Personality profiles: Using latent profile analysis to model personality typologies. (1st February 2018)

• Record Type: Journal Article
• Authors: Ferguson, Sarah L.; Hull, Darrell M.
• Is Part Of: Personality and individual differences. Volume 122(2018)
• Page Start: 177
• Page End: 183
• Publication Date: 2018-02-01
• Subjects: Personality -- Latent profile analysis -- Five-factor model -- IPIP; Personality -- Periodicals; Individuality -- Periodicals; Individuality -- Periodicals; Personality Development -- Periodicals; Personnalité -- Périodiques; Individualité -- Périodiques; 155.205
• DOI: 10.1016/j.paid.2017.10.029
• Languages: English
• ISSNs: 0191-8869

Abstract

Abstract: Latent profile analysis (LPA) has been presented as an effective tool to model interactions between the dimensions of personality. LPA models personality typologies to reflect the hierarchical nature of the personality construct, which can then be used to make further inferences in applied research. This approach may also address consistent modeling concerns in personality research related to lack of model fit in structural equation modeling analyses. However, further research is needed to support the use of LPA in personality research, including a need for consensus on the nature of function of the resulting personality profiles. The present study uses data from a sample of high school students ( n = 374) on an IPIP five-factor inventory to exemplify the use of LPA in personality research and continue the discussion around the benefits and challenges associated with this approach. The resulting three-profile solution in the present study agrees with a prior study, but does not align with some studies that identified a five-profile solution. Suggestions are offered as to why these two different personality typology models may have been identified. Highlights: Latent profile analysis is presented as a modern approach to modeling personality data. The person-centered approach models personality as latent profiles or typologies. Data from high school students resulted in three personality profiles. Discussion focuses on benefits of latent profile analysis in modeling personality data. Further research is needed to explore the implications of the resulting profiles.