BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Stats Camp Statistics Course - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Stats Camp Statistics Course
X-ORIGINAL-URL:https://www.statscamp.org
X-WR-CALDESC:Events for Stats Camp Statistics Course
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20220313T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20221106T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20230312T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20231105T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20270314T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20271107T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20280312T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20281105T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20290311T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20291104T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20300310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20301103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20310309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20311102T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20230312T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20231105T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20240310T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20241103T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20250309T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20251102T080000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260405T035111
CREATED:20220909T003648Z
LAST-MODIFIED:20250410T161246Z
UID:4477-1672531200-1893542399@www.statscamp.org
SUMMARY:Self-Paced: SEM Foundations & Extended Applications (Instant Download)
DESCRIPTION:Instant Asynchronous Video Download – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: SEM Foundations & Extended Applications\n\nDo you want to take your measurement to the latent level? Well\, this 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 4 day short course covering SEM Foundations & Extended Applications 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: 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\n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe 4-day training institute on Structural Equation Modeling (SEM Foundations) 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 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. \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\nDay 1\n\n \n\n\n1:30 – 3:15\nWelcome and Introductions. Philosophy\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nPsychometrics; Defining Constructs\n\n\n\n\n\n\nDay 2\n\n \n\n\n9:00 – 10:45\nIdentification & Scale setting\n\n\n10:45 – 11:00\nSnack and Refreshment Break\n\n\n11:00 – 12:30\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n12:30 – 1:30\nLunch Break\n\n\n1:30 – 3:15\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nMultiple-Group CFA – Testing for invariance\n\n\n\n\n\n\nDay 3\n\n \n\n\n9:00 – 10:45\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n10:45 – 11:00\nSnack and Refreshment Break\n\n\n11:00 – 12:30\nParcels and Parceling; Missing Data & Power\n\n\n12:30 – 1:30\nLunch Break\n\n\n1:30 – 3:15\nMultiple-Group SEM & Latent Regression Models\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nCatch up and Discussion\n\n\n\n\n\n\nDay 4\n\n \n\n\n9:00 – 10:45\nMediators\n\n\n10:45 – 11:00\nSnack and Refreshment Break\n\n\n11:00 – 12:30\nModerators\n\n\n12:30 – 1:30\nLunch Break\n\n\n1:30 – 3:15\nInteractions Multi-Trait Multi-Method (MTMM) models\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nCatch up and Discussion\n\n\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/sem-foundations-and-extended-applications/
LOCATION:MT
CATEGORIES:On-Demand,SEM Foundations & Extended Applications
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/09/sem-foundations-training-course-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260405T035111
CREATED:20230825T183614Z
LAST-MODIFIED:20241114T000716Z
UID:7137-1672531200-1893542399@www.statscamp.org
SUMMARY:Self Paced: The Craft of Longitudinal Structural Equation Modeling (Instant Download)
DESCRIPTION:Dive into a transformative learning experience! Our seminar goes beyond the ordinary\, offering a dynamic series of engaging lectures and hands-on computer workshops. Elevate your skills with advanced training in Structural Equation Modeling (SEM) tailored specifically for the analysis of longitudinal data. Seize this opportunity to unlock new insights – join us and propel your expertise to the next level!\nASYNCHRONOUS – 5-day Statistics Short Course (4hrs per day)\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nMastering Longitudinal Structural Equation Modeling: An In-Depth Workshop for Advanced Analysis \nWelcome to an immersive and advanced seminar experience! Join us for an intensive short camp focused on mastering the analysis of longitudinal data through Structural Equation Modeling (SEM). Engage in a comprehensive program featuring a blend of expert lectures and hands-on computer workshops. This unique opportunity ensures participants gain advanced proficiency in utilizing SEM for the in-depth analysis of longitudinal data. Elevate your skills—enroll now for an unparalleled learning journey! \n  \n\n\n\nDay 1\n\n \n\n\n5:00 – 6:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 9:00\nReview of Foundations of SEM\n\n\n\n\n\n\nDay 2\n\n \n\n\n5:00 – 6:45\nParcels and Parceling\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:00\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n8:00 – 8:30\nLongitudinal Panel Models: Basics\n\n\n8:30 – 9:00\nMultiple-group Longitudinal Panel Models: CFA\, SEM\, & RI-CLPM\n\n\n\n\n\n\nDay 3\n\n \n\n\n5:00 – 6:45\nMixture Modeling\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:00\nLongitudinal Mediation\n\n\n8:00 – 