Predictive HR analytics: mastering the HR metric
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; Philadelphia ; New Delhi
KoganPage
[2016]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 457 Seiten Illustrationen 24 cm |
| ISBN: | 9780749473914 |
Internformat
MARC
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| 245 | 1 | 0 | |a Predictive HR analytics |b mastering the HR metric |c Martin R. Edwards and Kirsten Edwards |
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| 264 | 4 | |c © 2016 | |
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Datensatz im Suchindex
| _version_ | 1804176414697586688 |
|---|---|
| adam_text | Titel: Predictive HR analytics
Autor: Edwards, Martin R
Jahr: 2016
CONTENTS
Preface xi
Acknowledgements xiv
01 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
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 HR information Systems and data 10
Information sources 11
Analysis Software options 13
UsingSPSS 15
Preparing the data 21
Big data 53
References 56
03 Analysis strategies 57
From descriptive reports to predictive analytics 57
Statistical signifkance 59
Data integrity 61
Types of data 62
Categorical variable types 62
Continuous variable types 64
Using group/team-level or individual-level data 66
Dependent variables and independent variables 66
Your toolkit: types of Statistical tests 68
Statistical tests for categorical data (binary, nominal, ordinal) 75
Statistical tests for continuous/interval-level data 85
Factor analysis and reliability analysis 103
What you will need 106
Contents
Summary 106
References 107
04 Case study 1: Diversity analytics 108
Equality, diversity and inclusion 108
Approaches to measuring and managing DSd 109
Example 1: gender and job grade analysis using frequency tables
and chi square 111
Example 2a: exploring ethnic diversity across teams using
descriptive statistics 122
Example 2b: comparing ethnicity and gender across two functions
in an Organization using the independent samples t-test 128
Example 3: using multiple linear regression to model and predict
ethnic diversity Variation across teams 135
Testing the impact of diversity: interacting diversity categories in
predictive modelling 141
A final note 143
References 143
05 Case study 2: Employee attitude surveys -
engagement and workforce perceptions 144
What is employee engagement? 145
How do we measure employee engagement? 147
Interrogating the measures 150
Conceptual explanation of factor analysis 153
Example 1: two constructs - exploratory factor analysis 158
Reliability analysis 165
Example 2: reliability analysis on a four-item
engagement scale 166
Example 3: reliability and factor testing with group-level
engagement data 170
Analysis and outcomes 174
Example 4: using the independent samples t-test to
determine differences in engagement levels 177
Example 5: using multiple regression to predict team-level
engagement 183
Actions and business context 188
References 189
06 Case study 3: Predicting employee turnover 190
Employee turnover and why it is such an important part of
HR management information 190
Descriptive turnover analysis as a day-to-day activity 192
Contents
Measuring turnover at individual or team level 192
Exploring differences in both individual and team-level
turnover 193
Example la: using frequency tables to explore regional
differences in staff turnover 194
Example lb: using chi-square analysis to explore regional
differences in individual staff turnover 198
Example 2: using one-way ANOVA to analyse team-level
turnover by country 203
Example 3: predicting individual turnover 217
Example 4: predicting team turnover 226
Modelling the costs of turnover and the business
case for action 231
Summary 235
References 235
07 Case study 4: Predicting employee Performance 237
What can we measure to indicate Performance? 238
What methods might we use? 239
Practical examples using multiple linear regression to
predict Performance 240
Ethical considerations caveat in Performance data analysis 282
Considering the possible ränge of Performance analytic
modeis 283
References 284
08 Case study 5: Recruitment and selection analytics 285
Reliability and validity of selection methods 286
Human bias in recruitment selection 287
Example 1: consistency of gender and BAME proportions in
the applicant pool 287
Example 2: investigating the influence of gender and BAME on
shortlisting and offers made 290
Validating selection techniques as predictors of Performance 302
Example 3: predicting Performance from selection data using
multiple linear regression 307
Example 4: predicting turnover from selection data - validating
selection techniques by predicting turnover 310
