Big Data at Work The amount of data in our world has been exploding, and analyzing large data sets—so called big data—will become a key basis of competition in business Statisticians and researchers will be updating their analytic approaches, methods, and research to meet the demands created by the availability of big data The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management, and statistics Scott Tonidandel is Associate Professor, Department of Psychology, Davidson College, NC He received his PhD in Industrial Organizational Psychology from Rice University in 2001 He teaches courses in Psychological Research, Design and Analysis, and Research Methods and Issues in Psychology His research includes issues related to computerized testing, and statistical and methodological issues Eden B King is Associate Professor of Industrial Organizational Psychology at George Mason University She earned her PhD from Rice University in 2006 Her research is mostly in the area of diversity, inclusion, and women in business She is currently the Associate Editor of the Journal of Management and the Journal of Business and Psychology She is also on the Editorial Board of the Academy of Management Journal Jose M Cortina, Professor of Industrial Organizational Psychology at George Mason University, is President Elect of SIOP He received his PhD in Psychology from Michigan State University He serves as Editor of the I-O research methods journal Organizational Research Methods He has an outstanding publication record and a tremendously high level of visibility in this field SIOP Organizational Frontiers Series The Organizational Frontiers Series is sponsored by the Society for Industrial and Organizational Psychology (SIOP) Launched in 1983 to make scientific contributions accessible to the field, the series publishes books addressing emerging theoretical developments, fundamental and translational research, and theory-driven practice in the field of Industrial-Organizational Psychology and related organizational science disciplines including organizational behavior, human resource management, and labor and industrial relations Books in this series aim to inform readers of significant advances in research; challenge the research and practice community to develop and adapt new ideas; and promote the use of scientific knowledge in the solution of public policy issues and increased organizational effectiveness The Series originated in the hope that it would facilitate continuous learning and spur research curiosity about organizational phenomena on the part of both scientists and practitioners The Society for Industrial and Organizational Psychology (SIOP) is an international professional association with an annual membership of more than 8,000 industrial-organizational (I-O) psychologists who study and apply scientific principles to the workplace I-O psychologists serve as trusted partners to business, offering strategically focused and scientifically rigorous solutions for a number of workplace issues SIOP’s mission is to enhance human well-being and performance in organizational and work settings by promoting the science, practice, and teaching of I-O psychology For more information about SIOP, please visit www.siop.org The Organizational Frontiers Series Series Editor Richard Klimoski George Mason University Editorial Board Neal M Ashkanasy University of Queensland Jill Ellingson The Ohio State University Ruth Kanfer Georgia Institute of Technology Eden King George Mason University Fred Oswald Rice University Stephen Zaccaro George Mason University Deborah Rupp Purdue University Mo Wang University of Florida Howard Weiss Georgia Institute of Technology Gilad Chen University of Maryland SIOP Organizational Frontiers Series Series Editor Richard Klimoski George Mason University Tonidandel/King/Cortina: (2015) Big Data at Work: The Data Science Revolution and Organizational Psychology Finkelstein/Truxillo/Fraccaroli/Kanfer: (2014) Facing the Challenges of a MultiAge Workforce: A Use-Inspired Approach Coovert/Thompson: (2013) The Psychology of Workplace Technology Highhouse/Dalal/Salas: (2013) Judgment and Decision Making at Work Cortina/Landis: (2013) Modern Research Methods for the Study of Behavior in Organizations Olson-Buchanan/Koppes Bryan/Foster Thompson: (2013) Using Industrial Organizational Psychology for the Greater Good: Helping Those Who Help Others Eby/Allen: (2012) Personal Relationships: The Effect on Employee Attitudes, Behavior, and Well-being Goldman/Shapiro: (2012) The Psychology of Negotiations in the 21st Century Workplace: New Challenges and New Solutions Ferris/Treadway: (2012) Politics in Organizations: Theory and Research Considerations Jones: (2011) Nepotism in Organizations Hofmann/Frese: (2011) Error in Organizations Outtz: (2009) Adverse Impact: Implications for Organizational Staffing and High Stakes Selection Kozlowski/Salas: (2009) Learning, Training, and Development in Organizations Klein/Becker/Meyer: (2009) Commitment in Organizations: Accumulated Wisdom and New Directions Salas/Goodwin/Burke: (2009) Team Effectiveness in Complex Organizations Kanfer/Chen/Pritchard: (2008) Work Motivation: Past, Present and Future De Dreu/Gelfand: (2008) The Psychology of Conflict and Conflict Management in Organizations Ostroff/Judge: (2007) Perspectives on Organizational Fit Baum/Frese/Baron: (2007) The Psychology of Entrepreneurship Weekley/Ployhart: (2006) Situational Judgment Tests: Theory, Measurement and Application Dipboye/Colella: (2005) Discrimination at Work: The Psychological and Organizational Bases Griffin/O’Leary-Kelly: (2004) The Dark Side of Organizational Behavior Hofmann/Tetrick: (2003) Health and Safety in Organizations Jackson/Hitt/DeNisi: (2003) Managing Knowledge for Sustained Competitive Knowledge Barrick/Ryan: (2003) Personality and Work Lord/Klimoski/Kanfer: (2002) Emotions in the Workplace Drasgow/Schmitt: (2002) Measuring and Analyzing Behavior in Organizations Feldman: (2002) Work Careers Zaccaro/Klimoski: (2001) The Nature of Organizational Leadership Rynes/Gerhart: (2000) Compensation in Organizations Klein/Kozlowski: (2000) Multilevel Theory, Research and Methods in Organizations Ilgen/Pulakos: (1999) The Changing Nature of Performance Earley/Erez: (1997) New Perspectives on International I-O Psychology Murphy: (1996) Individual Differences and Behavior in Organizations Guzzo/Salas: (1995) Team Effectiveness and Decision Making Howard: (1995) The Changing Nature of Work Schmitt/Borman: (1993) Personnel Selection in Organizations Zedeck: (1991) Work, Families and Organizations Schneider: (1990) Organizational Culture and Climate Goldstein: (1989) Training and Development in Organizations Campbell/Campbell: (1988) Productivity in Organizations Hall: (1987) Career Development in Organizations Big Data at Work The Data Science Revolution and Organizational Psychology Edited by Scott Tonidandel, Eden B King, and Jose M Cortina First published 2016 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2016 Taylor & Francis The right of the editors to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Library of Congress Cataloging-in-Publication Data Big data at work : the data science revolution and organizational psychology / edited by Scott Tonidandel, Eden B King, & Jose M Cortina pages cm — (The organizational frontiers series) Includes bibliographical references and index 1. Organizational behavior. 2. Big data. 3. Organizational sociology. 4. Psychology, Industrial. I. Tonidandel, Scott. II. King, Eden. III. Cortina, Jose M HD58.7.B534 2016 302.3'5—dc23 selection and assessment, statistical analyses and quantitative methods, litigation support, and performance management He has consulted for Fortune 500 companies from a variety of industries as well as public sector organizations Michael earned his master’s degree and Ph.D in industrial-organizational (I-O) psychology from the Georgia Institute of Technology Michael joined APTMetrics in 2004, where he completed a one-year internship before accepting a full-time position on the consulting staff Prior to APTMetrics, Michael worked with various other organizations on a contract basis to provide consulting in the areas of statistical analyses, research methods, employee development, and performance assessment and feedback Daniel A Newman, Ph.D., is an associate professor of