Modern research methods for the study of behavior in organizations jose m cortina, ronald s landis, routledge, 2013 scan

“When I first heard of the idea for this book a few years back, I was truly excited Now that it's finished, I'm truly amazed Professors Cortina and Landis not only identified a set of topics that will move organizational research forward, but also recruited some of the most knowledgeable people in the world to write on them This book needs to be required reading in any research methods course oriented toward the organizational sciences It will truly get students to think about research design issues very differently.” —Robert Vandenberg, University of Georgia, Professor of Management, Past Editor, Organizational Research Methods “Cortina and Landis bring a wide range of research methods that are not familiar to I/O psychologists to the attention of this community Their introductions of techniques such as catastrophe theory, social network analysis, latent class analysis, Petri nets, and experience sampling (to name only a few of the techniques described in this volume) will add breadth and depth to the toolbox of I/O scientists and practitioners alike.” —Kevin R Murphy, Colorado State University “Scientific progress accelerates when newer methodological approaches allow for the novel examination of enduring issues I am confident that the methodological approaches described in this wonderful volume will lead to advancements in many important domains for years to come.” —Herman Aguinis, Kelley School of Business, Indiana University Modern Research Methods for the Study of Behavior in Organizations The goal for the chapters in this SIOP Organizational Frontiers series volume is to challenge researchers to break away from the rote application of traditional methodologies and to capitalize upon the wealth of data-collection and analytic strategies available to them In that spirit, many of the chapters in this book deal with methodologies that encourage organizational scientists to reconceptualize phenomena of interest (e.g., experience sampling, catastrophe modeling), employ novel data-collection strategies (e.g., data mining, Petri nets), and/or apply sophisticated analytic techniques (e.g., latent class analysis) The editors believe that these chapters provide compelling solutions for the complex problems faced by organizational researchers Jose M Cortina is a Professor in the Industrial/Organizational Psychology program at George Mason University His recent research has involved topics in meta-analysis, structural equation modeling, significance testing, and philosophy of science, as well as predictors and outcomes of emotions in the workplace He currently serves as Editor of Organizational Research Methods and is a former Associate Editor of the Journal of Applied Psychology Dr Cortina was honored by SIOP with the 2001 Ernest J McCormick Award for Distinguished Early Career Contributions, by the Research Methods Division of the Academy of Management with the 2004 Robert O McDonald Best Paper Award, and by the Organizational Research Methods Editorial Board with the 2012 Best Paper Award He was also honored by George Mason University with a 2010 Teaching Excellence Award and by SIOP with the 2011 Distinguished Teaching Award Ronald S Landis is Nambury S Raju Endowed Professor in the College of Psychology at Illinois Institute of Technology He has also served on the faculty at Tulane University, where he was awarded the Tulane President’s Award for Excellence in Graduate and Professional Teaching in 2004 He is a Fellow of SIOP and was honored by the Organizational Research Methods Editorial Board with the 2012 Best Paper Award He has primary research interests in the areas of structural equation modeling, multiple regression, and other issues associated with measurement and the prediction of performance He is currently an Associate Editor of the Journal of Business and Psychology and a former Associate Editor of Personnel Psychology The Organizational Frontiers Series Series Editor Eduardo Salas University of Central Florida EDITORIAL BOARD Tammy Allen University of South Florida Neal M Ashkanasy University of Queensland Adrienne Colella Tulane University Jose Cortina George Mason University Lisa Finkelstein Northern Illinois University Gary Johns Concordia University Joan R Rentsch University of Tennessee John Scott APT Inc SIOP Organizational Frontiers Series Series Editor Eduardo Salas University of Central Florida 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 Modern Research Methods for the Study of Behavior in Organizations Edited by Jose M Cortina George Mason University Ronald S Landis Illinois Institute of Technology First published 2013 by Routledge 711 Third Avenue, New York, NY 10017 Simultaneously published in the UK by Routledge 27 Church Road, Hove, East Sussex BN3 2FA Routledge is an imprint of the Taylor & Francis Group, an informa business © 2013 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 utilized 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 Modern research methods for the study of behavior in organizations / edited by Jose M Cortina, Ronald S Landis p cm.—(SIOP organizational frontier series) Organizational behavior—Research Psychology, Industrial—Research I Cortina, Jose M II Landis, Ronald S HD58.7.M6273 2013 302.3Ј5072—dc23 2012035472 ISBN: 978-0-415-88559-1 (hbk) ISBN: 978-0-203-58514-6 (ebk) Typeset in Minion by Florence Production Ltd, Stoodleigh, Devon, UK The Editors dedicate this book to their advisor, Neal Schmitt Many people rave about their advisors Actually, most complain, but a few rave, and with good reason A good advisor gives time and expertise without any expectation of receiving anything in return But Neal wasn’t a good advisor He wasn’t a great advisor Neal Schmitt was simply the best possible advisor He was (and is) a great scholar and teacher to be sure, but the quality that distances him from all others was his absolute commitment to putting students first Neal has had dozens and dozens of advisees (he is quite old, you know), and every single one of them that we know felt that Neal ALWAYS prioritized them over all of his many other commitments We can never pay you back Neal We can only offer our gratitude and esteem Plus the occasional book dedication Jose M Cortina Fairfax, Virginia Ronald S Landis Chicago, Illinois Index References to Figures or Tables will be in italics I/O psychology stands for industrial/ organizational psychology a priori theory 306–307 absenteeism 52, 54 accident analysis/prevention 45–48, 56 active interviews 297, 298 actor attributes 101, 104 adaptability knowledge 451 additive models 215 adjacency matrices 102 Administrative Science Quarterly 294 Affective Events Theory (AET) 173 affiliation matrices 102 affinity analysis 201 Agentic Bias 240–242, 247, 249, 257 Aguinis, H 364 Akaike’s information criterion (AIC) 145, 155 algorithmic research, data mining 199, 200, 201, 210, 211, 221, 222; see also data mining Allitt Inquiry, UK 304 alternating-k-2-paths parameter 112 alternating-k-star parameter 111 alternating-k-triangle parameter 112 Alvarez, J 238–239, 252 American Time-Use Survey (ATUS) 206, 208 American Time-Use Survey extract builder (ATUS-X) 208, 209, 210, 212 Amway distributors 279 analogous points, catastrophe theory 34 analysis of variance (ANOVA) 2, 164, 418 Anderson, N 66 ANOVA (analysis of variance) 2, 164, 418 anti-fatigue 44 Appelbaum, M I 93 Applegate, J L 457 Arvey, R D 91 association rules learning 201, 203, 204 asymmetry control parameter, catastrophe theory 32, 34, 37, 38, 41, 43, 47, 48, 50, 54 ATLIS.ti software 305 attractors 34 attrition 94 Augmented Dickey Fuller (ADF) test 189, 190 autoregressive integrated-moving average (ARIMA) models 191, 193 autoregressive latent trajectory models (ALTs) 7, 90–92 avatars 369, 371 AWACS team (US Air Force), Petri nets 384, 394, 395, 396, 397, 398–399 axons, brain 407 Bamberger, P A 99 Bandura, A 243 Bank Wiring Observation Room study (1932) 268–269 bar charts 210 Barcikowski, R S 173 Bargh, J A 238–239, 252 Barker, J R 286–287, 299, 301 Baron, R A 328–329 Barry, D 304 basic growth model 95n; extensions to 88–94; limits 74–76 Baumeister, R F 172 Bayer, U 323, 334 Bayesian information criterion (BIC) 145, 155 Bayesian type hypothesis testing 38 Berkhof, J 329 between groups change models 7, 93–94 483 484 • Index between-person research technique: ESM as 323, 329, 337–338; neuroimaging methods 418 between-person variance 70 bias 120; Agentic Bias 240–242, 247, 249, 257; catastrophe theory 36, 41–42, 49, 50, 51, 54; conditional reasoning and power motive 239, 240, 241, 246–247, 250, 255; Hostile Attribution Bias 254, 257; Leader Intuition Bias 244–245, 246; longitudinal growth modeling, dynamic 67, 89, 90; Potency Bias 254; Power Attribution Bias 243–244, 247; qualitative research 277; Retribution Bias 254; stochastic trends and growth curve modeling 162, 164, 171, 175, 177, 179–180, 181, 182, 183, 184–185, 187, 188, 191, 193 Biesanz, J C 71 bifurcation control parameter, catastrophe theory 32, 34, 38, 39, 41, 42, 44, 48–49, 50, 51, 54 Binnewies, C 321 binning strategies, data mining 211, 217 biofeedback 427 Blake, W 93 bleaching 39 Bliese, P D 66, 71 blood pressure analysis 340–342 Boje, D M 294 BOLD (blood oxygen level-dependent response) 414, 415, 416, 417 Bolino, M C 424 Bollen, K A 73, 91 bond strength 78 Bondanella, J R 367 Boocock, G 323 boosted regression trees 215, 216 bootstrap procedures 145 bottom–up effects 8, 11, 99, 100 Bowers, C A 354, 355, 356–357 Bowman, R F 359 box plots 210 brain: Broca’s area 427; gray matter 407, 408, 409; hemispheres 423; leadership and fMRI 421; medial orbitofrontal cortex 411; medial prefrontal cortex 421–422, 423, 426; positive and negative intentions 425; prefrontal cortex 413; social brain hypothesis 405; white matter 407, 408, 409; see also neuroimaging methods Brass, D 102 Braun, M T 183 breaking points 92 Bretz, R D 272 Broca’s area, brain 427 Brown, A D 304 Bryk, A S 63 buckling stress 42–43, 55–56 Bunge, M 405 Burchinal, M 93 butterfly model, catastrophe theory 32, 52, 53 C3 Fire 366 Cabeza, R 421 Calás, M 304 Campbell, D 293 Campbell, J 424 Cannon-Bowers, J A 363 capitalism 284 Carter, N T 300 case study: data mining 206, 207, 208–223; qualitative research 292–294 catastrophe theory 5, 6, 29–61; accident analysis/prevention 45–48, 56; asymmetry control parameter 32, 34, 37, 38, 41, 43, 47, 48, 50, 54; bias parameter 51; bifurcation control parameter 32, 34, 38, 39, 41, 42, 44, 48–49, 50, 51, 54; butterfly model 32, 52, 53; comparison models 37; coping strategies 43; cusp model 32–35; description of principles, models and methods 31–41; examples of catastrophes 30; future research 55–57; hierarchical models 31–32; leadership emergence 48–52; linear regression 35, 37; local linearity 39–40; log-linear relationships 37; management policies 41–42; nonlinear regression 30, 38, 39–41, 47; phase shifts 35; polynomial regression method 30, 35–39, 53; scale parameter 36; static nonlinear regression method 39–41; stress and human performance 41–45; swallowtail model 32, 48–52; work motivation 52–54 Chan, D 73, 87 chaos theory 31 charismatic leadership 114 Chaturvedi, S 91 Chen, G 66 chi-square tests 145, 150, 155, 187 