An Introduction to Latent Variable Mixture Modeling (Part 1): Overview and Cross-Sectional Latent Class and Latent Profile Analyses
An Introduction to Latent Variable Mixture Modeling (Part 1): Overview and Cross-Sectional Latent Class and Latent Profile Analyses
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DOI：
10.1093/jpepsy/jst084
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发表时间：
2014-03-01
影响因子：
3.6
通讯作者：
Parra, Gilbert R.
中科院分区：
文献类型：
--
作者：
Berlin, Kristoffer S.;Williams, Natalie A.;Parra, Gilbert R.
目的儿童心理学家经常对在异质性横断面数据中发现模式感兴趣。潜变量混合建模是一种新兴的以人为中心的统计方法，通过将个体分类为具有相似（更同质）模式的未观察到的分组（潜类）来建模异质性。本文的目的是提供一个非技术性的介绍横截面混合建模。方法对隐变量混合模型进行了综述，并对两个横截面的例子进行了回顾和区分。结果使用1998-1999年幼儿纵向研究-幼儿园类数据文件提供了一步一步的潜在类和潜在的配置文件分析的儿科心理学的例子。结论潜变量混合模型是一种技术，是有用的儿科心理学家谁希望找到分组的个人共享相似的数据模式，以确定在何种程度上这些模式可能涉及到感兴趣的变量。
Objective Pediatric psychologists are often interested in finding patterns in heterogeneous cross-sectional data. Latent variable mixture modeling is an emerging person-centered statistical approach that models heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of this article is to offer a nontechnical introduction to cross-sectional mixture modeling. Method An overview of latent variable mixture modeling is provided and 2 cross-sectional examples are reviewed and distinguished. Results Step-by-step pediatric psychology examples of latent class and latent profile analyses are provided using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999 data file. Conclusions Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar data patterns to determine the extent to which these patterns may relate to variables of interest.