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thesis.pdf (605.65 KB)
ETD Abstract Container
Abstract Header
Causal Inference of Human Resources Key Performance Indicators
Author Info
Kovach, Matthew
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175
Abstract Details
Year and Degree
2018, Master of Science (MS), Bowling Green State University, Applied Statistics (Math).
Abstract
The purpose of this study is to examine the relationship between attrition rates and key performance indicators in a corporate workforce by using the propensity score (PS) matching. The study shows the possibilities of using logistic regression and propensity score matching methods in human capital strategic decisions. The data used here was from a fictional data set created by IBM data scientists based on active and separated employees to uncover the factors that lead to employee attrition. For each of the 1,470 employee records, information was generated about demographic characteristics such as age, gender, marital status, education level, employment status and culture, compensation, and performance factors. 1 Two logistic equations are defined for two key performance objectives, culture and work life balance. A logistic regression analysis on each equation, with support from contrast estimation, reveals a comparison between the most and least favorable responses to key performance indicators is most significant. After successfully balancing a treatment and control group using the nearest neighbor matching technique on propensity score estimates from the logistic regression, a paired t-test reveals a statistically significant difference for the work life balance key performance indicator. This result is interpreted as having the highest probability of successfully reducing attrition when the focus is on increasing employee responses to satisfaction levels of work life balance in comparison to other key performance indicators.
Committee
Wei Ning, Dr. (Advisor)
John Chen, Dr. (Committee Member)
Junfeng Shang, Dr. (Committee Member)
Pages
34 p.
Subject Headings
Statistics
Keywords
Logistic Regression
;
Propensity Score Matching
;
Key Performance Indicators
;
Causal Inference
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Citations
Kovach, M. (2018).
Causal Inference of Human Resources Key Performance Indicators
[Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175
APA Style (7th edition)
Kovach, Matthew.
Causal Inference of Human Resources Key Performance Indicators.
2018. Bowling Green State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175.
MLA Style (8th edition)
Kovach, Matthew. "Causal Inference of Human Resources Key Performance Indicators." Master's thesis, Bowling Green State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175
Chicago Manual of Style (17th edition)
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Document number:
bgsu1542361652897175
Download Count:
997
Copyright Info
© 2018, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.