第2版が出ていたようだ。
6.2.6 LCAモデルにおける補助変数の考え方
ワンステップ法の問題を解決するために、様々なアプローチが開発されてきた。よく知られているアプローチとしては、擬似クラス(PC)法(Clark and Muthen 2009; Wang et al. 2005a)、3 段階法(Vermunt 2010; Asparouhov and Muthen 2013)、Lanza法(Lanza et al. 2013)、BCH法(Bakk and Vermunt 2016)があり、これらはMplusで利用可能である。Mplusにおけるこれらの方法の比較、推奨事項、および実装については、Asparouhov and Muthen (2015a)で詳細に議論されている。
Clark, S. and Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis.
https://www.statmodel.com/download/relatinglca.pdfWang, C.P., Brown, C.H., and Bandeen-Roche, K. (2005a). Residual diagnostics for growth mixture models: examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association 100: 1054–1076.
https://www.researchgate.net/publication/4741694_Residual_Diagnostics_for_Growth_Mixture_Models_Examining_the_Impact_of_a_Preventive_Intervention_on_Multiple_Trajectories_of_Aggressive_BehaviorVermunt, J.K. (2010). Latent class modeling with covariates: two improved three-step approaches. Political Analysis 18: 450–469.
https://www.researchgate.net/publication/228389117_Latent_Class_Modeling_with_Covariates_Two_Improved_Three-Step_ApproachesAsparouhov, T. and Muthén, B. (2013). Auxiliary variables in mixture modeling: a 3-step approach using Mplus. Mplus Web Notes, 15. Los Angeles: Muthén & Muthén.
https://www.statmodel.com/download/webnotes/webnote15.pdfLanza, S.T., Tan, X., and Bray, B.C. (2013). Latent class analysis with distal outcomes: a flexible model-based approach. Structural Equation Modeling: A Multidisciplinary Journal 20: 1–26.
https://www.tandfonline.com/doi/abs/10.1080/10705511.2013.742377Bakk, Z. and Vermunt, J.K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal 23 (1): 20–31.
https://www.researchgate.net/publication/275960665_Robustness_of_Stepwise_Latent_Class_Modeling_With_Continuous_Distal_OutcomesAsparouhov, T. and Muthén, B. (2015a). Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Secondary Model. Mplus Web Notes: No. 21, Version 2, Muthén & Muthén, Los Angeles.
https://www.statmodel.com/download/asparouhov_muthen_2014.pdf
具体的なAuxiliary Settingはこちらを参照のこと。
