Predictive HR Analytics

Höfundar: Martin R Edwards; Kirsten Edwards; Daisung Jang (Útgáfa: 3)
Predictive HR Analytics

Kaup valmöguleikar

This is the essential guide for HR practitioners who want to gain the statistical and analytical knowledge to fully harness the potential of HR metrics and organizational people-related data. The ability to use and analyse data has become an invaluable skill for HR professionals to not only identify trends and patterns, but also make well-informed business decisions. The third edition of Predictive HR Analytics provides a clear, accessible framework for understanding people data, working with people analytics and advanced statistical techniques.

Readers will be taken step-by-step through worked examples, showing them how to carry out analyses and interpret HR data in areas such as employee engagement, performance and turnover. Learn how to make effective business decision with this updated edition that includes the latest materials on biased algorithms and data protection, supported by online resources consisting of R and Excel data sets.

Nánar um bókina

Útgefandi
Kogan Page
ISBN
9781398615892
Print ISBN
9781398615656
Format
ePub
Útgáfa
3
Höfundar
Martin R Edwards; Kirsten Edwards; Daisung Jang
Tungumál
English
Útgefið
2024-06-03
Prent takmörkun á líftíma
100
Prent takmörkun
10
Afritunar takmörkun
10

Kaflar

  • Cover
  • Endorsements
  • Titlepage
  • Dedication
  • Contents
  • List of Figures
  • List of Tables
  • Preface
  • Acknowledgements
  • 01 Understanding HR analytics
  • Predictive HR analytics defined
  • Understanding the need (and business case) for mastering and utilizing predictive HR analytic techniques
  • Human capital data storage and ‘big (HR) data’ manipulation
  • Predictors, prediction and predictive modelling
  • Current state of HR analytic capabilities and professional or academic training
  • Business applications of modelling
  • HR analytics and HR people strategy
  • Becoming a persuasive HR function
  • References
  • 02 HR information systems and data
  • Information sources
  • Analysis software options
  • Using SPSS
  • Preparing the data
  • Big data
  • References
  • 03 Analysis strategies
  • From descriptive reports to predictive analytics
  • Statistical significance
  • Examples of key HR analytic metrics/measures often used by analytics teams
  • Data integrity
  • Types of data
  • Categorical variable types
  • Continuous variable types
  • Using group/team-level or individual-level data
  • Dependent variables and independent variables
  • Your toolkit: types of statistical tests
  • Statistical tests for categorical data (binary, nominal, ordinal)
  • Statistical tests for continuous/interval-level data
  • Factor analysis and reliability analysis
  • What you will need
  • Summary
  • References
  • 04 Case study 1: Diversity analytics
  • Equality, diversity and inclusion
  • Approaches to measuring and managing D&I
  • Example 1: gender and job grade analysis using frequency tables and chi-square
  • Example 2a: exploring ethnic diversity across teams using descriptive statistics
  • Example 2b: comparing ethnicity and gender across two functions in an organization using the independent samples t-test
  • Example 3: using multiple linear regression to model and predict ethnic diversity variation across teams
  • Testing the impact of diversity: interacting diversity categories in predictive modelling
  • A final note
  • References
  • 05 Case study 2: Employee attitude surveys – engagement and workforce perceptions
  • What is employee engagement?
  • How do we measure employee engagement?
  • Interrogating the measures
  • Conceptual explanation of factor analysis
  • Example 1: two constructs – exploratory factor analysis
  • Reliability analysis
  • Example 2: reliability analysis on a four-item engagement scale
  • Example 3: Principal Components Analyses with group-level engagement data
  • Analysis and outcomes
  • Example 4: using the independent samples t-test to determine differences in engagement levels
  • Example 5: using multiple regression to predict team-level engagement
  • Survey comments analysis
  • Actions and business context
  • References
  • 06 Case study 3: Predicting employee turnover
  • Employee turnover and why it is such an important part of HR management information
  • Descriptive turnover analysis as a day-to-day activity
  • Measuring turnover at individual or team level
  • Exploring differences in both individual and team-level turnover
  • Example 1a: using frequency tables to explore regional differences in staff turnover