8:30\nIndividual Consultations\n\n\n8:30 – 9:00\nCatch up and Discussion\n\n\n\n\n\n\nDay 4\n\n \n\n\n5:00 – 6:45\nLatent Growth Curve Modeling: Basics & Multivariate and Multiple Groups\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:30\nMissing Data: Planned and Unplanned\n\n\n8:30 – 9:00\nCatch up and Discussion\n\n\n\n\n\n\nDay 5\n\n \n\n\n5:00 – 6:45\nLongitudinal Moderation\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:30\nCatch up and Discussion\n\n\n8:30 – 9:00\nWrap-up then Individual Consultations\n\n\n\n\n\n\n\nCourse Topics:\nDesign and measurement issues in cross-sectional and longitudinal research\, Traditional panel designs\, Overview of missing data\, Latent growth curve modeling\, Testing for Mediation and Moderation\, Multilevel and multiple group SEM\, Using Phantom Constructs\, Multiple group modeling. \nCourse Description:\nEmbark on an advanced journey of expertise with our intensive seminar focused on the nuanced analysis of longitudinal data through Structural Equation Modeling (SEM). Join a dynamic program featuring expert-led lectures and hands-on computer workshops\, meticulously designed to provide participants with unparalleled training in utilizing SEM for the comprehensive analysis of longitudinal data. Elevate your skills\, refine your approach\, and gain mastery in the craft of Longitudinal Structural Equation Modeling. Seize this opportunity to dive deep into advanced methodologies and enhance your proficiency in handling longitudinal data sets. Enroll now for a transformative learning experience at the forefront of statistical analysis. \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: 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\n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 5-day statistics training institute on Longitudinal SEM will enable participants to:\n\nUnderstand the strengths and weaknesses of the different models that can be applied to longitudinal data.\nDevelop a clear understanding of how the models can be specified and adapted to address the specific needs and questions of the investigator.\nGain knowledge of the ways in which one should formulate models test alternative models\, and evaluate models with regard to statistical and practical significance.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nIf you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on SEM Foundations as a pre-requisite to taking this 5-day advanced seminar. If you have not taken the foundations Seminar\, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling. \nParticipants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. Previous knowledge of LISREL\, Mplus or Laavan is preferred but not required. \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.Download 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/the-craft-of-longitudinal-structural-equation-modeling-a-comprehensive-seminar/
LOCATION:MT
CATEGORIES:Longitudinal SEM,Longitudinal Structural Equation Modeling,On-Demand
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2023/08/longitudinal-structural-equation-modeling-statistics-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260405T035111
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260405T035111
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:20260405T035111
CREATED:20220701T085536Z
LAST-MODIFIED:20231209T041530Z
UID:2684-1717405200-1717779600@www.statscamp.org
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:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T035111
CREATED:20220701T092234Z
LAST-MODIFIED:20231209T043756Z
UID:2697-1718010000-1718384400@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nLongitudinal Structural Equation Modeling (LSEM) Seminar Overview:\nDo you have repeated measurements? Have you collected data over multiple timepoints? Do you need help designing your longitudinal study? If so\, this is your course! Let us help you appropriately design your longitudinal study and analyze your data in the SEM latent variable framework using Longitudinal Structural Equation Modeling (LSEM). This framework will allow you more flexibility in evaluating your research questions over time as well as test assumptions that traditional techniques like ANOVA ignore. \nSeminar Topics:\n\nDesign and measurement issues in cross-sectional and longitudinal research\nTraditional panel designs\nOverview of missing data\nLatent growth curve modeling\nTesting for Mediation and Moderation\nUsing Phantom Constructs\nLongitudinal Measurement Invariance – Multiple Group LSEM\nGrowth Mixture Models\n\nSeminar Description:\nThe seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data. \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: 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\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\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\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Longitudinal Structural Equation Modeling (LSEM) will enable participants to:\n\nAddress design and measurement issues in longitudinal modeling.\nAcquire understanding of the SEM concepts that are foundational to longitudinal SEM design.\nAnalyze longitudinal panel models in both a single group and multi-group configuration in CFA and SEM framework.\nIncorporate mediation and moderation in a longitudinal framework.\nConstruct item parcels in a longitudinal framework.\nEvaluate latent growth curve models.\nApply latent growth curve models in a multivariate and multiple group context.\nEvaluate finite mixture models.\nInterpret and evaluate covariance pattern mixture models.\nEvaluate Growth mixture models.\nAddress missing data using FIML and MI methods.\nUse modern missing data treatments to implement a planned missing data design.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nProficiency in multiple linear regression.\nAt least limited experience (e.g.