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Bivariate latent difference score models are recommended for repeated measurements and multiple mediators, because they allow for a more dynamic approach to mediation.
“This type of analysis is recommended for repeated measurements and multiple mediators, because they allow for a more dynamic approach to mediation, by assessing changes in multiple variables and their interrelations over time [103–105].”
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- Authors
- van Montfort, K., Oud, J. H. L., & Satorra, A.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Radboud University Nijmegen (NL)
- Universitat Pompeu Fabra (ES)
- ORCID
- Albert Satorra
- Venue
- Springer eBooks
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- OpenAlex
- W1546312374
- Cited by
- 110
- References
- 0
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 11 · 2021: 12 · 2020: 3 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 5 · 2015: 10 · 2014: 8 · 2013: 8 · 2012: 8
- FWCI
- 3.23
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- longitudinal study, latent variables, latent variable models, longitudinal analysis, structural equation modeling, growth curve models
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