Literature DB >> 33634554

Latent profile transition analyses and growth mixture models: A very non-technical guide for researchers in child and adolescent development.

Sara K Johnson1.   

Abstract

Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and growth mixture models, are highly relevant to developmental science because they can identify subgroups of people who are similar in their patterns of change. This guide highlights foundational aspects of these two types of models and is intended for readers who have not previously conducted either an LPTA or a GMM, or perhaps no mixture model analyses at all. It includes four primary sections. The first focuses on understanding mixture models conceptually and applying that knowledge to identifying appropriate research questions. The second section addresses data requirements, including planning for data collection or evaluating the suitability of previously collected data, and data preparation. The third section focuses on conducting analyses, with step-by-step instructions and syntax, and the final section discusses presenting the results. I illustrate these concepts and procedures with an example data set and research questions derived from the Five Cs model of positive youth development.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  growth mixture models; latent profile analyses; latent profile transition analyses; mixture models; positive youth development

Year:  2021        PMID: 33634554     DOI: 10.1002/cad.20398

Source DB:  PubMed          Journal:  New Dir Child Adolesc Dev        ISSN: 1520-3247


  2 in total

1.  Latent trajectories of anxiety and depressive symptoms among adults in early treatment for nonmedical opioid use.

Authors:  Jennifer D Ellis; Jill A Rabinowitz; Jonathan Wells; Fangyu Liu; Patrick H Finan; Michael D Stein; Denis G Antoine Ii; Gregory J Hobelmann; Andrew S Huhn
Journal:  J Affect Disord       Date:  2021-12-04       Impact factor: 4.839

2.  Better self-care through co-care? A latent profile analysis of primary care patients' experiences of e-health-supported chronic care management.

Authors:  Carolina Wannheden; Marta Roczniewska; Henna Hasson; Klas Karlgren; Ulrica von Thiele Schwarz
Journal:  Front Public Health       Date:  2022-09-23
  2 in total

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