\, graduate-level course) with continuous latent variable models\, e.g.\, exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).\nWe strongly recommend that you attend our foundations of SEM as a pre-requisite to taking this advanced course. If you have not taken the foundations course\, you should have extensive experience or have taken a graduate-level course on SEM before enrolling.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, LISREL\, Mplus\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nLongitudinal Structural Equation Modeling (LSEM).\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam 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. \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. \nSeminar Audience\nSeminar Audience:\nIf you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on the foundations of SEM as a pre-requisite to taking this five-day advanced seminar. If you have not taken the foundations Seminar\, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling. \nParticipants 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. Previous knowledge of LISREL\, Mplus or Laavan is preferred but not required. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \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: Longitudinal Structural Equation Modeling (LSEM)\n\n\nMonday\nJune 10\, 2024\n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nReview of Foundations of SEM\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nLongitudinal Panel Models: Basics\n\n\nTuesday\nJune 11\, 2024\n\n\n9:00-10:45\nMultiple-group Longitudinal Panel Models; CFA and SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nParcels and Parceling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLongitudinal Mediation & Moderation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nConsultation\n\n\nWednesday\nJune 12\, 2024\n\n\n9:00-10:45\nLatent Growth Curve Modeling: Basics\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent Growth Curve Modeling: Multivariate and Multiple Groups\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Finite Mixture Modeling\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to growth mixture modeling / Consultation\n\n\nThursday\nJune 13\, 2024\n\n\n9:00-10:45\nLatent class growth analysis (LCGA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCovariance Pattern Mixture Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nGrowth mixture modeling (GMM)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGrowth mixture modeling (GMM)\n\n\nFriday\nJune 14\, 2024\n\n\n9:00-10:45\nMissing Data: Planned and Unplanned\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nWrap-up then Individual Consultations\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/longitudinal-structural-equation-modeling-lsem-training/
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:Longitudinal SEM,Longitudinal Structural Equation Modeling,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/longitudinal-structural-equation-modeling-statistics-course-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T035111
CREATED:20220701T093446Z
LAST-MODIFIED:20240618T021119Z
UID:2701-1718010000-1718384400@www.statscamp.org
SUMMARY:Multivariate Statistical Modeling using R
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nMultivariate Modeling Seminar Overview:\nAn introductory 5-day course on using R software for common analytic methods in behavioral and social sciences. Topics covered include\, regression\, mediation and moderation\, multilevel modeling (MLM)\, factor analysis and structural equation modeling (SEM). \nSeminar Topics:\n\nIntroduction to R software and importing data into R\nFitting regression models in R\nTesting mediation and moderation models in R\nMLM in R\nFactor analysis and SEM in R\n\nSeminar Description:\nThis seminar is intended to introduce participants to popular multivariate statistical methods using the R software program. R is a free\, open-source software program which continues to grow in popularity across a wide variety of fields. R provides cutting edge functionality for most popular multivariate analyses used by researchers in behavioral and social sciences. \nThis seminar will help you begin to learn how to analyze multivariate models using R. The seminar will cover regression\, mediation\, moderation\, multilevel\, factor and SEM models in R. 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 seminar will focus on best practices approaches to model specification and interpretation across all covered methods. Coverage of confirmatory factor analysis and SEM will use the lavaan package. \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\nAcquire an understanding of modeling techniques using R as applied in the educational\, social\, health\, and behavioral sciences\nSpecify\, estimate\, evaluate\, and compare regression models using R software\nSpecify\, estimate\, evaluate\, and compare mediation and moderation models using R software\nSpecify\, estimate\, evaluate\, and compare multilevel models using R software\nSpecify\, estimate\, evaluate\, and compare factor analysis and SEM models using R software\n\nParticipants will also complete the course with a foundation for future learning about statistical modeling with R and knowledge about available resources to guide such endeavors. \nSeminar Prerequisites\nSeminar 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 using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nFactor Analysis\, SEM\, or MLM.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam 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. \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. \nSeminar Audience\nMultivariate Modeling Seminar Audience:\nThe ideal audience for our statistical methods training course in multivariate modeling would be: \n\nData analysts and data scientists who have a solid foundation in statistical methods and want to learn advanced techniques for analyzing complex datasets with multiple variables.