1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Organizational Psychology and Organizational Behavior
  4. Volume 10, 2023
  5. Article

Annual Review of Organizational Psychology and Organizational Behavior

Volume 10, 2023

Structural Equation Modeling in Organizational Research: The State of Our Science and Some Proposals for Its Future

Abstract

The use of structural equation modeling (SEM) has grown substantially over the past 40 years within organizational research and beyond. There have been many different developments in SEM that make it increasingly useful for a variety of data types, research designs, research questions, and research contexts in the organizational sciences. To give researchers a better understanding of how and why SEM is used, our article () presents a review of SEM applications within organizational research; () discusses SEM best practices; and () explores advanced SEM applications, including instrumental variable methods, latent variable interactions and nonlinear measurement models, multilevel SEM, cross-lagged panel models and dynamic structural equation models, and meta-analytic SEM. We conclude by discussing concerns and debates that are both methodological (i.e., cross-validation and regularization) and theoretical (i.e., understanding causal evidence) as they relate to SEM and its application in organizational research and beyond.

Keyword(s): latent interactions, latent variable models, model fit, SEM, structural equation model, structural equation modeling
Loading

Article metrics loading...

/content/journals/10.1146/annurev-orgpsych-041621-031401
2023-01-23
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/orgpsych/10/1/annurev-orgpsych-041621-031401.html?itemId=/content/journals/10.1146/annurev-orgpsych-041621-031401&mimeType=html&fmt=ahah

Literature Cited

  1. Allison PD. 2009. Fixed Effects Regression Models Thousand Oaks, CA: SAGE
  2. Ancona DG, Goodman PS, Lawrence BS, Tushman ML. 2001. Time: a new research lens. Acad. Manag. Rev. 26:645–83
     [Google Scholar]
  3. Andersen HK. 2021. Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models? Investigating the benefits and drawbacks of the latent curve model with structured residuals and the random intercept cross-lagged panel model. Psychol. Methods. In press. https://doi.org/10.1037/met0000285
    [Google Scholar]
  4. Anderson JC, Gerbing DW. 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103:3411–23
     [Google Scholar]
  5. Antonakis J, Bendahan S, Jacquart P, Lalive R. 2010. On making causal claims: a review and recommendations. Leadersh. Q. 21:1086–1120
     [Google Scholar]
  6. Asparouhov T, Hamaker EL, Muthén B. 2018. Dynamic structural equation models. Struct. Equ. Model. 25:3359–88
     [Google Scholar]
  7. Asparouhov T, Muthén B. 2010. Plausible values for latent variables using Mplus Work. Pap. Mplus Los Angel: https://www.statmodel.com/download/Plausible.pdf
  8. Asparouhov T, Muthén B. 2021. Bayesian estimation of single and multilevel models with latent variable interactions. Struct. Equ. Model. 28:2314–28
     [Google Scholar]
  9. Baum JAC, Haveman HA. 2020. Editors' comments: the future of organizational theory. Acad. Manag. Rev. 45:268–72
     [Google Scholar]
  10. Belkin M, Hsu D, Ma S, Mandal S. 2019. Reconciling modern machine-learning practice and the classical bias-variance trade-off. PNAS 116:3215849–54
     [Google Scholar]
  11. Bentler PM, Bonett DG. 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88:588–606
     [Google Scholar]
  12. Bentler PM, Satorra A. 2010. Testing model nesting and equivalence. Psychol. Methods 15:111–23
     [Google Scholar]
