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Predictive HR Analytics

Book Description

This book shows Human Resource professionals and consultants how to confidently use predictive analysis with HR Metrics to enable them to predict and model employee attitudes and behaviour, and to demonstrate the value added by the HR function.

Table of Contents

  1. Cover
  2. Title Page
  3. Contents
  4. Dedication
  5. Preface
  6. Acknowledgements
  7. 01    Understanding HR analytics
    1. Predictive HR analytics defined
    2. Understanding the need (and business case) for mastering and utilizing predictive HR analytic techniques
    3. Human capital data storage and ‘big (HR) data’ manipulation
    4. Predictors, prediction and predictive modelling
    5. Current state of HR analytic professional and academic training
    6. Business applications of modelling
    7. HR analytics and HR people strategy
    8. Becoming a persuasive HR function
    9. References
    10. Further reading
  8. 02    HR information systems and data
    1. Information sources
    2. Analysis software options
    3. Using SPSS
    4. Preparing the data
    5. Big data
    6. References
  9. 03    Analysis strategies
    1. From descriptive reports to predictive analytics
    2. Statistical significance
    3. Data integrity
    4. Types of data
    5. Categorical variable types
    6. Continuous variable types
    7. Using group/team-level or individual-level data
    8. Dependent variables and independent variables
    9. Your toolkit: types of statistical tests
    10. Statistical tests for categorical data (binary, nominal, ordinal)
    11. Statistical tests for continuous/interval-level data
    12. Factor analysis and reliability analysis
    13. What you will need
    14. Summary
    15. References
  10. 04    Case study 1: Diversity analytics
    1. Equality, diversity and inclusion
    2. Approaches to measuring and managing D&I
    3. Example 1: gender and job grade analysis using frequency tables and chi square
    4. Example 2a: exploring ethnic diversity across teams using descriptive statistics
    5. Example 2b: comparing ethnicity and gender across two functions in an organization using the independent samples t-test
    6. Example 3: using multiple linear regression to model and predict ethnic diversity variation across teams
    7. Testing the impact of diversity: interacting diversity categories in predictive modelling
    8. A final note
    9. References
  11. 05    Case study 2: Employee attitude surveys – engagement and workforce perceptions
    1. What is employee engagement?
    2. How do we measure employee engagement?
    3. Interrogating the measures
    4. Conceptual explanation of factor analysis
    5. Example 1: two constructs – exploratory factor analysis
    6. Reliability analysis
    7. Example 2: reliability analysis on a four-item engagement scale
    8. Example 3: reliability and factor testing with group-level engagement data
    9. Analysis and outcomes
    10. Example 4: using the independent samples t-test to determine differences in engagement levels
    11. Example 5: using multiple regression to predict team-level engagement
    12. Actions and business context
    13. References
  12. 06    Case study 3: Predicting employee turnover
    1. Employee turnover and why it is such an important part of HR management information
    2. Descriptive turnover analysis as a day-to-day activity
    3. Measuring turnover at individual or team level
    4. Exploring differences in both individual and team-level turnover
    5. Example 1a: using frequency tables to explore regional differences in staff turnover
    6. Example 1b: using chi-square analysis to explore regional differences in individual staff turnover
    7. Example 2: using one-way ANOVA to analyse team-level turnover by country
    8. Example 3: predicting individual turnover
    9. Example 4: predicting team turnover
    10. Modelling the costs of turnover and the business case for action
    11. Summary
    12. References
  13. 07    Case study 4: Predicting employee performance
    1. What can we measure to indicate performance?
    2. What methods might we use?
    3. Practical examples using multiple linear regression to predict performance
    4. Ethical considerations caveat in performance data analysis
    5. Considering the possible range of performance analytic models
    6. References
  14. 08    Case study 5: Recruitment and selection analytics
    1. Reliability and validity of selection methods
    2. Human bias in recruitment selection
    3. Example 1: consistency of gender and BAME proportions in the applicant pool
    4. Example 2: investigating the influence of gender and BAME on shortlisting and offers made
    5. Validating selection techniques as predictors of performance
    6. Example 3: predicting performance from selection data using multiple linear regression
    7. Example 4: predicting turnover from selection data – validating selection techniques by predicting turnover
    8. Further considerations
    9. References
  15. 09    Case study 6: Monitoring the impact of interventions
    1. Tracking the impact of interventions
    2. Example 1: stress before and after intervention
    3. Example 2: stress before and after intervention by gender
    4. Example 3: value-change initiative
    5. Example 4: value-change initiative by department
    6. Example 5: supermarket checkout training intervention
    7. Example 6: supermarket checkout training course – Redux
    8. Evidence-based practice and responsible investment
    9. References
  16. 10    Business applications: Scenario modelling and business cases
    1. Predictive modelling scenarios
    2. Example 1: customer reinvestment
    3. Example 2: modelling the potential impact of a training programme
    4. Obtaining individual values for the outcomes of our predictive models
    5. Example 3: predicting the likelihood of leaving
    6. Making graduate selection decisions with evidence obtained from previous performance data
    7. Example 4: constructing the business case for investment in an induction day
    8. Example 5: using predictive models to help make a selection decision in graduate recruitment
    9. Example 6: which candidate might be a ‘flight risk’?
    10. Further consideration on the use of evidence-based recommendations in selection
    11. References
  17. 11    More advanced HR analytic techniques
    1. Mediation processes
    2. Moderation and interaction analysis
    3. Multi-level linear modelling
    4. Curvilinear relationships
    5. Structural equation models
    6. Growth models
    7. Latent class analysis
    8. Response surface methodology and polynomial regression analysis
    9. The SPSS syntax interface
    10. References
  18. 12    Reflection on HR analytics: Usage, ethics and limitations
    1. HR analytics as a scientific discipline
    2. The metric becomes the behaviour driver: Institutionalized Metric-Oriented Behaviour (IMOB)
    3. Balanced scorecard of metrics
    4. What is the analytic sample?
    5. The missing group
    6. The missing factor
    7. Carving time and space to be rigorous and thorough
    8. Be sceptical and interrogate the results
    9. The importance of quality data and measures
    10. Taking ethical considerations seriously
    11. Ethical standards for the HR analytics team
    12. The metric and the data are linked to human beings
    13. References
  19. Index
  20. Copyright