classification and regression tree (CART) models 215, 216, 217, 222 Index • 485 classification uncertainty 139 Clementine (data-mining tool) 224 cluster analysis 138, 139 Cobb, L 40 coefficient of determination 203 cognition selling paradigm 466 cognitive ability 66, 72, 92 cognitive loads 41, 42 cognitive processing 422 cognitive task analysis (CTA) 353–354 Cole, D A 66 comma-separated variable (CSV) files 224 commercial-off-the-shelf (COTS) software 15, 357, 359; versus custom-built software 354–355 Comparative Fit Index (CFI) 187 complexity theory 31 computed tomography (CT) 407, 408 computer games 359 computer-aided interpretive textual analysis 305–306 conditional growth models 75; with Level predictors 179–180; with random effect for slopes 181–182; without random effect for slopes 180–181 conditional reasoning 12, 233–263; analysis 259–260; hierarchical authority structures 235–236, 237; leadership, conditional-reasoning test for 250–251; measurement systems 246–247; research results 258–259; theory of conditional reasoning test for power motive 234–240; toxic leaders 12, 254–256; see also justification mechanisms for power; power motive conditional-reasoning measurement system 246–247 Conditional-Reasoning Test for Aggression (CRT-A) 233, 256 Conditional-Reasoning Test for Leadership (CRT-L) 250–251, 260; initial validities 259; toxic leaders, identifying using 256–257, 258, 259, 260 consensual scoring 465 consistent Akaike’s information criterion (CAIC) 145 constant comparative method 307, 308–309 construct generation measures 21; description of methodology 455–457; organizational research 460–461; overview 454–455; research using methodology 457–460 content analysis 302–303, 461 contextual performance 424 contralateral delay activity (CDA) 430 conversation analysis 305 Conway, J M 449 Conway, M A 421 Cooper-Thomas, H D 66 Coovert, M D 391, 394 “corner boys” studies 101 Cortina, J M 65, 92, 325, 446, 459, 467 creativity: and idea generation tasks 464, 465; and voxel-based morphometry 411–413 Cressie-Read (CR) fit statistic 145 critical theory and research 284–288 Cronbach’s alpha 212 cross-level moderator research 336 cross-sectional hypothesis 66, 67 Csikszentmihalyi, M 321 Curran, P J 73, 91 curve of factors model 86, 88 cusp catastrophe model 32–35 Daly, J A 459 Daniels, K 323 data collection/analysis: case study 292–294; computer-aided interpretive textual analysis 305–306; document analysis 301–305; grounded theory 13, 306–309, 310–311; interviews 294–298; longitudinal data collection 163; observational methods/ethnography 298–301; qualitative research 292–309; social network analysis 121 data mining 10–11, 199–230; American Time-Use Survey (ATUS) 206, 208; association rules learning 201, 203, 204; available tools 224–226; background 200–202; binning strategies 211, 217; CART models 215, 216, 217, 222; case study and demonstration 206, 207, 208–223; confidence 202; data preparation 208–211; defining 199–200; dimension reduction 212–214; dimensionality problem 204–205; flowchart 206, 207; machine-learning 200, 203, 204, 205, 211, 214, 215; market basket analysis 201, 204; model development and validation 214–223; multiple correspondence analysis 212, 213; neural networks 215, 216, 221; 486 • Index “noise” issue 213; overview of techniques 203–206; processes 200; shopping cart data 201, 202; summary of case study 223; supervised learning 203–204, 214–220; support 201, 202; SVM models 216, 217, 218, 219, 223; unsupervised learning 203, 204, 220–223 Daus, C S 324 De Leeuw, J 173 De Pater, I E 334–335, 336, 340–341 deCharms, R C 427 declarative knowledge 444 deconstruction 289, 304–305 deductive theory 307 degenerate models 110 Delia, J G 457 deoxygenated hemoglobin (dHb) 413 dependence assumptions (social network analysis): directed graphs 107, 113, 127–128; exponential random graph (ERG) models 106–107; Markov 107, 111, 117, 124–125; new specifications 126–128; nondirected graphs 107, 124–126 Derrida, J 289 DeShon, R P 93 deterministic trends 161–162, 167, 169, 174, 176, 191; versus stochastic trends 162, 168, 170, 171, 172, 190, 192, 194 detrending 39 DeYoung, C G 411 diathesis stress 41–42 Diener, E 319–320 difference scores, diathesis stress 41 Dimotakis, N 321, 323, 324, 334–335, 336, 340–341 discontinuous growth models 7, 92–93 discourse, postmodern perspective 289 Djamasbi, S 370 document analysis 301–305 Driskell, J E 351 Dudley, N M 446, 459, 467 Dunford, R 303 Durlauf, S N 175, 177, 178, 179 dustbowl empiricism 139, 227 dynamic relationships, and time-varying predictors 76–88 electroencephalography (EEG) 406, 414–415, 427–433, 428; data analysis 429–430; hyperscanning 432–433; in I/O psychology 430–432 electromagnet, structural MRI 409 Elliott, G 190 Elmes, M 304 emergent leadership: catastrophe theory 48–52; hypotheses 114–118; networks see emergent leadership networks; selection of leaders 114–115; social processes giving rise to 113–119 emergent leadership networks: hierarchical structure, exhibiting 115, 116; reciprocation, exhibiting 115; transitivity, exhibiting 115–116 Emmons, R A 327 empathy 425 endogeneity 77, 88, 89, 91 Enterprise Miner (data-mining tool) 224 entropy index 150 environmental events 172–173 Epstein, D 63 error-related negativity (ERN) 430, 431, 432 Ethnograph software 305–306 ethnography 13, 296–301; interviews 296–297; observational methods 299–300 ethnomethodology 305 event-related potentials/fields (ERPs/ERFs) 429; components 430 evolved leader psychology model 237–238 expectation-maximization (EM) algorithm 144 experience sampling methodology (ESM) 5, 14–15, 162, 319–348; analytical techniques 338–344; basic considerations 