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SUMMARY:Latent Profile Analysis
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\n\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent profile analysis (aka\, latent class cluster analysis)\, and modeling predictors and outcomes of latent profile membership. Hands-on practice with Mplus is provided. \nSeminar Topics:\nLatent Profile Analysis (LPA) steps including research questions appropriate for latent profile analysis\, profile (class) enumeration and assessing profile model results (classification quality\, profile homogeneity and separation)\, predicting profile membership with other variables and profile membership predicting outcomes. Practice analyses will be completed to build comfort with syntax and reading of output. We will also cover how to interpret and present the results to maximize audience understanding. \nSeminar Description:\nThis four-day camp is an intensive short seminar in the fundamentals of latent profile analysis (LPA).\nLPA is a type of latent variable model-based finite mixture models that express the overall distribution of one or more continuous variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of continuous\, manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent profiles\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic latent profile analysis to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA)\, including model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership as well as distal outcomes of latent profile membership will be presented. The implementation of these models in the most recent version of the Mplus software will be demonstrated and practiced throughout the seminar. \nInstructor: Whitney Moore\, Ph.D.\n \nDr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM\, Longitudinal SEM\, and MLM). Whitney has taught graduate courses in research design\, introduction to statistics\, ANOVA\, SEM\, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport\, exercise\, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs\, finite mixture modeling\, plus mediation and moderation in SEM. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course provides 16 credit hours for continuing education. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 4-day statistics training institute on Latent Profile Analysis will enable participants to:\n\nAcquire understanding of latent profile analysis techniques as applied in the social and behavioral sciences.\nDevelop an appreciation for the research questions and data best suited for latent profile analysis models and the common pitfalls leading to the misuse of mixture models.\nGain detailed knowledge of current “best practices” for mixture model specification\, estimation\, selection\, evaluation\, comparison\, interpretation\, and presentation.\nUnderstand how latent profile variables may be integrated into a larger (latent) variable system.\nBecome acquainted with a variety of mixture modeling extensions.\nBecome proficient in the use of Mplus for analysis of mixture models.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies with continuous variables using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent profiles) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, latent profile analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should have a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software.\nNote: We will also make use of Excel to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\n\n\n\n\n\nDay 1\n \n\n\n9:00-9:30\nWelcome and introductions plus Zoom Orientation\n\n\n9:30-10:30\nOverview of mixture modeling in a general latent variable framework\n\n\n10:30-10:45\nSnack and refreshment break\n\n\n10:45-12:15\nOverview of mixture modeling in a general latent variable framework\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nOverview of LPA steps\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIntroduction to Mplus syntax introduction with Latent Profile Analysis (LPA) example\n\n\n5:30~7:30\nSocial “hour” reception for all Stats Campers\n\n\nDay 2\n\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLPA class enumeration across variance-covariance structures introduction\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nSyntax and interpretation of output for LPA enumeration across variance-covariance structures\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nIndividual consultation & Practice of LPA enumeration process\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation & Review of multinomial logistic regression\n\n\nDay 3\n\n \n\n\n9:00-9:30\nReview of LPA enumeration process and decision-making\n\n\n9:30-10:45\nExamination of Profile homogeneity and separation\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nIntroduction to latent class regression (LCR) with inclusion of predictive covariates\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nLCR continued with inclusion of distal outcomes\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation\n\n\nDay 4\n\n \n\n\n9:00-9:30\nInformation Coming Soon…\n\n\n9:30-10:45\nInformation Coming Soon…\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nInformation Coming Soon…\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nInformation Coming Soon…\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation\n\n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/latent-profile-analysis/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Latent Profile Analysis,Winter Camp
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