psychology and labor and employment relations at the University of Illinois at Urbana-Champaign His research deals with diversity/adverse impact in HR hiring practices, emotional intelligence, narcissism in the workplace, job satisfaction and work withdrawal/work engagement, and research methods (multilevel and social network approaches, missing data) Frederick L Oswald, Ph.D., is a professor in the Department of Psychology at Rice University His research collaborations and grants center around personnel selection and test development issues within organizational, educational and military settings Currently, Dr Oswald serves as an associate editor for four journals (Journal of Management, Psychological Methods, Research Synthesis Methods, and Journal of Research in Personality) and for nine editorial boards He is a fellow of APS, APA, and two APA divisions (Div 5 and SIOP, Div 14) He received his Ph.D in industrial-organizational psychology with an emphasis in psychometrics and statistics from the University of Minnesota in 1999 Dan J Putka, Ph.D., is a principal staff scientist at the Human Resources Research Organization (HumRRO) in Alexandria, Virginia He has over a decade of hands-on experience developing and evaluating assessments for selection and promotion, and managing and modeling large, messy archival datasets for purposes of predicting and understanding key outcomes such as job performance, turnover, counterproductive work behavior, and job satisfaction Aside from his client centered work, Dan has maintained an active presence in the I-O psychology scientific community having authored numerous book chapters and journals articles on a variety of methods-related topics, and serving on the editorial boards of four journals He is a past-president of the Personnel Testing Council of Metropolitan Washington, and a fellow of APA and three of its divisions (5, 14, and 19) Dan received his Ph.D in I-O psychology with a specialization in quantitative methods from Ohio University Jacqueline Ryan is the Director of Science & Analytics for IBM Smarter Workforce Product Management She is responsible for IBM’s Smarter Workforce talent analytics business that improves employee and business performance through evidence-based decisions based on big data, advanced analytics and workforce sciences With over 20 years experience in information management, analytics, and big data technologies, Jackie has led worldwide software development, product management, and marketing teams that have led the market in client value and innovation Evan F Sinar, Ph.D., is Development Dimensions International’s (DDI’s) chief scientist and director of the Center for Analytics and Behavioral Research (CABER), a team of analytical specialists deploying its expertise globally across DDI’s solutions portfolio Evan partners with client organizations to build and execute analytic initiatives to demonstrate the impact of assessment and development programs on individual-level behavior change and organizationallevel business objectives Evan is also the lead author of DDI’s Global Leadership Forecast, a major trend research study providing deep and actionable insights on how organizations can optimize their management of leadership talent in alignment with strategic priorities Prior to his role as chief scientist Evan held the role of Manager of Assessment and Selection Analytics, leading design, development, and technology integrity for DDI’s screening and testing solutions Evan is an editorial board member of Journal of Applied Psychology and Journal of Business and Psychology and has also authored over 60 professional presentations and publications for major journals and professional conferences He is a thought leader for DDI on topics such as data visualization, leadership development, generational differences, social media, and pre-employment assessment Jeffrey Stanton, Ph.D (psychology, University of Connecticut, 1997), is professor and interim dean at Syracuse University’s School of Information Studies Stanton is an active scholar who has received more than $5.5M in extramural support including the National Science Foundation’s CAREER award for early career researchers