325–333; basic features 321–325; blood pressure analysis 340–342; comparison with other methodologies 324–325; cross-level moderator research 336; day-level analyses 334–335; defining 321; delivery schedule 328; design issues 322–329, 332; event-contingent studies 323–324; interval-contingent design 323, 324; versus longitudinal designs 345n; moderation and median considerations 343–344; multilevel modeling 338–343; overlapping network configurations 104; within-person research 319–320, 333–336, 345n; between-person research technique 323, 329, 337–338; power analyses 329; research studies 333–338; sampling schedule 328; signal-based designs 322–323, 324; technological considerations 330–333 Index • 487 Experience Sampling Program (ESP) 332 exploratory factor analysis (EFA) 204, 220 exponential random graph (ERG) models 100, 103–113; actor attributes 104; dependence assumptions 106–107; graph statistics 105, 106, 110, 111; Markov ERG model, parameter estimates 108; new specification parameters for 110–113; observed network 104, 105; PNet, use for fitting models to social network data 128–130; programs for estimating 131; triangle 104; t-statistic 109–110; see also social network analysis (SNA) extrinsic motivation 52 fatigue 43–44, 45 Festinger, L 115 fidelity, synthetic task environments 353, 354, 355; high-fidelity simulators 361, 362 Fitzgibbons, D E 294 fixed effects 179 Fleeson, W 337 Flügel, J C 320 Foo, M D 328–329, 332 Ford, J K 368 Frank, E 225 Frankfurt School, Germany 284 frequency histograms 210 Fried, Y 99, 120 Frith, C D 422 Frith, U 422 full-information maximum-likelihood (FIML) method 149, 155 functional fidelity 353, 354, 355, 361 functional MRI (fMRI) 406, 413–427, 433–434; advantages 425; design of studies 415–418, 419; and leadership 419–423; and organizational citizenship behaviors 423–427; real-time 426–427 games: defining 16, 358–359; future uses 363–365; and organizational research 359–360; roadblocks 362–363; simulations compared 360; versus virtual worlds 369 Garfinkel, H 303 Gaussian distributions 35 Gavin, M B 340 gender 304 general linear model (GLM) 70 Gentsch, A 430 George, J M 64 Gephart, R P 305, 308, 309 Gerhart, B 272 Glover, J 323 Goebel, M 224–225 Goel, L 371 Gollob, H F 90 goodness of fit (GOF) statistics 109, 110, 117, 122–124 Graham, L 285–286, 294, 301 grand theory 306–307 Granger, C W J 174, 175 Granlund, R 366–367 graph statistics 105, 106, 110, 111 gray matter, brain 407, 408, 409 Gregson, R A M 35, 55 Griffin, M A 340 grounded theory 13, 306–309; uses 310–311 group statistics 105 growth curve modeling 161–197; application to latent growth models 187–188; conditional growth see conditional growth models; current trends in longitudinal modeling 164; deviance statistics 186–187; general results 185–187; limitations/future directions 192–193; psychological longitudinal data collection 163, 167; spurious regression 173–188; summary 193–194; Time (predictor) 174–175, 178, 179, 181, 185, 186; with timevarying covariates 182–185; unconditional models 177–179; see also latent growth modeling (LGM); random coefficient models (RCM); stochastic trends growth mixture modeling (GMM) 9, 146–151; estimation 149; selection 149–151; specification 147–149 growth models: autoregressive latent trajectory models 7, 90–92; basic 64, 70–71, 74–76, 88–94, 95n; conditional see conditional growth models; between groups change models 93–94; growth curve modeling see growth curve modeling; growth mixture modeling see growth mixture modeling; lagged 81, 89–90; latent growth models 72–73, 74; longitudinal see longitudinal growth modeling; nonlinear and discontinuous 7, 92–93; role of time in 79–80; traditional 67–73; unconditional 75, 165, 178–179 488 • Index Gruenwald, L 224–225 Guastello, S J 35, 37, 44, 46, 54, 55 Gugerty, L 353–354 Guilford, J P 462 Habermas, J 284 Hall, D T 293 Hanson, J 120 Hartley, R 323 Harvey, O J 101 Hawthorne Study (Western Electric Company) 101, 267–268, 269, 270 Hays, R T 359 hemodynamic lag 414 heterogeneity 138, 139, 151; stochastic trends 161, 179, 182 hierarchical authority structures 235–236, 237 hierarchical linear modeling (HLM) 8, 99, 100, 167; experience sampling methodology 338, 339; longitudinal growth modeling 63, 64, 67; see also random coefficient models (RCM) high-fidelity simulators 361, 362 HLM see hierarchical linear modeling (HLM) Hofmann, D A 340 Holland, J 323 Holtz, B C 71 homogeneity 106, 107, 138, 140, 143 Hood, W R 101 Hostile Attribution Bias 254, 257 Huffcutt, A I 449 human capital, changes in 66–67 human-computer interaction Hunter, J E and R F 450 Hurst, C 336 hydrogen atoms 408, 409 hyperscanning 432–433 hysteresis 33, 47 idea generation methodology 454; description 463–465; organizational research 467–468; overview 461–463; research using 465–467 Ilies, R 323, 325, 327, 332, 333, 334–335, 336, 340–341 Im, K S 190 Implicit Leadership Theory (ILT) 114 implicit trait policies (ITPs) 451 in degree centrality 116–117, 121 independent component analysis (ICA) 220, 221, 223 industrial/organizational (I/O) psychology: catastrophe theory applications in 29–61; electroencephalography in 430–432; neuroimaging methods for 405–441; and qualitative methodologies 265–317; see also organizational research intercept 71, 72, 73, 86, 170 Internet-enabled devices 331–332 interpersonal knowledge 451, 460 interpretive paradigm 281–283, 303, 304; interpretive textual analysis, computeraided 305–306 intersubjective meaning 281 Inter-university Consortium for Political and Social Research (ICPSR) 208 interviews 274–275, 294–298, 447, 449; active 297, 298; data-oriented 283; ethnographic 296–297; life-story 