Difference between revisions of "References"

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*Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
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==Constructs and Validity==
*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.
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*Devellis, R. F. (2003). Scale Development: Theory and Applications Second Edition (Applied Social Research Methods).
*Malhotra N. K., Dash S. (2011). Marketing Research an Applied Orientation. London: Pearson Publishing.
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*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.
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*Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 64-73.
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*Yaniv, E. (2011). Construct clarity in theories of management and organization. Academy of Management Review, 36(3), 590-592.
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* Editor’s Comments. (2011). Construct clarity in theories of management and organization. Academy of Management Review, 36(3), 590-592.
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*Law, K. S., Wong, C. S., & Mobley, W. M. (1998). Toward a taxonomy of multidimensional constructs. Academy of management review, 23(4), 741-755.
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*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.
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*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.
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*Krosnick, J. A. (1999). Survey research. Annual review of psychology, 50(1), 537-567.
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*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.
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*Bolton, R. N. (1993). Pretesting questionnaires: content analyses of respondents' concurrent verbal protocols. Marketing science, 12(3), 280-303.
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*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.
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*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.
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*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.
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*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.
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*MacKenzie, S. B. (2003). The dangers of poor construct conceptualization. Journal of the Academy of Marketing Science, 31(3), 323-326.
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*Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social indicators research, 46(2), 137-155.
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*Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. Structural equation modeling: Present and future, 195-216.
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==Measurement Models==
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===Exploratory Factor Analysis==
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*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.
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*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.
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*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.
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*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.
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*Ferguson, E., & Cox, T. (1993). Exploratory factor analysis: A users’ guide. International Journal of Selection and Assessment, 1(2), 84-94.
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*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.
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===Confirmatory Factor Analysis==
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*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.
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*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.
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*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.
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*Doty, D. H., & Glick, W. H. (1998). Common methods bias: does common methods variance really bias results?. Organizational research methods, 1(4), 374-406.
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*Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20(4), 872-882.
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*Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling, 11(2), 272-300.
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*Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210-222.
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*Brown, T. A. (2014). Confirmatory factor analysis for applied research (2nd ed.). Guilford Publications.
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*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.
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*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.
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*MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542-555.
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*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.
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*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.
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*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.
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===Other==
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*Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological methods, 5(2), 155.
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*Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological assessment, 7(3), 286.
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*Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of marketing research, 186-192.
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*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.
 
