References
- Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
- Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika, 61, 109–121. https://doi.org/10.1007/BF02296961
- Browne, M. W. (1974). Generalized least squares estimators in the analysis of covariance structures. South African Statistical Journal, 8, 1–24. https://journals.co.za/content/sasj/8/1/AJA0038271X_175
- Browne, M. W. (1977). The analysis of patterned correlation matrices by generalized least squares. British Journal of Mathematical and Statistical Psychology, 30, 113–124. https://doi.org/10.1111/j.2044-8317.1977.tb00730.x
- Browne, M. W. (1982). Covariance structures. In D. M. Hawkins (Ed.), Topics in applied multivariate analysis (pp. 72–141). Cambridge University Press.
- Browne, M. W. (1984). Asymptotically distribution‐free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83. https://doi.org/10.1111/j.2044-8317.1984.tb00789.x
- Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81. https://doi.org/10.1037/h0046016
- Fuller, W. A. (2009). Measurement error models (Vol. 305). John Wiley & Sons.
- Golub, G. H., & Pereyra, V. (1973). The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate. SIAM Journal on Numerical Analysis, 10, 413–432. https://epubs.siam.org/doi/abs/10.1137/0710036
- Golub, G. H., & Pereyra, V. (2003). Separable nonlinear least squares: The variable projection method and its applications. Inverse Problems, 19, R1–R26. https://doi.org/10.1088/0266-5611/19/2/201
- Hägglund, G. (1982). Factor analysis by instrumental variables methods. Psychometrika, 47, 209–222. https://doi.org/10.1007/BF02296276
- Holzinger, K., & Swineford, F. (1939). A study in factor analysis: The stability of a bifactor solution. Supplementary Educational Monograph, no. 48. University of Chicago Press.
- Joreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/BF02289343
- Jöreskog, K. G., Olsson, U. H., & Wallentin, F. Y. (2016). Multivariate analysis with LISREL. Springer.
- Kreiberg, D., Söderström, T., & Yang-Wallentin, F. (2016). Errors-in-variables system identification using structural equation modeling. Automatica, 66, 218–230. https://doi.org/10.1016/j.automatica.2015.12.007
- Magnus, J. R., & Neudecker, H. (1999). Matrix differential calculus with applications in statistics and econometrics. John Wiley & Sons.
- Marcoulides, G. A., & Schumacker, R. E. (Eds). (1996). Advanced structural equation modeling: Issues and techniques. Lawrence Erlbaum Associates, Inc.
- Marsh, H., & Bailey, M. (1991). Confirmatory factor analysis of multitrait-multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15, 47–70. https://doi.org/10.1177/014662169101500106
- MATLAB. (2019). version R2019b. The MathWorks Inc.
- Miller, F. G., Johnson, A. H., Yu, H., Chafouleas, S. M., McCoach, D. B., Riley-Tillman, T. C., & Welsh, M. E. (2018). Methods matter: A multi-trait multi-method analysis of student behavior. Journal of school psychology, 68, 53–72. https://doi.org/10.1016/j.jsp.2018.01.002
- Muthén, B., & Asparouhov, T. (2011). Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework. In Handbook of advanced multilevel analysis (pp. 23–48). Routledge.
- Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
- R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
- Rosseel, Y. (2012). lavaan:An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of statistical software, 48, 1–36.
- Sörbom, D. (1989). Model modification. Psychometrika, 54, 371–384. https://doi.org/10.1007/BF02294623
- Ye, K., & Lim, L. H. (2016). Every matrix is a product of Toeplitz matrices. Foundations of Computational Mathematics, 16, 577–598. https://doi.org/10.1007/s10208-015-9254-z