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DTSTART;TZID=America/Chicago:20240722T090000
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DTSTAMP:20260405T182557
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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
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T182557
CREATED:20220728T235219Z
LAST-MODIFIED:20240529T225830Z
UID:3491-1718010000-1718384400@www.statscamp.org
SUMMARY:Psychometrics
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nWe’re all looking to measure something\, but we first need to understand what how that measurement process works! Psychometrics is the underlying science behind all of your tests and measurements used to evaluate psychological attributes\, whether those are ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. This course will introduce you to the measurement and statistical concepts that are central to psychometrics\, as well as the psychometric fundamentals of factor analysis and Item Response Theory. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nThe latent variable modeling perspective\nReliability – classical and modern approaches\nValidity – conceptual and statistical aspects\nFactor analysis – exploratory and confirmatory\nInstrument development & validation process\nMeasurement bias & strategies to address bias (e.g.\, MIMIC models)\nMulti-group analyses & measurement invariance testing\nMissing data\nItem Response Theory: 1PL (Rasch) & 2PL & 3PL models\nHow to use MPlus for psychometric analyses\n\nSeminar Description:\nPsychometrics is the science of how we measure things\, such as the psychological attributes of people. These psychological attributes include abilities\, aptitudes\, achievement\, attitudes\, interests\, personality traits\, cognitive functioning\, and mental health. Psychometrics\, theoretically-informed and precise measurement\, is an essential component of many of the things we hold dear. Scientific advances (e.g.\, can I make a claim that I am measuring what I purport to measure?)\, educational placement decisions (e.g.\, should a child be placed into a gifted program?)\, statistical power (e.g.\, is my measure precise enough to suggest that X predicts Y?)\, and other key considerations are all affected by psychometrics. This seminar emphasizes the conceptual understanding of and the application of psychometric principles. \n\nInstructor: Larry Price\, Ph.D.\n \nLarry Price\, Ph.d. is a Professor of Psychometrics & Statistics and was previously Director of the Office of Data Analytics & Methodology at Texas State University for 13 years. Between 1999 and 2002\, Dr. Price was employed at The Psychological Corporation in San Antonio as a Senior Psychometrician/Statistician where his work focused on improving the psychometric properties of the Wechsler Scales of Intelligence Memory (e.g.\, WISC-III\, WISC-IV\, WAIS-III\, WMS-III\, and WPPSI-III)\, and Achievement (WIAT-II) and other psychological measures such as the Beck Depression Inventory (BDI) and Clinical Evaluation of Language Fundamentals (CELF-IV). His research interests include the theoretical development and testing of Bayesian and non-Bayesian psychometric models in psychological and neuropsychological research (neuroimaging network analysis)\, theoretical development\, testing\, and refinement of classical and modern psychometric methods in the behavioral sciences\, development of dynamic multivariate time series models for the psychological\, social and neurosciences. Before working at Psychological Corporation\, he worked at Emory University from 1986 to 1999 as a Biostatistician and Psychometrician in the School of Medicine. Funding mechanisms for Dr. Price’s work include NIH\, NSF\, DOE\, and private organizations. \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 24 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nApply a latent variable perspective to psychometrics\nExamine how psychometrics fits into the scientific enterprise and educational policy\nIdentify construct validity as the “one ring to rule them all” and where other dimensions of validity fit in\nExamine why Cronbach’s Alpha is biased (a recent article was titled “Thanks Coefficient Alpha\, we’ll take it from here”)\nUnderstand why other reliability estimates (e.g.\, mean inter-item correlations\, Coefficient H\, Omega total) are preferred\, and how to calculate and interpret them\nCode\, analyze\, and interpret exploratory factor analyses (EFA)\nCode\, analyze\, and interpret confirmatory factor analyses (CFA)\nSynthesize the preceding ideas to understand the instrument development & validation process\nUnderstand the difference between “true score” bias and item bias\nCode\, analyze\, and interpret Multiple Indicator and Multiple Causes (MIMIC) models\nCode\, analyze\, and interpret measurement invariance analyses\nUnderstand the differences between configural\, metric & scalar invariance\nUnderstand why Item Response Theory (IRT) is useful to assess ability & shorten scales\nCode\, analyze\, and interpret 1PL\, 2PL\, 3PL IRT models\nUse figures to communicate the psychometric properties of measures\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) with classical measurement theory and concepts.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nIt is important to bring a notebook computer to the seminar\, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms. \nSeminar Audience\nSeminar Audience:\nThe intended audience is advanced students\, faculty\, and other researchers\, from all disciplines\, who want a ground-floor introduction to doing Psychometrics data analysis. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access Bayesian Data Analysis Seminar Files \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n  \n\n\n\nSummer Stats Camp 2024: Psychometrics\n\n\n\nMonday\n June 10\, 2024\n\n\n9:00-9:30\nWelcome and introduction.\n\n\n9:30-10:30\nA latent variable modeling perspective on psychometrics.\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:30\nCoefficient Alpha: Widely known\, poorly understood\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIntro to MPlus code. \nBringing latent variables into focus: Exploratory Factor Analyses (EFA).\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nConstruct validity: “One ring to rule them all”\n\n\nTuesday\n June 11\, 2024\n\n\n9:00-10:45\nTesting hypothesized factor structures: Confirmatory Factor Analyses (CFA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCFA: Hands-on practice & statistical power\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nBias & Fairness in Psychometrics: Multiple Indicators and Multiple Causes (MIMIC) models\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nWednesday\n June 12\, 2024\n\n\n9:00-10:30\nMulti-group models & Measurement Invariance\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:30\nMeasurement Invariance (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nBest practices: Instrument development & validation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nThursday\n June 13\, 2024\n\n\n9:00-10:45\nIntroduction to Item Response Theory (IRT)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIRT: Discrimination (1PL)\, Difficulty (2PL) & Guessing (3PL) models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIRT: The Graded Response Model\n\n\n3:00-3:15\nRest Break\n\n\n3:15-4:00\nReview\, integration & catch-up time\n\n\n4:00-5:00\nIndividual Consultations\n\n\nFriday\n June 14\, 2024\n\n\n9:00-10:30\nMissing data: Focus on the auxiliary variables strategy\n\n\n10:30-10:45\nRest Break\n\n\n10:45-12:00\nIndividual consultation\n\n\n12:00\nEnd of Workshop\n\n\n12:30-1:30\nRest Break\n\n\n\n  \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/psychometrics/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Psychometrics,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/psychometrics-training-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T182557
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
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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T182557
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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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240610T090000
DTEND;TZID=America/Denver:20240614T170000
DTSTAMP:20260405T182557
CREATED:20220701T092234Z
LAST-MODIFIED:20231209T043756Z
UID:2697-1718010000-1718384400@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nLongitudinal Structural Equation Modeling (LSEM) Seminar Overview:\nDo you have repeated measurements? Have you collected data over multiple timepoints? Do you need help designing your longitudinal study? If so\, this is your course! Let us help you appropriately design your longitudinal study and analyze your data in the SEM latent variable framework using Longitudinal Structural Equation Modeling (LSEM). This framework will allow you more flexibility in evaluating your research questions over time as well as test assumptions that traditional techniques like ANOVA ignore. \nSeminar Topics:\n\nDesign and measurement issues in cross-sectional and longitudinal research\nTraditional panel designs\nOverview of missing data\nLatent growth curve modeling\nTesting for Mediation and Moderation\nUsing Phantom Constructs\nLongitudinal Measurement Invariance – Multiple Group LSEM\nGrowth Mixture Models\n\nSeminar Description:\nThe seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, Ph.D. is a Professor of Educational Psychology at Texas Tech University (TTU). Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g.\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). Prior to joining TTU\, …  Little has guided quantitative training and provided consultation to students\, staff\, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998)\, Yale University’s Department of Psychology (1998-2002)\, and researchers at KU (2002-2013\, including as director of the RDA unit at the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001\, Little was elected to membership in the Society for Multivariate Experimental Psychology\, a restricted-membership society of quantitative specialists in the behavioral and social sciences.\n \nIn 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He founded\, organizes\, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. As an interdisciplinary-oriented collaborator\, Little has published with over 280 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 11\,000 citations. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology\, including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF\, NIH\, and IES\, and private foundations such as the Jacobs Foundation. He has been the principal investigator or co-principal investigator on over 15 grants and contracts and he has served as a statistical consultant on over 70 grants and contracts. In the conduct of his collaborative research\, he has participated in the development of over 12 different measurement tools\, including the CAMI\, the Multi-CAM\, the BALES\, the BISC\, the I FEEL\, and the form/function decomposition of aggression. \n\n\n\nInstructor: Whitney Moore\, Ph.D.