\nResearchers in various fields who have some background in statistical analysis and want to learn how to analyze data with multiple variables to draw meaningful conclusions.\nBusiness analysts and decision-makers who want to use multivariate analysis to understand the factors that affect business performance\, customer behavior\, or market trends.\nEngineers and scientists who have some familiarity with statistical analysis and want to learn how to model and analyze systems with multiple variables\, such as chemical processes\, mechanical systems\, or biological systems.\nHealthcare professionals who have a background in statistical analysis and want to learn how to analyze data from clinical trials or patient records to evaluate treatment effectiveness or identify risk factors for diseases.\n\nIn general\, the ideal audience for the Stats Camp statistical methods training course in multivariate modeling would be looking to expand their skills and learn more advanced techniques for analyzing complex datasets with multiple variables. \nSeminar Files\nSeminar Files\n\nInstructor will provide password on first day of seminar. \n\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 10\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nBasics of R and reading data into R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nRegression with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nRegression with R (continued)\n\n\nTuesday\nJune 11\, 2024\n\n\n9:00-10:45\nMediation with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nModeration with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nCombining Mediation and Moderation with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMissing Data Handling with R\n\n\nWednesday\nJune 12\, 2024\n\n\n9:00-10:45\nMLM with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMLM with R (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nLongitudinal MLM with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nLongitudinal MLM with R (continued)\n\n\nThursday\nJune 13\, 2024\n\n\n9:00-10:45\nExploratory Factor Analysis (EFA) with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nConfirmatory Factor Analysis (CFA) with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMultiple group CFA with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiple group CFA with R (continued)\n\n\nFriday\nJune 14\, 2024\n\n\n9:00-10:45\nSEM with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nSEM with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nOne-on-one consultations with instructor\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\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/multivariate-statistical-modeling-using-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:Multivariate Statistical Modeling using R,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/multivariate-statistical-modeling-using-r.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T035111
CREATED:20220715T045043Z
LAST-MODIFIED:20231209T044751Z
UID:3343-1718010000-1718384400@www.statscamp.org
SUMMARY:Structural Equation Modeling (SEM) with Mplus
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 5-day training course on using Mplus Structural Equation Modeling. \nMore and more researchers in the social and behavioral sciences use\, or want to use\, Mplus to analyze their structural equation models. This Stats Camp course is a 5-day hands on workshop using Mplus. \nSeminar Topics:\nThe five-day training institute on SEM with Mplus will enable participants to: \n\nBrief review of Structural Equation modeling (SEM)\nPreparing and reading data into Mplus\nModel specification and dealing with defaults\nProcedures for fitting and testing Structural Equation Modeling (SEM) models (regression\, path analysis\, multiple groups\, moderation ) in Mplus\nTesting mediation (bootstrapping)\nHow to fit longitudinal models (Panel vs Growth curve models)\n\nNote: This course will focus primarily on multivariate normal data that meets the assumptions of maximum likelihood estimation although other estimators available in Mplus will be briefly discussed. \nSeminar Description:\nThe course starts with a brief review of structural equation modeling with emphasis on the specific way Mplus is used to specify and estimate models. We will also discuss some ways to deal with warnings and error messages. Next\, we work with model specification and comparisons\, multigroup models\, DIF testing\, moderation\, and how to deal with Mplus defaults. We continue with testing predictive hypotheses and mean differences. Then\, we will cover more advanced topics related to longitudinal data along with its additional assumptions and how to fit longitudinal SEM models in Mplus (e.g. how to specify longitudinal panel models\, growth curve models\, and how to test for mediation). The last day provides time for an additional topic based on camper requests (i.e. MLM\, Power Analysis\, etc.). The available choices of different estimation methods and statistical tests are also discussed. \nOn each day\, the morning session consists of mini-lectures and examples\, and the afternoon session is a computer lab where the topics of the morning are applied on example data. There will also be time to work on your own data and get feedback on your models. Bringing your own data is a plus for you\, but definitely not a requirement for this course as we will have plenty of examples. \nParticipants 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 course. \nOn the first day of class you will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. You will also have access to a video recording of the course. \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\n  \n\n\nAPA Continuing Education Credits:\n \nPlease contact us for the exact # 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\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities\, participants will:\n\nAcquire an understanding of how Mplus syntax is structured and used to analyze structural equation models.