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Here are some helpful references for structural equation modeling. To search for a specific term, in Windows hit CTRL+F, on a Mac hit COMMAND+F.

  • 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,”
  • 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