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Take the online course on MyEducator: SEM Online 3 credit Graduate Course Invitation Video

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.

Constructs and Validity

  • Devellis, R. F. (2003). Scale Development: Theory and Applications Second Edition (Applied Social Research Methods).
  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods, 19(2), 159-203.
  • Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 64-73.
  • Yaniv, E. (2011). Construct clarity in theories of management and organization. Academy of Management Review, 36(3), 590-592.
  • Editor’s Comments. (2011). Construct clarity in theories of management and organization. Academy of Management Review, 36(3), 590-592.
  • Law, K. S., Wong, C. S., & Mobley, W. M. (1998). Toward a taxonomy of multidimensional constructs. Academy of management review, 23(4), 741-755.
  • Shaffer, J. A., DeGeest, D., & Li, A. (2016). Tackling the problem of construct proliferation: A guide to assessing the discriminant validity of conceptually related constructs. Organizational Research Methods, 19(1), 80-110.
  • Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.
  • Krosnick, J. A. (1999). Survey research. Annual review of psychology, 50(1), 537-567.
  • MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334.
  • Bolton, R. N. (1993). Pretesting questionnaires: content analyses of respondents' concurrent verbal protocols. Marketing science, 12(3), 280-303.
  • Podsakoff, N. P., Podsakoff, P. M., MacKenzie, S. B., & Klinger, R. L. (2013). Are we really measuring what we say we're measuring? Using video techniques to supplement traditional construct validation procedures. Journal of Applied Psychology, 98(1), 99.
  • Nahm, A. Y., Rao, S. S., Solis-Galvan, L. E., & Ragu-Nathan, T. S. (2002). The Q-sort method: assessing reliability and construct validity of questionnaire items at a pre-testing stage. Journal of Modern Applied Statistical Methods, 1(1), 15.
  • Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of consumer research, 30(2), 199-218.
  • MacKenzie, S. B. (2003). The dangers of poor construct conceptualization. Journal of the Academy of Marketing Science, 31(3), 323-326.
  • Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social indicators research, 46(2), 137-155.
  • Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. Structural equation modeling: Present and future, 195-216.
  • Hancock, Gregory R., and Ralph O. Mueller. "Rethinking construct reliability within latent variable systems." Structural equation modeling: Present and future (2001): 195-216. (discusses MaxR(H))

Measurement Models

Exploratory Factor Analysis

  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological methods, 4(3), 272.
  • Costello, A. B., & Osborne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation,10(7), 1-9.
  • Reio Jr, T. G., & Shuck, B. (2015). Exploratory factor analysis: Implications for theory, research, and practice. Advances in Developing Human Resources, 17(1), 12-25.
  • Treiblmaier, H., & Filzmoser, P. (2010). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information & management, 47(4), 197-207.
  • Ferguson, E., & Cox, T. (1993). Exploratory factor analysis: A users’ guide. International Journal of Selection and Assessment, 1(2), 84-94.

Confirmatory Factor Analysis

  • Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational research methods, 3(1), 4-70.
  • Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20(4), 872-882.
  • Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling, 11(2), 272-300.
  • Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210-222.
  • Brown, T. A. (2014). Confirmatory factor analysis for applied research (2nd ed.). Guilford Publications.
  • Matsunaga, M. (2015). How to factor-analyze your data right: do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97-110.
  • Malhotra N. K., Dash S. (2011). Marketing Research an Applied Orientation. London: Pearson Publishing.
  • Hermida, R. 2015. "The Problem of Allowing Correlated Errors in Structural Equation Modeling: Concerns and Considerations," Computational Methods in Social Sciences (3:1), p. 5.

