References
- Alhamzawi, R., & Ali, H. T. M. (2018). The Bayesian elastic net regression. Communications in Statistics - Simulation and Computation, 47, 1168–1178. https://doi.org/10.1080/03610918.2017.1307399
- Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815–824. https://doi.org/10.1016/j.paid.2006.09.018
- Benjamini, Y. (2010). Simultaneous and selective inference: Current successes and future challenges. Biometrical Journal, 52, 708–721. https://doi.org/10.1002/bimj.200900299
- Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L. (2013). Valid post-selection inference. Annals of Statistics, 41, 802–837. https://doi.org/10.1214/12-AOS1077
- Clark, A. E., & Troskie, C. G. (2006). Ridge regression - A simulation study. Communications in Statistics: Simulation and Computation, 35, 605–619. https://doi.org/10.1080/03610910600716811
- Devlieger, I., & Rosseel, Y. (2017). Factor score path analysis. Methodology, 13, 31–38. https://doi.org/10.1027/1614-2241/a000130
- Devlieger, I., & Rosseel, Y. (2019). Multilevel factor score regression. Multivariate Behavioral Research, 55, 600–624. https://doi.org/10.1080/00273171.2019.1661817
- Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., Mcclean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
- Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap ([Repr.]). Chapman & Hall/CRC. https://katalog.ub.uni-leipzig.de/Record/0003155546
- Friedman, L., & Wall, M. (2005). Graphical views of suppression and multicollinearity in multiple linear regression. American Statistician, 59, 127–136. https://doi.org/10.1198/000313005X41337
- Genz, A., & Bretz, F. (2009). Computation of multivariate normal and t probabilities. Lecture Notes in Statistics, 195. Springer-Verlag, Heidelberg. ISBN 978-3-642-01688-2
- Gökpinar, E., & Ebegil, M. (2016). A study on tests of hypothesis based on ridge estimator. Gazi University Journal of Science, 29, 769–781. https://dergipark.org.tr/tr/pub/gujs/issue/27537/289687
- Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23, 519–529. https://doi.org/10.1287/mksc.1040.0070
- Groß, J. (2003). Linear regression (Vol. 175). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-55864-1
- Halawa, A. M., & El Bassiouni, M. Y. (2000). Tests of regression coefficients under ridge regression models. Journal of Statistical Computation and Simulation, 65, 341–356. https://doi.org/10.1080/00949650008812006
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Elements, 1, 337–387. https://doi.org/10.1007/b94608
- Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: The lasso and generalizations. Chapman & Hall/CRC.
- Hoerl, A. E., Kannard, R. W., & Baldwin, K. F. (1975). Ridge regression: Some simulations. Communications in Statistics, 4, 105–123. https://doi.org/10.1080/03610927508827232
- Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for Nonorthogonal problems. Technometrics, 12, 55–67. https://doi.org/10.1080/00401706.1970.10488634
- Huang, P.-H. (2018). A penalized likelihood method for multi-group structural equation modelling. British Journal of Mathematical and Statistical Psychology, 71, 499–522. https://doi.org/10.1111/bmsp.12130
- Huang, P.-H. (2020a). lslx: Semi-confirmatory structural equation modeling via penalized likelihood. Journal of Statistical Software, 93, 1–37. https://www.jstatsoft.org/article/view/v093i07
- Huang, P.-H. (2020b). Postselection inference in structural equation modeling. Multivariate Behavioral Research, 55, 344–360. https://doi.org/10.1080/00273171.2019.1634996
- Huang, P.-H., Chen, H., & Weng, L.-J. (2017). A penalized likelihood method for structural equation modeling. Psychometrika, 82, 329–354. https://doi.org/10.1007/s11336-017-9566-9
- Jacobucci, R. (2017). regsem: Regularized structural equation modeling (pp. 1–13). arXiv. http://arxiv.org/abs/1703.08489
- Jacobucci, R., Brandmaier, A. M., & Kievit, R. A. (2019). A practical guide to variable selection in structural equation modeling by using regularized multiple-indicators, multiple-causes models. Advances in Methods and Practices in Psychological Science, 2, 55–76. https://doi.org/10.1177/2515245919826527
- Jacobucci, R., & Grimm, K. J. (2018). Comparison of frequentist and bayesian regularization in structural equation modeling. Structural Equation Modeling, 00, 1–11. https://doi.org/10.1080/10705511.2017.1410822
- Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling, 234, 555–566. https://doi.org/10.1080/10705511.2016.1154793
- Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202. https://doi.org/10.1007/BF02289343
