Predictive HR Analytics: Mastering the HR Metric

473 Pages·2018·15.08 MB·English

Copyright @ 2016. Kogan Page.All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
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iv
Publisher’s note
Every possible effort has been made to ensure that the information contained in this book
is accurate at the time of going to press, and the publisher and authors cannot accept
w. responsibility for any errors or omissions, however caused. No responsibility for loss or
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damage occasioned to any person acting, or refraining from action, as a result of the ma-
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g terial in this publication can be accepted by the editor, the publisher or the authors.
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ab First published in Great Britain and the United States in 2016 by Kogan Page Limited
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ap Apart from any fair dealing for the purposes of research or private study, or criticism or review,

or as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be

S. reproduced, stored or transmitted, in any form or by any means, with the prior permission in
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U writing of the publishers, or in the case of reprographic reproduction in accordance with the terms
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de and licences issued by the CLA. Enquiries concerning reproduction outside these terms should
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u be sent to the publishers at the undermentioned addresses:
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m 2nd Floor, 45 Gee Street 1518 Walnut Street, Suite 900 4737/23 Ansari Road
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p London EC1V 3RS Philadelphia PA 19102 Daryaganj
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se United Kingdom USA New Delhi 110002
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r www.koganpage.com India
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pt © Martin R Edwards and Kirsten Edwards, 2016
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, The right of Martin R Edwards and Kirsten Edwards to be identified as the authors of this work
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he has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
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pu ISBN 978 0 7494 7391 4

he E-ISBN 978 0 7494 7392 1
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f British Library Cataloguing-in-Publication Data

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is A CIP record for this book is available from the British Library.
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t Library of Congress Cataloging-in-Publication Data
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wi Names: Edwards, Martin R., author. | Edwards, Kirsten.

rm Title: Predictive HR analytics : mastering the HR metric / Martin R. Edwards,
o
f Kirsten Edwards.
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an Description: London ; Philadelphia : Kogan Page, [2016] | Includes

in bibliographical references and index.

