the Hazard rates for the Involuntary Resignation
and Job Termination event types are:
• Business travel: Business travel: When an employee doesn’t travel or
travels rarely this decreases the hazard rate or the hazard ratio falls to 0.267
which affects the event to happen.
• Over Time: When an employee work over time this attribute decreases the
hazard rate or the hazard ratio falls to 0.3240 which affects the voluntary
resignation event to happen.
• Job satisfaction: The employees who have low job satisfaction levels or level
1 are less likely to leave or this attribute decreases the hazard ration to 0.414.

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c. Does bonus affect employee turnover? If yes, how?
We created a new variable called Employ_bonus to estimate the effect of Bonus on
attrition. We used the cumulative method and took the average bonus for each
employee in the Employ_bonus variable.
❖ Event=1, Type=Retirement
It can be observed from the screenshot below that, Employee Bonus is a
significant factor for employees who are retiring from the organization, that is for
(Type=1). The screenshot below
The "Type 3 Tests" table is displayed if the model contains a CLASS variable.
The table displays, for each specified statistic, the Type 3 chi-square, the degrees
of freedom, and the p-value for each effect in the model.
The p value for the employee bonus is 0.0021.

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Salaries of retiring employees might be high because of their experience and expect
a higher bonus.
The screenshot below is the Analysis of Maximum Likelihood estimates. It shows
the p-value of the Wald chi-square statistic with respect to a chi-square distribution
with one degree of freedom; the hazard ratio estimate.
It can be inferred that Employee Bonus is a significant factor for employees who
are retiring from the company

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❖ Event=2, Type=Voluntary Retirement
It can be inferred from the Type 3 Tests and analysis of maximum likelihood
estimates that that Employee Bonus is not a significant factor for employees who
are voluntarily retiring.

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❖ For Event=3 And 4, Job Termination And Involuntary Resignation:
It can be inferred from the analysis of maximum likelihood estimates that that
Employee Bonus is not a significant factor for employees who are getting
terminated or who is involuntarily resigning from the organization.

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d. Are there any variables which affect hazards non-proportionally?
There are built in functions in SAS that allows us to evaluate the covariates through
its assess statement. As per COX model, these cumulative martingale sums should
have values around 0. The Martingale model has been used to find out the variables
which are non-proportional. The following is the result obtained.
Variable Maximum Absolute Value Replications Seed Pr > MaxAbsVal
Age 1.3189 1000 1027058582 0.22
BusinessTravel 11 1.4774 1000 1027058582 0.046
BusinessTravel 12 0.5198 1000 1027058582 0.844
DailyRate 1.6345 1000 1027058582 0.003
Department_11 300.8745 1000 1027058582 0.955
Department_12 4.9516 1000 1027058582 0.476
DistanceFromHome 1.361 1000 1027058582 0.033
Educationl 1.3468 1000 1027058582 0.845
Education2 2.97 1000 1027058582 0.202
Education3 2.6658 1000 1027058582 0.507
Education4 1.4591 1000 1027058582 0.911
EducationField 11 0.9314 1000 1027058582 0.525
EducationField 12 2.4155 1000 1027058582 0.166
EducationField 13 1.5229 1000 1027058582 0.353
EducationField_14 2.9692 1000 1027058582 0.028
EducationField_15 3.118 1000 1027058582 0.007
EnvironmentSatisfactl 0.5182 1000 1027058582 0.878
EnvironmentSatisfact2 1.2681 1000 1027058582 0.103
EnvironmentSatisfact3 1.1661 1000 1027058582 0.201
Gender_10 0.5387 1000 1027058582 0.741
HourlyRate 0.6857 1000 1027058582 0.55
J obl nvolvement1 1.7472 1000 1027058582 0.134
J obl nvolvement2 0.5445 1000 1027058582 0.994
Jobl nvolvement3 1.5493 1000 1027058582 0.482
JobLevel, 10.7527 1000 1027058582 0.042
JobLevel2 6.804 1000 1027058582 0.258
JobLevel3 2.5013 1000 1027058582 0.83
JobLevel4 1.4882 1000 1027058582 0.409
JobRole_11 2.1909 1000 1027058582 0.913
JobRole_12 1.7498 1000 1027058582 0.987
JobRole_13 1.8486 1000 1027058582 0.382
JobRole_14 1.0465 1000 1027058582 0.562
JobRole_15 2.0944 1000 1027058582 0.568
JobRole_16 2.8046 1000 1027058582 0.325
JobRole 17 300.8744 1000 1027058582 0.955
JobRole 18 2.7162 1000 1027058582 0.166
JobSatisfaction1 0.464 1000 1027058582 0.956
JobSatisfaction2 0.8812 1000 1027058582 0.471
JobSatisfaction3 1.7 1000 1027058582 0.035
MaritalStatus_11 2.0547 1000 1027058582 0.054
MaritalStatus_12 0.8397 1000 1027058582 0.709
Monthlylncome 3.8773 1000 1027058582 0.026
Supremum Test for Proportionals Hazards Assumption