as well as funding from the Institute for Museum and Library Services, NASA, the Department of Homeland Security, the KeyBank Foundation, and the SIOP Foundation His research expertise is in research methods, psychometrics, and survey design He is the author of three books, dozens of scholarly articles in peer-reviewed behavioral science and technology journals as well as numerous book chapters on data science, privacy, research methods, and program evaluation He has won several best paper awards and several awards for graduate student mentoring Stanton’s background also includes more than a decade of business experience in start-ups and established companies Scott Tonidandel, Ph.D., is the Wayne M and Carolyn A Watson Professor of Psychology at Davidson College and a faculty affiliate of the organizational science program at the University of North Carolina-Charlotte He earned his Ph.D in industrial-organizational psychology from Rice University His research spans a variety of topics including reactions to selection procedures, leadership, mentoring, diversity, and statistical techniques Dr Tonidandel currently serves as the associate editor for both the Journal of Business and Psychology and Organizational Research Methods, and he is a fellow of the Society for Industrial and Organizational Psychology (APA div 14) Index ability: cognitive 22, 218, 297–8; organization’s 314, 322 absenteeism 92–3, 188, 193, 345 accuracy 49–50, 110, 121, 149, 152, 193, 196, 214, 219, 330–1; levels 110 activation 47, 290 adverse impact 313, 315–17, 319, 330; allegation 315; measurement 315–16, 330 agent-based simulation (ABS) 296–7 agents, embedded 297, 299 aggregation 65–7, 112, 245, 293, 312, 319 algorithmic modeling methods, modern 206 algorithms 6, 8, 45, 49, 51–3, 70–1, 107, 169, 184, 199, 217, 234, 238, 324 American Psychological Association 219, 243, 245, 318, 346 analytics 14, 38, 189, 192, 272, 283, 285; movement 183, 187, 202, 205, 207, 209; processes 28, 32, 35, 37–8; projects, multiple workforce 29, 32; team 186, 190, 198, 201 animated data visualizations 141–2 annotations 150–3 applicants: large number of 221–2; pool 223, 229, 317, 320–2 applicant tracking system (ATS) 23, 188, 221, 227, 235, 338 application programming interface (API) 76–8, 99–100, 105, 174; streaming 99–100 artificial neural networks (ANNs) 46–8 assessment processes 213–14, 220–1, 223, 226, 231, 234, 238, 242, 244–5; 360-degree 230; internal talent 220, 222–3; organization’s talent 229 assessments 8–9, 12, 191–2, 194, 213–23, 225–9, 231, 233, 235, 237–46, 278, 280, 285, 290–2, 344; context 218, 237, 241, 340; daily 292; data 217, 227–8, 241–2; improving of 194; practices 214, 219–20, 237, 241–2; principles 214–15; procedures 222; scores 192, 194, 338, 340; situation 216; systems 23; work 214–16, 218, 220 ATS see applicant tracking system badge data 289–90, 293, 303 badges 288, 290, 293 bar graphs 158–9, 161, 171 behavioral data 20, 22–3, 40, 298 behaviors 4–5, 10, 23, 64–5, 76, 96, 193–4, 232, 239–41, 272, 284, 302, 311, 325, 329; counterproductive work 58; individual 8, 217 bias, unconscious 320, 325 big data 7, 43, 45, 47, 49, 51, 53, 55, 57, 59, 101–2, 225, 230, 233, 243–4; availability of 233, 236–7; benefits 228, 232, 236; challenges 227, 231, 234–5; for diversity and inclusion 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331; movement 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209; potential applications 3, 6–7, 124, 250; reactions to 244; in selection and assessment 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 245–7; sensing 158–9, 161, 163, 165, 167, 169, 171, 173; and team effectiveness 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301; in turnover and retention 250–1, 257, 259, 261, 263, 265, 267; types of 230; velocity of 65, 120, 329 biodata measures 194–5 bubble plot 136, 150 business: organizations 295, 349; outcomes 241; problem 229, 232; rules 27; value 232, 236, 239 calibration set 199–200 CAT see computer adaptive testing categories 20–1, 46, 48, 69–70, 73, 86, 106–10, 112, 123–6, 128–9, 131–3, 135, 146, 205; focal 112 charts, horizon 134–5 choropleth 140; map 140 circle packing diagram 129–30 city level of analysis 