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A Systematic Literature Review of Latent Variable Mixture Modeling in Communication Scholarship
Communication Methods and Measures ( IF 11.4 ) Pub Date : 2023-02-23 , DOI: 10.1080/19312458.2023.2179612
Colton E. Krawietz 1 , Rudy C. Pett 2
Affiliation  

ABSTRACT

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.



中文翻译:

传播学研究中潜变量混合模型的系统文献综述

摘要

最近,潜变量混合模型因其在更广泛的人群中发现未知群体的独特能力而在许多学科中受到关注。事实上,该方法假设群体内存在有限数量的混合物(即未知群体),并且可以通过评估参与者对一组明显指标的反应模式来发现。尽管采用了直观的方法,但仍提出了一些建议来克服与潜变量混合建模相关的一些方法论问题。本研究的主要目的是了解传播研究中潜变量混合模型的特征,并评估现有研究满足这些建议的程度。对 2010 年至 2022 年间发表在 18 种传播期刊上的 95 篇手稿进行了识别和系统分析。审查发现(1)潜变量混合模型的使用有所增加;(2)潜在类别分析和潜在概况分析是最常见的模型;(3)大部分稿件在随机起始值、辅助变量程序、指标要求、缺失数据程序等方面不符合规定标准。与禁止的标准相比,这些发现得到了更多的讨论。此外,还提供了概念性和适用性的建议,以提高传播学术水平。审查发现(1)潜变量混合模型的使用有所增加;(2)潜在类别分析和潜在概况分析是最常见的模型;(3)大部分稿件在随机起始值、辅助变量程序、指标要求、缺失数据程序等方面不符合规定标准。与禁止的标准相比,这些发现得到了更多的讨论。此外,还提供了概念性和适用性的建议,以提高传播学术水平。审查发现(1)潜变量混合模型的使用有所增加;(2)潜在类别分析和潜在概况分析是最常见的模型;(3)大部分稿件在随机起始值、辅助变量程序、指标要求、缺失数据程序等方面不符合规定标准。与禁止的标准相比,这些发现得到了更多的讨论。此外,还提供了概念性和适用性的建议,以提高传播学术水平。

更新日期:2023-02-23
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