297; long 297–298; open-ended 272–273; see also qualitative research intraclass correlation coefficient (ICC(1)) 165, 177–178, 185 intrinsic motivation 52, 54 I/O psychology see industrial/organizational (I/O) psychology Isabella, L 282–283, 293–294 isolates parameter 112–113 item response theory (IRT) 140; see also mixed-measurement item response (MM-IRT) models James, L R 64, 233 Jentsch, F 354, 355, 356–357 job analysis 17, 18, 382–383, 400 job knowledge tests 450 job performance: and knowledge 446; longitudinal growth modeling 6, 65, 68, 69, 70, 75, 79, 80, 82, 84, 89; synthetic task environments 364 job-analysis techniques 382 Johansen likelihood ratio test 190 Johnson, M D 327 Johnson, M J 37 Jones, G R 64 Judge, T A 325, 327, 332, 333, 334, 336 justification mechanisms for power 12, 239–245, 247–250; Agentic Bias 240–242, 247, 249, 257; aggressive 252, 256; alternatives 248–250; assessment 247–250; Leader Intuition Bias 244–245, 246; Power Attribution Bias 243–244, Index • 489 247; Social Hierarchy Orientation 242–243; see also conditional reasoning; power motive Kabanoff, B 303 Kahn, W A 309 Kang, H 174 Karwowski, W 42 Keeney, J 327 Keil, C T 65, 92 Kellerman, B 255 Kelly, G 454–455 kernal (“mapping” algorithm) 217 Kilduff, M 99 Kim-Prieto, C 319–320 Kirk, J 274 k-means clustering 221, 222 knowledge: accumulated 449; adaptability 451; and basic growth model 75; changes in 80; declarative 444; defining 444–445; electroencephalography 431–432; expansion of performance domain 445–446; interpersonal 451, 460; interpretive paradigm 282; job 450; limited research, contributing factors 445–448; negotiation 451; procedural 444, 451; tacit 245, 444–445, 452; target 460; team role 451; technological capabilities, limitations 447–448; trainability, perception of 447 knowledge, skills, abilities, and other characteristics (KSAOs) 17, 65, 451 knowledge measurement 20–21; advances 443–481; complexity of 446–447; integration of technology advancement 468–469; interviews 449; organizational research 448–454; performance-based 452–454; self- and other-reports 448–449; tests 449–452 Koopman, J 321 Koopman, M 31 Kosslyn, S M 418 Krackhardt, D 120 Kraiger, K 364, 368 Kreft, I 173 Kuljanin, G 177, 178, 179–180, 182, 190 Kuppens, P 329 Kwatlitan software 305 Kwiatkowski, D 190 Kwong, K K 417 laboratory research 351 lagged growth models 7, 81, 89–90 Lahman, S 208 Landis, R S 325 Larson, R 321 latent class analysis (LCA)/latent class procedures 8–9, 93, 137–160; appropriate use 157; growth mixture modeling 9, 146–151; mixedmeasurement item response models 140–145; mixture latent Markov modeling 9, 151–156 latent growth models (LGM) 67, 74, 75, 88, 187; applications 187–188; conventional 146; dynamic 84, 85, 86–88; latent growth structural equation models 72–73, 74; multiple group LGM 7, 93, 94 Leader Intuition Bias 244–245, 246 leader-member exchange (LMX) 114 leadership: aggressive leaders 252, 256; catastrophe theory 48–52; charismatic 114; conditional reasoning test for 250–251; evolved leader psychology model 237–238; and functional fMRI 419–423; implicit leadership theory 117; judgments of leaders 234–235, 245; Leader Intuition Bias 244–245, 246; nominations 102, 121; power motive, channeling into leader behavior 251–257; primary leaders 49, 51; secondary leaders 49–50, 51; selection of leaders 114–115; toxic leaders 253–257; see also conditional-reasoning test for leadership (CRT-L); emergent leadership; emergent leadership networks lean production 285–286 least squares analysis 203 least-squares solutions 39 Lee, T 276–277 Level predictors (L2Ps) 166, 177, 179–180, 183 Lewin, K 101 life-story interview 297 linear regression: catastrophe theory 35, 37; and data mining 219; see also nonlinear regression (NLR) Lips-Wiersma, M 293 Locke, K 308 log-likelihood ratio 144, 145, 150, 155 Loiacono, E 370 Lo–Mendell–Rubin likelihood ratio test (LMR) 150 longitudinal growth modeling 63–98, 193; cross-domain 84; cross-sectional 490 • Index hypothesis 66, 67; current trends 164; definition of longitudinal research 67; descriptive longitudinal research 75; dynamic LGM 84, 86–88, 86; dynamic RCM 82–84; future action 95; job performance 6, 65, 68, 69, 70, 75, 79, 80, 82, 84, 89; latent growth structural equation models 72–73, 74; maintenance stages 65; psychological longitudinal data collection 163, 167; random coefficient models 70–72; theoretical background 64–67; timeinvariant predictors 63, 73, 75–76; timevarying predictors and dynamic relationships 76–88; traditional growth models 67–73; transition stages 65 longitudinal research, open-ended 272 Lowder, M W 421 Lynn, M 46 machine-learning 200, 203, 204, 205, 211, 214, 215 MacKenzie, W 76 magnetic resonance imaging (MRI) 19–20; computed tomography compared 408; functional 406, 413–427, 433–434; structural 406, 407–413, 427; 3-D images 407 magnetoencephalography (MEG) 428, 429–430 Malone, T W 359 Marco, C A 329, 330–331 market basket analysis 201, 204 Markov chain analysis 9, 383–384; Markov chain Monto Carlo maximum likelihood estimation (MCMCMLE) 107 Markov dependence assumptions 107, 111, 117, 124–125 Martin, J 289–290, 304 Martin, R 459 Marx, K 284 Maryland, University of 208, 209 maximum likelihood (ML) estimates 144–145; full-information maximumlikelihood (FIML) method 149, 155 MAXQDA software 305 Maxwell, S E 66 McArdle, J J 63 McCloskey, D L 305 McGhee, D W 44 McGrath, R G 351 means model, unconditional 165, 177–178 measurement approaches: construct generation measures 454–461; fields other than knowledge measurement 454–468; idea generation methodology 461–468; knowledge measurement see knowledge measurement medial orbitofrontal cortex 