  13. Bergh DD, Aguinis H, Heavey C, Ketchen DJ, Boyd BK et al. 2016. Using meta-analytic structural equation modeling to advance strategic management research: guidelines and an empirical illustration via the strategic leadership-performance relationship. Strateg. Manag. J. 37:3477–97
     [Google Scholar]
  14. Binning JF, Barrett GV. 1989. Validity of personnel decisions: a conceptual analysis of the inferential and evidential bases. J. Appl. Psychol. 74:478–94
     [Google Scholar]
  15. Bollen KA. 2002. Latent variables in psychology and the social sciences. Annu. Rev. Psychol. 53:605–34
     [Google Scholar]
  16. Bollen KA, Brand JE. 2010. A general panel model with random and fixed effects: a structural equations approach. Soc. Forces 89:11–34
     [Google Scholar]
  17. Bollen KA, Long JS, eds. 1993. Testing Structural Equation Models Thousand Oaks, CA: SAGE
  18. Borsboom D, Mellenbergh GJ, van Heerden J. 2003. The theoretical status of latent variables. Psychol. Rev. 110:203–19
     [Google Scholar]
  19. Browne MW, Cudeck R. 1993. Alternative ways of assessing model fit. Sociol. Methods Res. 21:2230–58
     [Google Scholar]
  20. Bullock HE, Harlow LL, Mulaik SA. 1994. Causation issues in structural equation modeling research. Struct. Equ. Model. 1:3253–67
     [Google Scholar]
  21. Butts MM, Casper WJ, Yang TS. 2013. How important are work–family support policies? A meta-analytic investigation of their effects on employee outcomes. J. Appl. Psychol. 98:11–25
     [Google Scholar]
  22. Byrne BM, Shavelson RJ, Muthén B. 1989. Testing for the equivalence of factor covariances and mean structures: the issue of partial measurement invariance. Psychol. Bull. 105:456–66
     [Google Scholar]
  23. Cartwright N 2017. Single case causes: what is evidence and why. Philosophy of Science in Practice H-C Chao, J Reiss 11–24. Cham, Switz: Springer
     [Google Scholar]
  24. Cartwright N, Goldfinch A, Howick J. 2009. Evidence-based policy: Where is our theory of evidence?. J. Child. Serv. 4:46–14
     [Google Scholar]
  25. Cartwright N, Hardie J. 2012. Evidence-Based Policy: A Practical Guide to Doing It Better Oxford, UK: Oxford Univ. Press
  26. Cheung GW, Cooper-Thomas HD, Lau RS, Wang LC. 2021. Testing moderation in business and psychological studies with latent moderated structural equations. J. Bus. Psychol. 36:1009–33
     [Google Scholar]
  27. Cheung MWL, Chan W. 2005. Meta-analytic structural equation modeling: a two-stage approach. Psychol. Methods 10:140–64
     [Google Scholar]
  28. Connelly BS, McAbee ST, Oh I-S, Jung Y, Jung C-W 2022. A multirater perspective on personality and performance: an empirical examination of the trait–reputation–identity model. J. Appl. Psychol. 107:81352–68
     [Google Scholar]
  29. Cortina JM, Green JP, Keeler KR, Vandenberg RJ. 2017. Degrees of freedom in SEM: Are we testing the models we claim to test?. Organ. Res. Methods 20:3350–78
     [Google Scholar]
  30. D'Innocenzo L, Luciano MM, Mathieu JE, Maynard MT, Chen G. 2016. Empowered to perform: a multilevel investigation of the influence of empowerment on performance in hospital units. Acad. Manag. J. 59:41290–1307
     [Google Scholar]
  31. Davidov E, Muthen B, Schmidt P. 2018. Measurement invariance in cross-national studies: challenging traditional approaches and evaluating new ones. Sociol. Methods Res. 47:631–36
     [Google Scholar]
  32. Deaton A, Cartwright N. 2018. Understanding and misunderstanding randomized controlled trials. Soc. Sci. Med. 210:2–21
     [Google Scholar]
  33. Denis D, Legerski J. 2006. Causal modeling and the origins of path analysis. Theory Sci. 7:12–10
     [Google Scholar]
  34. Diener E, Northcott R, Zyphur MJ, West SG. 2022. Beyond experiments. Perspect. Psychol. Sci. 17:41101–19