\nEvaluate and respond to common error messages generated by Mplus.\nSet the scale in an SEM or CFA model in Mplus by overriding the program defaults for marker variable\, fixed factor\, and effects coding scaling methods.\nPrepare data for input into Mplus.\nFit CFA models using Mplus.\nConduct model comparisons and invariance testing using Mplus.\nEvaluate structural invariance in predictive models.\nEvaluate panel models and growth curve models using Mplus.\nImplement longitudinal designs\, mediation\, and bootstrapping in Mplus.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in multiple linear regression (e.g.\, a second course in statistics is usually sufficient).\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\nAt least limited experience (or basic awareness) with continuous latent variable models\, e.g.\, exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).\nPlease email the instructor if you have any questions or concerns. Some introductory literature/articles can be recommended/provided before the course begins.\n\nNot required but advantageous: \n\nAt least limited experience with multivariate data analysis.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nLatent variable modeling using Mplus.\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. \nInstruction 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.  Information for purchasing a personal license can be found at www.statmodel.com. \nSeminar Audience\nSeminar Audience:\nIf you need to analyze your data in Mplus or if you want to know when to switch to Mplus\, this seminar is for you. You should have some (basic) experience with other SEM software\, for example AMOS\, LISREL\, openMX\, SAS. No previous knowledge of Mplus is assumed. You do not need to know matrix algebra\, calculus\, or likelihood theory\, or any knowledge on Bayesian statistics. 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. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \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 10\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nSEM: Brief Review\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIntro to Mplus syntax and common error messages\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nScale setting in CFA/SEM (overriding Mplus defaults)\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and getting your data ready for Mplus\n\n\nTuesday\nJune 11\, 2024\n\n\n9:00-10:45\nQ&A\, Fitting CFA models\, Model comparisons\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIntro to Invariance testing (Multiple group models and moderation)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMplus examples\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises\n\n\nWednesday\nJune 12\, 2024\n\n\n9:00-10:45\nQ&A and catch up\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nStructural Invariance\, predictive models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMplus examples\, exercises\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and camper hypothesized models\n\n\nThursday\nJune 13\, 2024\n\n\n9:00-10:45\nQ&A and catch up\, longitudinal SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nPanel vs growth curve model specification in Mplus\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMore on longitudinal\, mediation\, bootstrapping in Mplus\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and working with your own data\n\n\nFriday\nJune 14\, 2024\n\n\n9:00-10:45\nQ&A and catch up\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMisc topic (based on camper response to survey)\n\n\n12:30-1:30\nRest Break\n\n\n1:30 ~ 3:00\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/sem-with-mplus/
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 with Mplus,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/sem-with-mplus-statistics-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T035111
CREATED:20220728T235219Z
LAST-MODIFIED:20240529T225830Z
UID:3491-1718010000-1718384400@www.statscamp.org
SUMMARY:Psychometrics
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nWe’re all looking to measure something\, but we first need to understand what how that measurement process works! Psychometrics is the underlying science behind all of your tests and measurements used to evaluate psychological attributes\, whether those are ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. This course will introduce you to the measurement and statistical concepts that are central to psychometrics\, as well as the psychometric fundamentals of factor analysis and Item Response Theory. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nThe latent variable modeling perspective\nReliability – classical and modern approaches\nValidity – conceptual and statistical aspects\nFactor analysis – exploratory and confirmatory\nInstrument development & validation process\nMeasurement bias & strategies to address bias (e.g.\, MIMIC models)\nMulti-group analyses & measurement invariance testing\nMissing data\nItem Response Theory: 1PL (Rasch) & 2PL & 3PL models\nHow to use MPlus for psychometric analyses\n\nSeminar Description:\nPsychometrics is the science of how we measure things\, such as the psychological attributes of people. These psychological attributes include abilities\, aptitudes\, achievement\, attitudes\, interests\, personality traits\, cognitive functioning\, and mental health. Psychometrics\, theoretically-informed and precise measurement\, is an essential component of many of the things we hold dear. Scientific advances (e.g.\, can I make a claim that I am measuring what I purport to measure?)