ed Identifiers: LCCN 2015050726 (print) | LCCN 2016004035 (ebook) | ISBN
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d 9780749473914 (paperback) | ISBN 9780749473921 (ebook)
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p Subjects: LCSH: Personnel management--Statistical methods. | BISAC: BUSINESS
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e & ECONOMICS / Human Resources & Personnel Management. | BUSINESS &
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e.t ECONOMICS / Organizational Development.
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Pa n Classification: LCC HF5549 .E4155 2016 (print) | LCC HF5549 (ebook) | DDC
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anMa 658.3001/5195--dc23
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Kod. LC record available at http://lccn.loc.gov/2015050726
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20es Typeset by Graphicraft Limited, Hong Kong
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@s Print production managed by Jellyfish
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ghgh Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY
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Contents
Contents v
Preface xi
Who this book is for xi
Why we wrote this book xi
Little Professor xii
DIY xii
How to use this book xiii
Acknowledgements xiv
01 1
Understanding HR analytics 1
Predictive HR analytics defined 2
Understanding the need (and business case) for mastering and utilizing predictive HR analytic techniques 2
Human capital data storage and ‘big (HR) data’ manipulation 3
v Predictors, prediction and predictive modelling 4
Current state of HR analytic professional and academic training 5
Business applications of modelling 7
HR analytics and HR people strategy 7
Becoming a persuasive HR function 8
References 8
Further reading 9
02 10
Contents HR information systems and data 10
Information sources 11
Analysis software options 13
Using SPSS 15
Preparing the data 21
Big data 53
References 56
03 57
Analysis strategies 57
aw. FSDtraaottmais t idincetaeslgc srriiigptynt iivfiec arenpcoe rts to 56p91redictive analytics 57
ght l TCCUyaospitnneetggsin ogourrfioo cduaualsp t v/avta aerariimaabb-llleee v tteyylpp oeesrs in666d224ividual-level data 66
i Dependent variables and independent variables 66
opyr Preface xi YSSttoaauttiirss tttiioccoaalllk ttieetss: tttssy ffpooerrs ccoaoftn estgtianoturiisoctuaiclsa /diln attteeasr tv(sba iln-laervye,l ndoamtai nal, ord68i85nal) 75
able c Acknowledgements xiv FWSR0u4aehmc fetaomrte ryan aorcnyue as w lyislils naenedd reliabilit1111y0000 a6678nalysis 103
pplic 01 Understanding HR analytics 1 CEAEqxpauaspemar olspitatulyce,dh d1yei: sv 1 get eorns midtyeer aa sanundrd iin njgoc blau ngs1dir0oa 8mnd ea naangailnygsi sD u&siIn g frequen11cy00 89tables and chi square 111
a Example 2a: exploring ethnic diversity across teams using descriptive statistics 122
. or Predictive HR analytics defined 2 EETAexx fisaatnmminappgl ll neetho 23etb:e i :um cspionamgc ptm aourfi lndtigipv leeetr hsliin1ntiy4ec:3ai tiryn r taeengrardec sgtsieninogdn de triov a emcrrsooitdsyse clt awatneodg f ouprnrieecdst iiiocntn pse trihendn aiiccnt diovirevg emarsnoiitdzyae lvtliaionrngia utisoinng a tchreo sisn dteeapmensd ent sam11p34l51es t-test 128
r U.S Understanding the need (and business case) for mastering and R0CW5eah fseaer tes itnsuc deemys p2l oyee engageme111n444t?344 145
d unde Huumtialinzi ncagp pitraeld dicattiav es tHorRa gaen aalnydti ‘cb tigec (hHnRiq)u deas ta2’ manipulation 3 HICERnxoeotlaenwirmacr beodppigollteia ut wyt1ai n:ela gtenmw xatepholyale assc iumonsrna eetsa iteosrmuunrc poetlssfo –fy11a e56ecext05 oepnrl ogaranagateolmyrsyei snf at?c tor analysis11 4573 158
mitte Predictors, prediction and predictive modelling 4 EEAExxxnaaaammmlypppsilllseee a234n::: drru eesolliiiunaatgbbc iiotllhiimtteyye isaan nndadel pyfeas1nic7std o4oernn tt e ass aftoimnugpr l-weitsei ttmh-t egesnrtog tuaopg de-lmeetveeenrlmt e sinncgaeal edg eifmfeernent cdeast ain engagem11e67n60t levels 177