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We have checked for the Pr > MaxAbsVal parameter for each of the variables to
check if they are significant. Considering the standard 5% significance level we have
found there to be 8 variables that are significant. These significant variables are
non-proportional in nature. The variables are:
• Education Field
• Job Satisfaction
Variable Maximum Absolute Value Replications Seed Pr > MaxAbsVal
JobRole_15 2.0944 1000 1027058582 0.568
JobRole_16 2.8046 1000 1027058582 0.325
JobRole 17 300.8744 1000 1027058582 0.955
JobRole 18 2.7162 1000 1027058582 0.166
JobSatisfaction1 0.464 1000 1027058582 0.956
JobSatisfaction2 0.8812 1000 1027058582 0.471
JobSatisfaction3 1.7 1000 1027058582 0.035
MaritalStatus_11 2.0547 1000 1027058582 0.054
MaritalStatus_12 0.8397 1000 1027058582 0.709
Monthlylncome 3.8773 1000 1027058582 0.026
MonthlyRate 0.4325 1000 1027058582 0.888
NumCompaniesWorked 0.6125 1000 1027058582 0.711
OverTime_10 1.1703 1000 1027058582 0.1
PercentSalaryHike 0.9333 1000 1027058582 0.67
PerformanceRating3 0.8458 1000 1027058582 0.739
RelationshipSatisfac1 0.7058 1000 1027058582 0.651
RelationshipSatisfac2 0.8782 1000 1027058582 0.396
Relations hipSatisfac3 0.6622 1000 1027058582 0.725
StockOptionLevel0 1.5513 1000 1027058582 0.6
StockOptionLevel1 1.2427 1000 1027058582 0.634
StockOptionLevel2 1.2668 1000 1027058582 0.247
TotalWorkingYears 2.801 1000 1027058582 0.016
TrainingTimesLastYear 0.4966 1000 1027058582 0.859
WorkLifeBalance1 1.0162 1000 1027058582 0.453
WorkLifeBalance2 0.8849 1000 1027058582 0.683
WorkLifeBelance3 0.6247 1000 1027058582 0.923
YearsInCurrentRole 2.9665 1000 1027058582 <.0001
YearsSinceLastPromotion 1.1919 1000 1027058582 0.09
YearsWithCurrManager 1.8764 1000 1027058582 <.0001
Supremum Test for Proportionals Hazards Assumption

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• Business Travel
• Monthly Income
• Total Working Year
• Years in Current Role
• Daily rate
• Years with Current Manager
The Martingale Residual plots for these 8 variables are shown below:

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Following this, we used the Schoenfeld method to verify the test of non-
proportionality of the 5 numeric variables. Most of the results are randomly

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distributed. From the graphs, we can conclude that the 5 numeric variables are
non-proportional in nature as it is randomly distributed which signify that the
covariates are non-proportional. Below are the screen shots of the Schoenfeld
residual plots:

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6. Business Recommendations and Conclusion:
In addition to the recommendations extended to the business as part of Project 1
we would also like to share some additional recommendations. The company’s HR
should have separate retention strategies in place for employees who fall in
different event types. The following are a few of the strategies that you can look
at:
Work from Home and Travel Option:
The people who are voluntarily resigning from the company value their
family life which gets affected when they travel frequently. The company should
focus on strategies that enable employees working from home remotely or have
strategies to achieve the project goals with minimum business travel by its
employees or give a day off if the business travel exceeds more than a week. This
may increase the job satisfaction and reduce turnover.
Growth within the Company:
The employees working under the same manager for a longer duration feel
stagnated and take the step of voluntary retirement. The business should design
strategies that enable employees to switch between various projects and work
under different managers.
After Action Survey:
An after-action survey should be conducted quarterly or after every project
to learn and understand about the satisfaction level of the employees and to devise
corrective strategies to retain them.

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New Role for about to retire employees:
The company should use employees who have worked for a longer duration
in the company and are about to retire. Giving them a bigger role such as to train
the new employees can be beneficial to the company. They can act as inspiration
to newer employees to work in the company.
Reallocation of resources:
By proper resource allocation the company can reduce the overload on a
specific few employees and make sure no one is working overtime which affects
the morale of the employee and thereby impacts the turnover rate.
Promotions and Yearly Appraisals:
The company should have a yearly appraisal system and promotion exercise
every year and award the deserving employees with a promotion. This is also linked
with the number of years an employee works in a role as a change in role will
motivate the employee to reach his/her goals so that the role doesn’t get
monotonous to the employee which can cause a slacking behavior.
Employee Benefit Scheme:
Provide bonus which can include full health benefits until employees reach the
age of 65 (i.e., the age of eligibility for Medicare). Provide additional benefits such
as disability benefits, medical plan membership, tuition benefits employee’s
dependents, free admission to campus activities. This would ensure that the
employees stick to the company.

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7. References:
Paul D. Allison - Survival Analysis Using SAS A Practical Guide, Second Edition
http://www.ats.ucla.edu/stat/sas/topics/survival.htm
http://www.ats.ucla.edu/stat/sas/examples/asa2/asa2_sas_ch2.htm
http://www.ats.ucla.edu/stat/sas/seminars/sas_survival/
https://communities.sas.com/t5/tag/sgplot/tg-p/tag-id/332/category-id/sas_programming
https://communities.sas.com/t5/SAS-Procedures/PROC-LIFETEST/td-p/145280
https://communities.sas.com/t5/SAS-Procedures/Using-PROC-LIFEREG-parameter-estimates/td-p/162486
https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#phreg_toc.htm
https://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#phreg_toc.htm