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Predictive HR Analytics | Heimkaup.is
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Heimkaup Námsbækur Viðskipti og hagfræði Predictive HR Analytics
Útgáfa: 1 - Höfundur: Edwards, Martin; Edwards, Kirsten
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Höfundur: Edwards, Martin; Edwards, Kirsten
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01 Understanding HR analytics
Predictive HR analytics defined
Understanding the need (and business case) for mastering and utilizing predictive HR analytic techni
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
02 HR information systems and data
From descriptive reports to predictive analytics
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
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 indepen
Example 3: using multiple linear regression to model and predict ethnic diversity variation across t
Testing the impact of diversity: interacting diversity categories in predictive modelling
05 Case study 2: Employee attitude surveys – engagement and workforce perceptions
What is employee engagement?
How do we measure employee engagement?
Conceptual explanation of factor analysis
Example 1: two constructs – exploratory factor analysis
Example 2: reliability analysis on a four-item engagement scale
Example 3: reliability and factor testing with group-level engagement data
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
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: predicting team turnover
Modelling the costs of turnover and the business case for action
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
Ethical considerations caveat in performance data analysis
Considering the possible range of performance analytic models
08 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
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
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
11 More advanced HR analytic techniques
Moderation and interaction analysis
Response surface methodology and polynomial regression analysis
The SPSS syntax interface
12 Reflection on HR analytics
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?
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
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