MARC details
| 000 -LEADER |
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| fixed length control field |
10708cam a22003851i 4500 |
| 001 - CONTROL NUMBER |
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18947356 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
JGU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230429020017.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
Paper bound |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
160125s2016 enka b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2015050726 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780749473914 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
HF5549 |
| Item number |
.E4155 2016 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.30015195 |
| Edition number |
23 |
| Item number |
ED-P |
| 084 ## - OTHER CLASSIFICATION NUMBER |
|---|
| Classification number |
BUS030000 |
| -- |
BUS103000 |
| Source of number |
bisacsh |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Edwards, Martin R |
| 9 (RLIN) |
43082 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Edwards, Kirsten |
| 9 (RLIN) |
43083 |
| 245 10 - TITLE STATEMENT |
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| Title |
Predictive HR analytics |
| Remainder of title |
mastering the HR metric |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
London |
| Name of publisher, distributor, etc |
Kogan Page |
| Date of publication, distribution, etc |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 457p. |
| Other physical details |
illustrations |
| Dimensions |
23 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Machine 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 ## - SUMMARY, ETC. |
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| Summary, etc |
"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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Personnel management |
| General subdivision |
Statistical methods. |
| 9 (RLIN) |
43084 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
BUSINESS & ECONOMICS / Human Resources & Personnel Management. |
| Source of heading or term |
bisacsh |
| 9 (RLIN) |
31650 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
BUSINESS & ECONOMICS / Organizational Development. |
| Source of heading or term |
bisacsh |
| 9 (RLIN) |
33680 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Online version: |
| Main entry heading |
Edwards, Martin R., author. |
| Title |
Predictive HR analytics |
| Place, publisher, and date of publication |
London ; Philadelphia : Kogan Page, [2016] |
| International Standard Book Number |
9780749473921 |
| Record control number |
(DLC) 2016004035 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| a |
7 |
| b |
cbc |
| c |
orignew |
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1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Print |
| Koha issues (borrowed), all copies |
6 |
