HR AnalysisApril 4, 2014
MBP
Professor Judson
Glenice Booker-Butler, Mark Dominik, Tammi Dorion & Fred Paul
Team Shenanigans
Glenice Booker-Butler
Mark DominikTammi DorionFred Paul
Table of Contents
1. Executive Summary
2. Problem Statement
3. Purpose Statement & Research Question
4. Business Case
5. Variables Analyzed
6. Methods
7. Demographics
8. Hypothesis
9. Analysis & Results
10.Conclusions
Executive Summary The analysis of key demographic information
is important to the new executive team in order to understand if any policies need immediate review. This study will provide the following:
General overview of company demographics Significance between key factors Statistical analysis for determination of
potential bias Descriptions of analysis methods utilized Ethical considerations
Problem Statement
Problem: The new executive team wants to better understand the critical issues related to demographics and processes such as compensation and job grade.
Purpose & Research Question
Statement of Purpose:
The purpose of this presentation is to outline key HR statistical data for the new executive team to understand if the various demographics affect salary.
Research Question:
Which demographic(s) within the company most affects the salary of the employees?
Business Case
Close in-depth look into demographics
Benefits of analysis
Forward looking…
Variables Analyzed
Gender Cultural Identity Age Grade Effectiveness Years of Education Years of Experience Company Experience ESL
Methods
Demographics Scatter Plot Correlations Regression Analysis Voice of the data – ethical
considerations
AA = 36%$79,512.17
H = 37%$76,016.11
E = 27%$80,612.93
F = 53%$73,939.53
M = 47%$83,673.19
ESL N = 47%$82,330.30
ESL Y = 53%$75,130.32
Basic Hypothesis: There is no relationship between the independent variables (1-8) and the dependent variable salary.
Independent Variables
1. Gender
2. Cultural Identity
3. Age
4. Effectiveness
5. Years of Education
6. Years of Experience
7. Company Experience
8. ESL
Dependent Variable Salary
Hypothesis
Variables Related to Salary
Correlations – Pearson’s
Correlations – Kendall Tau’s
Multi Regression Independent Variables Age Grade Effectiveness Years of Education Years of Experience Company Experience Satisfaction with company Gender Cultural Identity ESL
Multiple Regression – All Variables
Gender and ESL are the only statistically significant variables.
R-Square – All Variables
22.9% of the variation within salary.
R-Square – ESL & Gender
14.7% of the variation within salary.
R-Square - Gender
10.2% of the variation within salary.
Salary & Job Grade Analysis
Job “grade” is not statistically significant, nor is it predictive of an individual’s salary.
Job grade is directly correlated with “Age”.
Conclusions
ESL and gender are the demographics that are statistically significant related to salary.
No strong predictive model for salary.
Grade of individuals is based on age of employees.
Overall determination of why differences exist would need to be investigated further.
Top Related