     [Google Scholar]
  35. Edwards JR. 2010. The fallacy of formative measurement. Organ. Res. Methods 14:2370–388
     [Google Scholar]
  36. Edwards JR, Bagozzi RP. 2000. On the nature and direction of relationships between constructs and measures. Psychol. Methods 5:155–74
     [Google Scholar]
  37. Ferris DL, Yan M, Lim VK, Chen Y, Fatimah S 2016. An approach–avoidance framework of workplace aggression. Acad. Manag. J. 59:1777–1800
     [Google Scholar]
  38. Foulk TA, Woolum A, Erez A. 2016. Catching rudeness is like catching a cold: the contagion effects of low-intensity negative behaviors. J. Appl. Psychol. 101:150–67
     [Google Scholar]
  39. Frone MR, Russell M, Cooper ML. 1992. Antecedents and outcomes of work-family conflict: testing a model of the work-family interface. J. Appl. Psychol. 77:165–78
     [Google Scholar]
  40. Frone MR, Russell M, Cooper ML 1994. Relationship between job and family satisfaction: Causal or noncausal covariation?. J. Manag. 20:3565–79
     [Google Scholar]
  41. Fulmer CA, Ostroff C. 2017. Trust in direct leaders and top leaders: a trickle-up model. J. Appl. Psychol. 102:4648–57
     [Google Scholar]
  42. Gabriel AS, Podsakoff NP, Beal DJ, Scott BA, Sonnentag S et al. 2018. Experience sampling methods. Organ. Res. Methods. 22:4969–1006
     [Google Scholar]
  43. Geldhof GJ, Preacher KJ, Zyphur MJ. 2013. Reliability estimation in a multilevel confirmatory factor analysis framework. Psychol. Methods. 19:172–91
     [Google Scholar]
  44. Grace JB. 2021. Instrumental variable methods in structural equation models. Methods Ecol. Evol. 12:71148–57
     [Google Scholar]
  45. Hamaker EL, Asparouhov T, Brose A, Schmiedek F, Muthén B. 2018. At the frontiers of modeling intensive longitudinal data: dynamic structural equation models for the affective measurements from the COGITO study. Multivar. Behav. Res. 53:6820–41
     [Google Scholar]
  46. Hamaker EL, Kuiper RM, Grasman RP. 2015. A critique of the cross-lagged panel model. Psychol. Methods 20:1102–16
     [Google Scholar]
  47. Hamaker EL, Muthén B. 2020. The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychol. Methods 25:3365–79
     [Google Scholar]
  48. Hayes T, Usami S. 2020. Factor score regression in the presence of correlated unique factors. Educ. Psychol. Meas. 80:15–40
     [Google Scholar]
  49. Hershcovis MS, Bhatnagar N. 2017. When fellow customers behave badly: witness reactions to employee mistreatment by customers. J. Appl. Psychol. 102:111528–44
     [Google Scholar]
  50. Hoyle RH, Smith GT. 1994. Formulating clinical research hypotheses as structural equation models: a conceptual overview. J. Consult. Clin. Psychol. 62:429–40
     [Google Scholar]
  51. Hu LT, Bentler PM. 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6:1–55
     [Google Scholar]
  52. Hülsheger UR, Alberts HJEM, Feinholdt A, Lang JWB. 2013. Benefits of mindfulness at work: the role of mindfulness in emotion regulation, emotional exhaustion, and job satisfaction. J. Appl. Psychol. 98:310–25
     [Google Scholar]
  53. Jacobucci R, Grimm KJ, McArdle JJ. 2016. Regularized structural equation modeling. Struct. Equ. Model. 23:4555–66
     [Google Scholar]
  54. Jak S, Cheung MWL. 2020. Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychol. Methods 25:4430–55
     [Google Scholar]
  55. Jorgensen TD, Pornprasertmanit S, Schoemann AM, Rosseel Y. 2022. semTools: useful tools for structural equation modeling. R package version 0.5–6. semTools. https://CRAN.R-project.org/package=semTools
    [Google Scholar]
  56. Kim J-H. 2009. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Comput. Stat. Data Anal. 53:113735–45
     [Google Scholar]
