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DTSTART;VALUE=DATE:20230101
DTEND;VALUE=DATE:20300102
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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 Most campers report their prior training was insufficient and/or outdated.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
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DTSTART;VALUE=DATE:20230101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260407T100201
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:20230612T090000
DTEND;TZID=America/Denver:20230616T170000
DTSTAMP:20260407T100201
CREATED:20220701T090804Z
LAST-MODIFIED:20240222T203237Z
UID:2690-1686560400-1686934800@www.statscamp.org
SUMMARY:Intro to Data Mining and Machine Learning
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nData Mining and Machine Learning Seminar Overview:\nAn intermediate 5-day course introducing several popular machine learning approaches such as regression based methods (ridge and lasso regularized regression\, regression splines)\, tree methods (random forests\, boosted trees)\, support vector machines\, and Interpretative Machine Learning (ILM) as well as their application to empirical data. The course combines lectures and hands-on practice using R. \nSeminar Topics:\n\nReview of linear regression and the least squares criterion\nRegularization methods (ridge regression\, lasso\, elastic net)\nRegression splines\nPrediction error and k-fold cross validation\nTree methods to predict categorical or continuous outcomes (CART\, random forest\, boosting\nInterpretative Machine Learning (IML)\nSupport vector machines for classification\n\nSeminar Description:\nMachine learning refers to leveraging data to build statistical models or algorithms. The objective is usually to gain knowledge about the structure in the data in order to make predictions or decisions. \nThis short course is based on \n\nAn Introduction to Statistical Learning (James\, Witten\, Hastie\, Tibshirani)\nHands-on Machine Learning with R (Boemke & Greenwell)\nInterpretable Machine Learning (Molnar)\n\nThe course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g.\, linear regression\, structural equation modeling) and machine learning (aka statistical learning\, aka data mining). The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso\, regression splines)\, tree methods (CART\, random forests\, boosting)\, interpretable machine learning (IML)\, and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly\, these methods are useful not only for large data collections\, but also more generally for exploratory analyses when the substantive theory to design and fit parametric models (e.g. SEM) is lacking. Machine learning is used in a wide variety of fields including but not limited to public health\, education\, biology\, and the different social sciences. \nParticipants are invited to discuss potential machine learning applications to their data during individual consultations with the instructor scheduled at the end of days 2-5. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts (R)\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n  \n\nInstructor: Gitta Lubke\, Ph.D.\n \nGitta Lubke is a Professor Emerita in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests included machine learning and general latent variable modeling. Empirical applications were mainly in the field of psychiatric disorders and behavioral genetics. Other areas of expertise include mixture models\, twin models\, multi-group factor analysis and measurement invariance\, longitudinal analyses\, and the analysis of categorical data. \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\nUnderstand some of the key differences and similarities between parametric modeling and machine learning methods.\nExpand the acquired basic knowledge of several popular machine learning methods and apply these methods to empirical data.\nImplement ridge regression and Lasso.\nAssess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors.\nImplement and evaluate regression splines.\nImplement and evaluate decision trees to categorize data.\nUtilize CART and bagging techniques.\nImplement random forests to evaluate data.\nImplement and evaluate boosted trees.\nUnderstand several basic interpretable machine learning methods\nUnderstand and utilize support vector machines\nUtilize R packages for machine learning.\nUnderstand and evaluate scientific papers covering basic machine learning applications to empirical data.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nAdvanced proficiency in linear regression\, including the estimation of regression coefficients using least squares\nIntermediate proficiency with R\nIntermediate knowledge of exploratory data analysis\nBasic familiarity with iterative optimization (e.g. Newton-Raphson algorithm to find a maximum)\n\nNot required but advantageous: \n\nExperience in calculus (e.g.\, graduate-level course)\nUnderstanding the relation between multiple testing and Type I error\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMachine learning methods\nMore advanced mathematical or statistical topics such as constrained estimation using Laplace multipliers\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. \nAll instruction for this course will be based on the freely available software program R. Please make sure to have a recent version installed. \nSeminar Audience\nSeminar Audience:\nTypically the ideal audience for this course in data mining and machine learning includes: \n\nStudents pursuing a degree in computer science\, engineering\, statistics\, or related fields who have a strong background in mathematics and programming.