Predictive HR Analytics: Mastering HR Metric
Contents (473 pages)
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 professional and academic training
Business applications of modelling
HR analytics and HR people strategy
Becoming a persuasive HR function
HR information systems and data
Information sources
Analysis software options
Using SPSS
Preparing the data
Big data
Analysis strategies
From descriptive reports to predictive analytics
Statistical significance
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
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
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: reliability and factor testing 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
Actions and business context
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 analyze team-level turnover by country
Example 3: predicting individual turnover
Example 4: predicting team turnover
Modelling the costs of turnover and the business case for action
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
Ethical considerations caveat in performance data analysis
Considering the possible range of performance analytic models
Case study 5: Recruitment and selection analytics
Reliability and validity of selection methods
Human bias in recruitment selection
Example 1: consistency of gender and BAME proportions in the applicant pool
Example 2: investigating the influence of gender and BAME 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
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
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
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
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
The metric and the data are linked to human beings