  57. Kline R. 2004. Principles and Practice of Structural Equation Modeling New York: Guilford Press. , 2nd ed..
  58. Kunze F, Raes AML, Bruch H. 2015. It matters how old you feel: antecedents and performance consequences of average relative subjective age in organizations. J. Appl. Psychol. 100:51511–26
     [Google Scholar]
  59. Kuykendall L, Craig L, Tay L. 2019. Work-contingent self-esteem: a boon or bane for worker well-being?. J. Organ. Behav. 41:11–16
     [Google Scholar]
  60. Landis RS. 2013. Successfully combining meta-analysis and structural equation modeling: recommendations and strategies. J. Bus. Psychol. 28:3251–61
     [Google Scholar]
  61. Lester HF, Cullen-Lester KL, Walters RW 2021. From nuisance to novel research questions: using multilevel models to predict heterogeneous variances. Organ. Res. Methods 24:2342–88
     [Google Scholar]
  62. Li X, Jacobucci R. 2021. Regularized structural equation modeling with stability selection. Psychol. Methods 27:4497–518
     [Google Scholar]
  63. Livne-Ofer E, Coyle-Shapiro JA, Pearce JL. 2019. Eyes wide open: perceived exploitation and its consequences. Acad. Manag. J. 62:61989–2018
     [Google Scholar]
  64. Loi TI, Kuhn KM, Sahaym A, Betterfield KD, Tripp TM. 2020. From helping hands to harmful acts: when and how employee volunteering promotes workplace deviance. J. Appl. Psychol. 105:9944–58
     [Google Scholar]
  65. Lucas R. 2022. It's time to abandon the cross-lagged panel model. PsyArXiv pkec7. https://psyarxiv.com/pkec7/
  66. MacCallum RC, Austin JT. 2000. Applications of structural equation modeling in psychological research. Annu. Rev. Psychol. 51:201–26
     [Google Scholar]
  67. MacCallum RC, Wegener DT, Uchino BN, Fabrigar LR. 1993. The problem of equivalent models in applications of covariance structure analysis. Psychol. Bull. 114:185–99
     [Google Scholar]
  68. MacKenzie SB, Podsakoff PM, Jarvis CB. 2005. The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. J. Appl. Psychol. 90:4710–30
     [Google Scholar]
  69. Maydeu-Olivares A, Shi D, Fairchild AJ. 2020. Estimating causal effects in linear regression models with observational data: the instrumental variables regression model. Psychol. Methods 25:2243–58
     [Google Scholar]
  70. McCarthy JM, Trougakos JP, Cheng BH. 2016. Are anxious workers less productive workers? It depends on the quality of social exchange. J. Appl. Psychol. 101:2279–91
     [Google Scholar]
  71. McDonald RP. 1962. A general approach to nonlinear factor analysis. Psychometrika 27:397–415
     [Google Scholar]
  72. McDonald RP. 2010. Structural models and the art of approximation. Perspect. Psychol. Sci. 5:6675–86
     [Google Scholar]
  73. McDonald RP, Ho M-HR. 2002. Principles and practice in reporting structural equation analyses. Psychol. Methods 7:164–82
     [Google Scholar]
  74. McGrath JE. 1981. The study of research choices and dilemmas. Am. Behav. Sci. 25:179–210
     [Google Scholar]
  75. McNeish D. 2021. Specifying location-scale models for heterogeneous variances as multilevel SEMs. Organ. Res. Methods 24:630–53
     [Google Scholar]
  76. McNeish D, Hamaker EL. 2020. A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychol. Methods 25:5610–35
     [Google Scholar]
  77. McNeish D, Wolf MG. 2021. Dynamic fit index cutoffs for confirmatory factor analysis models. Psychol. Methods In press. https://doi.org/10.1037/met0000425
    [Google Scholar]
  78. Muthén B, Asparouhov T. 2015. Causal effects in mediation modeling: an introduction with applications to latent variables. Struct. Equ. Model. 22:12–23
     [Google Scholar]
  79. Muthén B, Asparouhov T. 2017. Recent methods for the study of measurement invariance with many groups. Sociol. Methods Res. 47:4637–64
     [Google Scholar]
  80. Muthén BO, Muthén LK, Asparouhov T. 2017. Regression and Mediation Analysis Using Mplus Los Angeles: Mplus
  81. Nye CD, Brummel BJ, Drasgow F. 2014. Understanding sexual harassment using aggregate construct models. J. Appl. Psychol. 99:61204–21
     [Google Scholar]
  82. O'Boyle EH Jr., Williams LJ. 2011. Decomposing model fit: measurement versus theory in organizational research using latent variables. J. Appl. Psychol. 96:11–12
     [Google Scholar]
  83. Ozkok O, Vaulont MJ, Zyphur MJ, Zhang Z, Preacher KJ et al. 2021. Interaction effects in cross-lagged panel models: SEM with latent interactions applied to work-family conflict, job satisfaction, and gender. Organ. Res. Methods. 25:4673–715
     [Google Scholar]
  84. Pearl J. 2010. The foundations of causal inference. Sociol. Methodol. 40:75–149
     [Google Scholar]
  85. Petter S, Straub D, Rai A. 2007. Specifying formative constructs in information systems research. MIS Q. 31:4623–56
     [Google Scholar]
  86. Ployhart RE, Oswald FL. 2004. Application of mean and covariance structures analysis: integrating correlational and experimental approaches. Organ. Res. Methods 7:27–65
     [Google Scholar]
  87. Preacher KJ, Zhang Z, Zyphur MJ. 2010. A general multilevel SEM framework for assessing multilevel mediation. Psychol. Methods 15:209–33
     [Google Scholar]
  88. Preacher KJ, Zhang Z, Zyphur MJ. 2011. Alternative methods for assessing mediation in multilevel data: the advantages of multilevel SEM. Struct. Equ. Model. 18:2161–82
     [Google Scholar]
  89. Preacher KJ, Zhang Z, Zyphur MJ. 2016. Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychol. Methods 21:2189–205
     [Google Scholar]
  90. Priesemuth M, Schminke M, Ambrose ML, Folger R. 2014. Abusive supervision climate: a multiple-mediation model of its impact on group outcomes. Acad. Manag. J. 57:51513–34
     [Google Scholar]
  91. Quigley TJ, Hubbard TD, Ward A, Graffin SD. 2020. Unintended consequences: information releases and CEO stock option grants. Acad. Manag. J. 63:155–80
     [Google Scholar]