\, educational placement decisions (e.g.\, should a child be placed into a gifted program?)\, statistical power (e.g.\, is my measure precise enough to suggest that X predicts Y?)\, and other key considerations are all affected by psychometrics. This seminar emphasizes the conceptual understanding of and the application of psychometric principles. \n\nInstructor: Larry Price\, Ph.D.\n \nLarry Price\, Ph.d. is a Professor of Psychometrics & Statistics and was previously Director of the Office of Data Analytics & Methodology at Texas State University for 13 years. Between 1999 and 2002\, Dr. Price was employed at The Psychological Corporation in San Antonio as a Senior Psychometrician/Statistician where his work focused on improving the psychometric properties of the Wechsler Scales of Intelligence Memory (e.g.\, WISC-III\, WISC-IV\, WAIS-III\, WMS-III\, and WPPSI-III)\, and Achievement (WIAT-II) and other psychological measures such as the Beck Depression Inventory (BDI) and Clinical Evaluation of Language Fundamentals (CELF-IV). His research interests include the theoretical development and testing of Bayesian and non-Bayesian psychometric models in psychological and neuropsychological research (neuroimaging network analysis)\, theoretical development\, testing\, and refinement of classical and modern psychometric methods in the behavioral sciences\, development of dynamic multivariate time series models for the psychological\, social and neurosciences. Before working at Psychological Corporation\, he worked at Emory University from 1986 to 1999 as a Biostatistician and Psychometrician in the School of Medicine. Funding mechanisms for Dr. Price’s work include NIH\, NSF\, DOE\, and private organizations. \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 24 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\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\nApply a latent variable perspective to psychometrics\nExamine how psychometrics fits into the scientific enterprise and educational policy\nIdentify construct validity as the “one ring to rule them all” and where other dimensions of validity fit in\nExamine why Cronbach’s Alpha is biased (a recent article was titled “Thanks Coefficient Alpha\, we’ll take it from here”)\nUnderstand why other reliability estimates (e.g.\, mean inter-item correlations\, Coefficient H\, Omega total) are preferred\, and how to calculate and interpret them\nCode\, analyze\, and interpret exploratory factor analyses (EFA)\nCode\, analyze\, and interpret confirmatory factor analyses (CFA)\nSynthesize the preceding ideas to understand the instrument development & validation process\nUnderstand the difference between “true score” bias and item bias\nCode\, analyze\, and interpret Multiple Indicator and Multiple Causes (MIMIC) models\nCode\, analyze\, and interpret measurement invariance analyses\nUnderstand the differences between configural\, metric & scalar invariance\nUnderstand why Item Response Theory (IRT) is useful to assess ability & shorten scales\nCode\, analyze\, and interpret 1PL\, 2PL\, 3PL IRT models\nUse figures to communicate the psychometric properties of measures\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) with classical measurement theory and concepts.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nIt is important to bring a notebook computer to the seminar\, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms. \nSeminar Audience\nSeminar Audience:\nThe intended audience is advanced students\, faculty\, and other researchers\, from all disciplines\, who want a ground-floor introduction to doing Psychometrics data analysis. \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 Bayesian Data Analysis 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\n\nSummer Stats Camp 2024: Psychometrics\n\n\n\nMonday\n June 10\, 2024\n\n\n9:00-9:30\nWelcome and introduction.\n\n\n9:30-10:30\nA latent variable modeling perspective on psychometrics.\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:30\nCoefficient Alpha: Widely known\, poorly understood\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIntro to MPlus code. \nBringing latent variables into focus: Exploratory Factor Analyses (EFA).\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nConstruct validity: “One ring to rule them all”\n\n\nTuesday\n June 11\, 2024\n\n\n9:00-10:45\nTesting hypothesized factor structures: Confirmatory Factor Analyses (CFA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCFA: Hands-on practice & statistical power\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nBias & Fairness in Psychometrics: Multiple Indicators and Multiple Causes (MIMIC) models\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nWednesday\n June 12\, 2024\n\n\n9:00-10:30\nMulti-group models & Measurement Invariance\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:30\nMeasurement Invariance (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nBest practices: Instrument development & validation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nThursday\n June 13\, 2024\n\n\n9:00-10:45\nIntroduction to Item Response Theory (IRT)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIRT: Discrimination (1PL)\, Difficulty (2PL) & Guessing (3PL) models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIRT: The Graded Response Model\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nFriday\n June 14\, 2024\n\n\n9:00-10:30\nMissing data: Focus on the auxiliary variables strategy\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:00\nIndividual consultation\n\n\n12:00\nEnd of Workshop\n\n\n12:30-1:30\nRest Break\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/psychometrics/
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:Psychometrics,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/psychometrics-training-course.jpg
END:VEVENT
END:VCALENDAR