\nResearchers and professionals in the fields of data science\, data analysis\, artificial intelligence\, and machine learning who want to learn new techniques and keep up with the latest developments in the field.\nData analysts and data engineers who are interested in learning how to extract insights from large datasets using machine learning algorithms.\nBusiness professionals who are interested in understanding how data mining and machine learning can be applied to solve real-world business problems.\nAnyone who wants to gain a deeper understanding of the techniques and algorithms used in data mining and machine learning\, and their applications in various fields.\n\nThe audience for a course in data mining and machine learning can be quite diverse\, but typically consists of individuals with a strong background in quantitative analysis and a desire to apply machine learning techniques to real-world problems. \nCourse Learning Goals\nCourse Learning Goals:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with empirical data\, participants will be able to: \n\nUnderstand some of the key differences and similarities between parametric modeling and data mining methods\nExpand the acquired basic knowledge of several popular data mining methods and apply these methods to empirical data\nAssess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors\nUtilize R packages for data mining\nUnderstand and evaluate scientific papers covering data mining applications to empirical data\n\nSeminar Files\nSeminar Files\nSeminar files will be provided by the instructor on the first day of the seminar. You do not need to download anything prior to the event date. All materials will be provided during or after the class. \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 12\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nSimple and Multiple Linear Regression\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nRidge Regression and Lasso\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nPrediction Error and Cross Validation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nR lab: Ridge Regression and Lasso\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nRegression Splines\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nR lab: Regression Splines\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIntroduction to Tree Methods\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00-10:45\nCART\, bagging\, Random Forests\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nR lab: Random Forests\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nBoosted Trees\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nR lab: Boosted Trees\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nInterpretable Machine Learning (IML)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nDeductive Data Mining\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nSupport Vector Machines\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nR lab: Support Vector Machines\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIndividual consultation with instructor\n\n\n\n\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/intro-to-data-mining-and-machine-learning/
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:Intro to Data Mining and Machine Learning,Summer Camp
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DTSTART;TZID=America/Chicago:20230626T170000
DTEND;TZID=America/Chicago:20230630T210000
DTSTAMP:20260407T100201
CREATED:20230301T011050Z
LAST-MODIFIED:20240214T193454Z
UID:5512-1687798800-1688158800@www.statscamp.org
SUMMARY:Item Response Theory
DESCRIPTION:Statistics Training Course Offerings\n\nLIVE STREAM – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nA 5-day course in the theory and application of item response theory. As a prerequisite\, participants should be proficient in the material covered in a one-semester graduate-level psychometrics course or an equivalent level of knowledge from professional experience in test development. \nSeminar Topics:\n\nFoundations\, principles\, and evolution of IRT\nThe relationship between measurement\, classical test theory\, IRT\, and factor analysis\nLinking terms and concepts in IRT\, factor analysis\, and classical test theory\nAssumptions of IRT models and their evaluation – test dimensionality and local independence\nThe principle and implications of IRT model invariance\nTypes of IRT models – model selection relative to test/instrument development and utility\nUnidimensional and multidimensional IRT\nEstimation of item parameters and person’s ability or latent traits\nItem and test information – definition\, application\, and implications for test development\nIRT-based test and instrument construction\, evaluation\, and refinement\nSteps in conducting an IRT analysis in relation to the testing problem\nInterpreting IRT output to inform test refinement\nIdentification of potentially biased items and tests – techniques and practical solutions\nIntroduction to longitudinal IRT\nSoftware used for conducting IRT analyses for a variety of models\n\nSeminar Description:\nItem response theory (IRT)\, also known as modern test theory\, is a system of modeling procedures that uses latent characteristics of persons or examinees and test items as predictors of observed responses (de Ayala\, 2022; Price\, 2016; Lord\, 1980). IRT is a model-based theory of statistical estimation that conveniently places persons and items on the same metric based on the probability of response outcomes. IRT offers a powerful statistical framework that is useful for experts in disciplines such as cognitive psychology\, education\, psychiatry\, or social/developmental psychology when the goal is to construct explanatory models of behavior and/or performance in relation to theory. Participants 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: 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 \nThis course offers 18hrs of credit hours for continuing education. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\n\nAcquire a basic understanding of the role of IRT as applied to psychological measurement\, test development\, refinement\, and evaluation in the social and behavioral sciences.\nDevelop an understanding of the conceptual and theoretical basis of IRT approaches to psychometrics.\nConceptualization and estimation of reliability.\nThe role of validity in IRT in comparison to classical test theory.\nAcquire knowledge of IRT models used for item response data acquired as dichotomous\, polytomous\, and nominal.\nAcquire knowledge of how to properly apply IRT principles and techniques in test development\, and item analysis/refinement.\nGain knowledge of how to assess requisite assumptions of IRT including dimensionality\, invariance\, and local item independence.\nGain knowledge of how to estimate and report conditional or person-specific reliability using IRT latent trait information.\nGain knowledge of how to use IRT models for detecting differential functioning of items and tests (a.k.a. model invariance in factor analysis and structural equation modeling).\nAcquire knowledge of how to use IRT for test equating and norms development.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in psychometric methods and/or theory in a graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) in probability and mathematical statistics.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nStudents must have access to R Studio and the R MIRT package installed. For computer practice\, students will primarily use the R MIRT package. Examples will also be presented using IRTPRO or flex MIRT available from Vector Psychometric Group (https://store.vpgcentral.com). \nTo facilitate learning\, examples in the course will draw primarily on those included in the book Handbook of Educational Measurement and Psychometrics Using R. It is recommended that participants have access to this book during the course. A companion site for the book that includes R code and sample data by chapter is available at: (http://bit.ly/hemp_code). Examples will also use intelligence test data provided from Psychometric Methods: Theory into Practice\, by the course instructor. \nSyllabus\n\n\n\nDay 1\n \n\n\n5:00-5:30\nWelcome and introductions\n\n\n5:30-6:30\nFoundations\, principles\, and current practice of IRT in testing research\, and development.  The relationship between measurement\, classical test theory\, IRT\, and factor analysis. \n \n\n\n6:30-6:45\nBreak\n\n\n6:30-7:45\nLinking terms and concepts in IRT\, factor analysis\, and classical test theory with implications for practical IRT use. The role of reliability and validity in relation to modern test theory. Types of unidimensional IRT models (dichotomous\, polytomous\, nominal)\, assumptions\, and their evaluation. Introduction to software for IRT – MIRT program in R; IRTPRO; flex MIRT. \n  \n \n\n\n7:45-9:00\nIntroduction and practice with software for IRT – MIRT program in R; IRTPRO; flex MIRT. \n \n\n\nDay 2\n\n\n\n5:00-6:30\nUnidimensional IRT dichotomous response model – estimation of item parameters\, model fit\, person’s ability/latent traits\, and interpreting the output Computer exercise with example data. \n \n\n\n6:30-6:45\nBreak\n\n\n6:45-7:45\nUnidimensional IRT polytomous and nominal response models – estimation of item parameters\, model fit\, person’s ability/latent traits\, and interpreting the output. Computer exercise with example data. \n \n\n\n7:45-9:00\nUsing IRT results to inform test development practice\, refinement\, and decision-making. IRT-based conditional and test reliability estimation\, use\, and reporting\n\n\nDay 3\n \n\n\n5:00-6:30\nIntroduction to multidimensional IRT (MIRT) – theory and practical application examples. Overview and advantages of fully Bayesian IRT/MIRT.\n\n\n6:30-6:45\nBreak\n\n\n6:45-8:00\nSoftware applications for MIRT analysis\, including interpretation of the output.\n\n\n8:00-9:00\nUsing IRT results to inform test development practice\, refinement\, and decision-making.\n\n\nDay 4\n\n\n\n5:00-6:30\nDetecting functioning of items and tests (a.k.a. model invariance in factor analysis and structural equation modeling). \n \n\n\n6:30-6:45\nBreak\n\n\n6:45-8:00\nComputer practice for conducting differential item and test functioning\, including interpretation of output and reporting results.\n\n\n8:00-9:00\nContinuation of practice for conducting differential item and test functioning\, including interpretation of output and reporting results.\n\n\nDay 5\n\n\n\n5:00-6:30\nIntroduction to test score equating and linking. Using IRT for equating different forms of tests.\n\n\n6:30-6:45\nBreak\n\n\n6:45-9:00\nTime for addressing student-specific questions and needs to enhance learning. Additional practice using IRT programs and interpretation of results.\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/item-response-theory/
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
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