\n \nDr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM\, Longitudinal SEM\, and MLM). Whitney has taught graduate courses in research design\, introduction to statistics\, ANOVA\, SEM\, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport\, exercise\, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs\, finite mixture modeling\, plus mediation and moderation in SEM. \nRead More\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary\, Ph.D. is a senior research scientist at Yhat Enterprises LLC. where he pursues his research interests in measurement design\, applied latent variable modeling\, and modern approaches to missing data. Dr. Stickley has also served as an instructor and coordinator for the Stats Camp Foundation since first joining the team as a graduate student in 2018. He received his Ph.D. in Educational Psychology from College of Education at Texas Tech University with a focus on research methodology\, measurement design\, and statistical modeling. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University. \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 26 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Longitudinal Structural Equation Modeling (LSEM) will enable participants to:\n\nAddress design and measurement issues in longitudinal modeling.\nAcquire understanding of the SEM concepts that are foundational to longitudinal SEM design.\nAnalyze longitudinal panel models in both a single group and multi-group configuration in CFA and SEM framework.\nIncorporate mediation and moderation in a longitudinal framework.\nConstruct item parcels in a longitudinal framework.\nEvaluate latent growth curve models.\nApply latent growth curve models in a multivariate and multiple group context.\nEvaluate finite mixture models.\nInterpret and evaluate covariance pattern mixture models.\nEvaluate Growth mixture models.\nAddress missing data using FIML and MI methods.\nUse modern missing data treatments to implement a planned missing data design.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nProficiency in multiple linear regression.\nAt least limited experience (e.g.\, graduate-level course) with continuous latent variable models\, e.g.\, exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).\nWe strongly recommend that you attend our foundations of SEM as a pre-requisite to taking this advanced course. If you have not taken the foundations course\, you should have extensive experience or have taken a graduate-level course on SEM before enrolling.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, LISREL\, Mplus\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nLongitudinal Structural Equation Modeling (LSEM).\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nSeminar Audience\nSeminar Audience:\nIf you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on the foundations of SEM as a pre-requisite to taking this five-day advanced seminar. If you have not taken the foundations Seminar\, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling. \nParticipants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. Previous knowledge of LISREL\, Mplus or Laavan is preferred but not required. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2024: Longitudinal Structural Equation Modeling (LSEM)\n\n\nMonday\nJune 10\, 2024\n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nReview of Foundations of SEM\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nLongitudinal Panel Models: Basics\n\n\nTuesday\nJune 11\, 2024\n\n\n9:00-10:45\nMultiple-group Longitudinal Panel Models; CFA and SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nParcels and Parceling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLongitudinal Mediation & Moderation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nConsultation\n\n\nWednesday\nJune 12\, 2024\n\n\n9:00-10:45\nLatent Growth Curve Modeling: Basics\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent Growth Curve Modeling: Multivariate and Multiple Groups\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Finite Mixture Modeling\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to growth mixture modeling / Consultation\n\n\nThursday\nJune 13\, 2024\n\n\n9:00-10:45\nLatent class growth analysis (LCGA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCovariance Pattern Mixture Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nGrowth mixture modeling (GMM)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGrowth mixture modeling (GMM)\n\n\nFriday\nJune 14\, 2024\n\n\n9:00-10:45\nMissing Data: Planned and Unplanned\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nWrap-up then Individual Consultations\n\n\n12:30-1:30\nRest Break\n\n\n1:30-~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/longitudinal-structural-equation-modeling-lsem-training/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Longitudinal SEM,Longitudinal Structural Equation Modeling,Summer Camp,Summer Camp 2
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/longitudinal-structural-equation-modeling-statistics-course-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260405T182557
CREATED:20220701T085536Z
LAST-MODIFIED:20231209T041530Z
UID:2684-1717405200-1717779600@www.statscamp.org
SUMMARY:SEM Foundations & Extended Applications