64, 91–2 cluster dendogram 133–4, 145 coaching effectiveness 223, 229–31 cognitive systems 38, 41 cohesion 277, 279, 285, 289, 291–3, 297 communication patterns 297, 343 compensation data 20–2, 29, 40 computer adaptive testing (CAT) 8, 240 consistency, internal 73–5, 87, 89, 96 constructs 55, 59–60, 71, 75, 193, 197, 239, 262, 303, 340; emergent 275–6; latent 58, 71, 293; static 274–5 Cornell model 90 criterion values 46–7 cross-validation 48, 52–3, 60, 184, 196, 199, 341 cultures 4, 22, 44, 69, 183–4, 265, 322, 340; algorithmic modeling 183–4; data modeling 183–4 data: aggregation 67, 318, 320; analyses 8, 56–8, 60, 226, 342; analytics, 12–13, 274, 310, 312–14, 325, 328–9; applications 9–11, 67, 117, 123, 129, 131, 142–3, 147, 152; characteristics 24, 32, 215, 220, 222–3, 231; elements 119, 136–7, 143–4, 190, 261; large volumes of 34, 312–14; management 19, 36, 64, 120, 217–18, 246; matters 336–7, 339, 341, 343, 345, 347, 349; methods 45–6, 48–9, 56, 58–60, 186; mining 184, 219, 243, 290, 294, 299–300, 342; models 45, 57–8; movement 11, 43, 181–5, 187, 191–2, 194, 202, 206, 208, 238, 245–6; platform 19–21, 23, 25, 27–9, 31–3, 35, 37–9, 41, 342; quality 27, 29, 31; science 2, 12, 14, 342; scientist 310, 342; sources 4, 11, 27, 31, 120, 159, 188, 199, 219, 252, 290, 310, 312–13; storage 33, 160; techniques 274, 303; velocity 25, 226, 230, 234, 314, 338 data science revolution 3, 5, 7, 9, 11, 13 datasets 2–3, 45, 48, 51, 53, 55, 133, 135, 142–4, 152, 154, 158–9, 161, 218, 234, 257; organizational 19, 40 data visualizations 12, 115–23, 125, 129, 131, 133, 135, 137, 139, 141–55, 158, 161; advantages of 116, 121; category 124, 128–9, 134, 136; design 148; haptic 172; highlights 151; interactive 142; methods 151; software packages 145, 153; tools 12, 150, 206; types 115, 123, 145 DDM see dynamic decision-making decisions 7–8, 25, 48, 116–17, 120–1, 214, 221, 226, 228, 262, 266, 295, 297, 300, 329 decision trees 48, 57 deduction 205, 277, 283, 290, 297, 303, 340 demographic data 20–1, 173, 343–4 density 121, 143, 173, 323, 325 device users, non-mobile 228 diagrams 125, 133, 145; chord 138–9, 145 dictionaries 70–1, 73–6, 86–7, 89, 96, 104, 109; job dissatisfaction word count 90, 93, 95 differences: individual 8, 190–1, 329, 338; region-level 198–9 dimensionality 45, 51, 53 disciplines: multiple 191, 341–2; organizational science 342 discriminant validity 74–5, 89–90 discrimination 73, 311, 314–16, 330–1; subtle 314, 328, 330–1 disparities 315, 319 dissatisfaction 93, 110–12, 259; tweets 110–11 diversity 4, 9, 21, 311–14, 320, 322–4, 328–9, 343–4; inclusion and 310–11, 313, 315, 317, 319, 321, 323, 325, 327–9, 331, 343 dot plot maps 140 droids 298–9 Duncan Socioeconomic Index (DSEI) 88, 91 dynamic decision-making (DDM) 328–9 dynamic processes 158, 261, 274–5, 279 EDW see enterprise data warehouse elastic net 50 employee resource groups (ERG) 321, 325 employees 20–5, 34–7, 55–6, 216–17, 220–1, 227–30, 232–6, 240, 242–5, 257, 259–60, 263–5, 323–8, 342–4, 346; behaviors 236, 240; engagement 5, 22, 24, 229, 231–2; individual 189, 233–4, 236; information 35, 41; interactions 24, 37, 314; outcomes 223, 231, 232; performance 22, 232, 236, 240, 244; retention 24, 264, 343; selection 242; selection processes on 232; sentiment analysis 24; turnover 250–2, 258–61, 264–5, 267 employment lifecycle 187–9, 206 enterprise data warehouse (EDW) 30–1, 41 Equal Employment Opportunity Commission (EEOC) 219, 243, 316 ERG see employee resource groups evaluation set 199–201 event structure 282, 284–5 event time windows 282, 284 Executive Order (EO) 316 experience sampling methods (ESM) 278, 288–9, 291–2, 302 false negatives (FN) 112 five-V framework 214, 223, 246 gender 21, 56, 311, 321–3, 325, 343, 345 geospatial data 140 governance processes 26, 28 graphical overlays 150, 152–3 group and organizational-level turnover theory 260 haptic interfaces 165, 170 haptics 159, 166, 169–71, 173 heat map 137, 140 hierarchical structure 129, 131 hiring process 223, 239, 244 histograms 159, 161, 164 human capital management (HCM) 187–9, 191 human decision processes 252 human resource management 22–3, 213 human resources information system (HRIS) 21–2, 193, 227, 235 hypotheses 19, 