Further considerations 317
References 318
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
Example 2: stress before and after Intervention by gender 330
Example 3: value-change initiative 336
Example 4: value-change initiative by department 344
Example 5: supermarket checkout training Intervention 352
Example 6: supermarket checkout training course - Redux 359
Evidence-based practice and responsible Investment 363
References 364
10 Business applications: Scenario modelling and
business cases 365
Predictive modelling scenarios ^66
Example 1: customer reinvestment 367
Example 2: modelling the potential impact of a training
Programme 373
Obtaining individual values for the outcomes of our predictive
modeis 382
Example 3: predicting the likelihood of leaving 383
Making graduate selection decisions with evidence obtained from
previous Performance data 390
Example 4: constructing the business case for investment in an
induction day 394
Example 5: using predictive modeis to help make a selection
decision in graduate recruitment 398
Example 6: which candidate might be a flight risk ? 406
Further consideration on the use of evidence-based
recommendations in selection 410
References 411
11 More advanced HR analytic techniques 412
Mediation processes 414
Moderation and interaction analysis 416
Multi-level linear modelling 421
Curvilinear relationships 423
Structural equation modeis 427
Growth modeis 427
Latent class analysis 430
Response surface methodology and polynomial regression
analysis 431
The SPSS syntax interface 435
References 436
Contents
12 Reflection on HR analytics: Usage, ethics and
limitations 437
HR analytics as a scientific discipline 437
The metric becomes the behaviour driver: Institutionalized Metric-
Oriented Behaviour (IMOB) 439
Balanced scorecard of metrics 441
What is the analytic sample? 441
The missing group 443
The missing factor 444
Carving time and Space to be rigorous and thorough 445
Be sceptical and interrogate the results 445
The importance of quality data and measures 446
Taking ethical considerations seriously 447
Ethical Standards for the HR analytics team 450
The metric and the data are linked to human beings 451
References 452
Index 453
Supporting resources to accompany this book are available at the following url.
(Please scroti to the bottomof the page and complete the form toaccess the
resources.)
www.koganpage.com/PHRA
|
| any_adam_object | 1 |
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| discipline | Wirtschaftswissenschaften |
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| language | English |
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| spelling | Edwards, Martin R. Verfasser (DE-588)1090899823 aut Predictive HR analytics mastering the HR metric Martin R. Edwards and Kirsten Edwards London ; Philadelphia ; New Delhi KoganPage [2016] © 2016 XIII, 457 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Personnel management Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Humanvermögen (DE-588)4240300-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Personalinformationssystem (DE-588)4045259-1 gnd rswk-swf Humanvermögen (DE-588)4240300-5 s Statistik (DE-588)4056995-0 s Personalinformationssystem (DE-588)4045259-1 s Datenverarbeitung (DE-588)4011152-0 s DE-604 Edwards, Kirsten Verfasser (DE-588)1090901771 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029074051&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Edwards, Martin R. Edwards, Kirsten Predictive HR analytics mastering the HR metric Personnel management Datenverarbeitung (DE-588)4011152-0 gnd Humanvermögen (DE-588)4240300-5 gnd Statistik (DE-588)4056995-0 gnd Personalinformationssystem (DE-588)4045259-1 gnd |
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| title | Predictive HR analytics mastering the HR metric |
| title_auth | Predictive HR analytics mastering the HR metric |
| title_exact_search | Predictive HR analytics mastering the HR metric |
| title_full | Predictive HR analytics mastering the HR metric Martin R. Edwards and Kirsten Edwards |
| title_fullStr | Predictive HR analytics mastering the HR metric Martin R. Edwards and Kirsten Edwards |
| title_full_unstemmed | Predictive HR analytics mastering the HR metric Martin R. Edwards and Kirsten Edwards |
| title_short | Predictive HR analytics |
| title_sort | predictive hr analytics mastering the hr metric |
| title_sub | mastering the HR metric |
| topic | Personnel management Datenverarbeitung (DE-588)4011152-0 gnd Humanvermögen (DE-588)4240300-5 gnd Statistik (DE-588)4056995-0 gnd Personalinformationssystem (DE-588)4045259-1 gnd |
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