  • Example 1b: using chi-square analysis to explore regional differences in individual staff turnover
  • Example 2: using one-way ANOVA to analyse team-level turnover by country
  • Example 3: predicting individual turnover
  • Example 4: comparing expected length of service for men vs women using the Kaplan-Meier survival analysis technique
  • Example 5: predicting team turnover
  • Modelling the costs of turnover and the business case for action
  • Summary
  • References
  • 07 Case study 4: Predicting employee performance
  • What can we measure to indicate performance?
  • What methods might we use?
  • Practical examples using multiple linear regression to predict performance
  • Example 1a: using multiple linear regression to predict customer loyalty in a financial services organization
  • Example 1b: using multiple linear regression to predict customer reinvestment in a financial services organization
  • Example 2: using multiple linear regression to predict customer loyalty
  • Example 3: using multiple linear regression to predict individual performance
  • Example 4: using stepwise multiple linear regression to model performance
  • Example 5: using stepwise multiple linear regression to model change in performance over time
  • Example 6: using multiple regression to predict sickness absence
  • Example 7: exploring patterns in performance linked to employee profile data
  • Example 8: exploring patterns in supermarket checkout scan rates linked to employee demographic data
  • Example 9: determining the presence or otherwise of high-performing age groups
  • Ethical considerations caveat in performance data analysis
  • Considering the possible range of performance analytic models
  • References
  • 08 Case study 5: Recruitment and selection analytics 301
  • Reliability and validity of selection methods
  • Human bias in recruitment selection
  • Example 1: consistency of gender and UG proportions in the applicant pool
  • Example 2: investigating the influence of gender and UG on shortlisting and offers made
  • Validating selection techniques as predictors of performance
  • Example 3: predicting performance from selection data using multiple linear regression
  • Example 4: predicting turnover from selection data – validating selection techniques by predicting turnover
  • Further considerations
  • Reference
  • 09 Case study 6: Monitoring the impact of interventions
  • Tracking the impact of interventions
  • Example 1: stress before and after intervention
  • Example 2: stress before and after intervention by gender
  • Example 3: value-change initiative
  • Example 4: value-change initiative by department
  • Example 5: supermarket checkout training intervention
  • Example 6: supermarket checkout training course – Redux
  • Evidence-based practice and responsible investment
  • Reference
  • 10 Business applications: Scenario modelling and business cases
  • Predictive modelling scenarios
  • Example 1: customer reinvestment
  • Example 2: modelling the potential impact of a training programme
  • Obtaining individual values for the outcomes of our predictive models
  • Example 3: predicting the likelihood of leaving
  • Making graduate selection decisions with evidence obtained from previous performance data
  • Example 4: constructing the business case for investment in an induction day
  • Example 5: using predictive models to help make a selection decision in graduate recruitment
  • Example 6: which candidate might be a ‘flight risk’?
  • Further consideration on the use of evidence-based recommendations in selection
  • References
  • 11 More advanced HR analytic techniques
  • Mediation processes
  • Moderation and interaction analysis
  • Multi-level linear modelling
  • Curvilinear relationships
  • Structural equation models
  • Growth models
  • Latent class analysis
  • Response surface methodology and polynomial regression analysis
  • The SPSS syntax interface
  • Machine learning
  • References
  • 12 Reflection on HR analytics: Usage, ethics and limitations
  • HR analytics as a scientific discipline
  • The metric becomes the behaviour driver: Institutionalized Metric-Oriented Behaviour (IMOB)
  • Balanced scorecard of metrics
  • What is the analytic sample?
  • The missing group
  • The missing factor
  • Carving time and space to be rigorous and thorough
  • Be sceptical and interrogate the results
  • The importance of quality data and measures
  • Taking ethical considerations seriously
  • Ethical standards for the HR analytics team
  • Data privacy
  • The metric and the data are linked to human beings
  • References
  • Appendix R
  • Index
  • Endmatter
  • Endmatter
  • Copyright