A Systematic Literature Review of Latent Variable Mixture Modeling in Communication Scholarship

Recently, latent variable mixture modeling has gained traction in many disciplines, given its unique ability to discover unknown groups within a broader population. Indeed, this method assumes that a finite number of mixtures (i.e. unknown groups) exist within the population and can be discovered by evaluating participants' response patterns to a set of manifest indicators. Despite the intuitive approach, recommendations have been proposed to overcome some methodological concerns associated with latent variable mixture modeling. The primary purpose of this study was to understand the characteristics of latent variable mixture modeling in communication research and to evaluate the extent to which the existing research meets these recommendations. Ninety-five manuscripts published between 2010 and 2022 in 18 communication journals were identified and systematically analyzed. The review found that (1) the use of latent variable mixture modeling has increased; (2) latent class analysis and latent profile analysis are the most common models; and (3) most manuscripts did not meet the proscribed standards for random start values, auxiliary variable procedures, indicator requirements, and missing data procedures. These findings are discussed more in comparison with the proscribed standards. In addition, conceptual and applicable recommendations are provided to improve communication scholarship.