{"title":"作为潜在变量多组因子模型的潜在增长混合模型:对 McNeish 等人(2023 年)的评论。","authors":"Phillip K Wood,Wolfgang Wiedermann,Jules K Wood","doi":"10.1037/met0000693","DOIUrl":null,"url":null,"abstract":"McNeish et al. argue for the general use of covariance pattern growth mixture models because these models do not involve the assumption of random effects, demonstrate high rates of convergence, and are most likely to identify the correct number of latent subgroups. We argue that the covariance pattern growth mixture model is a single random intercept model. It and other models considered in their article are special cases of a general model involving slope and intercept factors. We argue growth mixture models are multigroup invariance hypotheses based on unknown subgroups. Psychometric models in which trajectories are modeled using slope factor loadings which vary by latent subgroup are often conceptually preferable. Convergence rates for mixture models can be substantially improved by using a variance component start value taken from analyses with one fewer class and by specifying multifactor models in orthogonal form. No single latent growth model is appropriate across all research contexts and, instead, the most appropriate latent mixture model must be \"right-sized\" to the data under consideration. Reanalysis of a real-world longitudinal data set of posttraumatic stress disorder symptomatology reveals a three-group model involving exponential decline, further suggesting that the four-group \"cat's cradle\" pattern frequently reported is artefactual. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Latent growth mixture models as latent variable multigroup factor models: Comment on McNeish et al. (2023).\",\"authors\":\"Phillip K Wood,Wolfgang Wiedermann,Jules K Wood\",\"doi\":\"10.1037/met0000693\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"McNeish et al. argue for the general use of covariance pattern growth mixture models because these models do not involve the assumption of random effects, demonstrate high rates of convergence, and are most likely to identify the correct number of latent subgroups. We argue that the covariance pattern growth mixture model is a single random intercept model. It and other models considered in their article are special cases of a general model involving slope and intercept factors. We argue growth mixture models are multigroup invariance hypotheses based on unknown subgroups. Psychometric models in which trajectories are modeled using slope factor loadings which vary by latent subgroup are often conceptually preferable. Convergence rates for mixture models can be substantially improved by using a variance component start value taken from analyses with one fewer class and by specifying multifactor models in orthogonal form. No single latent growth model is appropriate across all research contexts and, instead, the most appropriate latent mixture model must be \\\"right-sized\\\" to the data under consideration. Reanalysis of a real-world longitudinal data set of posttraumatic stress disorder symptomatology reveals a three-group model involving exponential decline, further suggesting that the four-group \\\"cat's cradle\\\" pattern frequently reported is artefactual. (PsycInfo Database Record (c) 2024 APA, all rights reserved).\",\"PeriodicalId\":20782,\"journal\":{\"name\":\"Psychological methods\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychological methods\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/met0000693\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000693","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Latent growth mixture models as latent variable multigroup factor models: Comment on McNeish et al. (2023).
McNeish et al. argue for the general use of covariance pattern growth mixture models because these models do not involve the assumption of random effects, demonstrate high rates of convergence, and are most likely to identify the correct number of latent subgroups. We argue that the covariance pattern growth mixture model is a single random intercept model. It and other models considered in their article are special cases of a general model involving slope and intercept factors. We argue growth mixture models are multigroup invariance hypotheses based on unknown subgroups. Psychometric models in which trajectories are modeled using slope factor loadings which vary by latent subgroup are often conceptually preferable. Convergence rates for mixture models can be substantially improved by using a variance component start value taken from analyses with one fewer class and by specifying multifactor models in orthogonal form. No single latent growth model is appropriate across all research contexts and, instead, the most appropriate latent mixture model must be "right-sized" to the data under consideration. Reanalysis of a real-world longitudinal data set of posttraumatic stress disorder symptomatology reveals a three-group model involving exponential decline, further suggesting that the four-group "cat's cradle" pattern frequently reported is artefactual. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.