Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers 论文

2019International Journal of Behavioral Development引用 802

Forest ecology and managementStatistical Methods and Bayesian InferenceBayesian Methods and Mixture Models

Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models Forest ecology and management

作者

摘要

The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models, (c) model fit and interpretability, (d) investigation of patterns of profiles in a retained model, (e) covariate analysis, and (f) presentation of results. A worked example is provided with syntax and results to exemplify the steps.

作者查看全部 (3)

Darrell M. Hull

E. Whitney G. Moore

Sarah L. Ferguson

Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers 论文

详细信息

摘要

作者查看全部 (3)

相关技术查看全部 (2)

相关事件

相关文章