Using Pandas to Clean and Analyze Employee Exit Surveys

INTRODUCTION

This project involved working with exit surveys from the employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. Our main goal was to clean and transform the data to help answer the following questions -

1. Are employees who worked for a short time resigning due to some kind of dissatisfaction? What about employees who have worked there longer?
2. Are younger employees resigning due to some kind of dissatisfaction? What about older employees?

DATA

The DETE exit survey can be found here, and exit survey for TAFE here. A slightly modified version of the two datasets (encoding changed from cp1252 to UTF-8) was already provided to us by DataQuest as dete_survey.csv and tafe_survey.csv.

The dete_survey dataframe contains 822 entries and 56 columns. Every other column except Id is object or boolean datatype. The missing values in the data are filled as Not Stated . Some of the columns we used from the dete_survey.csv are:

SeparationType: The reason why the person's employment ended
Cease Date: The year or month the person's employment ended
DETE Start Date: The year the person began employment with the DETE
Job dissatisfaction: Whether job dissatisfaction was the reason for separation

The tafe_survey dataframe has 702 entries with 72 columns and all columns with the exception of Record ID and CESSATION YEAR are object data types. Below is a preview of some of the columns we worked with:

Contributing Factors. Job Dissatisfaction: Whether job dissatisfaction was one of the contributing factors for ending employment.
Reason for ceasing employment: The reason why the person's employment ended
LengthofServiceOverall. Overall Length of Service at Institute (in years): The length of the person's employment (in years)

TECHNOLOGIES AND TECHNIQUES

Our end goal was to combine both the surveys and answer the questions mentioned above. Even though both the surveys were designed using the same template, one of them had customized some of its answers. As a data dictionary wasn't provided with the dataset, we used common knowledge to combine and clean some of the columns. Below are the steps we took to prepare the data for analysis -

• Dropped columns that were not necessary for this particular analysis, for e.g., Aboriginal, Torres Strait, South Sea in dete_survey dataframe and Workplace. Topic:Would you recommend the Institute as an employer to others? in tafe_survey dataframe among others. We chose to keep only those columns that directly related to job dissatisfaction.
• Filtered the data to only show information of employees that had marked 'Resignation' as a reason for separation.
• Cleaned and standardized column names required for our analysis. The table below shows some of the columns we were intereseted in:
  

dete_survey	tafe_survey	Definition	New Name
SeparationType	Reason for ceasing employment	The reason why the participant's employment ended	separationtype
Cease Date	CESSATION YEAR	The year or month the participant's employment ended	cease_date
DETE Start Date		The year the participant began employment with the DETE	dete_start_date
	LengthofServiceOverall. Overall Length of Service at Institute (in years)	The length of the person's employment (in years)	institute_service
Age	CurrentAge. Current Age	The age of the participant	age
Gender	Gender. What is your Gender?	The gender of the participant	gender

• Added new columns including institute_service in dete_survey (cease_date - start_date) to calculate the number of years the employee had worked in the organization. A similar column already existed for tafe_survey.
• Combined the data, handled missing values and cleaned columns required for analysis.

CONCLUSION

Organized and cleaned employee exit survey data from the Department of Education, Training and Employment(DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.

The following observations were noted -

• Dissatisfaction was more prevelant in employees with over 7 years of experience, with 51% of them resigning over it.
• Employees over 60 years of age were 52% more dissatisfied at the time of leaving compared to only 37% of employees between the age of 21-30.
• Both male and female employees were equally likely to resign due to dissatisfaction, percentage of males being slightly higher(42%) compared to females (37%).
• DETE employees (49%) showed higher dissatisfaction compared to TAFE employees (28%) at the time of resignation.
• Majority of the dissatisfied ex-employees worked in teaching jobs(including 'Teacher', 'Teacher Aide', 'Guidance Officer' and 'Tutor') and had been hired as permanent employees.
  
  

FUTURE ANALYSIS

In this project, we grouped dissatisfied employees based on their position, institute, employment status and demographic features. Future analysis can take into account WorkUnit Views and Institute Views columns to see if any specific feature or features stand out as highly disagreeable. This may help us further pinpoint the reason as for employee dissatisfied. For e.g., were the dissatisfied employees not happy with the growth opportunities provided or the work-life balance, etc.

WHAT I LEARNED

• Data extraction, cleaning and organization are a very important part of the data science process.
• How to use pivot tables to summarizes data in a meaningful way as well as apply functions to rows/columns of a dataframe
• Combining datasets