lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved]
Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated wit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wellcome
2022-05-01
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| Series: | Wellcome Open Research |
| Subjects: | |
| Online Access: | https://wellcomeopenresearch.org/articles/7-149/v1 |
| _version_ | 1797996277812690944 |
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| author | Milan Wiedemann Urška Košir Graham Thew Anke Ehlers |
| author_facet | Milan Wiedemann Urška Košir Graham Thew Anke Ehlers |
| author_sort | Milan Wiedemann |
| collection | DOAJ |
| description | Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling. |
| first_indexed | 2024-04-11T10:13:51Z |
| format | Article |
| id | doaj.art-5162e47203204622a014043f20e12b65 |
| institution | Directory Open Access Journal |
| issn | 2398-502X |
| language | English |
| last_indexed | 2024-04-11T10:13:51Z |
| publishDate | 2022-05-01 |
| publisher | Wellcome |
| record_format | Article |
| series | Wellcome Open Research |
| spelling | doaj.art-5162e47203204622a014043f20e12b652022-12-22T04:30:00ZengWellcomeWellcome Open Research2398-502X2022-05-01719390lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved]Milan Wiedemann0https://orcid.org/0000-0003-1991-282XUrška Košir1https://orcid.org/0000-0003-2132-4090Graham Thew2https://orcid.org/0000-0003-2851-1315Anke Ehlers3Department of Experimental Psychology, University of Oxford, Oxford, UKDepartment of Experimental Psychology, University of Oxford, Oxford, UKDepartment of Experimental Psychology, University of Oxford, Oxford, UKDepartment of Experimental Psychology, University of Oxford, Oxford, UKLatent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling.https://wellcomeopenresearch.org/articles/7-149/v1latent change score modelling structural equation modelling longitudinal data analysis lavaan Reng |
| spellingShingle | Milan Wiedemann Urška Košir Graham Thew Anke Ehlers lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] Wellcome Open Research latent change score modelling structural equation modelling longitudinal data analysis lavaan R eng |
| title | lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] |
| title_full | lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] |
| title_fullStr | lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] |
| title_full_unstemmed | lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] |
| title_short | lcsm: An R package and tutorial on latent change score modelling [version 1; peer review: 2 approved] |
| title_sort | lcsm an r package and tutorial on latent change score modelling version 1 peer review 2 approved |
| topic | latent change score modelling structural equation modelling longitudinal data analysis lavaan R eng |
| url | https://wellcomeopenresearch.org/articles/7-149/v1 |
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