Across various industries, compensation professionals work to organize and analyze aspects of employment that deal with elements of pay, such as deciding base salary, bonus, and commission provided by an employer to its employees for work performed. Acknowledging the numerous quantitative analyses of data that are a part of this everyday work, Statistics for Compensation provides a comprehensive guide to the key statistical tools and techniques needed to perform those analyses and to help organizations make fully informed compensation decisions.
This self–contained book is the first of its kind to explore the use of various quantitative methods from basic notions about percents to multiple linear regression that are used in the management, design, and implementation of powerful compensation strategies. Drawing upon his extensive experience as a consultant, practitioner, and teacher of both statistics and compensation, the author focuses on the usefulness of the techniques and their immediate application to everyday compensation work, thoroughly explaining major areas such as:
Frequency distributions and histograms
Measures of location and variability
Exponential curve models
Maturity curve models
Market models and salary survey analysis
Linear and exponential integrated market models
Job pricing market models
Throughout the book, rigorous definitions and step–by–step procedures clearly explain and demonstrate how to apply the presented statistical techniques. Each chapter concludes with a set of exercises, and various case studies showcase the topic′s real–world relevance. The book also features an extensive glossary of key statistical terms and an appendix with technical details. Data for the examples and practice problems are available in the book and on a related FTP site.
Statistics for Compensation is an excellent reference for compensation professionals, human resources professionals, and other practitioners responsible for any aspect of base pay, incentive pay, sales compensation, and executive compensation in their organizations. It can also serve as a supplement for compensation courses at the upper–undergraduate and graduate levels.
Chapter 1 Introduction.
1.1 Why Do Statistical Analysis?
1.3 Numbers Raise Issues.
1.4 Behind Every Data Point, There is a Story.
1.5 Aggressive Inquisitiveness.
1.6 Model Building Framework.
1.7 Data Sets.
Chapter 2 Basic Notions.
2.2 Percent Difference.
2.3 Compound Interest.
Chapter 3 Frequency Distributions and Histograms.
3.1 Definitions and Construction.
3.2 Comparing Distributions.
3.3 Information Loss and Comprehensive Gain.
3.4 Category Selection.
3.5 Distribution Shapes.
Chapter 4 Measures of Location.
4.4 Trimmed Mean.
4.5 Overall Example and Comparison.
4.6 Weighted and Unweighted Average.
4.7 Simpson s Paradox.
4.9 Percentile Bars.
Chapter 5 Measures of Variability.
5.1 Importance of Knowing Variability.
5.2 Population and Sample.
5.3 Types of Samples.
5.4 Standard Deviation.
5.5 Coefficient f Variation.
5.8 Comparison and Summary.
Chapter 6 Model Building.
6.1 Prelude to Models.
6.3 Scientific Method.
6.5 Model Building Process.
Chapter 7 Linear Model.
7.2 Straight Line Basics.
7.3 Fitting the Line to the Data.
7.4 Model Evaluation.
7.5 Summary of Interpretations and Evaluation.
7.7 Digging Deeper.
7.8 Keep the Horse Before the Cart.
Chapter 8 Exponential Model.
8.3 Exponential Model.
8.4 Model Evaluation.
Chapter 9 Maturity Curve Model.
9.1 Maturity Curves.
9.2 Building the Model.
9.3 Comparison of Models.
Chapter 10 Power Model.
10.1 Building the Model.
10.2 Model Evaluation.
Chapter 11 Market Models and Salary Survey Analysis.
11.2 Commonalities of Approaches.
11.3 Final Market–Based Salary Increase Budget.
11.4 Other Factors Influencing the Final Salary Increase Budget Recommendation.
11.5 Salary Structure.
Chapter 12 Integrated Market Model Linear.
12.1 Gather Market Data.
12.2 Age Data to a Common Date.
12.3 Create an Integrated Market Model Interpretations.
12.4 Compare Employee Pay with Market Model.
Chapter 13 Integrated Market Model Exponential.
Chapter 14 Integrated Market Model Maturity Curve.
Chapter 15 Job Pricing Market Model Group of Jobs.
Chapter 16 Job Pricing Market Model Power Model.
Chapter 17 Multiple Linear Regression.
17.1 What It Is.
17.2 Similarities and Differences with Simple Linear Regression.
17.3 Building the Model.
17.4 Model Evaluation.
17.5 Mixed Messages in Evaluating a Model.
17.6 Summary of Regressions.
17.7 Digging Deeper.
A.1 Value Exchange Theory.
A.2 Factors Determining a Person s Pay.
A.3 Types of Numbers.
A.4 Significant Figures.
A.5 Scientific Notation.
A.6 Accuracy and Precision.
A.7 Compound Interest Additional.
A.8 Rule of 72.
A.9 Normal Distribution.
A.10 Linear Regression Technical Note.
A.11 Formulas for Regression Terms.
A.12 Logarithmic Conversion.
A.13 Range Spread Relationships.
A.14 Statistical Inference in Regression.
A.15 Additional Multiple Linear Regression Topics.
Answers to Practice Problems.