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如何使用 R 包 lmfa 探索密集纵向数据中的个体内和个体间测量模型差异。

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

机构信息

Department of Methodology and Statistics, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.

出版信息

Behav Res Methods. 2023 Aug;55(5):2387-2422. doi: 10.3758/s13428-022-01898-1. Epub 2022 Sep 1.

Abstract

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.

摘要

密集纵向数据 (ILD) 已成为研究心理结构内个体动态 (或其中的个体间差异) 的热门方法。在研究动态之前,必须检查测量模型 (MM) 是否在不同的主体和时间上相同,因此,所测量的结构是否具有相同的含义。如果 MM 不同(例如,由于项目解释或反应模式的变化),则观察结果无法进行有效比较。可以使用潜在马尔可夫因子分析 (LMFA) 来探索 ILD 中 MM 的差异,该方法根据潜在的 MM 对观察结果进行分类(同时针对许多主体和时间点),从而显示哪些观察结果是可比较的。然而,该方法的复杂性或直到现在都没有用于 LMFA 的开源软件这一事实可能会阻碍研究人员在实践中应用该方法。在本文中,我们为新的用户友好型软件包 lmfa 提供了分步教程,该软件允许研究人员在免费提供的 R 软件中轻松执行 LMFA 分析,以调查他们自己的 ILD 中的 MM 差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/332e/10439104/bf673803a7e0/13428_2022_1898_Fig1_HTML.jpg

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