Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

Key Points

• The guide aims to equip researchers with the knowledge to conduct latent profile analysis using Mplus software effectively.
• Provided a six-step procedure for latent profile analysis: data inspection, iterative evaluation, model fit assessment, profile investigation, covariate analysis, and results presentation.
• Included a worked example demonstrating syntax and results in Mplus to aid understanding.
• Targeted at researchers already familiar with latent variable modeling.
• Detailed instructions for each step of conducting LPA to ensure clarity and ease of application.
• An example offers insights into interpreting model outcomes and covariate effects.
• Guidelines for presenting results effectively for future reference and application.