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DTSTART;TZID=America/Denver:20240603T090000
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DTSTAMP:20260419T071009
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SUMMARY:SEM Foundations & Extended Applications
DESCRIPTION:IN PERSON – 5-day Structural Equation Modeling Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nStructural Equation Modeling Course Overview:\n\nDo you want to take your measurement to the latent level? Well\, this Structural Equation Modeling Course is it\, you have found it\, the foundation to what you need to know for latent variable modeling – structural equation modeling (SEM)! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to ‘craft’ an exquisite SEM model. \n\nSeminar Topics:\n\nPhantom Constructs\nFitting measurement models\nThree methods of scale setting – including effects coding!\nUpdated recommendations for Scale Validation\nMultiple-Group Comparisons with applications for experimental and observational groups!\nFactorial/Measurement Invariance – Are you measuring the same construct?\nExtended Applications Such as Parceling and Missing Data\nMediation and Indirect Effects using Bootstrapping\nModeration\, creating latent interaction terms!\n\nSeminar Description:\nThis summer institute is an intensive short seminar on the principles of structural equation modeling. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, Ph.D. is a Professor of Educational Psychology at Texas Tech University (TTU). Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g.\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). Prior to joining TTU\, …  Little has guided quantitative training and provided consultation to students\, staff\, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998)\, Yale University’s Department of Psychology (1998-2002)\, and researchers at KU (2002-2013\, including as director of the RDA unit at the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001\, Little was elected to membership in the Society for Multivariate Experimental Psychology\, a restricted-membership society of quantitative specialists in the behavioral and social sciences.\n \nIn 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He founded\, organizes\, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. As an interdisciplinary-oriented collaborator\, Little has published with over 280 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 11\,000 citations. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology\, including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF\, NIH\, and IES\, and private foundations such as the Jacobs Foundation. He has been the principal investigator or co-principal investigator on over 15 grants and contracts and he has served as a statistical consultant on over 70 grants and contracts. In the conduct of his collaborative research\, he has participated in the development of over 12 different measurement tools\, including the CAMI\, the Multi-CAM\, the BALES\, the BISC\, the I FEEL\, and the form/function decomposition of aggression. \n\n\n\nInstructor: Elizabeth Grandfield\, Ph.D.\n \nElizabeth received her Ph.D. in Quantitative Psychology at the University of Kansas. She is currently an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands. Her research focuses on evaluating …measurement invariance with an emphasis in longitudinal designs. In areas of applied research\, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center\, both in Kansas City. She also received the 2011 Multivariate Software Award\, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012. \nRead More\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary\, Ph.D. is a senior research scientist at Yhat Enterprises LLC. where he pursues his research interests in measurement design\, applied latent variable modeling\, and modern approaches to missing data. Dr. Stickley has also served as an instructor and coordinator for the Stats Camp Foundation since first joining the team as a graduate student in 2018. He received his Ph.D. in Educational Psychology from College of Education at Texas Tech University with a focus on research methodology\, measurement design\, and statistical modeling. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 26 hours of 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. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Structural Equation Modeling will enable participants to: \n\nDescribe the psychometric properties that underly Structural Equation Modeling (SEM).\nDefine a latent construct using manifest variables.\nIdentify a latent construct using numerous methods of identification\, including marker method\, fixed factor\, and effects coding.\nConduct confirmatory factor analysis (CFA) and evaluate model fit using several fit indices.\nCompare CFA models using several comparison metrics.\nGenerate and implement item parceling schemes.\nEvaluate multiple groups using the CFA framework using weak and strong invariance.\nTest and compare latent parameters in a multiple group framework.\nEvaluate and address missing data with both FIML and Multiple Imputation.\nImplement a planned missing data design.\nEvaluate mediation and moderation in an SEM framework.\nEvaluate multi-trait\, multi-method (MTMM) models.\nEvaluate hierarchical models.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you need to analyze the covariance structure of multivariate data and have a basic statistical background\, 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 do not need to know matrix algebra\, calculus\, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL\, Mplus or Laavan is assumed. Furthermore\, nearly all the techniques taught in the seminar can be translated fairly easily to most other packages. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor Will Provide Password on First Day of Seminar:\nClick Here to View Seminar Materials Page for SEM Foundations and Extended Applications \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\nSummer Stats Camp 2024: Structural Equation Modeling Foundations Course\n\n\nMonday\nJune 3\, 2024\n\n\n9:00 – 10:45\nWelcome and Introductions. Philosophy\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nPsychometrics\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nDefining Constructs\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIdentification\n\n\nTuesday \nJune 4\, 2024\n\n\n9:00 – 10:45\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nCFA: The foundation of any SEM model\, continued\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nParcels and Parceling; Start of Individual Consultations\n\n\nWednesday\nJune 5\, 2024\n\n\n9:00 – 10:45\nMultiple-Group CFA – Testing for configural and weak invariance\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group CFA – Testing for Strong Invariance\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nWrap-up then Individual Consultations\n\n\nThursday\nJune 6\, 2024\n\n\n9:00 – 10:45\nMissing data and Power\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group SEM and Latent Regression Models\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMediation and Moderation\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nCatch-up time then Individual Consultations\n\n\nFriday\nJune 7\, 2024\n\n\n9:00 – 10:45\nMulti-trait\, Multi-Method (MTMM) and Hierarchical Models\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nHierarchical Models\, continued; Writing results\, Cautions & Wrap-up\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – ~3:30\nIndividual Consultations\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/structural-equation-modeling-course/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:SEM Foundations & Extended Applications,Summer Camp,Summer Camp 1