DESCRIPTION:IN PERSON – 5-day Structural Equation Modeling Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nStructural Equation Modeling Course Overview:\n\nDo you want to take your measurement to the latent level? Well\, this Structural Equation Modeling Course is it\, you have found it\, the foundation to what you need to know for latent variable modeling – structural equation modeling (SEM)! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to ‘craft’ an exquisite SEM model. \n\nSeminar Topics:\n\nPhantom Constructs\nFitting measurement models\nThree methods of scale setting – including effects coding!\nUpdated recommendations for Scale Validation\nMultiple-Group Comparisons with applications for experimental and observational groups!\nFactorial/Measurement Invariance – Are you measuring the same construct?\nExtended Applications Such as Parceling and Missing Data\nMediation and Indirect Effects using Bootstrapping\nModeration\, creating latent interaction terms!\n\nSeminar Description:\nThis summer institute is an intensive short seminar on the principles of structural equation modeling. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, Ph.D. is a Professor of Educational Psychology at Texas Tech University (TTU). Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g.\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). Prior to joining TTU\, …  Little has guided quantitative training and provided consultation to students\, staff\, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998)\, Yale University’s Department of Psychology (1998-2002)\, and researchers at KU (2002-2013\, including as director of the RDA unit at the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001\, Little was elected to membership in the Society for Multivariate Experimental Psychology\, a restricted-membership society of quantitative specialists in the behavioral and social sciences.\n \nIn 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He founded\, organizes\, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. As an interdisciplinary-oriented collaborator\, Little has published with over 280 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 11\,000 citations. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology\, including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF\, NIH\, and IES\, and private foundations such as the Jacobs Foundation. He has been the principal investigator or co-principal investigator on over 15 grants and contracts and he has served as a statistical consultant on over 70 grants and contracts. In the conduct of his collaborative research\, he has participated in the development of over 12 different measurement tools\, including the CAMI\, the Multi-CAM\, the BALES\, the BISC\, the I FEEL\, and the form/function decomposition of aggression. \n\n\n\nInstructor: Elizabeth Grandfield\, Ph.D.\n \nElizabeth received her Ph.D. in Quantitative Psychology at the University of Kansas. She is currently an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands. Her research focuses on evaluating …measurement invariance with an emphasis in longitudinal designs. In areas of applied research\, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center\, both in Kansas City. She also received the 2011 Multivariate Software Award\, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012. \nRead More\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary\, Ph.D. is a senior research scientist at Yhat Enterprises LLC. where he pursues his research interests in measurement design\, applied latent variable modeling\, and modern approaches to missing data. Dr. Stickley has also served as an instructor and coordinator for the Stats Camp Foundation since first joining the team as a graduate student in 2018. He received his Ph.D. in Educational Psychology from College of Education at Texas Tech University with a focus on research methodology\, measurement design\, and statistical modeling. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 26 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Structural Equation Modeling will enable participants to: \n\nDescribe the psychometric properties that underly Structural Equation Modeling (SEM).\nDefine a latent construct using manifest variables.\nIdentify a latent construct using numerous methods of identification\, including marker method\, fixed factor\, and effects coding.\nConduct confirmatory factor analysis (CFA) and evaluate model fit using several fit indices.\nCompare CFA models using several comparison metrics.\nGenerate and implement item parceling schemes.\nEvaluate multiple groups using the CFA framework using weak and strong invariance.\nTest and compare latent parameters in a multiple group framework.\nEvaluate and address missing data with both FIML and Multiple Imputation.\nImplement a planned missing data design.\nEvaluate mediation and moderation in an SEM framework.\nEvaluate multi-trait\, multi-method (MTMM) models.\nEvaluate hierarchical models.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you need to analyze the covariance structure of multivariate data and have a basic statistical background\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You do not need to know matrix algebra\, calculus\, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL\, Mplus or Laavan is assumed. Furthermore\, nearly all the techniques taught in the seminar can be translated fairly easily to most other packages. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor Will Provide Password on First Day of Seminar:\nClick Here to View Seminar Materials Page for SEM Foundations and Extended Applications \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2024: Structural Equation Modeling Foundations Course\n\n\nMonday\nJune 3\, 2024\n\n\n9:00 – 10:45\nWelcome and Introductions. Philosophy\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nPsychometrics\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nDefining Constructs\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIdentification\n\n\nTuesday \nJune 4\, 2024\n\n\n9:00 – 10:45\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nCFA: The foundation of any SEM model\, continued\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nParcels and Parceling; Start of Individual Consultations\n\n\nWednesday\nJune 5\, 2024\n\n\n9:00 – 10:45\nMultiple-Group CFA – Testing for configural and weak invariance\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group CFA – Testing for Strong Invariance\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nWrap-up then Individual Consultations\n\n\nThursday\nJune 6\, 2024\n\n\n9:00 – 10:45\nMissing data and Power\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group SEM and Latent Regression Models\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMediation and Moderation\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nCatch-up time then Individual Consultations\n\n\nFriday\nJune 7\, 2024\n\n\n9:00 – 10:45\nMulti-trait\, Multi-Method (MTMM) and Hierarchical Models\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nHierarchical Models\, continued; Writing results\, Cautions & Wrap-up\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – ~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/structural-equation-modeling-course/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:SEM Foundations & Extended Applications,Summer Camp,Summer Camp 1
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DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260405T182557
CREATED:20220701T075623Z
LAST-MODIFIED:20231209T043257Z
UID:2670-1717405200-1717779600@www.statscamp.org
SUMMARY:Multilevel Modeling
DESCRIPTION:IN PERSON – 5-day Multilevel Modeling Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data. Everything is nested\, so you need something more than multiple regression or analysis of variance to get the job done! Nested data structures can include students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. Multilevel modeling (MLM) is built to handle this kind of data. You will use real datasets and the R software environment to learn how to analyze multilevel data sets and interpret results of multilevel models. \nSeminar Topics:\n\nReview of regression and methods of handling nested data\nRandom-intercept and random-slope models\nTesting and interpreting interactions in multilevel models\nCross-sectional and Longitudinal multilevel models\nMultilevel models for binary outcomes\nCross-classified random effects modeling\n\nNote: MLM is sometimes referred to as mixed-effects modeling\, hierarchical linear modeling\, or random coefficients modeling. This course will focus primarily on with a single outcome variable.  As such\, this course (https://www.statscamp.org/courin combination with a course in SEM Foundations) would provide an ideal introduction to the foundations necessary to prepare for the advanced Summer Stats Camp course\, Multilevel SEM with xxM. \nSeminar Description:\nThis course is designed to provide theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include\, for example\, students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. In each of these cases and many more\, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling\, which is also sometimes referred to as mixed modeling\, hierarchical linear modeling\, or random coefficients modeling. \nThis course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Cross-sectional and longitudinal models\, the most common multilevel modeling applications\, are featured in the seminar. Using real datasets provided in the seminar\, participants will learn how to use the R software program to analyze data and interpret results. Further\, the course will emphasize proper interpretation of analysis results and illustrate procedures that can be used to specify multilevel models. Coverage of multilevel models for binary outcomes and cross-classified random effects modeling will also be included. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Alex Schoemann\, Ph.D.\n \nDr. Alexander M. Schoemann\, is an Alex Schoemann\, Ph.D. is Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 … (after spending several years as a “counselor”). Alex teaches graduate courses in research design\, regression\, multivariate statistics\, structural equation modeling and multilevel modeling. His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation\, power analyses\, missing data estimation\, meta-analysis\, structural equation models and multilevel models. Alex is also interested in developing user friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/). \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nIdentify the basic functions of R that are relevant to multilevel modeling.\nUnderstand the principles behind multiple linear regression.\nDescribe the methods of handling nested data.\nModify regression models by adding predictors and random effects.\nApply methods of centering.\nEvaluate models with interactions.\nConduct multiparameter tests.\nMake informed decisions about model selection.\nEvaluate longitudinal models.\nImplement alternative error structures.