411 medial prefrontal cortex (mPFC) 421–422, 423, 426 mentalization 420, 422 Meredith, W 73 microdata 208 micro–macro gap, organizational research 99, 103 micro-unmanned aerial vehicles (MUAVs), Petri nets 384, 391, 392, 393–394 microworlds: defining 16, 365–366; future uses 367–368; in organizational research 366–367; roadblocks 367 Miller, M 274 missing at random (MAR) 149, 155 Mitchell, J P 422 Mitchell, T R 64 mixed-measurement item response (MMIRT) models 9, 140–145; applications 141–142; estimation 144–145; selection 145; specification 142–143 mixture latent Markov modeling 9, 151–156; estimation 154–155; selection 155–156; specification 153–154 MM-IRT models see mixed-measurement item response (MM-IRT) models Mobile Phone Subscriber Microworld 366 Mojza, E J 321 Monte Carlo simulation 177, 178, 183 mood, study of 320, 330–331 Morehouse, J W 396 Moreno, J L 101 motivation, work 52–54 Motowidlo, S J 451 multilevel modeling 338–343; centering in 342–343 multiplayer online role-playing games (MMORPG) 369 multiple correspondence analysis 212, 213 multiple linear regression 40, 57 multiple regression quadratic assignment procedures (MRQAPs) 103 multiple-group LGM (MGLGM) 7, 93, 94 Multivariate Augmented Dickey–Fuller test 190 Index • 491 Mumby, D.K 304–305 Mumford, M D 466 Muraven, M 172 Murphy, K R 65 narrative analysis 289, 304, 305 Nathan, D E 37 naturalistic research 266, 350–351 negotiation knowledge 451 Neimeyer, R A and G J 456 Nelson, C R 174, 175 network analysis see social network analysis (SNA) neural networks 215, 216, 221 neuroimaging methods 19, 405–441; electroencephalography 406, 414–415, 427–433, 428; functional MRI 406, 413–427, 433–434; structural MRI 406, 407–413, 427; see also brain new specifications dependence assumptions (social network analysis) 110–113; directed graphs 127–128; nondirected graphs 126 Newbold, P 174, 175 Newcomb, T 101 Nicholas, J P 324 Nicolson, N A 329 Nintendo, Wii game 358 nonlinear dynamical systems (NDS) 31, 35, 37, 55 nonlinear growth models 92–93 nonlinear regression (NLR) 7, 30, 38, 39–41, 47 nonrandom sampling 321 non-stationary processes 161 NPs (people with weak or non-existent motives) 241, 242, 243, 244, 246; alternative 249, 256, 258 NVIVO software 305 Nylund, K L 150 observational methods 298–301; ethnography 299–300 observed network 104, 105 Offe, C 284 O’Keefe, B J 457 Okhuysen, G 309 open coding 306, 308 operator load, accident analysis/prevention 45–46 Oravecz, Z 329 ordinary least squares (OLS) 2, 100, 101, 338, 344 organizational citizenship behaviors (OCBs) 15, 423–427 organizational research 2, 3; catastrophe theory 6, 8; construct generation measures 460–461; and data mining 199–230; deconstruction 304; grounded theory 306; idea generation methodology 467–468; knowledge measurement approaches 445, 448–454; latent class procedures 137, 138, 140, 141, 145, 146, 151, 156, 157; microworlds in 366–367; neuroimaging methods 19, 20; Petri nets 17, 18; and serious games 359–360; and simulations 361–362; social network analysis 8, 99, 103, 104, 119, 120; virtual worlds in 369–370; see also industrial/organizational (I/O) psychology Ostaszewski, K 42 Osterholm, P 190 overlearning/overtraining 205 oxygenated hemoglobin (Hb) 413 Pailing, P E 430 Palmer, I 303 paper-and-pencil tests 434, 447 Parsons, T 306 partial conditional dependence 111 participant observation 298, 299 path dependency 91 Pavlov, O 370 Pearson chi-square statistic 145, 155 performance-based measures 452–454 Perlow, L A 274, 275, 301, 309 Perron, P 190 personal digital assistant (PDA) devices 331, 332 personality, and voxel-based morphometry 410–411 personality neuroscience 410 person-centered approach, statistical methods 137 Pesaran, M H 190 Petri, C A 382 Petri nets 17–19, 381–403; AWACS team example 384, 394, 395, 396, 397, 398–399; basic components 384–389; current applications 384; directed arcs 385, 388; elements 387–388; examples 388–399, 390; explicit choice 388; fixed sequence 388; high-level 383; MUAV mission specialist example 384, 391, 492 • Index 392, 393–394; overview 382–389, 390; places 385, 387; place/transition (P/T) net 383; probabilistic choice 388; reachability 386–387; state-space 387; structure 386; tokens 387; transitions 385, 387 phase shifts 35 Phillips, P C B 175, 177, 178, 179, 190 physical fidelity 353, 354, 355, 361 Pitter, R 309 place/transition (P/T) net 383 Ployhart, R E 66, 67, 71, 75, 76, 93 PNet, use for fitting ERG models to social network data 128–130 Poldrack, R A 426 polynomial regression method 30, 35–39, 53 population heterogeneity 138, 139, 151 portable devices 331 POs (people with strong motives) 240, 241–242, 244, 245, 246, 247; alternative 248–249, 256, 258 positivism and post-positivism 226, 273, 276–280; content analysis 302–303; interviews 295 positron emission tomography (PET) 408 postmodern perspective 288–290 Potency Bias 254 Power Attribution Bias 243–244, 247 power in two level designs (PINT) 329 power motive: channeling into leader behavior 251–257; conditional reasoning test for, theory 234–240; justification mechanisms for power 12, 240–245, 247–250; NPs (people with weak or non-existent motives) 241, 242, 243, 244, 246, 249, 256, 258; POs (people with strong motives) 240, 241–242, 244, 245, 246, 247, 248–249, 256, 258; as primary motivating force 236; strong, effects of 233–234, 236, 239; weak, effects of 236–237 Pratt, M 279–280 prefrontal cortex 413 principal components analysis (PCA) 220, 221 probability density function, nonlinear dynamical systems 35, 40 procedural knowledge 444, 451 Prokopec, S 371 psychological fidelity 353, 354, 361 psychological longitudinal data collection 