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SUMMARY:Advanced Structural Equation Modeling: Bayesian SEM
DESCRIPTION:Sponsorship Opportunity:\nApply for $1150 scholarship and learn Bayesian SEM at Stats Camp. Instructor Mauricio Garnier Villarreal got awarded (co-PI) the grant “Scaling Bayesian Latent Variable Models to Big Education Data” from the United States Department of Education. We are able to sponsored 2 students with $1150 each for the registration to the Bayesian SEM course. \n\n\n\n\nTo apply for this sponsorship:\nPlease email the following information to the instructor at mgv@pm.me\n\n\n– CV\n– Explanation on how this course fits your professional development (maximum 300 words)\n– Statement of diversity\, how do you and\or your work is related to underrepresented groups\n\nLIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThe goal of the Bayesian SEM is to provide instruction in the application SEM from the Bayesian paradigm. It will cover the application of models commonly implemented in frequentist SEM\, and in models that are complicate or impossible to estimate in the frequentist paradigm. This seminar is design to portray the advantages of the Bayesian paradigm\, both philosophical and practical\, within the application of SEM. The material and examples will be provided in the open source platform R\, but if the students prefer to work with another program we will assist in the understanding and application. In addition\, students who sign up for this seminar can request topics to be included during the weeklong seminar. If we have the expertise\, we’ll gladly prepare and deliver a module on the topic! As with all Stats Camp seminars\, personal consultation time is available and ample support resources are provided on our web pages at statscamp.org. \nA perfect follow up for SEM Foundations or Bayesian Data Analysis! \nSeminar Topics:\n\nBayesian inference\nMCMC estimation algorithm\nPrior selection\nModel comparison\nCommomly applied SEM models: CFA\, SEM\, multiple group\, grorth curve\n\nSeminar Description:\nYou have a good to great understanding of structural equation modeling (SEM)\, maybe you have been doing traditional SEM for years\, but you’ve began a project where your observations are not normally distributed\, you have a small sample size\, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step! \nAdvanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM. \n\nInstructors: Mauricio Garnier-Villarreal\, Ph.D.\n \n\n\n\nEsteban Montenegro\, Ph.D.\n \nI’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R\, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas\, I collaborate with several teams around the world on topics related to healthy aging\, Alzheimer Disease and other health related topics. \nRead More\n\n\n  \n\nAPA Continuing Education Credits:\n \nPlease contact us for exact # of credit hours for continuing education credits. 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. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning and Bayes’ rule.\nDifferentiate between direct probability and indirect probability.\nUtilize Markov Chain Monte Carlo estimation using the Gibbs and Hamiltonian samplers.\nAnalyze Bayesian models in several software packages\, including JAGS\, STAN\, and blavaan.\nIdentify the properties of key distributions used in Bayesian analysis.\nConduct a confirmatory factor analysis using the Bayesian framework.\nImplement prior estimations in conducting CFA modeling.\nEvaluate model fit and compare CFA models.\nModel multiple group CFAs using the Bayesian framework.\nEvaluate latent regression models\, latent interactions\, quadratic effects\, and mediation in a Bayesian framework.\nUse Bayesian methods to evaluate non-normal continuous data.\nConduct SEM for categorical data using Bayesian Item Response Theory.\nAccount for missing data.\nEvaluate longitudinal models in a Bayesian framework.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nGeneral understanding of linear regression models\n\nNot required but advantageous: \n\nBasic understanding of Bayesian inference\nBasic understanding of SEM\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nWorking with R\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \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\n4-Day Schedule: Bayesian SEM\n\n\nDay 1\n\n\n\n9:00–10:45\nWelcome and Introductions.\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nBayesian reasoning and Baye’s rule : Direct probability vs indirect probability. Markov Chain Monte Carlo\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nKnow your distributions. Introduction to blavaan (R)\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nBayesian Confirmatory Factor Analysis\n\n\nDay 2\n\n\n\n9:00–10:45\nPriors: relevance\, choice\, strengths. CFA with cross-loadings\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nModel fit and model comparison\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMultiple-group CFA\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLatent regression models: latent interactions\, and mediation\n\n\nDay 3\n\n\n\n9:00–10:45\nLatent regression models: latent interactions\, and mediation\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data handling\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nSEM for categorical data\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLongitudinal CFA (Measurement Invariance)\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/advanced-structural-equation-modeling-bayesian-sem-seminar/
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:Advanced Structural Equation Modeling: Bayesian SEM Seminar,Livestream
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