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Here are some helpful references for structural equation modeling (in no particular order - I just keep adding to the list as they come).

To search for a specific term, in Windows hit CTRL+F, on a Mac hit COMMAND+F.

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
  • Hair, J. F., Jr., Black, W. C., Rabin, B. J., & Anderson, R. E. 2010. Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Hayes, Andrew. F., 2013. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, New York: The Guilford Press.
  • Byrne, B. M. 2009. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge Academic, 416 pages.
  • Privitera, G. J. 2015. Statistics for the Behavioral Sciences, (2nd ed.). Los Angeles, SAGE Publications, Inc.
  • Blair, J., Czaja, R.F. and Blair, E. A., (2014), Designing Surveys: A Guide to Decisions and Procedures, Third Edition, Sage Publishers, ISBN: 978-1-4129-9734-8.
  • Timothy A. Brown, (2015), Confirmatory Factor Analysis for Applied Research, Second Edition, The Guildford Press Publishers, ISBN: 978-1-4625-1536-3
  • Matsunaga, M. (2010), “How to Factor-Analyze Your Data Right: Do’s and Don’ts, and How-To’s,” International Journal of Psychological Research, vol. 3(1), pp. 97-110.
  • Costello A., Osborne, J. (2005), “Best Practices in EFA,” Practical Assessment, Research & Evaluation, vol. 10(7), pp. 1-9.
  • Thomas, G. Reio, Jr. and Shuck, B., (2015), “Exploratory Factor Analysis: Implications for Theory, Research, and Practice,” Advances in Developing Human Resources, vol. 17(1), pp. 12-25.
  • Treiblmaier, H., Filsmoser P., (2010), “Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research,” Information & Management, vol. 47, pp. 97-107.
  • Peterson, R.A., Kim, Y. (2012), “On the Relationship Between Coefficient Alpha and Composite Reliability,” Journal of Applied Psychology, vol. 98(1), pp. 194-198.
  • MacKenzie, Podsakoff, and Podsakoff (2011) “Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques,” MIS Quarterly, vol. 35(2), pp. 293-334.
  • Kenny, D.A. (2012), “Measuring Model fit,” http://davidakenny.net/cm/fit.htm
  • Kenny, D.A. (2011) “Respecification of Latent Variable Models,” http://davidakenny.net/cm/respec.htm (provides justification for covarying error terms)
  • MacKenzie, S.B., Podsakoff, P.M., Jarvis, C.B. (2005), “The Problem of Measurement Model Misspecification in Behavioral and Organizational Research and Some Recommended Solutions,” Journal of Applied Psychology, 90(4), pp. 710-730.
  • Bagozzi, R.P., and Yi, Y. 2012. "Specification, Evaluation, and Interpretation of Structural Equation Models," Academy of Marketing Science J (40:1), pp 8-34.
  • Hu, L., Bentler, P.M. (1999), “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives” SEM vol. 6(1), pp. 1-55.
  • Browne, M.W., Cudeck, R. (1992), “Alternative Ways of Assessing Model Fit,” SMR, vol. 21(2), pp. 230-258.
  • Baumgartner & Weijter (2016), SEM, Chapter 11 (see model fit indices explained, among other SEM topics)
  • Winklhofer , H.M., Diamantopoulos , A. (2002), “Managerial evaluation of sales forecasting effectiveness: A MIMIC modeling approach,” Intl Journal of Research in Marketing, vol. 19, pp. 153-166.
  • Diamantopoulos, A., Riefler, P., Roth, K.A. (2008), “Advancing formative measurement models,” Journal of Business Research, vol., 61, pp. 1203-1218.
  • Nahm, A.Y., Solis-Galvin, L.E., Rao, S.S., Ragun-Nathan, T.S. (2002), “The Q-Sort method: assessing reliability and construct validity of questionnaire items at a pre-testing stage,” IE working paper, DO8-103-I, May 22.
  • Thomas, D.M., Watson, R.T. (2003), “Q-Sorting and MIS Research: A Primer,” CAIS, vol. 8, pp. 141-156.
  • Osborne, J. (2013) “Power and Planning for Data Collection,” in Best Practices in Data Cleaning, Chapter 2, Sage pp. 19-41.
  • Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P. (2003), “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” Journal of Applied Psychology, vol. 88(5), pp. 879-903.
  • MacKenzie, S.B., Podsakoff, P.M., (2012), “Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies,” Journal of Retailing, vol. 88(4), pp. 542-555.
  • Steenkamp, J., DeJong, M., Baumgartner, H. (2010), “Socially Desirable Response Tendencies in Survey Research,” Journal of Marketing Research, vol. XLVII, pp. 199-214.
  • Williams, L.J., Hartman, N., Cavazotte, F. (2010), “Method Variance and Marker Variables: A review and Comprehensive CFA Marker Technique,” Organizational Research Methods, 13(3), pp. 477-514.
  • Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P. (2012), “Sources of Method Bias in Social Science Research and Recommendations on How to Control It,” Annu. Rev. Psychol., vol. 63, pp. 539-569.
  • Byrne B., (2008) “Testing for multigroup equivalence of a measuring instrument: A walk through the process,” Psicothema Vol. 20(4), pp. 872-882
  • Byrne, B. (2004), “Testing for Multigroup Invariance using AMOS Graphics: A Road Less Traveled,” Structural Equation Modeling, vol. 11(2) 272-300.