Method Bias, Response Bias, Specific Bias

  • Serrano Archimi, C., Reynaud, E., Yasin, H.M. and Bhatti, Z.A. (2018), “How perceived corporate social responsibility affects employee cynicism: the mediating role of organizational trust”, Journal of Business Ethics, Vol. 151 No. 4, pp. 907-921.
  • Fuller et al., (2016) "Common methods variance detection in business research", Journal of Business Research, Volume 69, Issue 8, pp. 3192-3198 (suggests Harman's single factor test is useful under certain circumstances).
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
  • MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542-555.
  • 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), 477-514.
  • Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods.
  • 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. Annual review of psychology, 63, 539-569.
  • Doty, D. H., & Glick, W. H. (1998). Common methods bias: does common methods variance really bias results?. Organizational research methods, 1(4), 374-406.
  • Estabrook, Ryne, and Michael Neale. “A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence.” Multivariate behavioral research 48.1 (2013): 1–27. PMC. Web. 1 Nov. 2017.
  • Arbuckle JL. Amos 7.0 user’s guide. Chicago, IL: SPSS; 2006.
  • Bartlett MS. The statistical conception of mental factors. British Journal of Psychology. 1937;28:97–104.
  • Lawley DN, Maxwell MA. Factor analysis as a statistical method. 2. London, UK: Butterworths; 1971.
  • Horn JL, McArdle JJ, Mason R. When invariance is not invariant: A practical scientist’s view of the ethereal concept of factorial invariance. The Southern Psychologist. 1983; 1:179–188.
  • Muthén L, Muthén B. Mplus user’s guide. 5. Los Angeles, CA: Author; 1998–2007.
  • Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods.
  • Eichhorn, B. R. (2014, October 5-7). Common method variance techniques MWSUG 2014, Chicago, Illinois.


  • Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological methods, 5(2), 155.
  • Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological assessment, 7(3), 286.
  • Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of marketing research, 186-192.
  • Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis in Personality and Social Psychology Bulletin. Personality and social psychology bulletin, 28(12), 1629-1646.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
  • Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. Mis Quarterly, 261-292.
  • 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), 710.
  • Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of business research, 61(12), 1203-1218.

Mediation, Moderation, and Moderated Mediation


  • Mathieu, J. E., & Taylor, S. R. (2006). Clarifying conditions and decision points for mediational type inferences in organizational behavior. Journal of Organizational Behavior, 27(8), 1031-1056.
  • Mathieu, J. E., DeShon, R. P., & Bergh, D. D. (2008). Mediational inferences in organizational research: Then, now, and beyond. Organizational Research Methods, 11(2), 203-223.
  • MacKinnon, D. P., Coxe, S., & Baraldi, A. N. (2012). Guidelines for the investigation of mediating variables in business research. Journal of Business and Psychology, 27(1), 1-14.
  • MacKinnon, D. P., & Pirlott, A. G. (2015). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19(1), 30-43.
  • Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825-852.
  • Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.
  • Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication monographs, 76(4), 408-420.
  • Homburg, C., Klarmann, M., & Schmitt, J. (2010). Brand awareness in business markets: When is it related to firm performance?. International Journal of Research in Marketing, 27(3), 201-212. (implementation of latent interactions)
  • Dave Kenny also has a bunch of good ones here: Dave Kenny Mediation

Moderation and Multigroup

  • Cheah, J.-H., Memon, M. A., Richard, J. E., Ting, H., & Cham, T.-H. (2020). CB-SEM latent interaction: Unconstrained and orthogonalized approaches. Australasian Marketing Journal.
  • Busenbark, J. R., Graffin, S. D., Campbell, R. J., & Lee, E. Y. A Marginal Effects Approach to Interpreting Main Effects and Moderation. Organizational Research Methods, 1094428120976838.
  • Byrne, B. M., & Stewart, S. M. (2006). Teacher's corner: The MACS approach to testing for multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling, 13(2), 287-321.
  • Schumacker, R. E., & Marcoulides, G. A. (1998). Interaction and nonlinear effects in structural equation modeling. Lawrence Erlbaum Associates Publishers.
  • Li, F., Harmer, P., Duncan, T. E., Duncan, S. C., Acock, A., & Boles, S. (1998). Approaches to testing interaction effects using structural equation modeling methodology. Multivariate Behavioral Research, 33(1), 1-39.
  • Floh, A., & Treiblmaier, H. (2006). What keeps the e-banking customer loyal? A multigroup analysis of the moderating role of consumer characteristics on e-loyalty in the financial service industry.
  • Chambel, M. J., Castanheira, F., & Sobral, F. (2016). Temporary agency versus permanent workers: A multigroup analysis of human resource management, work engagement and organizational commitment. Economic and Industrial Democracy, 37(4), 665–689.