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/sem-foundations-statistics-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260419T071009
CREATED:20220701T075623Z
LAST-MODIFIED:20231209T043257Z
UID:2670-1717405200-1717779600@www.statscamp.org
SUMMARY:Multilevel Modeling
DESCRIPTION:IN PERSON – 5-day Multilevel Modeling Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data. Everything is nested\, so you need something more than multiple regression or analysis of variance to get the job done! Nested data structures can include students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. Multilevel modeling (MLM) is built to handle this kind of data. You will use real datasets and the R software environment to learn how to analyze multilevel data sets and interpret results of multilevel models. \nSeminar Topics:\n\nReview of regression and methods of handling nested data\nRandom-intercept and random-slope models\nTesting and interpreting interactions in multilevel models\nCross-sectional and Longitudinal multilevel models\nMultilevel models for binary outcomes\nCross-classified random effects modeling\n\nNote: MLM is sometimes referred to as mixed-effects modeling\, hierarchical linear modeling\, or random coefficients modeling. This course will focus primarily on with a single outcome variable.  As such\, this course (https://www.statscamp.org/courin combination with a course in SEM Foundations) would provide an ideal introduction to the foundations necessary to prepare for the advanced Summer Stats Camp course\, Multilevel SEM with xxM. \nSeminar Description:\nThis course is designed to provide theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include\, for example\, students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. In each of these cases and many more\, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling\, which is also sometimes referred to as mixed modeling\, hierarchical linear modeling\, or random coefficients modeling. \nThis course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Cross-sectional and longitudinal models\, the most common multilevel modeling applications\, are featured in the seminar. Using real datasets provided in the seminar\, participants will learn how to use the R software program to analyze data and interpret results. Further\, the course will emphasize proper interpretation of analysis results and illustrate procedures that can be used to specify multilevel models. Coverage of multilevel models for binary outcomes and cross-classified random effects modeling will also be included. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Alex Schoemann\, Ph.D.\n \nDr. Alexander M. Schoemann\, is an Alex Schoemann\, Ph.D. is Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 … (after spending several years as a “counselor”). Alex teaches graduate courses in research design\, regression\, multivariate statistics\, structural equation modeling and multilevel modeling. His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation\, power analyses\, missing data estimation\, meta-analysis\, structural equation models and multilevel models. Alex is also interested in developing user friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/). \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of 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\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\nIdentify the basic functions of R that are relevant to multilevel modeling.\nUnderstand the principles behind multiple linear regression.\nDescribe the methods of handling nested data.\nModify regression models by adding predictors and random effects.\nApply methods of centering.\nEvaluate models with interactions.\nConduct multiparameter tests.\nMake informed decisions about model selection.\nEvaluate longitudinal models.\nImplement alternative error structures.\nEvaluate multiple group models.\nUnderstand the roles sample size and power play in a multilevel framework.\nEvaluate multivariate models.\nEvaluate three-level models.\nEvaluate cross-classified random effects models.\nEvaluate models with categorical outcome variables.\n\nSeminar Prerequisites\nMultilevel Modeling Prerequisites:\nRequired: \n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience in binary logistic regression\nAt least limited experience using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMultilevel Modeling.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\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. \nNote\, however\, that R and RStudio are the software programs that will be demonstrated. Both programs are free and can be downloaded from https://cloud.r-project.org/ and https://www.rstudio.com/products/rstudio/download/\, respectively. Additional directions will be shared with enrolled participants. \nNote: Limited examples will also be provided in SPSS and SAS but the majority of the course will be taught using R. \nSeminar Audience\nSeminar Audience:\nComing Soon… \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access The Multilevel Modeling Seminar Files \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\nMonday\nJune 3\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to multilevel modeling and basics of R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of multiple linear regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMethods of handling nested data\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nAdding predictors and random effects\n\n\nTuesday\nJune 4\, 2024\n\n\n9:00-10:45\nCentering\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nInteractions and contextual effects\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nEstimation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiparameter tests and model selection\n\n\nWednesday\nJune 5\, 2024\n\n\n9:00-10:45\nLongitudinal models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLongitudinal models (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nAlternative error structures\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiple group models\n\n\nThursday\nJune 6\, 2024\n\n\n9:00-10:45\nPower and sample size\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMultivariate Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nThree-level modeling\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nOne-on-one consultations with instructor\n\n\nFriday\nJune 7\, 2024\n\n\n9:00-10:45\nCross-classified random effects modeling\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCross-classified random effects modeling (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-5:00\nOne-on-one consultations with instructor\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/multilevel-modeling-in-r/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Multilevel Modeling,Summer Camp,Summer Camp 1