36, 45, 90–1, 93–4, 116, 219, 339 ideologies, inclusive 328 inclusion 4, 310–11, 313–15, 317, 319–21, 323–5, 327–31; frameworks 329 Individual-level turnover theory 259 individuals, diverse 320–5, 330–1 induction 202, 205, 274, 277, 279, 283, 290, 297, 303, 340 industrial-organizational psychology 13, 231, 243, 317 infographics 117 information: demographic 234, 257, 321; processors 295; supply chain 19, 32–3, 38, 41; unique 295, 297; visual 118, 121–2, 151 information systems 183; human resource 338, 343 information technology 224, 311, 342 Integrated Public Use Microdata Series (IPUMS) data 91, 93 interaction data, social 11, 20–1, 23–5, 29, 40 interactions 23, 29, 37–8, 48, 142, 184, 273, 275, 288, 293, 302, 322–5, 327, 329–30, 340; technique 142–4 interactivity 135, 142, 144 interfaces 11, 163, 165, 168–9; immersive information 159, 170 interpersonal discrimination 314, 323, 326, 330 interpretation 2, 11, 118–19, 121, 144, 146, 148–50, 153, 158–9, 166–7, 213, 227, 242–3, 340–1 interventions 147, 197, 201, 203–4, 264, 285, 296–7, 299, 314, 339, 343 investigators 274–5, 283, 290, 302–3 item-level relationships, stable 59 items, biodata 196–7 job: embeddedness 260; families 44, 54, 224–5, 228; performance 188, 193–4, 215, 233–4, 242, 327 job dissatisfaction 86, 88–93, 95, 106, 109–11; dictionary 87, 89 job-related tweets 86, 108–9 job satisfaction 46, 64, 75–8, 86, 88–96, 101, 106–12, 245, 259, 261–2, 337–8; city-level 89–91, 93, 95; dictionary 87, 89; levels of 90, 92, 94; measures 90, 92–3; twitter 89, 92, 95–6, 108, 109, 110 k-means, nearest neighbor 53–5 LASSO see least absolute shrinkage and selection operator labor costs 233–6 leadership, organizational 201, 231, 233 leadership practices, inclusive 324 least absolute shrinkage and selection operator (LASSO) 50 linguistic inquiry and word count (LIWC) 69–71, 74, 87–8, 90 logistic regression 107, 253 machine learning 10, 52, 106, 184, 195, 240, 294, 299–300; approaches 106 macrocognition 295–6 management 8, 182, 245–6, 250, 252 managers 47, 117–18, 230, 240, 252 map, heat 137, 140 market basket analysis (MBA) 55 meaningfulness 214, 220, 222–3, 228, 244 measurement quality 10–11 measures, psychological 58, 60 memory demands, working 150 meta-parameters 195–6; model 195 micro-dynamics 284, 291, 293, 298 microexpression analyses 3–4, 6, 323 missing completely at random (MCAR) 66 missing data bias 66, 101 mobile devices 224–5, 227–9, 263, 344 modeling: competency 215; latent growth 252; random coefficient 252, 292, 299; structural equation 56, 58, 172, 285 models: baseline 111; best fitting 200–1; development 187, 191–2, 197, 202; ensembles 58, 196–7, 199, 201, 341; linear 46–7, 182–3; linear regression 47; omitted variable 169; organization competency 240; performance 109–11; sample 57; structural equation 45, 182, 184; traditional 201; unfolding 259–60 Moore’s law 160 multilevel modeling 182 multimodal displays 168 multimodal interfaces 165–7, 172 Naïve Bayes 49–50, 107; theorem 49; classifier 107–9, 112 National Aeronautic and Space Administration (NASA) 273, 276, 287, 289 natural language processing (NLP) 6, 38, 327 negative correlations 91–3 negative relationship 67 network analysis 338–9 network models, neural 47 networks 11, 23, 47, 300, 320, 323–4, 338; formal 323, 325 neural networks 4, 47; artificial 46–7 neurons 46–8 NLP see natural language processing nomological validity 75, 90, 92–3 Oculus Rift 162, 167, 172 Office of Federal Contract Compliance Programs (OFCCP) 316 OLS regression models 52–3 online recruitment 237 online visualization tools 145 optimization, local 49, 52–3 organizational-level turnover theory 260 organizational psychology and behavior (OPB) 272, 275, 283, 292, 303 organizations 22–4, 29, 120, 188–94, 197–8, 201–8, 216–18, 223–4, 226–7, 229–38, 240–5, 250–2, 310–15, 320–31, 341–3; behavior 5, 173, 252; client 227 , 231; contexts 55, 338; create diversity and inclusions 310; data 51, 60; data-savvy 186–7; diverse 328, 330; effectiveness 3; inclusive 312–13, 320, 325, 328, 330; innovation adoption 329; large 53, 181–3, 189, 197, 202–4, 344; larger 44, 230; level performance 67; levels of 258, 261; outcomes 215, 222, 223, 232–3, 330, 344; performance of 