es per CBuusrirneensts sataptpel iocfa tHioRn sa onfa lmytoicd eplrlionfge ssi7onal and academic training 5 EAR0C6xcea tfasieemor nesptnslu ceade 5nys :d 3u b suinsgin mesus lctiopnlete rxe111tg 899r900ession to predict team-lev1e8l 8engagement 183
r us HR analytics and HR people strategy 7 EDMmeespacslrouipyrtienievg et u tturunrrnonovovevere rra anatdn i anwldyhisvyisi di tau sias a ls oudrac hyte- ataonm- di mlaeyvp eoalrc ttaivnitt yp art11 99o22f HR management information 190
i Exploring differences in both individual and team-level turnover 193
except fa BRFueecrfoethrmeenrin crgees aa d pi8negrs ua9sive HR function 8 EEEEEMSRuxxxxxeomfaaaaaedmmmmmmreeppppplanllllllirceeeeenye g11234 s ab:::t h::upp euusrr ieesscnddiionngiisccg gottt siifcnnn rhoeeggi-fq -w itstuneuqaeadruynmin avcAo ryi22tdevN u33t ueara55Orannb laVoal entvlAusyde r srt tnito osho eteavox neb praeu lxlosypirsnleeoe rstreese ag crmiaeogs-neliaeo fvlno ed22arli12 lfta 76fduceirtrfniefoeonnrvce eensrc ibensy sicntoa ifunfn dttuirvryin douvaelr s 122ta903f431f turnover 198
, 07 237
sher CWWahhsaae tts cmtuaednty hw o4ed sm meaisguhrte w toe uinsde2?i3c a7te performance? 223389
i Practical examples using multiple linear regression to predict performance 240
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m the Information sources 11 CRHExaeulasmiema basptniull eibdt y1iya : a s5cn iodnn vrseiascltireduniitctymy oeofnf stg es2eleen8lcde5tceiotri noa nnm de tBhAoMdsE proportion22s88 67in the applicant pool 287
on fro AUnsianlgy sSisP SsoS ftw15are options 13 EVEEFuxxxarlaaaitmmmdhaeppptrllli eeenc go234 n:::s seippnilrrdeveeceeddtrsiiiaotcctittngiiio anntntggeisc n pthguen rrtinfhqooeu3rv emi1ens7r afl anfusrc eopenm rcfer edso eoimclfet ocgstreeislnoe donceft d irpo aaentnra fdd o–a r BtvmaAa aulMinsdicEanet g oi nmng usshletolieprctltleii soltininn etgae cra hnrnedgi qoruefsfeess3ir obs0 ny2m pardeed icting turnover 233901070
missi Preparing the data 21 R0CT9reaa fsceekr esitnnugcde tysh 6e impact of inter333v111en899tions 319
ut per BRiegf edraetnac es5 356 EEEEExxxxxaaaaammmmmpppppllllleeeee 12345::::: ssvvsttuaarrplleeuuesseessr-- mbbcchhaeeffaarooknnrreggeetee aa ciihnnnneddiittc iiaakaaffottttiiueevvrrtee iitbnnryattee idnrrvveineepnnga ttriiitnoomtnnee rbnvyte ngteinodne 3333r2345 5642 330
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n Example 5: using predictive models to help make a selection decision in graduate recruitment 398
ed i Statistical significance 59 EFRuxerfaetmhreepnrl cece o6sn : swidheircaht icoann doind athtee4 m1u1sieg hotf beev iad e‘flnicgeh-tb arissekd’ ?r eco4m0m6endations in selection 410
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Kogand. Ma Dependent variables and independent variables 66 HTBWahRhlea a anmtn cieeastd lrty ihstciec cb osaer naceaocs lamayr tdesicsc o itsefhan memt ibefipteclreh id?ca isvs icoipulri nder iver: Institutionaliz444e344d711 Metric-Oriented Behaviour (IMOB) 439
2016. eserve YStoautirs ttiocoallk tiets: ttsy fpoers coaft esgtaotriisctaicla dl atteas t(sb in6a8ry, nominal, ordinal) 75 TTCBTehhha eeresv cmmiiemnpiigssptss iotciiinnramtggla eangf anarccodnet u dooipn rfs tpqearurcaoel gittaoyt ebd 44eath44 trae34i garoenrsdou ulmtsse aansudr tehso rough 444444556
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igig Factor analysis and reliability analysis 103 INDEX 453
rr
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opll What you will need 106
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vi Contents
Summary 106
References 107
w. 04 Case study 1: Diversity analytics 108
a
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ht Equality, diversity and inclusion 108
g
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yr Approaches to measuring and managing D&I 109
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co Example 1: gender and job grade analysis using frequency tables