\nEvaluate multiple group models.\nUnderstand the roles sample size and power play in a multilevel framework.\nEvaluate multivariate models.\nEvaluate three-level models.\nEvaluate cross-classified random effects models.\nEvaluate models with categorical outcome variables.\n\nSeminar Prerequisites\nMultilevel Modeling Prerequisites:\nRequired: \n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience in binary logistic regression\nAt least limited experience using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMultilevel Modeling.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nNote\, however\, that R and RStudio are the software programs that will be demonstrated. Both programs are free and can be downloaded from https://cloud.r-project.org/ and https://www.rstudio.com/products/rstudio/download/\, respectively. Additional directions will be shared with enrolled participants. \nNote: Limited examples will also be provided in SPSS and SAS but the majority of the course will be taught using R. \nSeminar Audience\nSeminar Audience:\nComing Soon… \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access The Multilevel Modeling Seminar Files \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nMonday\nJune 3\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to multilevel modeling and basics of R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of multiple linear regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMethods of handling nested data\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nAdding predictors and random effects\n\n\nTuesday\nJune 4\, 2024\n\n\n9:00-10:45\nCentering\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nInteractions and contextual effects\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nEstimation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiparameter tests and model selection\n\n\nWednesday\nJune 5\, 2024\n\n\n9:00-10:45\nLongitudinal models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLongitudinal models (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nAlternative error structures\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiple group models\n\n\nThursday\nJune 6\, 2024\n\n\n9:00-10:45\nPower and sample size\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMultivariate Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nThree-level modeling\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nOne-on-one consultations with instructor\n\n\nFriday\nJune 7\, 2024\n\n\n9:00-10:45\nCross-classified random effects modeling\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCross-classified random effects modeling (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-5:00\nOne-on-one consultations with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/multilevel-modeling-in-r/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Multilevel Modeling,Summer Camp,Summer Camp 1
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DTSTART;TZID=America/Denver:20240603T090000
DTEND;TZID=America/Denver:20240607T170000
DTSTAMP:20260405T182557
CREATED:20220701T073144Z
LAST-MODIFIED:20231209T043118Z
UID:2659-1717405200-1717779600@www.statscamp.org
SUMMARY:Applied Latent Class Analysis & Finite Mixture Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent class analysis\, latent class cluster analysis\, modeling predictors and outcomes of latent class membership\, and select extensions. Hands-on practice with Mplus is provided. \nThis five-day camp is an intensive short seminar in the fundamentals of finite mixture modeling. \nSeminar Description:\nFinite mixture models are a type of latent variable model that express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent classes\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic finite mixture modeling to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA. s)\, in terms of model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented. The seminar will also explore “hybrid” latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some   longitudinal extensions of mixture modeling\, as time allows (for a more in-depth treatment\, see the Stats Camp Session 2 seminar on longitudinal mixture modeling). The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar. \n\nInstructor: Whitney Moore\, Ph.D.\n \nDr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM\, Longitudinal SEM\, and MLM). Whitney has taught graduate courses in research design\, introduction to statistics\, ANOVA\, SEM\, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport\, exercise\, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs\, finite mixture modeling\, plus mediation and moderation in SEM. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe mixture modeling in a general latent variable framework.\nPerform binary\, ordinal\, and multinomial logistic regression.\nUtilize Mplus to evaluate generalized linear models.\nIdentify best practice methods to enumerate latent classes for latent class analysis.\nPerform latent class regression.\nConduct measurement invariance in a latent class analysis framework.\nDerive distal outcomes in latent class analysis.\nPerform structural equation mixture modeling.\nDescribe the process of finite mixture modeling\nEnumerate latent classes in a finite mixture modeling framework.\nConduct finite mixture modeling with non-normal indicators.\nDescribe advanced topics and applications of mixture modeling\, such as multilevel latent class analysis.