4, 218, 220–1, 236; phenomena 60, 181; practices 13, 182, 205, 311, 328; processes 205, 207, 213; propose 321, 327; psychologists 1–2, 14, 19, 43, 55, 330; psychology 3, 43, 173, 191, 272, 336, 341; research 44–5, 47, 49, 60, 173, 204–5, 341, 343, 348–9; researchers 173, 310, 329, 331, 337, 342; science 252; sciences 2–3, 7, 10, 52, 272, 339, 342, 348; service-based 327; stakeholders 181, 330; talent development process 230; talent management processes 229, 245 parallel coordinates 137–8 patterns: generalizable 58–9; of knowledge emergence 296–7 perception 121, 153, 159, 163, 230, 244, 278, 302 performance: behaviors 239; data 20, 22–3, 38, 40; lower 110, 277; management 4, 8, 188, 194, 220, 222–3, 240, 242 perspectives, data modeling 184 phase, pre-hire 220–3 phenomena, dynamic 274–5, 303 plateaus 298–9 platform, organization’s assessment 229 population 56–7, 65, 76, 99–100, 119, 287, 291, 313, 321–2, 337; data 56–7 practitioners 59, 117, 204–6, 208, 214, 246, 264, 310, 313–14, 328, 331, 343 predictive value 112; negative 110–11 predictors 45–6, 48, 50–3, 58, 189, 195–6, 199–200, 203, 215, 217, 262, 266, 340, 344; large number of 50 prestige, occupational 88, 91–3 probability 55, 66, 108–10, 123, 200 process dynamics 272, 275–7, 279, 284–5, 288, 291–2, 296–7, 300–1, 303; capturing team 278 processes: behavioral 273, 279–80, 283, 285; mechanisms 296–9; sharing 298–9 profitability 232, 234, 235 psychologists 1, 10–14, 23, 25, 115–16, 127–8, 134, 136, 144, 146, 154, 184, 186, 190–1, 205–9; industrial and organizational 19, 330; industrial-organizational 231, 242–3, 245, 310 psychology 2, 4, 14, 43, 58, 64–5, 68, 125, 132, 154, 158–9, 181–2, 185, 191–4, 203–8; industrial and organizational 184; processes 64, 69, 71; research 44, 64, 68, 71, 95; science 1, 12, 44, 65 Python code 101–3, 105 quit decisions 259, 261, 263 radial charts 125–6, 145 random forests 10, 48–9, 57, 199 recruiters 5, 222, 238, 320 redundant encodings 151 reference effects 90–2, 337 regression: coefficients 50, 52–3, 66; linear 50–1, 195; model 52, 109, 327 relationships: dynamic 340; reciprocal 292–3 representational state transfer (REST) API 99–101 research: design 182, 206, 208, 251, 274, 277, 279, 283–4, 301–2; methods 206, 272, 301, 348; questions 95, 116, 124, 125, 136, 146, 149, 227–8, 234–5, 285, 291, 316 resources 30–1, 38–9, 45, 186, 191, 209, 221, 227, 229, 239, 242, 279, 296, 321; allocation 9; new 182–3 return on investment (ROI) 233, 241 robotic surgery systems 170 sales goals 229–30, 232 samples, independent 48, 52, 59 sample sizes 3, 9, 47, 50, 228, 231, 252, 257, 317–18; larger 45, 184, 318; small 65, 112, 182, 184 sampling frequency 278–9, 284, 291 Sankey (alluvial) diagram 125, 145, 158 science 3, 12, 14, 43, 181–7, 191–2, 195, 197, 202–3, 206–8, 303, 313, 339, 341–2; normal 348; and practice 43, 202–3, 258, 341; of team effectiveness 272, 301; theory-based 243 scientists 159, 164, 246, 331, 347 screening 159, 161, 174, 220–2 script 78, 87, 99, 102–3 selection process 192, 217, 221–2, 223, 224, 225–6, 232–3; assessment and 213–21, 223, 234, 237–8, 241–4, 246, 344; organization 213; organization’s talent 234–6; procedures 222, 316 semi-structured data 25, 312 sensitivity 11, 55, 110–12, 190 sensors 5, 7, 19, 239, 257–8, 288–9, 311; wearable 10–11, 146, 301–2 sentiments 4, 23, 76, 87, 96, 107–8, 186, 310; analyses 3 shocks 259, 290, 294, 300 significance tests, statistical 313, 316–17, 319 SIOP see Society for Industrial and Organizational Psychology skills 22–3, 25, 28, 34, 38, 116, 118, 134, 138–9, 148, 232, 237–8, 240, 242–3, 280; assessment 222–3 slopegraph 124 small multiples (trellis) chart 126–7 social comparison processes 337–8 social media 5, 13, 19, 99, 245, 257–8, 262, 276, 311, 320–1; data 3, 5, 25, 77, 115 social psychology 64–5, 272, 274–5 social sciences 159, 273, 300–1, 317 social security numbers 21 Society for Human Resource Management 22–3, 213 Society for Industrial and Organizational Psychology (SIOP) 13, 125, 127, 140, 184–5, 191 sociometric sensors 3, 5, 301 sonification 12, 159, 163–4, 167–8, 170, 174 source data 147 standardization 26, 227, 235 StarCAVE 167 statistical learning methods 195–7, 200; modern 195–6, 199 statistical