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/multilevel-modeling-statistics-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260419T071009
CREATED:20220701T073144Z
LAST-MODIFIED:20231209T043118Z
UID:2659-1717405200-1717779600@www.statscamp.org
SUMMARY:Applied Latent Class Analysis & Finite Mixture Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent class analysis\, latent class cluster analysis\, modeling predictors and outcomes of latent class membership\, and select extensions. Hands-on practice with Mplus is provided. \nThis five-day camp is an intensive short seminar in the fundamentals of finite mixture modeling. \nSeminar Description:\nFinite mixture models are a type of latent variable model that express the overall distribution of one or more 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 manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent classes\, 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 finite mixture modeling to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA. s)\, in terms of model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented. The seminar will also explore “hybrid” latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some   longitudinal extensions of mixture modeling\, as time allows (for a more in-depth treatment\, see the Stats Camp Session 2 seminar on longitudinal mixture modeling). The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar. \n\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 offers 29 hours of 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\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 mixture modeling in a general latent variable framework.\nPerform binary\, ordinal\, and multinomial logistic regression.\nUtilize Mplus to evaluate generalized linear models.\nIdentify best practice methods to enumerate latent classes for latent class analysis.\nPerform latent class regression.\nConduct measurement invariance in a latent class analysis framework.\nDerive distal outcomes in latent class analysis.\nPerform structural equation mixture modeling.\nDescribe the process of finite mixture modeling\nEnumerate latent classes in a finite mixture modeling framework.\nConduct finite mixture modeling with non-normal indicators.\nDescribe advanced topics and applications of mixture modeling\, such as multilevel latent class analysis.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies 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\nLatent Class Analysis Course Prerequisites:\nYou 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\, or Mplus is assumed. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should bring 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 and R Studio 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. \nSeminar Audience\nLatent Class Analysis Course Audience:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent classes) 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\, 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. \nSeminar Files\nWho Can Benefit From a Latent Class Analysis Course:\nA latent class analysis course would be useful for researchers\, data analysts\, and practitioners who work with categorical data and want to identify unobserved subgroups or latent classes within their data. This includes individuals from a wide range of fields\, such as psychology\, sociology\, epidemiology\, marketing\, education\, and public health. \nSpecifically\, individuals who may benefit from a latent class analysis course include: \n\nResearchers who want to identify subgroups of individuals or objects with similar characteristics\, attitudes\, behaviors\, or preferences. For example\, a researcher studying consumer behavior may want to identify different types of shoppers based on their buying habits and demographic characteristics.\nData analysts who want to use latent class analysis as a tool for data reduction or variable selection. For example\, a data analyst working with survey data may want to reduce the number of survey items to a smaller set of latent factors that capture the most important dimensions of the data.\nPractitioners who want to use latent class analysis as a tool for program evaluation or needs assessment. For example\, a public health practitioner may want to identify different types of health behaviors or risk factors among a population in order to tailor intervention programs to specific subgroups.\n\nA latent class analysis course would be useful for anyone who wants to gain a deeper understanding of how to use latent class analysis to identify meaningful subgroups within categorical data and make informed decisions based on the results. \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\nSummer Stats Camp 2024: LCA\n\n\nMonday\nJune 3\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nOverview of mixture modeling in a general latent variable framework\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of binary\, ordinal\, and multinomial logistic regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Mplus\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGeneralized linear modeling in Mplus\n\n\nTuesday\nJune 6\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Latent Class Analysis (LCA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to Latent Class Regression (LCR)\n\n\nWednesday\nJune 4\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLCR (continued)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMeasurement invariance in LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nDistal outcomes in LCA\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nStructural equation mixture modeling\n\n\nThursday\nJune 5\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Finite Mixture Modeling (FMM)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for FMM\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration for FMM (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nFMM with non-Normal indicators\n\n\nFriday\nJune 6\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nOverview of “hybrid” factor mixture models (including growth mixture models)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nOverview of advanced topics in mixture modeling (e.g.\, multilevel LCA)\n\n\n12:30-1:30\nRest Break\n\n\n1:30~4:30\nIndividual consultations\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/applied-latent-class-analysis-finite-mixture-modeling/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Applied Latent Class Analysis & Finite Mixture Modeling,Summer Camp,Summer Camp 1
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/latent-class-analysis.jpg
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