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nLatent Class Analysis Course Prerequisites:\nYou do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, or Mplus is assumed. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should bring a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software. \nNote: We will also make use of Excel and R Studio to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \nSeminar Audience\nLatent Class Analysis Course Audience:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent classes) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSeminar Files\nWho Can Benefit From a Latent Class Analysis Course:\nA latent class analysis course would be useful for researchers\, data analysts\, and practitioners who work with categorical data and want to identify unobserved subgroups or latent classes within their data. This includes individuals from a wide range of fields\, such as psychology\, sociology\, epidemiology\, marketing\, education\, and public health. \nSpecifically\, individuals who may benefit from a latent class analysis course include: \n\nResearchers who want to identify subgroups of individuals or objects with similar characteristics\, attitudes\, behaviors\, or preferences. For example\, a researcher studying consumer behavior may want to identify different types of shoppers based on their buying habits and demographic characteristics.\nData analysts who want to use latent class analysis as a tool for data reduction or variable selection. For example\, a data analyst working with survey data may want to reduce the number of survey items to a smaller set of latent factors that capture the most important dimensions of the data.\nPractitioners who want to use latent class analysis as a tool for program evaluation or needs assessment. For example\, a public health practitioner may want to identify different types of health behaviors or risk factors among a population in order to tailor intervention programs to specific subgroups.\n\nA latent class analysis course would be useful for anyone who wants to gain a deeper understanding of how to use latent class analysis to identify meaningful subgroups within categorical data and make informed decisions based on the results. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2024: LCA\n\n\nMonday\nJune 3\, 2024\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nOverview of mixture modeling in a general latent variable framework\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of binary\, ordinal\, and multinomial logistic regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Mplus\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGeneralized linear modeling in Mplus\n\n\nTuesday\nJune 6\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Latent Class Analysis (LCA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to Latent Class Regression (LCR)\n\n\nWednesday\nJune 4\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLCR (continued)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMeasurement invariance in LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nDistal outcomes in LCA\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nStructural equation mixture modeling\n\n\nThursday\nJune 5\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Finite Mixture Modeling (FMM)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for FMM\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration for FMM (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nFMM with non-Normal indicators\n\n\nFriday\nJune 6\, 2024\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nOverview of “hybrid” factor mixture models (including growth mixture models)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nOverview of advanced topics in mixture modeling (e.g.\, multilevel LCA)\n\n\n12:30-1:30\nRest Break\n\n\n1:30~4:30\nIndividual consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/applied-latent-class-analysis-finite-mixture-modeling/
LOCATION:Embassy Suites – Albuquerque\, If you are unavailable to join in-person\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Applied Latent Class Analysis & Finite Mixture Modeling,Summer Camp,Summer Camp 1
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240308T120000
DTEND;TZID=America/New_York:20240311T170000
DTSTAMP:20260405T182557
CREATED:20230825T215540Z
LAST-MODIFIED:20240124T173906Z
UID:7147-1709899200-1710176400@www.statscamp.org
SUMMARY:Latent Profile Analysis
DESCRIPTION:LIVESTREAM / ASYNCHRONOUS – 4-day Statistics Short Course\n\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent profile analysis (aka\, latent class cluster analysis)\, and modeling predictors and outcomes of latent profile membership. Hands-on practice with Mplus is provided. \nSeminar Topics:\nLatent Profile Analysis (LPA) steps including research questions appropriate for latent profile analysis\, profile (class) enumeration and assessing profile model results (classification quality\, profile homogeneity and separation)\, predicting profile membership with other variables and profile membership predicting outcomes. Practice analyses will be completed to build comfort with syntax and reading of output. We will also cover how to interpret and present the results to maximize audience understanding. \nSeminar Description:\nThis four-day camp is an intensive short seminar in the fundamentals of latent profile analysis (LPA).