+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Statistics for Compensation. A Practical Guide to Compensation Analysis

  • ID: 2171352
  • Book
  • April 2011
  • Region: Global
  • 456 Pages
  • John Wiley and Sons Ltd
1 of 3
An insightful, hands–on focus on the statistical methods used by compensation and human resources professionals in their everyday work

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

  • Model building

  • Linear models

  • Exponential curve models

  • Maturity curve models

  • Power 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.

Note: Product cover images may vary from those shown
2 of 3

Chapter 1 Introduction.

1.1 Why Do Statistical Analysis?

1.2 Statistics.

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.

1.8 Prerequisites.

Chapter 2 Basic Notions.

2.1 Percent.

2.2 Percent Difference.

2.3 Compound Interest.

Practice Problems.

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.

Practice Problems.

Chapter 4 Measures of Location.

4.1 Mode.

4.2 Median.

4.3 Mean.

4.4 Trimmed Mean.

4.5 Overall Example and Comparison.

4.6 Weighted and Unweighted Average.

4.7 Simpson s Paradox.

4.8 Percentile.

4.9 Percentile Bars.

Practice Problems.

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.

5.6 Range.

5.7 P90/P10.

5.8 Comparison and Summary.

Practice Problems.

Chapter 6 Model Building.

6.1 Prelude to Models.

6.2 Introduction.

6.3 Scientific Method.

6.4 Models.

6.5 Model Building Process.

Practice Problems.

Chapter 7 Linear Model.

7.1 Examples.

7.2 Straight Line Basics.

7.3 Fitting the Line to the Data.

7.4 Model Evaluation.

7.5 Summary of Interpretations and Evaluation.

7.6 Cautions.

7.7 Digging Deeper.

7.8 Keep the Horse Before the Cart.

Practice Problems.

Chapter 8 Exponential Model.

8.1 Examples.

8.2 Logarithms.

8.3 Exponential Model.

8.4 Model Evaluation.

Practice Problems.

Chapter 9 Maturity Curve Model.

9.1 Maturity Curves.

9.2 Building the Model.

9.3 Comparison of Models.

Practice Problems.

Chapter 10 Power Model.

10.1 Building the Model.

10.2 Model Evaluation.

Practice Problems.

Chapter 11 Market Models and Salary Survey Analysis.

11.1 Introduction.

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.

Practice Problems.

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.

Practice Problems.

Chapter 13 Integrated Market Model Exponential.

Practice Problems.

Chapter 14 Integrated Market Model Maturity Curve.

Practice Problems.

Chapter 15 Job Pricing Market Model Group of Jobs.

Practice Problems.

Chapter 16 Job Pricing Market Model Power Model.

Practice Problems.

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.

Practice Problems.


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.


Note: Product cover images may vary from those shown
3 of 3


4 of 3
John H. Davis
Note: Product cover images may vary from those shown