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:VEVENT
DTSTART;VALUE=DATE:20230101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260405T093406
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:20260405T093406
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/Chicago:20240722T090000
DTEND;TZID=America/Chicago:20240725T150000
DTSTAMP:20260405T093406
CREATED:20221014T045552Z
LAST-MODIFIED:20240704T002959Z
UID:4647-1721638800-1721919600@www.statscamp.org
SUMMARY:Network Psychometric Training Course
DESCRIPTION:Unlock your potential with our immersive 4-day online psychometric training! Gain proficiency in applying psychometrics and elevate your skills to new heights. Ideal for those with a solid foundation in the material covered in a two-semester graduate-level social science statistics course sequence. Propel your career forward – enroll now and become a master in psychometrics!\nLIVESTREAM / Asynchronous – 4-day Statistics Psychometric Training Online Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nCutting-Edge Network Psychometric Training\nThis is an introductory four-day course in network psychometrics theory and application. Participants should be proficient in the material covered in one-semester graduate-level psychometrics\, statistics\, and structural equation modeling courses. Foundational experience using R open-source software is strongly recommended. \nSyllabus:\n\n\n\n\nDay 1 \n\n\nIntroduction and Fundamentals \n\n\n\n\n9:00-9:30 \n\n\nWelcome and introductions \n\n\n\n\n9:30-10:30 \n\n\nIntroduction to Network Psychometrics: Overview of network psychometrics\, comparison with traditional psychometric models\, applications in psychology\, neuroscience\, education\, and social science \n\n\n\n\n10:30-11:30 \n\n\nHands-on Exercise: Introduction to R and R Studio \n\n\n\n\n11:30-12:30 \n\n\nLunch break \n\n\n\n\n12:30-1:30 \n\n\nBasic concepts in network analysis: Types of networks (undirected\, directed\, weighted\, unweighted)\, Adjacency and correlation matrices \n\n\n\n\n1:30-3:00 \n\n\nNetwork estimation methods: Thresholding techniques\, Regularization techniques (e.g.\, LASSO and graphical LASSO) \n\n\n\n\nDay 2 \n\n\nNetwork Analysis and Interpretation \n\n\n\n\n9:00-11:00 \n\n\nCentrality Measures: Degree centrality\, Betweenness centrality\, Closeness centrality\, Eigenvector centrality \n\n\n\n\n11:00-11:30 \n\n\nHands-on Exercise: Detecting communities in the network using ‘qgraph’ and ‘igraph’ R packages\, Interpreting community structures in psychological data \n\n\n\n\n11:30-12:30 \n\n\nLunch break \n\n\n\n\n12:30-2:00 \n\n\nCommunity Detection: Concepts of communities and clustering\, Algorithms for community detection (e.g.\, Walktrap\, Louvain)\, Applications in psychological and neuroimaging analyses\, Introduction to Pairwise Markov Random Fields (PMRFs): Graphical Models for Binary Data (GMBDs); Gaussian Graphical Models (GGMs) for continuous data; Mixed Graphical Models (MGMs) for continuous\, categorical and count data \n\n\n\n\n2:00-3:00 \n\n\nHands-on Exercise: Detecting communities in the network using ‘qgraph’ and ‘igraph’ R packages\, Interpreting community structures in psychological data \n\n\n\n\nDay 3 \n\n\nAdvanced Topics in Network Psychometrics \n\n\n\n\n9:00-10:30 \n\n\nStability and Robustness Analysis: Bootstrapping and permutation tests\, Assessing the stability of network parameters\, visualizing stability results \n\n\n\n\n10:30-11:30 \n\n\nHands-on Exercise: Performing stability analysis using the R ‘bootnet’ package\,  Interpreting stability plots \n\n\n\n\n11:30-12:30 \n\n\nLunch break \n\n\n\n\n12:30-1:30 \n\n\nLunch break \n\n\n\n\n1:30-2:30 \n\n\nClassical Item Analysis and Factor Analysis Using Networks:  Network approaches to classical item analysis\, Network-based factor analysis \n\n\n\n\n2:30-3:00 \n\n\nHands-on Exercise: Conducting item analysis and factor analysis using network models in R \n\n\n\n\nDay 4 \n\n\nApplications and Case Studies \n\n\n\n\n9:00-10:15 \n\n\nNetwork Psychometrics for Reliability Estimation: Network-based reliability estimation methods\, Comparing network-based\, classical\, and structural equation modeling reliability estimates \n\n\n\n\n10:15-11:30 \n\n\nItem Response Theory (IRT) and Network Models: Linking IRT with network psychometrics\, Network-based IRT analysis \n\n\n\n\n11:30-12:30 \n\n\nLunch Break \n\n\n\n\n12:30-1:30 \n\n\nModel Invariance and Structural Validation: Assessing model invariance cross-sectionally and longitudinally\, Structural model validation using network models \n\n\n\n\n1:30-3:00 \n\n\nFinal Project and Discussion: Students present their network analysis projects\, Discuss applications and future directions in network psychometrics\, Q & A\, and course wrap-up. Final Project: Students will apply the techniques they learned during the course to a dataset of their choice\, constructing\, analyzing\, and interpreting a psychological network. \n\n\n\n\nCourse Topics:\n\nIntroduction to Network Psychometrics\nBasic Concepts in Network Analysis\nNetwork Estimation Methods\nCentrality Measures\nCommunity Detection\nStability and Robustness Analysis\nClassical Item Analysis Using Networks\nNetwork-based Factor Analysis\nReliability Estimation with Network Models\nItem Response Theory (IRT) and Network Models\nModel Invariance Assessment (Cross-sectional and Longitudinal)\nConstruct Validation Using Network Models\nPractical Applications and Case Studies\n\nPsychometric Training Description:\nThis course introduces the principles and applications of network psychometrics\, a cutting-edge approach to understanding psychological constructs through network models. Students will learn how to construct\, analyze\, and interpret networks where nodes represent psychological variables (e.g.\, symptoms\, behaviors\, and item-level response data) and edges represent relationships between them. The course covers key concepts such as network estimation\, centrality measures\, community detection\, and stability analysis. Practical applications will use network psychometrics for classical item analysis\, factor analysis\, reliability estimation\, item response theory\, model invariance assessment (cross-sectionally and longitudinally)\, and construct validation in psychological testing. Through hands-on exercises and real-world examples\, students will gain basic proficiency using R for network analysis. By the end of the course\, participants will be equipped with the skills to apply network psychometrics in research and practice\, enhancing their ability to explore complex psychological phenomena. \nAs a participant\, you’ll receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. These comprehensive resources are designed to enhance your learning experience and support your application of network psychometrics in your work. You’ll also have access to a video recording of the course\, allowing you to revisit the content at your convenience. This additional resource is a valuable tool for reinforcing your understanding of the course material and applying it in your professional practice. \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\nAPA Continuing Education Credits:\n \nPlease contact us for exact # of credit hours for continuing education credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nPsychometric Training Course Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\n\nUnderstand the principles of network psychometrics and its applications in psychology.\nConstruct\, analyze\, and interpret psychological networks using R.\nEstimate networks and understand the implications of different estimation methods.\nCalculate and interpret centrality measures to identify key nodes in networks.\nApply community detection algorithms to uncover clusters within networks.\nConduct stability and robustness analyses to ensure the reliability of network findings.\nUse network models for classical item analysis and factor analysis.\nEstimate reliability using network-based methods.\nIntegrate Item Response Theory with network psychometrics for deeper insights.\nAssess model invariance both cross-sectionally and longitudinally.\nValidate psychological constructs through network analysis.\nApply network psychometrics in real-world scenarios and research settings.\nExpertise in using R for network analysis through hands-on exercises and examples.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \nPrerequisites: Basic knowledge of psychometrics and statistics. \nAdvantageous: \n\nLimited experience (e.g.\, graduate-level course) in structural equation modeling.\nLimited experience using the R statistical platform for coding and analysis.\n\nNo level of proficiency beyond basic awareness is required for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. Students must have access to R Studio. \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/network-psychometric-training-online/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Livestream,Network Psychometrics,Psychometrics
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/10/network-psychometrics-statistics-training-seminar.jpg
END:VEVENT
END:VCALENDAR