| RESEARCH SERIES NO. 26 |
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| Year : 2022 | Volume
: 23
| Issue : 2 | Page : 127-133 |
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Latent profile analysis – An emerging advanced statistical approach to subgroup identification
Asha Mathew1, Ardith Z Doorenbos2
1 PhD Scholar, Department of Biobehavioral Nursing Science, University of Illinois, Department of Biobehavioral Nursing Science; Professor, College of Nursing, Christian Medical College, Vellore, Tamil Nadu, India
2 Professor, Biobehavioral Sciences, University of Illinois, Chicago, Illinois, USA
Correspondence Address:
Dr. Asha Mathew
College of Nursing, Christian Medical College, Vellore, Tamil Nadu
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/ijcn.ijcn_24_22
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Latent profile analysis (LPA) is emerging as an advanced statistical clustering approach. It is a type of mixture modeling that uses a person-centred approach to classify individuals from a heterogeneous population into homogenous subgroups. LPA identifies the distinct patterns of responses to a set of observed continuous variables in a sample of individuals, and these response patterns are known as latent profiles. This article presents an overview of LPA with key assumptions, sample size considerations, advantages, and limitations. Using an example of LPA application in research, the article also presents the process of conducting LPA and its implications for nursing research. LPA has valuable potential in nursing and could provide new insights into a particular research concept and offer more nuanced information regarding patterns of responses. Further, researchers could examine the impact of targeted assessment and interventions, identify predictors of subgroup membership and explore differences in outcomes across the profiles.
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