Latent Growth Curve Modeling
Abstract
About the Authors Series Editor Introduction Acknowledgements 1. Introduction 2. Applying LGM to Empirical Data 3. Specialized Extensions 4. Relationships Between LGM and Multilevel Modeling 5. Summary Appendix References
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- Authors
- Preacher, K., Wichman, A., MacCallum, R., & Briggs, N.
- Venue
- Sage eBooks
- Publisher
- SAGE Publications, Inc.
- Published
- 2008
- OpenAlex
- W609313093
- Cited by
- 742
- References
- 0
- Citations by year
- 2026: 33 · 2025: 33 · 2024: 31 · 2023: 30 · 2022: 44 · 2021: 58 · 2020: 58 · 2019: 43 · 2018: 69 · 2017: 56 · 2016: 53 · 2015: 52 · 2014: 43 · 2013: 43 · 2012: 31
- FWCI
- 7.17
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Sage eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- latent growth curve models, longitudinal analysis, growth trajectories, multilevel modeling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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