| 回归混合模型：方法进展与软件实现 |
| |
| 引用本文： | 王孟成,毕向阳. 回归混合模型：方法进展与软件实现[J]. 心理科学进展, 2018, 26(12): 2272-2280. DOI: 10.3724/SP.J.1042.2018.02272 |
| |
| 作者姓名： | 王孟成 毕向阳 |
| |
| 作者单位： | 1. 广州大学心理系;2. 广州大学心理测量与潜变量建模研究中心;3. 广东省未成年人心理健康与教育认知神经科学实验室, 广州 510006;4. 中国政法大学社会学院, 北京 102249 |
| |
| 基金项目： | *国家自然科学基金(31400904);广州大学“创新强校工程”青年创新人才类项目(2014WQNCX069);广州大学青年拔尖人才培养项目(BJ201715) |
| |
| 摘 要： | 近来以个体为分析对象的方法日益受到研究者的重视, 其中潜类别和潜剖面模型最为流行。研究者在潜类别和潜剖面模型建模时往往需要进一步探讨协变量与潜分组之间的关系(即带有协变量的潜类别模型)。例如, 哪些变量预测个体类别归属, 以及个体的类别归属对结果变量的预测。本文对近年来研究者提出的各种方法进行了回顾和比较。包括当结果变量是分类变量的LTB法; 当结果变量是连续变量时的BCH和稳健三步法。在此基础上, 文章为应用研究者提供了Mplus软件示例, 并在最后对当前研究存在的问题和未来研究趋势进行了简要评价。
|
| 关 键 词： | 个体中心方法 混合模型 潜类别分析 潜变量建模 Mplus |
| 收稿时间： | 2017-03-04 |
Regression mixture modeling: Advances in method and its implementation |
| |
| WANG Meng-Cheng,BI Xiangyang. Regression mixture modeling: Advances in method and its implementation[J]. Advances In Psychological Science, 2018, 26(12): 2272-2280. DOI: 10.3724/SP.J.1042.2018.02272 |
| |
| Authors: | WANG Meng-Cheng BI Xiangyang |
| |
| Affiliation: | 1. Department of Psychology, Guangzhou University;2. The Center for Psychometric and Latent Variable Modeling, Guangzhou University;3. The Key Laboratory for Juveniles Mental Health and Educational Neuroscience in Guangdong Province, Guangzhou University, Guangzhou 510006, China;4. School of Sociology, China University of Political Science and Law, Beijing 102249, China |
| |
| 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. |
| |
| Keywords: | person-centered method mixture modeling latent class analysis latent variable modeling Mplus |
|
| 点击此处可从《心理科学进展》浏览原始摘要信息 |
|
点击此处可从《心理科学进展》下载全文 |
|