  • Schmitt, N. and Kuljanin, G. (2008) “Measurement Invariance: Review of practice and implications,” Human Resource Management Review, vol.18, 210–222
  • Hooper, D., Coughlan, J., and Mullen, M. (2008), "Structural equation modelling: guidelines for determining model fit," The Electronic Journal of Business Research Methods 6(1), pp. 53-60.
  • Bacharach, S. (1989), "Organizational theories: Some criteria for evaluation," Academy of Management Review 14(4), pp. 496-515.
  • Zhoa et al (2010) “Reconsidering Baron and Kenny” JCR.
  • Mathieu, J. and Taylor, S. (2006), "Clarifying conditions and decision points for mediational type inferences in organizational behavior," Journal of Organizational Behavior 27(8), pp. 1031-1056.
  • Becker, T. (2005), "Potential problems in the statistical control of variables in organizational research: A qualitative analysis with recommendations," Organizational Research Methods 8(3), pp. 274.
  • Dietz Jr, W. and Gortmaker, S. (1985), "Do we fatten our children at the television set? Obesity and television viewing in children and adolescents," Pediatrics 75(5), pp. 807.
  • Floh & Treiblmaier 2006 on multigroup moderation in eBanking.
  • Jarvis, C., MacKenzie, S., and Podsakoff, P. (2003), "A critical review of construct indicators and measurement model misspecification in marketing and consumer research," Journal of Consumer Research 30(2), pp. 199-218.
  • MacKenzie, S. (2003), "The dangers of poor construct conceptualization," Journal of the Academy of Marketing Science 31(3), pp. 323.
  • Suits, D. B. (1957), "Use of Dummy Variables in Regression Equations," Journal of the American Statistical Association 52(280), pp. 548-551.
  • Lyubomirsky, S. and Lepper, H. (1999), "A measure of subjective happiness: Preliminary reliability and construct validation," Social Indicators Research 46(2), pp. 137-155.
  • Peterson, C., Park, N., and Seligman, M. (2005), "Orientations to happiness and life satisfaction: The full life versus the empty life," Journal of Happiness Studies 6(1), pp. 25-41.
  • Paul Benjamin Lowry and James Gaskin (2014). "Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it," IEEE Transactions on Professional Communication (57:2), pp. 123-146. http://www.kolobkreations.com/PLSIEEETPC2014.pdf
  • Rigdon, E.E. (2014) “Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead” Long Range Planning (47), pp. 161-167.
  • Marko Sarstedt, Jörg Henseler, Christian M. Ringle. "Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results" In Measurement and Research Methods in International Marketing. Published online: 10 Mar 2015; 195-218. Permanent link to this document: http://dx.doi.org/10.1108/S1474-7979(2011)0000022012
  • Perry, Carlos Monge, Jesús Cruz Álvarez, and Jesús Fabián López. "Manufacturing and continuous improvement areas using partial least square path modeling with multiple regression comparison." CBU International Conference Proceedings. Vol. 2. 2014.
  • Gefen, David, Detmar W. Straub, and Edward E. Rigdon. "An update and extension to SEM guidelines for administrative and social science research. "Management Information Systems Quarterly 35.2 (2011): iii-xiv.
  • Hayes, Andrew F. "Beyond Baron and Kenny: Statistical mediation analysis in the new millennium." Communication monographs 76.4 (2009): 408-420.
  • Wong, Ken Kwong-Kay. "Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS." Marketing Bulletin 24.1 (2013): 1-32.
  • Henseler, Jörg, Christian M. Ringle, and Rudolf R. Sinkovics. "The use of partial least squares path modeling in international marketing." Advances in international marketing 20.1 (2009): 277-319.
  • Hair, Joseph F., et al. "The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications." Long range planning 45.5 (2012): 320-340.
  • F. Hair Jr, Joe, et al. "Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research." European Business Review 26.2 (2014): 106-121.
  • Joe F. Hair , Christian M. Ringle & Marko Sarstedt (2011) PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory and Practice, 19:2, 139-152
  • Hair, Joseph F., Christian M. Ringle, and Marko Sarstedt. "Editorial-partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance." Long Range Planning 46.1-2 (2013): 1-12.
  • Henseler, Jörg, et al. "Common beliefs and reality about PLS comments on Rönkkö and Evermann (2013)." Organizational Research Methods (2014): 1094428114526928.
  • Richardson, Hettie A., Marcia J. Simmering, and Michael C. Sturman. "A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance." Organizational Research Methods (2009). (gives justification for the zero-constraint approach)
  • Cook, R. Dennis; Weisberg, Sanford (1982). Residuals and Influence in Regression. New York, NY: Chapman & Hall. ISBN 0-412-24280-X.
  • Sposito, V., Hand, M., and Skarpness, B. "On the efficiency of using the sample kurtosis in selecting optimal lpestimators," Communications in Statistics-Simulation and Computation (12:3) 1983, pp 265-272.
  • N.J. Blunch, Introduction to structural equation modeling using IBM SPSS statistics and AMOS, 2nd ed. Los Angeles, Calif.: SAGE, 2013.
  • R.B. Kline, Principles and practice of structural equation modeling, 3rd ed. New York: Guilford Press, 2011.
  • G.Argyrous, Statistics for research: with a guide to spss, 3rd ed. Thousand Oaks, CA: Sage Publications, 2011.
  • Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information systems, 16(1), 5.
  • Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods.