Regression mixture modeling: Advances in method and its implementation

doi:10.3724/SP.J.1042.2018.02272

Abstract

Abstract:

The person-centered methods, including latent class analysis (LCA) and latent profile analysis (LPA), are increasingly popular in recent years. Researchers often add covariate variables (i.e., predictor and distal variables) into LCA and LPA models. This kind of models are also called regression mixture models. In this paper, we introduce several new methods. Those methods include (1) the LTB method proposed by Lanza, Tan and Bray (2013) to model categorical outcome variables; and (2) the BCH method proposed by Bolck, Croon and Hagenaars (2004) to deal with continuous distal variables. Using an empirical example, we demonstrate the process of analyses in Mplus. The future directions of those new methods were also discussed.

Key words: person-centered method, mixture modeling, latent class analysis, latent variable modeling, Mplus

CLC Number:

B841

WANG Meng-Cheng, BI Xiangyang. Regression mixture modeling: Advances in method and its implementation[J]. Advances in Psychological Science, 2018, 26(12): 2272-2280.

Figures/Tables 15

References 16

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适用情况		方法	Mplus语句: Auxiliary=()	评价
结果变量	分类变量	单步法	无单独语句	直接将类别结果变量作为LCA的测量指标; 这种做法显然会影响测量模型; 纳入不同的结果变量会造成测量模型结果的差异, 因此不推荐使用。
	分类变量	LTB	DCAT	是处理类别结果变量最好的方法之一, 推荐使用。
	连续变量	单步法	无单独语句	非正态时表现不佳。
		BCH	BCH	是处理连续结果变量最好的方法之一, 在 DU3STEP不报告结果时使用。
		稳健三步法：类别方差不等	DU3STEP	在结果变量类别内正态分布, 方差不等时表现佳。但会出现类别顺序变化的不足。
		稳健三步法：类别方差相等	DE3STEP	在结果变量类别内正态分布, 方差相等时表现佳。
		LTB	DCON	对假设前提比较敏感, 当假设违反时会扭曲估计结果, 不推荐使用
		PC method	E	精确性较差, 不推荐实际使用
预测变量		PC method	R	结果有偏, 不推荐使用。
		单步法	无单独语句	表现良好, 当变量较多时使用不便。
		稳健三步法	R3STEP	表现良好, 操作方便, 推荐使用。

适用情况		方法	Mplus语句: Auxiliary=()	评价
结果变量	分类变量	单步法	无单独语句	直接将类别结果变量作为LCA的测量指标; 这种做法显然会影响测量模型; 纳入不同的结果变量会造成测量模型结果的差异, 因此不推荐使用。
	分类变量	LTB	DCAT	是处理类别结果变量最好的方法之一, 推荐使用。
	连续变量	单步法	无单独语句	非正态时表现不佳。
		BCH	BCH	是处理连续结果变量最好的方法之一, 在 DU3STEP不报告结果时使用。
		稳健三步法：类别方差不等	DU3STEP	在结果变量类别内正态分布, 方差不等时表现佳。但会出现类别顺序变化的不足。
		稳健三步法：类别方差相等	DE3STEP	在结果变量类别内正态分布, 方差相等时表现佳。
		LTB	DCON	对假设前提比较敏感, 当假设违反时会扭曲估计结果, 不推荐使用
		PC method	E	精确性较差, 不推荐实际使用
预测变量		PC method	R	结果有偏, 不推荐使用。
		单步法	无单独语句	表现良好, 当变量较多时使用不便。
		稳健三步法	R3STEP	表现良好, 操作方便, 推荐使用。

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