Using Latent Profile Analysis and Related Approaches in Adolescent Health Research

In this issue ofJournal of Adolescent Health, Giano et al.. use latent profile analysis (LPA) to examine how risk factors differentially combine to impact suicide risk among lesbian, gay, and bisexual youth. This article joins a fast-growing body of literature using statistical approaches to underst...

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Bibliographic Details
Published in:Journal of adolescent health 2020-08, Vol.67 (2), p.153-154
Main Author: Hensel, Devon J.
Format: Article
Language:English
Subjects:
Adolescent
Adolescent Health
Adolescents
Bisexuality
Childrens health
Female
Health behavior
Health status
Homosexuality, Female
Humans
Latent Class Analysis
Lesbianism
Medical personnel
Medical research
Risk factors
Sexual and Gender Minorities
Suicide
Suicides & suicide attempts
Teenagers
Young adults
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Summary:In this issue ofJournal of Adolescent Health, Giano et al.. use latent profile analysis (LPA) to examine how risk factors differentially combine to impact suicide risk among lesbian, gay, and bisexual youth. This article joins a fast-growing body of literature using statistical approaches to understand variability in adolescent health experiences. This editorial will focus on providing a brief review of how and when health professions researchers might use LPA or related methods in their own work. LPA is one method in a family of statistical approaches called mixture modeling, which has been described in excellent lay-focused reviews by Lanza and Cooper. Oberski, and Williams and Kibowski. These methods focus on uncovering how adolescents and young adults can be similarly "grouped" or "profiled" on a combination of different variables. The idea of looking for "sets" of related factors is not a new idea in adolescent health. Researchers often want to know how the intersection of social, interpersonal, and environmental/structural influences may have impact on health outcomes. However, the method that we choose to investigate these questions will influence our understanding of how these variable effects are interpreted in the population.
ISSN:1054-139X
1879-1972
DOI:10.1016/j.jadohealth.2020.05.005