*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.
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*Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. Mis Quarterly, 261-292.
*Hayes, Andrew. F., 2013. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, New York: The Guilford Press.
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*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.
*Byrne, B. M. 2009. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge Academic, 416 pages.
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*Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of business research, 61(12), 1203-1218.
*Privitera, G. J. 2015. Statistics for the Behavioral Sciences, (2nd ed.). Los Angeles, SAGE Publications, Inc.
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*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.
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==Mediation, Moderation, and Moderated Mediation==
*Timothy A. Brown, (2015), Confirmatory Factor Analysis for Applied Research, Second Edition, The Guildford Press Publishers, ISBN: 978-1-4625-1536-3
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===Mediation===
*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.
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*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.
*Costello A., Osborne, J. (2005), “Best Practices in EFA,” Practical Assessment, Research & Evaluation, vol. 10(7), pp. 1-9.
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*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.
*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.
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*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.
*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.
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*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.
*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.
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*Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825-852.
*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.
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*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.
*Kenny, D.A. (2012), “Measuring Model fit,” http://davidakenny.net/cm/fit.htm
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*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.
*Kenny, D.A. (2011) “Respecification of Latent Variable Models,” http://davidakenny.net/cm/respec.htm (provides justification for covarying error terms)
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*Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication monographs, 76(4), 408-420.
*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.
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*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.
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===Moderation and Multigroup===
*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.
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*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.
*Browne, M.W., Cudeck, R. (1992), “Alternative Ways of Assessing Model Fit,” SMR, vol. 21(2), pp. 230-258.
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*Schumacker, R. E., & Marcoulides, G. A. (1998). Interaction and nonlinear effects in structural equation modeling. Lawrence Erlbaum Associates Publishers.
*Baumgartner & Weijter (2016), SEM, Chapter 11 (see model fit indices explained, among other SEM topics)
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*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.
*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.
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*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.
*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.
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===Both or Other===
*Thomas, D.M., Watson, R.T. (2003), “Q-Sorting and MIS Research: A Primer,” CAIS, vol. 8, pp. 141-156.
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*Aguinis, H., Edwards, J. R., & Bradley, K. J. (2016). Improving our understanding of moderation and mediation in strategic management research. Organizational Research Methods, 1094428115627498.
*Osborne, J. (2013) “Power and Planning for Data Collection,” in Best Practices in Data Cleaning, Chapter 2, Sage pp. 19-41.
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*Sardeshmukh, S. R., & Vandenberg, R. J. (2016). Integrating Moderation and Mediation A Structural Equation Modeling Approach. Organizational Research Methods, 1094428115621609.
*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.
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*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.
*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.
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*Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.
*Steenkamp, J., DeJong, M., Baumgartner, H. (2010), “Socially Desirable Response Tendencies in Survey Research,” Journal of Marketing Research, vol. XLVII, pp. 199-214.
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*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.
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==Partial Least Squares==
*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.
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*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.
*Byrne B., (2008) “Testing for multigroup equivalence of a measuring instrument: A walk through the process,” Psicothema Vol. 20(4), pp. 872-882
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*McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17(2), 210-251.
*Byrne, B. (2004), “Testing for Multigroup Invariance using AMOS Graphics: A Road Less Traveled,” Structural Equation Modeling, vol. 11(2) 272-300.
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*Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Planning, 47(3), 161-167.
*Schmitt, N. and Kuljanin, G. (2008) “Measurement Invariance: Review of practice and implications,” Human Resource Management Review, vol.18, 210–222
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*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.
*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.
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*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).
*Bacharach, S. (1989), "Organizational theories: Some criteria for evaluation," Academy of Management Review 14(4), pp. 496-515.
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*Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
*Zhoa et al (2010) “Reconsidering Baron and Kenny” JCR.  
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*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.
*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.
+
*Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (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), 320-340.
*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.
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*F. Hair Jr, 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.
*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.
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*Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
*Floh & Treiblmaier 2006 on multigroup moderation in eBanking.
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*Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Editorial-partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance.
*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.
+
*Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182-209.
*MacKenzie, S. (2003), "The dangers of poor construct conceptualization," Journal of the Academy of Marketing Science 31(3), pp. 323.
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*Suits, D. B. (1957), "Use of Dummy Variables in Regression Equations," Journal of the American Statistical Association 52(280), pp. 548-551.
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*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.
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*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
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*Rigdon, E.E. (2014) “Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead” Long Range Planning (47), pp. 161-167.
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*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
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*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.
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*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.
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*Hayes, Andrew F. "Beyond Baron and Kenny: Statistical mediation analysis in the new millennium." Communication monographs 76.4 (2009): 408-420.
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*Wong, Ken Kwong-Kay. "Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS." Marketing Bulletin 24.1 (2013): 1-32.
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*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.
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*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.
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*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.
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*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  
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*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.
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*Henseler, Jörg, et al. "Common beliefs and reality about PLS comments on Rönkkö and Evermann (2013)." Organizational Research Methods (2014): 1094428114526928.
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*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)
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*Cook, R. Dennis; Weisberg, Sanford (1982). Residuals and Influence in Regression. New York, NY: Chapman & Hall. ISBN 0-412-24280-X.
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*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.
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*N.J. Blunch, Introduction to structural equation modeling using IBM SPSS statistics and AMOS, 2nd ed. Los Angeles, Calif.: SAGE, 2013.
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*R.B. Kline, Principles and practice of structural equation modeling, 3rd ed. New York: Guilford Press, 2011.
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*G.Argyrous, Statistics for research: with a guide to spss, 3rd ed. Thousand Oaks, CA: Sage Publications, 2011.
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*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.
 
*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.
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*McDonald, R. P., “The Dimensionality of test and items,” British journal of mathematical and statistical psychology, 34: 100-117. 1981. (talks about MaxR(H))
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==General Topics==
*Ferguson, E., & Cox, T. (1993). Exploratory factor analysis: A users’ guide. International Journal of Selection and Assessment, 1(2), 84-94. (gives justification for 0.20 difference in cross-loadings)
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*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.
*Podsakoff, Philip M., Scott B. MacKenzie, and Nathan P. Podsakoff. "Sources of method bias in social science research and recommendations on how to control it." Annual review of psychology 63 (2012): 539-569.
+
*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.
 +
 
 +
===Model Fit===
 +
*Kenny, D. A. (2012). Measuring Model Fit. http://davidakenny.net/cm/fit.htm
 +
*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. Structural Equation Modelling: Guidelines for Determining Model Fit. Journal of Business Research, 6(1), 53-60.
 +
 
 +
==Miscellaneous==
 +
*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. http://davidakenny.net/cm/respec.htm
 +
*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.
 +
*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.

Revision as of 09:37, 23 January 2017

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.
  • 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.
  • 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.

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.
  • 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.

=Confirmatory Factor Analysis

  • 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.
  • 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.
  • Doty, D. H., & Glick, W. H. (1998). Common methods bias: does common methods variance really bias results?. Organizational research methods, 1(4), 374-406.
  • 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.
  • 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.
  • 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.
  • 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.

=Other

  • 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

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.
  • 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.
  • Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication monographs, 76(4), 408-420.

Moderation and Multigroup

  • 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.

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

  • 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.
  • Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Planning, 47(3), 161-167.
  • 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.
  • 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).
  • Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
  • 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.
  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (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), 320-340.
  • F. Hair Jr, 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.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Editorial-partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance.
  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182-209.
  • 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.

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.
  • 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.

Model Fit

  • Kenny, D. A. (2012). Measuring Model Fit. http://davidakenny.net/cm/fit.htm
  • 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. Structural Equation Modelling: Guidelines for Determining Model Fit. Journal of Business Research, 6(1), 53-60.

Miscellaneous

  • 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. http://davidakenny.net/cm/respec.htm
  • 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.
  • 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.