  92. Raudenbush SW, Bryk AS. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: SAGE. , 2nd ed..
  93. Raykov T, Tomer A, Nesselroade JR. 1991. Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines. Psychol. Aging 6:4499–503
     [Google Scholar]
  94. Rosseel Y, Loh WW. 2021. A “Structural-After-Measurement” (SAM) approach to SEM. OSF: Center for Open Science's Open Source Web Application Charlottesville, VA: retrieved August 15, 2022. https://osf.io/pekbm/
    [Google Scholar]
  95. Russell DW, Kahn JH, Spoth R, Altmaier EM. 1998. Analyzing data for experimental studies: a latent variable structural equation modeling approach. J. Couns. Psychol. 45:18–29
     [Google Scholar]
  96. Saylors R, Trafimow D. 2020. Why the increasing use of complex causal models is a problem: on the danger sophisticated theoretical narratives pose to truth. Organ. Res. Methods 24:3616–29
     [Google Scholar]
  97. Scandura TA, Williams EA. 2000. Research methodology in management: current practices, trends, and implications for future research. Acad. Manag. J. 43:61248–64
     [Google Scholar]
  98. Schaubroeck JM, Peng AC, Hannah ST. 2013. Developing trust with peers and leaders: impacts on organizational identification and performance during entry. Acad. Manag. J. 56:41148–68
     [Google Scholar]
  99. Schreiber JB, Nora A, Stage FK, Barlow EA, King J. 2010. Reporting structural equation modeling and confirmatory factor analysis results: a review. J. Educ. Res. 99:6323–38
     [Google Scholar]
  100. Schuler MS, Rose S. 2017. Targeted maximum likelihood estimation for causal inference in observational studies. Am. J. Epidemiol. 185:165–73
     [Google Scholar]
  101. Schultzberg M, Muthén B. 2018. Number of subjects and time points needed for multilevel time-series analysis: a simulation study of dynamic structural equation modeling. Struct. Equ. Model. 25:4495–515
     [Google Scholar]
  102. Shamsollahi A, Zyphur MJ, Ozkok O. 2021. Long-run effects in dynamic systems: new tools for cross-lagged panel models. Organ. Res. Methods. 25:3435–58
     [Google Scholar]
  103. Skrondal A, Laake P. 2001. Regression among factor scores. Psychometrika 66:563–75
     [Google Scholar]
  104. Solomon BC, Nikolaev BN, Shepherd DA. 2021. Does educational attainment promote job satisfaction? The bittersweet trade-offs between job resources, demands, and stress. J. Appl. Psychol. 10:71227–41
     [Google Scholar]
  105. Su R, Zhang Q, Liu Y, Tay L. 2019. Modeling congruence in organizational research with latent moderated structural equations. J. Appl. Psychol. 104:111404–33
     [Google Scholar]
  106. Tarka P. 2018. An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Qual. Quant. 52:313–54
     [Google Scholar]
  107. Tay L, Meade AW, Cao M. 2015. An overview and practical guide to IRT measurement equivalence analysis. Organ. Res. Methods 18:3–46
     [Google Scholar]
  108. Tay L, Woo SE, Vermunt JK. 2014. A conceptual and methodological framework for psychometric isomorphism: validation of multilevel construct measures. Organ. Res. Methods 17:77–106
     [Google Scholar]
  109. Toker S, Biron M. 2012. Job burnout and depression: unraveling their temporal relationship and considering the role of physical activity. J. Appl. Psychol. 97:3699–710
     [Google Scholar]
  110. Vandenberg RJ, Grelle DM 2009. Alternative model specifications in structural equation modeling. Statistical and Methodological Myths and Urban Legends CE Lance, RJ Vandenberg 165–91. London: Routledge
     [Google Scholar]
  111. Vandenberg RJ, Lance CE. 2000. A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organ. Res. Methods 3:4–70
     [Google Scholar]
  112. Viswesvaran C, Ones DS. 1995. Theory testing: combining psychometric meta-analysis and structural equations modeling. Pers. Psychol. 48:865–85
     [Google Scholar]