163, 167 Purdue Momentary Assessment Tool (PMAT) 332 Putnam, L 304–305 Q-statistic 145 quadratic assignment procedures (QAP) 103 qualitative research 12–13, 265–317; approaches to 275–292; case study 292–294; comparing perspectives 290, 291, 292; critical theory and research 284–288; data collection/analysis methods 292–309; evaluating 309–311; flexibility 266; inquiry limitations 273–275; interpretive paradigm 281–283; nature 266–270; paradigms 275, 281–283, 291, 302; positivism and post-positivism 226, 273, 276–280; postmodern perspective 288–290; purposes 277; quantitative research compared 270–273; reality, perception of 271, 273, 275, 281, 282, 284, 288, 289, 290, 291, 295, 296; strong and weak 274; techniques 270–272; terminology 266; use with quantitative 310; work-life balance 274, 275, 301 quantitative research: compared to qualitative 270–273; and critical theory 287–288; numbers and statistics, emphasis on 265; strong and weak 274; techniques 271–272; use with qualitative 310; see also specific statistical methods such as random coefficient models Ramsey, J 66 random coefficient models (RCM) 6–7, 70–72, 74, 75, 76, 88, 162, 164, 187; dynamic 82–84; intercept 71, 72; intraclass correlation coefficient (ICC(1)) 165, 177–178, 185; Level predictors 166; Level predictors 166, 177, 179–180; mathematical and conceptual overview 164–167; betweenobservation effects 71–72; potential pitfalls in using 167–168; slope terms 71; spurious regression in 177–188; and structural equation models 68; time, coding of 71 random forests 216 random sampling 321 random walks, and stochastic trends 10, 168–173 Index • 493 random-number generators 39 Rao, R A 63 rapid event-related design 416 Rasch models 145 Raudenbush, S W 63 reading effects 289 reality, perception of 156–157; qualitative and quantitative research 271, 273, 275, 281, 282, 284, 288, 289, 290, 291, 295, 296 real-time fMRI (rtfMRI) 426–427 Reason, J 46 reciprocation, emergent leadership network exhibiting 115 recruitment 17, 273; qualitative research 272, 295–296; virtual worlds 369, 370, 371, 372 regression analysis 162, 167; linear regression 35, 37; multiple linear regression 40, 57; neuroimaging methods 418; nonlinear regression 7, 30, 38, 39–41, 47; ordinary least squares 2, 100, 101, 338, 344; polynomial regression method 30, 35–39, 53; spurious regression see spurious regression; static nonlinear regression 39–41 regression coefficients 174, 175 Reichardt, C S 90 Reis, H T 321, 327 repellors 34 Repenning, N 309 Reserve Officers’ Training Corps (ROTC) 21, 459, 466, 467 Resick, C J 360 Retribution Bias 254 return on investment (ROI) 364 rhetorical analysis 289, 304, 305 Riccie, K E 363 rich description 287 Robbers’ cave experiment 101 Robins, G 113 Rogosa, D R 71 ROI (return on investment) 364 role category questionnaire (RCQ) 457–459 Romme, A G L 366 Root Mean Squared Error of Approximation (RMSEA) 187 Rost, J 144, 145 Roth, P L 449 Rothenberg, T J 190 Rousseau, D M 99, 120 Rynes, S L 272–273, 295 saddle points, catastrophe theory 34 Salas, E 351, 357, 363, 368 sample-size adjusted BIC 155 Savinshisky, J 297 Schmidt, P 190 Scollon, C N 319–320 Scott, B A 321, 325 SEALS (Sea, Air and Land Teams), US Navy 364 Segalowitz, S J 430 self- and other-reports 448–449 self-organizing maps 221 self-regulatory depletion 173 serious games see games Sherif, M 101 Shin, Y 190 Silverman, D 272, 273–274, 311 SimCity (game) 359–360 Simon, H A 78 simulations 227, 447; defining 360–361; future uses 363–365; and organizational research 361–362; roadblocks 362–363 Singer, J D 71, 75, 77–78, 80, 83, 93, 165, 166 situational judgment tests (SJTs) 447, 450–452, 453 skull 407 slopes: conditional growth models 180–182; longitudinal growth modeling 71, 73, 86 Smircich, L 304 snapshot scoring 465 Snijders, T A B 111, 113 social brain hypothesis 405 social comparison theory 115 Social Hierarchy Orientation 242–243 social network analysis (SNA) 7–8, 99–135; benefits 120–121; data for 102, 121–124; definition of network data 102–103; definition of social networks 101–102; in degree centrality 116–117, 121; dependence assumptions 124–128; directed graphs, dependence assumptions 107, 113, 127–128; emergent leadership, social process giving rise to 113–119; exponential random graph models see exponential random graph (ERG) models; goodness of fit (GOF) statistics 109, 110, 117, 122–124; leadership nominations between recruits, matrix 121; Markov dependence assumptions 107, 111, 124–125; matrices 121; network models 494 • Index other than ERG 119–120; nondirected graphs, dependence assumptions 107, 124–126; PNet, use for fitting ERG models to social network data 128–130; social selection models 103–104, 110; see also exponential random graph (ERG) models social selection models 103–104, 110 social-networking platforms, online 227 Song, Z 332 Sonnentag, S 321, 323, 334 Sony Playstation system 358 Space Fortress 354 Spearman correlation 117 speech acts, analysis 305 Spradley, J P 296 SPSS (statistical program) 35, 209, 338; SPSS/PASW 224 spurious regression 162, 167, 173–177, 182, 183–184; defining 171; effects 176–177; in RCMs 177–188; regression coefficients 174, 175 St Jacques, P L 421, 422 Statistica workbench interface 224 status degradation 308, 309 steady states 32, 34, 52 Steinbard, D S 294 stochastic Petri nets (SPNs) 384 stochastic trends: alternative statistical techniques 191–192; ARIMA models 191, 193; creation 173; dealing with 188–193; versus deterministic trends 162, 168, 170, 171, 172, 190, 192, 194; and random walks 10, 168–173; versus stochastic models 168; structural timeseries models 192; unit root tests 189–190; see also growth