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How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439104/ https://www.ncbi.nlm.nih.gov/pubmed/36050575 http://dx.doi.org/10.3758/s13428-022-01898-1 |
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author | Vogelsmeier, Leonie V. D. E. Vermunt, Jeroen K. De Roover, Kim |
author_facet | Vogelsmeier, Leonie V. D. E. Vermunt, Jeroen K. De Roover, Kim |
author_sort | Vogelsmeier, Leonie V. D. E. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-01898-1. |
format | Online Article Text |
id | pubmed-10439104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-104391042023-08-20 How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa Vogelsmeier, Leonie V. D. E. Vermunt, Jeroen K. De Roover, Kim Behav Res Methods Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-01898-1. Springer US 2022-09-01 2023 /pmc/articles/PMC10439104/ /pubmed/36050575 http://dx.doi.org/10.3758/s13428-022-01898-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vogelsmeier, Leonie V. D. E. Vermunt, Jeroen K. De Roover, Kim How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title | How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title_full | How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title_fullStr | How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title_full_unstemmed | How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title_short | How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa |
title_sort | how to explore within-person and between-person measurement model differences in intensive longitudinal data with the r package lmfa |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439104/ https://www.ncbi.nlm.nih.gov/pubmed/36050575 http://dx.doi.org/10.3758/s13428-022-01898-1 |
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