Literature DB >> 36050575

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

Leonie V D E Vogelsmeier1, Jeroen K Vermunt2, Kim De Roover2.   

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.
© 2022. The Author(s).

Entities:  

Keywords:  ESM; Factor analysis; Intensive longitudinal data; Latent Markov modeling; Measurement invariance; R; Software package; Three-step approach

Year:  2022        PMID: 36050575     DOI: 10.3758/s13428-022-01898-1

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  24 in total

1.  Computing and evaluating factor scores.

Authors:  J W Grice
Journal:  Psychol Methods       Date:  2001-12

2.  The Scree Test For The Number Of Factors.

Authors:  R B Cattell
Journal:  Multivariate Behav Res       Date:  1966-04-01       Impact factor: 5.923

3.  A latent markov model for the analysis of longitudinal data collected in continuous time: states, durations, and transitions.

Authors:  Ulf Böckenholt
Journal:  Psychol Methods       Date:  2005-03

4.  Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method.

Authors:  Eva Ceulemans; Henk A L Kiers
Journal:  Br J Math Stat Psychol       Date:  2006-05       Impact factor: 3.380

5.  What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research.

Authors:  Fang Fang Chen
Journal:  J Pers Soc Psychol       Date:  2008-11

6.  Hypothesis Testing Using Factor Score Regression: A Comparison of Four Methods.

Authors:  Ines Devlieger; Axel Mayer; Yves Rosseel
Journal:  Educ Psychol Meas       Date:  2015-09-29       Impact factor: 2.821

7.  The role of valence focus and appraisal overlap in emotion differentiation.

Authors:  Yasemin Erbas; Eva Ceulemans; Peter Koval; Peter Kuppens
Journal:  Emotion       Date:  2015-02-23

8.  CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers.

Authors:  Kirsten Bulteel; Tom F Wilderjans; Francis Tuerlinckx; Eva Ceulemans
Journal:  Behav Res Methods       Date:  2013-09

Review 9.  Dissecting components of reward: 'liking', 'wanting', and learning.

Authors:  Kent C Berridge; Terry E Robinson; J Wayne Aldridge
Journal:  Curr Opin Pharmacol       Date:  2009-01-21       Impact factor: 5.547

10.  Sample size considerations and predictive performance of multinomial logistic prediction models.

Authors:  Valentijn M T de Jong; Marinus J C Eijkemans; Ben van Calster; Dirk Timmerman; Karel G M Moons; Ewout W Steyerberg; Maarten van Smeden
Journal:  Stat Med       Date:  2019-01-06       Impact factor: 2.373

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.