curve modeling Stock, J H 190 Stone, N J 449 stress: buckling 42–43, 55–56; diathesis 41–42; fatigue 43–44, 45; and human performance 41–45 structural equation models (SEMs) 18, 63, 64, 67, 76, 393; latent growth 72–73, 74; and random coefficient models 68; software 200–201 structural MRI 406, 407–413, 427; personality and voxel-based morphometry 410–411 structural time-series models 191, 192 structural-developmental theory 455 Subaru–Isuzu plant, Lafayette (Indiana) 285, 294, 301 Suls, J 329, 330–331 supervised learning, data mining 203–204, 214–220 support vector machines (SVMs) 216, 217, 218, 219, 223 surveys 274–275 swallowtail model, catastrophe theory 32, 48–52 synthetic task environments (STEs) 15, 349–380; advancing science of 373–374; custom-built versus commercial-offthe-shelf software, evaluating before selecting 354–355; definitions 352–354; fidelity 353, 354, 355, 361, 362; investigation of specific construct of interest, allowing for 356; microworlds 16, 365–368; principles for use 354–357; purpose 352; security of data and individuals, safeguarding 355; selection of testbed and measurement of dependent variables 356–357; serious games 358–360; taking advantage of 358–372; traditional approaches to understanding work performance 350–352; training 363–364; virtual worlds 16–17, 369–372; work performance improvement 349–380 Sypher, H E 457 systematic self-observation (SSO) 310 tacit knowledge 245, 444–445, 452 tacit knowledge situational judgment tests (TKSJTs) 452, 453 TACT software 306 Takeuchi, H 412, 413 target knowledge 460 task performance 424 team role knowledge 451 team-based management 287 textual analysis 289, 301–305; computeraided interpretive 305–306 theoretical sampling 307–308 theory testing 277 thick descriptions 300 Thom, R 30, 32 Thompson, C M 67 Thompson, H L 43 time series 39, 40, 162, 163, 174, 189–190; see also structural time-series models time-invariant predictors 63, 73, 75–76 time-varying covariates: growth curve models with 182–185; latent class procedures 148; with only time-varying Index • 495 covariates 185; with time and timevarying covariates 183–185 time-varying predictors 76–88 time-varying predictors (TVCs): ancillary 77, 78; contextual 77, 78; defined 77, 78; internal 77, 78; model-comparison approach 81–82; reciprocal causation 78; and time 81; see also longitudinal growth modeling tipping points 92 Tisak, J 73 Top index 465 top–down effects 99 topological models, catastrophe theory 30 toxic leaders 12, 253–257; conditional reasoning of 254–256; Hostile Attribution Bias 254, 257; identifying using CRT-L 256–257, 258, 259, 260; Potency Bias 254; Retribution Bias 254; toxic alternative (TX) 256, 257, 258 training, synthetic task environments 363–364 transactive memory system (TMS) 431, 432 transitivity, emergent leadership network exhibiting 115–116 truth, perception of see reality, perception of Tsai, W 99 Tucker, L R 63 Tuerlinckx, F 329 Tulu, B 370 Type I errors see spurious regression UCINet 117 Ullsperger, M 430 Ullsperger, P 430 umbilic models, catastrophe theory 32 unconditional models: growth 75, 165, 178–179; means 165, 177–178 uninhabited aerial vehicles (UAVs) 354 unit root tests 189–190 unsupervised learning, data mining 203, 204, 220–223 Uy, M A 328–329, 332 virtual teams 372 virtual worlds: defining 16–17, 369; future uses 371–372; in organizational research 369–370; roadblocks 370–371 Von Davier, M 145 voxel-based morphometry (VBM) 409, 410; and creativity 411–413; limitations 434n; and personality 410–411 Wagner, D T 334 water molecules 408–409 Watson, D 323, 334 Watson, T 301 Weber, M 285, 286, 287 Weekley, J A 66 Weiss, H M 324 WEKA (Waikato Environment for Knowledge Analysis) 224, 225 Werner, H 454, 455 Wheeler, L 321, 327 White, B J 101 white matter, brain 407, 408, 409 Whyte, W F 101, 115 WikiPosit database 208–209 Willett, J B 71, 75, 77–78, 80, 83, 93, 165, 166 Williams, A A 67 Wilson, K A 363 Wilson, K S 334 Winter, D G 239 Wireless Application Protocol (WAP) 332 within-person research 319–320, 333–336, 345n Witten, I 225 Woolf, E F 336 work motivation 52–54 work performance: laboratory-based settings 351; naturalistic settings 350–351; synthetic task environments 349–380; traditional approaches 350–352 work samples 447, 453 work-life balance 274, 275, 301 X-rays 407 Van Eck, M 329 Van Iddekinge, C 76 Van Maanen, J 300–301 Vancouver, J B 67 Vandenberg, R J 66, 67, 75, 93 variable-centered approach, statistical methods 137 variance stealing 39 video games 358 Yagoda, R 391, 394 Yin, R K 293 Zickar, M J 300 Zivot, E 190 Zohar, D 46, 56 Zurada, J M 42 Zyphur, M J 91 Taylor & Francis eBooks FOR liBRARIES Over 23,000 eBook titles in the Humanities, Social Sciences, STM and Law from some of the world's leading imprints Choose from a range of subject packages or create your own! ~ Free MARC records ~ COUNTER-compliant usage statistics ~ Flexible purchase and pricing options ~ Off-site, anytime access via Athens or referring URL ~ Print or copy pages or chapters ~ Full 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Aguinis, Kelley School of Business, Indiana University Modern Research Methods for the Study of Behavior in Organizations The goal for the chapters in this SIOP Organizational Frontiers series... associate at the Institute for Simulation and Training (IST), Department for Human Systems Integration Research, where his research interests include teamwork, training, performance measurement, and... measures for a variety of clients across a broad range of industries His research interests include implicit attitude measurement, personality, training, legal issues, and the use of social media