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DTSTART;TZID=America/Denver:20240610T090000
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DTSTAMP:20260421T061559
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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
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DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260421T061559
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:20260421T061559
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
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DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260421T061559
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
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