methods 43, 45, 47, 49, 51, 53, 55–7, 59, 195, 273 statistical models 45, 183, 188 statistical power 65–6 statistical science 183 statistical significance 9, 45, 153, 196, 315, 317–18, 320, 330 stream graphs 135, 145 structural models 198–201 structure, reference 150 Sunburst diagrams 131–2 supervised learning methods 46, 51–2; modern 51–2 support vector machine (SVM) 46, 107, 199, 348 survival analysis 252 SVM see support vector machine table lens 139–40 talent assessment 224, 344 talent management 29, 213, 231, 233, 237, 241–2, 244, 246 talent management systems (TMS) 183 talent selection 213, 222, 224–7, 230–3, 234–6, 238–9, 242–4, 246; and assessment 213, 243, 246; role of big data in 213, 223, 242–3 teams 4–5, 8–9, 53, 56, 197–202, 273–6, 278–80, 283–6, 288–91, 293, 295–8, 300–2, 338, 340, 342; bottlenecks 296–7, 299; cohesion 273, 286, 293–4; cross-functional 227, 235; effectiveness 272–3, 275, 277–9, 281, 283, 285–7, 289, 291, 293, 295–7, 299, 301, 303, 305; human 296, 298–9; knowledge emergence of 279, 294, 296–8; medical 273, 276, 279–81; members of 8, 199, 273, 279, 288–91, 295, 298; noid 298; performance 275, 277, 283, 285, 340; phenomena 275–6, 278, 290; process behaviors 276, 281, 283–5; process dimensions 284; process dynamics 5, 274, 276–7, 279, 283, 288, 298, 301, 303; process patterns of 285 teamwork interactions 288 temporal alignment 284 technology 5–6, 40–1, 115, 119, 171, 174, 181, 183, 217–18, 221, 237–42, 244, 288–9, 301, 303 text: corpus of 73–4, 76; sample of 106–7 text-based datasets 68 text units 69, 73 theory: creation 204, 339–40; culture 340; generating 340; organizational 48, 205; system-oriented 339, 343 timeframes 25, 34, 197, 277 timeliness 28, 238 time pressure 295 time windows 282, 284; associated 284; targeted 283 TMS see talent management systems training data set 51–2, 54 transit time 93 tree maps 130–1, 143, 145 true negatives (TN) 112 true positives (TP) 112 trustworthiness 25, 347 turnover 9, 46, 53, 193, 197–203, 232, 233, 235, 241, 250–2, 257, 259–61, 263–5, 267, 314, 337; antecedents 262–3, 265, 337; intentions 262; models 198, 203; organizational 251; prediction of 199–200; processes 259; research 250–1, 258, 260–5; and retention research 252; role of 252; structural models 199–200; studies 263–4; theory 198, 250–1, 258, 261–2; traditional theory 265 tweets 6, 76–8, 86–9, 95, 99–104, 106–12, 337–8; category of 108–9; identifying 106; irrelevant 111–12; neutral 106, 111; unique 78; unique information 100 twitter 6, 24, 76–7, 95, 99–101, 105; analysis 64–7, 69, 71, 73–5, 77, 87, 89, 91, 93, 95, 99, 101, 103, 105, 107; API 99–101; data 10, 76, 91, 99 understanding, theoretical 59 unemployment 88, 91–3, 105, 226, 263 unsupervised learning methods 45, 53 validity 4, 10, 71, 73, 75, 87, 89–90, 192, 194, 219, 222, 225, 229, 235, 340; convergent 74–5; criterion-related 193–4, 219, 227 variables 9, 12–13, 51, 54–6, 88–9, 91, 105, 136–41, 158, 161, 171–3, 194–5, 198–9, 217–19, 226; data value 347; dependent 94, 252; grouping 171; independent 184, 252; large number of 3, 55, 160, 203, 244; larger sets of 9; organizational 218; outcome 215, 217–18; work-related 92–3 velocity 10–11, 24–5, 29, 64–5, 119, 129–32, 213–17, 221–3, 225–6, 228, 234, 237–8, 312, 314–15, 329; of big data 65, 120, 329 veracity 10, 24–6, 29, 120, 214–15, 217–20, 222–3, 231, 237, 246, 347 virtual environment 167 visualizations 4, 6, 12, 115–24, 127–9, 131, 134, 136, 138, 141–9, 151–4, 159–62, 167–8, 171–2, 323–4; design process 147; interactive 142, 144; methods 123, 128, 137; scientific 165; software packages 143–4; static 141–2; tasks 165; techniques 120, 125, 146, 159, 161; types 123, 125–9, 134, 143–7 voluntary turnover 251–2, 339 word (tag) cloud 127–8, 145; dictionaries 6, 64, 74–6, 78, 87 word counting 69 word order 107–8 workers 1, 8, 118, 241 workforce 19–20, 24, 39–40, 231, 233, 316, 320, 344; analysis 27, 31–2, 38; analytics 19–20, 24, 26, 28–9, 33, 35–6, 38, 40–1; data scientists 29, 31, 41; organization’s 236; sciences 43 workloads 35, 290 work performance 245 workplace 23, 160, 174, 314, 330, 338 work teams 323, 325–6 zones 32, 34–5, 37, 41; information ingestion 33, 41; landing 32–3, 41; real time analytics 34, 41