  113. Vrieze SI. 2012. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17:2228–43
     [Google Scholar]
  114. Wanberg CR, Csillag B, Douglass RP, Zhou L, Pollard MS. 2020. Socioeconomic status and well-being during COVID-19: a resource-based examination. J. Appl. Psychol. 105:121382–96
     [Google Scholar]
  115. Weston R, Gore PA. 2016. A brief guide to structural equation modeling. Couns. Psychol. 34:5719–51
     [Google Scholar]
  116. Williams LJ, Vandenberg RJ, Edwards JR. 2009. Structural equation modeling in management research: a guide for improved analysis. Acad. Manag. Ann. 3:543–604
     [Google Scholar]
  117. Wo DXH, Ambrose ML, Schminke M. 2015. What drives trickle-down effects? A test of multiple mediation processes. Acad. Manag. J. 58:61848–68
     [Google Scholar]
  118. Zhang XA, Liao H, Li N, Colbert AE 2020. Playing it safe for my family: exploring the dual effects of family motivation on employee productivity and creativity. Acad. Manag. J. 63:1923–50
     [Google Scholar]
  119. Zhang Z, Zyphur MJ, Preacher KJ. 2009. Testing multilevel mediation using hierarchical linear models: problems and solutions. Organ. Res. Methods 12:695–719
     [Google Scholar]
  120. Zhou L, Wang M, Zhang Z. 2019. Intensive longitudinal data analyses with dynamic structural equation modeling. Organ. Res. Methods 24:2219–50
     [Google Scholar]
  121. Zyphur MJ. 2009. When mindsets collide: switching analytical mindsets to advance organization science. Acad. Manag. Rev. 34:4677–88
     [Google Scholar]
  122. Zyphur MJ, Allison PD, Tay L, Voelkle MC, Preacher KJ et al. 2020a. From data to causes I: building a general cross-lagged panel model (GCLM). Organ. Res. Methods 23:651–87
     [Google Scholar]
  123. Zyphur MJ, Hamaker EL, Tay L, Voelkle M, Preacher KJ et al. 2021. From data to causes III: Bayesian priors for general cross-lagged panel models (GCLM). Front. Psychol. 12:612251
     [Google Scholar]
  124. Zyphur MJ, Pierides DC. 2017. Is quantitative research ethical? Tools for ethically practicing, evaluating, and using quantitative research. J. Bus. Ethics 143:1–16
     [Google Scholar]
  125. Zyphur MJ, Pierides DC. 2020a. Making quantitative research work: from positivist dogma to actual social scientific inquiry. J. Bus. Ethics 167:49–62
     [Google Scholar]
  126. Zyphur MJ, Pierides DC. 2020b. Statistics and probability have always been value-laden: an historical ontology of quantitative research methods. J. Bus. Ethics 167:1–18
     [Google Scholar]
  127. Zyphur MJ, Pierides DC, Roffe J 2015. Measurement and statistics in ‘organization science’: philosophical, sociological, and historical perspectives. The Routledge Companion to Philosophy in Organization Studies R Mir, H Willmott, M Greenwood 474–82. London: Routledge
     [Google Scholar]
  128. Zyphur MJ, Voelkle MC, Tay L, Allison PD, Preacher KJ et al. 2020b. From data to causes II: comparing approaches to panel data analysis. Organ. Res. Methods 23:688–716
     [Google Scholar]
  129. Zyphur MJ, Zhang Z, Preacher KJ, Bird L 2019. Moderated mediation in multilevel structural equation models: decomposing effects of race on math achievement within versus between high schools in the United States. The Handbook of Multilevel Theory SE Humphrey, JM LeBreton 473–94. Am. Psychol. Assoc.
     [Google Scholar]
/content/journals/10.1146/annurev-orgpsych-041621-031401
Loading
Structural Equation Modeling in Organizational Research: The State of Our Science and Some Proposals for Its Future
Annual Review of Organizational Psychology and Organizational Behavior 10, 495 (2023); https://doi.org/10.1146/annurev-orgpsych-041621-031401
/content/journals/10.1146/annurev-orgpsych-041621-031401
/content/journals/10.1146/annurev-orgpsych-041621-031401
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error