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The Current Practice of Latent Growth Curve Modeling in the Social and Behavioral Sciences: Observations and Recommendations

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Title: The Current Practice of Latent Growth Curve Modeling in the Social and Behavioral Sciences: Observations and Recommendations
Language: English
Authors: Matt L. Miller (ORCID 0000-0002-2377-6325), Emilio Ferrer, Paolo Ghisletta
Source: International Journal of Behavioral Development. 2025 49(4):389-397.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 9
Publication Date: 2025
Sponsoring Agency: National Institutes of Health (NIH) (DHHS)
Contract Number: T32A039772
Document Type: Journal Articles
Reports - Research
Descriptors: Social Science Research, Behavioral Science Research, Structural Equation Models, Statistical Analysis, Longitudinal Studies, Vocabulary
DOI: 10.1177/01650254241269723
ISSN: 0165-0254
1464-0651
Abstract: We examine recommendations for three key features of latent growth curve models in the structural equation modeling framework. As a basis for the discussion, we review current practice in the social and behavioral sciences literature as found in 441 reports published in the 19 months beginning in January 2019 and compare our findings to extant recommendations. We then provide suggestions for empirical researchers, reviewing the application of these very popular models, specifically focusing on comparison of alternative change models, time metric and interval features implemented, and the treatment of individually varying time intervals.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1479228
Database: ERIC
FullText Text:
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: The Current Practice of Latent Growth Curve Modeling in the Social and Behavioral Sciences: Observations and Recommendations
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  Data: <searchLink fieldCode="AR" term="%22Matt+L%2E+Miller%22">Matt L. Miller</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2377-6325">0000-0002-2377-6325</externalLink>)<br /><searchLink fieldCode="AR" term="%22Emilio+Ferrer%22">Emilio Ferrer</searchLink><br /><searchLink fieldCode="AR" term="%22Paolo+Ghisletta%22">Paolo Ghisletta</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Behavioral+Development%22"><i>International Journal of Behavioral Development</i></searchLink>. 2025 49(4):389-397.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 9
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  Data: National Institutes of Health (NIH) (DHHS)
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  Data: 10.1177/01650254241269723
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  Data: 0165-0254<br />1464-0651
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  Data: We examine recommendations for three key features of latent growth curve models in the structural equation modeling framework. As a basis for the discussion, we review current practice in the social and behavioral sciences literature as found in 441 reports published in the 19 months beginning in January 2019 and compare our findings to extant recommendations. We then provide suggestions for empirical researchers, reviewing the application of these very popular models, specifically focusing on comparison of alternative change models, time metric and interval features implemented, and the treatment of individually varying time intervals.
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      – SubjectFull: Structural Equation Models
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