000			10708cam a22003851i 4500
001			18947356
003			JGU
005			20230429020017.0
007			Paper bound
008			160125s2016 enka b 001 0 eng
010			_a 2015050726
020			_a9780749473914
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aHF5549 _b.E4155 2016
082	0	0	_a658.30015195 _223 _bED-P
084			_aBUS030000 _aBUS103000 _2bisacsh
100	1		_aEdwards, Martin R _943082
100	1		_aEdwards, Kirsten _943083
245	1	0	_aPredictive HR analytics _bmastering the HR metric
260			_aLondon _bKogan Page _c2016
300			_axiii, 457p. _billustrations _c23 cm
504			_aIncludes bibliographical references and index.
505	8		_aMachine generated contents note: Foreword -- Preface -- Acknowledgements01 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 modeling -- HR analytics and HR people strategy -- Becoming a persuasive HR function -- References -- Further reading02 HR information systems and data -- Information sources -- Analysis software options -- Using SPSS -- Preparing the data/data file -- Big data -- References03 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 tool kit: 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 -- References04 Case study 1: diversity analysis -- 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 -- References05 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 -- References06 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 -- Summary -- References07 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 -- Growth trajectories and analytic demands -- Ethical considerations caveat in performance data analysis -- Considering the possible range of performance analytics models -- References08 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 -- Example 3: validating selection techniques as predictors of performance -- Example 4: predicting performance from selection data using multiple linear regression -- Example 5: predicting turnover from selection data - validating selection techniques by predicting turnover -- Further considerations -- References09 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 -- References10 Business applications: scenario modelling and business cases -- Predictive modelling scenarios -- Example 1: predictive modelling scenario 1 - customer reinvestment -- Example 2: predictive modelling scenario 2 - modelling the potential impact of a training programme -- Obtaining individual values for the outcomes of our predictive models -- Example 3: predictive modelling scenario 3 - predicting the likelihood of leaving -- Example 4: making graduate selection decisions with evidence obtained from previous performance data -- Example 5: predictive modelling scenario 4 - constructing the business case for investment in an induction day -- Example 6: using predictive models to help make a selection decision in graduate recruitment -- Example 7: which candidate might be a 'flight risk'? -- Further consideration on the use of evidence-based recommendations in selection -- References11 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 -- References12 Reflection on HR analytics: ethics and limitations -- HR analytics as a scientific discipline -- The metric becomes the behaviour driver: institutionalized metricoriented 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 is linked to human beings -- ReferencesIndex.
520			_a"While other departments in an organization deal with profits, sales growth, and strategic planning, Human Resources (HR) is responsible for employee well-being, engagement, and staff motivation. Even though it may not be immediately obvious, the management of these duties often requires a great deal of measurement and technical skill. Predictive HR Analytics provides a clear and accessible framework to understanding and learning to work with HR analytics at an advanced level, using examples of particular predictive models, such as diversity analysis, predicting turnover, evaluating interventions, and predicting performance. When dealing with metrics, management information, and analytics, HR practitioners rarely use any advanced statistical techniques or go beyond describing the characteristics of the workforce. Authors Martin Edwards and Kirsten Edwards explain the business applications of HR predictive models; the ethics and limitations of HR analytics; how to carry out an analysis; predict turnover, performance, recruiting, and selection outcomes; and monitor the impact of interventions. "--
520			_a"Where other functions of an organization deal in profits, sales growth and forecasts and strategic planning, the HR function is responsible for employee well-being, engagement and motivation. Such concerns do not immediately conjure up images of technical know-how, despite the fact that in reality the management of such things may often require a lot of measurement and technical skill. Predictive HR Analytics: Mastering the HR Metric provides a clear, accessible framework with which to understand and work with HR analytics at an advanced level, taking the reader through examples of particular predictive models. When dealing with HR metrics, management information and HR analytics, HR practitioners rarely use any advanced statistical techniques to make the most of the data they have. This book will show the reader step-by-step, using simple terms, how to carry out the analysis (using the statistical package SPSS) and how to interpret the results, helping them to communicate the potential of HR analytics and get the most out of their HR function, whether they are carrying out the analysis themselves or briefing external consultants. Predictive HR Analytics: Mastering the HR Metric will help HR professionals to deliver a credible and reliable service to the businesses that they support by providing metrics on which executives will be able to make sound business decisions"--
650		0	_aPersonnel management _xStatistical methods. _943084
650		7	_aBUSINESS & ECONOMICS / Human Resources & Personnel Management. _2bisacsh _931650
650		7	_aBUSINESS & ECONOMICS / Organizational Development. _2bisacsh _933680
776	0	8	_iOnline version: _aEdwards, Martin R., author. _tPredictive HR analytics _dLondon ; Philadelphia : Kogan Page, [2016] _z9780749473921 _w(DLC) 2016004035
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