- Kaplan, D. (1994). Estimator conditioning diagnostics for covariance structure models. Sociological Methods & Research, 23, 200–229. https://doi.org/10.1177/0049124194023002003
- Kidwell, J. S., & Brown, L. H. (1982). Ridge regression as a technique for analyzing models with multicollinearity. Journal of Marriage and the Family, 44, 287. https://doi.org/10.2307/351539
- Lee, J. D., Sun, D. L., Sun, Y., & Taylor, J. E. (2016). Exact post-selection inference, with application to the lasso. The Annals of Statistics, 44, 907–927. https://doi.org/10.1214/15-AOS1371
- Liang, X., & Jacobucci, R. (2020). Regularized structural equation modeling to detect measurement bias: Evaluation of lasso, adaptive lasso, and elastic net. Structural Equation Modeling, 27, 722–734. https://doi.org/10.1080/10705511.2019.1693273
- Marsh, H. W., Dowson, M., Pietsch, J., & Walker, R. (2004). Why multicollinearity matters: A reexamination of relations between self-efficacy, self-concept, and achievement. Journal of Educational Psychology, 96, 518–522. https://doi.org/10.1037/0022-0663.96.3.518
- Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28, 268. https://doi.org/10.2307/3172863
- Mason, R., & Brown, W. G. (1975). Multicollinearity problems and ridge regression in sociological models. Social Science Research, 4, 135–149. https://doi.org/10.1016/0049-089X(75)90008-3
- Muthén, B. O. (2002). Beyond SEM: General latent variable modelling. Behaviormetrika, 29, 81–117. https://doi.org/10.2333/bhmk.29.81
- Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599–620. https://doi.org/10.1207/S15328007SEM0904{\_}8
- Obenchain, R. L. (1977). Classical F-tests and confidence regions for ridge regression. Technometrics, 19, 429. https://doi.org/10.2307/1267882
- R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/
- Rhemtulla, M., Van Bork, R., & Borsboom, D. (2019). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 8, 55. https://doi.org/10.1037/met0000220
- Rinaldo, A., Wasserman, L., & G’Sell, M. (2019). Bootstrapping and sample splitting for high-dimensional, assumption-lean inference. The Annals of Statistics, 47, 3438–3469. https://doi.org/10.1214/18-aos1784
- Rosseel, Y. (2012). {lavaan}: An{R} package for structural equation modeling. Journal of Statistical Software, 48, 1–36. https://doi.org/10.18637/jss.v048.i02
- Scharf, F., & Nestler, S. (2019). Should regularization replace simple structure rotation in exploratory factor analysis? Structural Equation Modeling, 26, 576–590. https://doi.org/10.1080/10705511.2018.1558060
- Serang, S., Jacobucci, R., Brimhall, K. C., & Grimm, K. J. (2017a). Exploratory mediation analysis via regularization. Structural Equation Modeling, 24, 733–744. https://doi.org/10.1080/10705511.2017.1311775
- Serang, S., Jacobucci, R., Brimhall, K. C., & Grimm, K. J. (2017b). Exploratory mediation analysis via regularization. Structural Equation Modeling, 24, 733–744.
- Shieh, G. (2010). On the misconception of multicollinearity in detection of moderating effects: Multicollinearity is not always detrimental. Multivariate Behavioral Research, 45, 483–507. https://doi.org/10.1080/00273171.2010.483393
- Sirimongkolkasem, T., & Drikvandi, R. (2019). On regularisation methods for analysis of high dimensional data. Annals of Data Science, 6, 737–763. https://doi.org/10.1007/s40745-019-00209-4
- Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66, 563–575. https://doi.org/10.1007/BF02296196
- Smid, S. C., Mcneish, D., Miočević, M., & Van De Schoot, R. (2019). Bayesian versus frequentist estimation for structural equation models in small sample contexts: A systematic review. Pre-Print, 1–31. https://doi.org/10.1080/10705511.2019.1577140
- Stahlecker, P., & Schmidt, K. (1996). Biased estimation and hypothesis testing in linear regression. Acta Applicandae Mathematicae, 43, 145–151. https://doi.org/10.1007/BF00046995
- Taylor, J., & Tibshirani, R. (2018). Post-selection inference for 1-penalized likelihood models. Canadian Journal of Statistics, 46, 41–61. https://doi.org/10.1002/cjs.11313
- Taylor, J., & Tibshirani, R. J. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112, 7629–7634. https://doi.org/10.1073/pnas.1507583112
- Wilcox, R. R. (2019). Multicolinearity and ridge regression: Results on type I errors, power and heteroscedasticity. Journal of Applied Statistics, 46, 946–957. https://doi.org/10.1080/02664763.2018.1526891
- Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 67, 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x