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references.yml
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---
- code: yuan2000three
ref: "Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological methodology, 30(1), 165-200."
cite: Yuan et al. (2000)
- code: logan2019should
ref: "Logan, J., Jiang, H., Helsabeck, N., & Yeomans-Maldonado, G. (2019). Should I Allow my Confirmatory Factors to Correlate During Factor Extraction? Implications for the Applied Researcher."
cite: Logan et al. (2019)
- code: ten1999some
ref: "Ten Berge, J. M., Krijnen, W. P., Wansbeek, T., & Shapiro, A. (1999). Some new results on correlation-preserving factor scores prediction methods. Linear Algebra and its Applications, 289(1-3), 311-318."
cite: ten Berge et al. (1999)
- code: joreskog1969general
ref: "Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183-202."
cite: Jöreskog (1069)
- code: joreskog1969general
ref: "Jöreskog, K.G. (1969) A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202."
cite: Joreskog (1969)
- code: wold1985partial
ref: "Wold, H. (1985). In S. Kotz & NL Johnson (Eds.), Encyclopedia of statistical sciences. Partial least squares (Vol. 6, pp. 581591)."
cite: Wold (1985)
- code: joreskog1973general
ref: "Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system In: Goldberger AS, Duncan OD, editors. Structural Equation Models in the Social Sciences. New York: Seminar Press."
cite: Joreskog (1973)
- code: lodder2019modeling
ref: "Lodder, P., Denollet, J., Emons, W. H., Nefs, G., Pouwer, F., Speight, J., & Wicherts, J. M. (2019). Modeling interactions between latent variables in research on Type D personality: A Monte Carlo simulation and clinical study of depression and anxiety. Multivariate behavioral research, 54(5), 637-665."
cite: Lodder et al. (2019)
- code: rosseel2012lavaan
ref: "Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software, 48(2), 1–36. http://www.jstatsoft.org/v48/i02/."
cite: Rosseel (2012)
- code: tenenhaus2005pls
ref: "Tenenhaus, M., V. E. Vinzi, Y.-M. Chatelin, and C. Lauro (2005) PLS path modeling. Computational Statistics & Data Analysis 48, 159-205."
cite: Tenenhaus et al. (2005)
- code: hair2016primer
ref: "Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd Ed., Sage: Thousand Oaks."
cite: Hair et al. (2017)
- code: dijkstra2015consistent
ref: "Dijkstra, T. K., and Henseler, J. (2015). Consistent partial least squares path modeling. MIS quarterly, 39(2)."
cite: Dijkstra & Henseler (2015)
- code: fornell1981evaluating
ref: "Fornell, C. and D. F. Larcker (1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, pp. 39-5"
cite: Fornell & Larcker (1981)
- code: davis2007differential
ref: "Dillon, W. R, and M. Goldstein. (1987). Multivariate Analysis: Methods and Applications. Biometrical Journal 29 (6)."
cite: Dillon & Goldstein (1987)
- code: henseler2015new
ref: "Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135."
cite: Henseler et al. (2014)
- code: becker2018estimating
ref: "Becker, J. M., Ringle, C. M., & Sarstedt, M. (2018). Estimating moderating effects in PLS-SEM and PLSc-SEM: interaction term generation*Data treatment. Journal of Applied Structural Equation Modeling, 2(2), 1-21."
cite: Becker et al. (2018)
- code: lohmoller1989predictive
ref: "Lohmoller, J.-B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica Verlag."
cite: Lohmoller (1989)
- code: henseler2010comparison
ref: "Henseler, J., and Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82-109."
cite: Henseler & Chin (2010)
- code: zhao2010reconsidering
ref: "Zhao, X., Lynch Jr, J. G., and Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206."
cite: Zhao et al. (2010)
- code: becker2012hierarchical
ref: "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(5-6), 359-394."
cite: Becker et al. (2012)
- code: hair2011pls
ref: "Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152."
cite: Hair et al. (2011)
- code: henseler2006testing
ref: "Henseler, J., and Fassot, G. (2006). Testing moderating effects in PLS path models. In: Esposito Vinzi, V., Chin,W.W., Henseler, J., & Wang, H. (Eds.), Handbook PLS and Marketing. Berlin, Heidelberg, New York: Springer."
cite: Henseler & Fassot (2006)
- code: henseler2014common
ref: "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. Organizational Research Methods, 17(2), 182–209"
cite: Henseler et al. (2014)
- code: henseler2015adanco
ref: "Henseler, J. and Dijkstra, T.K. (2015), “ADANCO 2.0”, Composite Modeling, Kleve, available at: www.compositemodeling.com (accessed December 14, 2015)."
cite: Henseler & Dijkstra (2015)
- code: monecke2012sempls
ref: "Monecke, A., and Leisch, F. (2012). semPLS: structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1–32."
cite: Monecke & Leisch (2012)
- code: ringle2015smartpls
ref: "Ringle, C. M., Wende, S., and Becker, J-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com"
cite: Ringle et al. (2015)
- code: ronkko2016matrixpls
ref: "Rönkkö, M. (2016). R package matrixpls: Matrix-based partial least squares estimation (version 0.7.0). https://CRAN.R-project.org/package=matrixpls"
cite: Rönkkö (2016)
- code: sarstedt2016estimation
ref: "Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010"
cite: Sarstedt et al. (2016)
- code: cohen2013applied
ref: "Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge."
cite: Cohen (2013)
- code: fornelllarcker1981
ref: "Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50."
cite: Fornell & Larcker (1981)
- code: raydanksvaldez2021
ref: "Ray, S., Danks, N.P., & Valdez A.C. (2021). Ray, Soumya and Danks, Nicholas and Calero Valdez, André, SEMinR: domain-specific language for building, estimating, and visualizing structural equation models in R (v2.1.0). SSRN"
cite: Ray, Danks, & Valdez (2021)
- code: henseler2009MGA
ref: "Henseler, J., Ringle, C. M. & Sinkovics, R. R. New Challenges to International Marketing. Adv Int Marketing 277–319 (2009) doi:10.1108/s1474-7979(2009)0000020014."
cite: Henseler, Ringle, & Sinkovics (2009)