e
bl and chi square 111
a
c
li Example 2a: exploring ethnic diversity across teams using
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ap descriptive statistics 122
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o Example 2b: comparing ethnicity and gender across two functions
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U. in an organization using the independent samples t-test 128

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e Example 3: using multiple linear regression to model and predict
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u ethnic diversity variation across teams 135
d
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tt Testing the impact of diversity: interacting diversity categories in
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er predictive modelling 141
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s A final note 143
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u References 143
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ep 05 Case study 2: employee attitude surveys –
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e engagement and workforce perceptions 144
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is What is employee engagement? 145
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ub How do we measure employee engagement? 147
p

he Interrogating the measures 150
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om Conceptual explanation of factor analysis 153
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f Example 1: two constructs – exploratory factor analysis 158
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si Reliability analysis 165
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rm Example 2: reliability analysis on a four-item
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p engagement scale 166
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ho Example 3: reliability and factor testing with group-level
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w engagement data 170
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for Analysis and outcomes 174

ny Example 4: using the independent samples t-test to
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in determine differences in engagement levels 177

d
e Example 5: using multiple regression to predict team-level
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od engagement 183
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re Actions and business context 188

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. b References 189
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Kd
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ghgh Descriptive turnover analysis as a day-to-day activity 192
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Contents vii
Measuring turnover at individual or team level 192
Exploring differences in both individual and team-level
turnover 193
Example 1a: using frequency tables to explore regional
.
w
la differences in staff turnover 194

t
gh Example 1b: using chi-square analysis to explore regional
i
r
py differences in individual staff turnover 198
o
c Example 2: using one-way ANOVA to analyse team-level
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ab turnover by country 203
c
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pl Example 3: predicting individual turnover 217
p
a Example 4: predicting team turnover 226
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. Modelling the costs of turnover and the business
S
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U case for action 231
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nde Summary 235
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d References 235
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p 07 Case study 4: Predicting employee performance 237
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r What can we measure to indicate performance? 238
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f What methods might we use? 239
t
ep Practical examples using multiple linear regression to
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e predict performance 240
,
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he Ethical considerations caveat in performance data analysis 282
s
li Considering the possible range of performance analytic
b
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p models 283
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t References 284
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si 08 Case study 5: Recruitment and selection analytics 285
s
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pe Reliability and validity of selection methods 286

ut Human bias in recruitment selection 287
o
h
it Example 1: consistency of gender and BAME proportions in
w

rm the applicant pool 287
o
f Example 2: investigating the influence of gender and BAME on
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a shortlisting and offers made 290
n
i
d Validating selection techniques as predictors of performance 302
e
uc Example 3: predicting performance from selection data using
d
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pr multiple linear regression 307
e
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e Example 4: predicting turnover from selection data – validating
b
ge.ot selection techniques by predicting turnover 310
an
Py Further considerations 317
na
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og. References 318
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viii Contents
09 Case study 6: Monitoring the impact of interventions 319
Tracking the impact of interventions 319
Example 1: stress before and after intervention 325
.
aw Example 2: stress before and after intervention by gender 330
l

ht Example 3: value-change initiative 336
g
ri Example 4: value-change initiative by department 344
y
p
co Example 5: supermarket checkout training intervention 352

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bl Example 6: supermarket checkout training course – Redux 359
a
lic Evidence-based practice and responsible investment 363
p
ap References 364

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U 10 Business applications: scenario modelling and
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nd business cases 365
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tt Predictive modelling scenarios 366
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rm Example 1: customer reinvestment 367
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s Example 2: modelling the potential impact of a training
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u programme 373
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ai Obtaining individual values for the outcomes of our predictive
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pt models 382
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ex Example 3: predicting the likelihood of leaving 383

r, Making graduate selection decisions with evidence obtained from
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is previous performance data 390
l
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pu Example 4: constructing the business case for investment in an

e
th induction day 394

om Example 5: using predictive models to help make a selection
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n decision in graduate recruitment 398
o
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ss Example 6: which candidate might be a ‘flight risk’? 406
i
rm Further consideration on the use of evidence-based
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t recommendations in selection 410
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th References 411
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y f 11 More advanced HR analytic techniques 412
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in Mediation processes 414
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ce Moderation and interaction analysis 416
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ro Multi-level linear modelling 421
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r Curvilinear relationships 423
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. b Structural equation models 427
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Pa n Growth models 427
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ga M Latent class analysis 430
o.
Ked Response surface methodology and polynomial regression
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01se analysis 431
2e
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@s The SPSS syntax interface 435
tt
ghgh References 436
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Contents ix
12 Reflection on HR analytics: Usage, ethics and
limitations 437
HR analytics as a scientific discipline 437
.
aw The metric becomes the behaviour driver: Institutionalized Metric-
l

ht Oriented Behaviour (IMOB) 439
g
ri Balanced scorecard of metrics 441
y
p
co What is the analytic sample? 441

e
bl The missing group 443
a
lic The missing factor 444
p
ap Carving time and space to be rigorous and thorough 445

r
o Be sceptical and interrogate the results 445
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S
U. The importance of quality data and measures 446

er Taking ethical considerations seriously 447
d
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u Ethical standards for the HR analytics team 450
d
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AN: 1193776 ; Edwards, Martin R..; Predictive HR Analytics : Mastering the HR Metric
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