Both or Other

  • Aguinis, H., Edwards, J. R., & Bradley, K. J. (2016). Improving our understanding of moderation and mediation in strategic management research. Organizational Research Methods, 1094428115627498.
  • Sardeshmukh, S. R., & Vandenberg, R. J. (2016). Integrating Moderation and Mediation A Structural Equation Modeling Approach. Organizational Research Methods, 1094428115621609.
  • Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate behavioral research, 42(1), 185-227.
  • Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

Partial Least Squares

  • Documentation Page on
  • Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  • Petter, S. (2018). "Haters Gonna Hate": PLS and Information Systems Research. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 49(2), 10-13.
  • Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial Least Squares Structural Equation Modeling-based Discrete Choice Modeling: An Illustration in Modeling Retailer Choice. Business Research, 12, 115-140.
  • Becker, J. M., Klein, K., and Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5), 359-394.
  • Becker, J.-M., Rai, A., Ringle, C. M., and Völckner, F. (2013). Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly, 37 (3), 665-694.
  • 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.
  • Hair, J. F., C. M. Ringle, and M. Sarstedt (2011). PLS-SEM. Indeed a Silver Bullet, Journal of Marketing Theory & Practice, 19 (2), 139-151.
  • Hair, J. F., M. Sarstedt, C. M. Ringle, and J. A. Mena (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research, Journal of the Academy of Marketing Science, 40 (3), 414-433.
  • Hair, J. F., M. Sarstedt, T. Pieper, and C. M. Ringle (2012). 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(5/6), 320-340.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Editorial-partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance.
  • Hair, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), 106-121.
  • Henseler, J., C. M. Ringle, and M. Sarstedt (2015). A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling, Journal of the Academy of Marketing Science, 43 (1), 115–135.
  • Henseler, J., C. M. Ringle, and M. Sarstedt (2016). Testing Measurement Invariance of Composites Using Partial Least Squares, International Marketing Review, 33 (3), 405-431.
  • Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Ketchen, D.J., Hair, J.F., Hult, G.T.M., and Calantone, R.J. (2014). Common Beliefs and Reality about Partial Least Squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(2), 182-209.
  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited.
  • Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
  • Lowry, P. B., & Gaskin, J. (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), 123-146.
  • McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17(2), 210-251.
  • Monge, C., Cruz, J., & López, F. (2014). Manufacturing and continuous improvement areas using partial least squares path modeling with multiple regression comparison. In Proceedings of CBU International Conference on Innovation, Technology Transfer and Education (2014), February (pp. 3-5).
  • Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Planning, 47(3), 161-167.
  • Ringle, C. M., M. Sarstedt, and D. W. Straub (2012). A Critical look at the Use of PLS-SEM in MIS Quarterly, MIS Quarterly, 36(1), iii-xiv.
  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In Measurement and research methods in international marketing (pp. 195-218). Emerald Group Publishing Limited.
  • Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
  • Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618-632.

General Topics

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Tabachnick & Fidell (2014). Using Multivariate Statistics (6th ed), Chapter 14: Structural Equation Modeling. Pp. 731-836.
  • Urdan, T. C. 2011. Statistics in Plain English. Routledge.
  • Newbold, P., Carlson, W., and Thorne, B. 2012. Statistics for Business and Economics. Pearson.
  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage learning.
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
  • Suits, D. B. (1957). Use of dummy variables in regression equations. Journal of the American Statistical Association, 52(280), 548-551.
  • Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor's comments: an update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, iii-xiv.
  • Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman and Hall.
  • Blunch, N. (2013). Introduction to structural equation modeling using IBM SPSS statistics and AMOS (2nd ed.). Los Angeles, CA: Sage.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford publications.
  • Argyrous, G. (2011). Statistics for research: with a guide to SPSS (3rd ed.). Thousand Oaks, CA: Sage Publications.
  • Byrne, B. M. (2009). Structural equation modeling with AMOS: basic concepts, applications, and programming (2nd ed.). Abingdon-on-Thames: Routledge.
  • Williams, L. J., Vandenberg, R. J., & Edwards, J. R. (2009). Structural equation modeling in management research: A guide for improved analysis. The Academy of Management Annals, 3 (1), 543-604.
  • Fife, D. (2019). The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis. Perspectives on Psychological Science

Model Fit

  • Kenny, D. A. (2012). Measuring Model Fit.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
  • Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230-258.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008) Structural Equation Modelling: Guidelines for Determining Model Fit. Journal of Business Research, 6(1), 53-60.
  • Barrett, P. (2007). Structural equation modelling: adjudging model fit. Personality and Individual Differences, 42, 815–824.
  • Bentler, P. M., & Chou, C. P. (1987) Practical issues in structural modeling. Sociological Methods & Research, 16, 78-117.
  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-600.
  • Bollen, K. A., & Long, J. S., Eds. (1993). Testing structural equation models. Newbury Park, CA: Sage
  • Enders, C.K., & Tofighi, D. (2008). The impact of misspecifying class-specific residual variances in growth mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 15, 75-95.
  • Hayduk, L., Cummings, G. G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! Testing! One, two three – Testing the theory in structural equation models! Personality and Individual Differences, 42, 841-50.
  • Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.
  • Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2014). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Resarch, in press.
  • Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling, 10, 333-3511.
  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
  • O'Boyle, E. H., Jr., Williams, L. J. (2011). Decomposing model fit: Measurement vs. theory in organizational research using latent variables. Journal of Applied Psychology, 96, 1-12.
  • Satorra, A., & Saris,W. E. (1985). The power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83–90.
  • Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W.R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58, 935-43.
  • Tanaka, J.S. (1987). "How big is big enough?": Sample size and goodness of fit in structural equation models with latent variables. Child Development, 58, 134-146.
  • Tofghi, D., & Enders, C. K. (2007). Identifying the correct number of classes in mixture models. In G. R. Hancock & K. M. Samulelsen (Eds.), Advances in latent variable mixture models (pp. 317-341). Greenwich, CT: Information Age.
  • Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services, 51, 72–82


  • Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66(4), 563-575.
  • Kolenikov, S., and Bollen, K. A. 2012. "Testing Negative Error Variances: Is a Heywood Case a Symptom of Misspecification?," Sociological Methods & Research (41:1), pp. 124-167.
  • Upadhaya, B., Munir, R., Blount, Y., and Su, S. 2018. "Does Organizational Culture Mediate the CSR–Strategy Relationship? Evidence from a Developing Country, Nepal," Journal of Business Research (91), pp. 108-122. (tests endogeneity)
  • Jalayer Khalilzadeh, Asli D.A. Tasci, Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research, In Tourism Management, Volume 62, 2017, Pages 89-96,
  • Green, J. P., Tonidandel, S., & Cortina, J. M. (2016). Getting through the gate: Statistical and methodological issues raised in the reviewing process. Organizational Research Methods, 19(3), 402-432.
  • Malhotra, Naresh K. Marketing research: An applied orientation, 5/e. Pearson Education India, 2008.
  • Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (2nd ed.). Los Angeles: SAGE Publications, Inc.
  • Blair, J., Czaja, R. F., & Blair, E. A. (2014). Designing surveys: A guide to decisions and procedures (3rd ed.). Sage Publications.
  • Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability.
  • Kenny, D. A. (2011). Respecification of Latent Variable Models.
  • Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the academy of marketing science, 40(1), 8-34.
  • Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, 16(2), 270-301. (for Cook's distance)
  • Winklhofer, H. M., & Diamantopoulos, A. (2002). Managerial evaluation of sales forecasting effectiveness: A MIMIC modeling approach. International Journal of Research in Marketing, 19(2), 151-166.
  • Thomas, D. M., & Watson, R. T. (2002). Q-sorting and MIS research: A primer. Communications of the Association for Information Systems, 8(1), 9.
  • Osborne, J. W. (2012). Power and Planning for Data Collection. In Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. Sage Publications.
  • Steenkamp, J. B. E., De Jong, M. G., & Baumgartner, H. (2010). Socially desirable response tendencies in survey research. Journal of Marketing Research, 47(2), 199-214.
  • Bacharach, S. B. (1989). Organizational theories: Some criteria for evaluation. Academy of management review, 14(4), 496-515.
  • Becker, T. E. (2005). Potential problems in the statistical control of variables in organizational research: A qualitative analysis with recommendations. Organizational Research Methods, 8(3), 274-289.
  • Dietz, W. H., & Gortmaker, S. L. (1985). Do we fatten our children at the television set? Obesity and television viewing in children and adolescents. Pediatrics, 75(5), 807-812.
  • Peterson, C., Park, N., & Seligman, M. E. (2005). Orientations to happiness and life satisfaction: The full life versus the empty life. Journal of happiness studies, 6(1), 25-41.
  • Sposito, V. A., Hand, M. L., & Skarpness, B. (1983). On the efficiency of using the sample kurtosis in selecting optimal lpestimators. Communications in Statistics-simulation and Computation, 12(3), 265-272.
  • McDonald, R. P. (1981). The dimensionality of tests and items. British Journal of mathematical and statistical Psychology, 34(1), 100-117.
  • Trochim, W. M., & Donnelly, J. P. (2006). The research methods knowledge base (3rd ed.). Cincinnati, OH:Atomic Dog.
  • Gravetter, F., & Wallnau, L. (2014). Essentials of statistics for the behavioral sciences (8th ed.). Belmont, CA: Wadsworth.
  • Field, A. (2000). Discovering statistics using spss for windows. London-Thousand Oaks- New Delhi: Sage publications.
  • Field, A. (2009). Discovering statistics using SPSS. London: SAGE.