\nLPA is a type of latent variable model-based finite mixture models that express the overall distribution of one or more continuous variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of continuous\, manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent profiles\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic latent profile analysis to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA)\, including model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership as well as distal outcomes of latent profile membership will be presented. The implementation of these models in the most recent version of the Mplus software will be demonstrated and practiced throughout the seminar. \nInstructor: Whitney Moore\, Ph.D.\n \nDr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM\, Longitudinal SEM\, and MLM). Whitney has taught graduate courses in research design\, introduction to statistics\, ANOVA\, SEM\, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport\, exercise\, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs\, finite mixture modeling\, plus mediation and moderation in SEM. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course provides 16 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. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 4-day statistics training institute on Latent Profile Analysis will enable participants to:\n\nAcquire understanding of latent profile analysis techniques as applied in the social and behavioral sciences.\nDevelop an appreciation for the research questions and data best suited for latent profile analysis models and the common pitfalls leading to the misuse of mixture models.\nGain detailed knowledge of current “best practices” for mixture model specification\, estimation\, selection\, evaluation\, comparison\, interpretation\, and presentation.\nUnderstand how latent profile variables may be integrated into a larger (latent) variable system.\nBecome acquainted with a variety of mixture modeling extensions.\nBecome proficient in the use of Mplus for analysis of mixture models.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies with continuous variables using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent profiles) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, latent profile analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should have a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software.\nNote: We will also make use of Excel to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \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\n\n2024 Stats Camp: 4-Day LPA\n\n\nDay 1\nDay 1 (Friday)\n\n\n\n12:00-12:30\nWelcome and introductions\n\n\n\n12:30-1:30\nOverview of mixture modeling in a general latent variable framework\n\n\n\n1:30-1:45\nBreak\n\n\n\n1:45-3:15\nOverview of LPA Analysis Steps\n\n\n\n3:15-3:30\nBreak\n\n\n\n3:30-5:00\nMplus syntax introduction with Latent Profile Analysis (LPA) example\n\n\n\nDay 2\nDay 2 (Saturday)\n\n\n\n12:00-1:30\nLPA class enumeration across variance-covariance structures introduction\n\n\n\n1:30-1:45\nSnack and refreshment break\n\n\n\n1:45-3:15\nPractice running LPA models in Mplus\n\n\n\n3:15-3:30\nLunch break\n\n\n\n3:30-5:00\nSyntax and interpretation of output for LPA enumeration across variance-covariance structures\n\n\n\nDay 3\nDay 3 (Sunday)\n\n\n\n12:00-1:30\nMultinomial Logistic Regression Review (On Own)\n\n\n\n1:30-1:45\nSnack and refreshment break\n\n\n\n1:45-3:15\nExamination of Profile homogeneity and separation\n\n\n\n3:15-3:30\nLunch break\n\n\n\n3:30-5:00\nIndividual consultation & Practice of LPA modeling process\n\n\n\nDay 4\nDay 4 (Monday)\n\n\n\n12:00-1:30\nIntroduction to latent class regression (LCR) with inclusion of predictive covariates\n\n\n\n1:30-1:45\nSnack and refreshment break\n\n\n\n1:45-3:15\nLCR continued with inclusion of distal outcomes\n\n\n\n3:15-3:30\nLunch break\n\n\n\n3:30-5:00\nIndividual consultation\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/latent-profile-analysis-online-training/
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:Latent Profile Analysis,Livestream
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240219T170000
DTEND;TZID=America/Chicago:20240223T210000
DTSTAMP:20260405T182557
CREATED:20230804T200642Z
LAST-MODIFIED:20240926T190746Z
UID:7119-1708362000-1708722000@www.statscamp.org
SUMMARY:The Craft of Structural Equation Modeling: A Comprehensive Quantitative Methods Training
DESCRIPTION:Unlock your learning journey with flexible registration options! Choose the perfect fit for your schedule—from a single day to the full 5-day immersive experience. Opt for the comprehensive 5-day All-Access Pass and enjoy a special $100 discount off the daily rate of $195/day. Seize the opportunity to maximize your savings while diving deep into the rich content of each day. Select your preferred pass and embark on a transformative learning adventure!\nLIVESTREAM / ASYNCHRONOUS – 5-day Statistics Short Course\, 4hrs per day\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nStructural Equation Modeling Mastery: An In-Depth Training in Quantitative Methods\n\nReady to elevate your measurement game to the latent level? Look no further – you’ve discovered the essential foundation for mastering latent variable modeling: Structural Equation Modeling (SEM)! Many participants have shared that their previous training fell short or was outdated. Join us as we not only introduce you to the latest SEM techniques but also guide you through the precise steps to ‘craft’ a sophisticated SEM model. Upgrade your skills and stay ahead of the curve in this transformative learning experience! \nSyllabus:\n\n\n\nDay 1\n\n \n\n\n5:00 – 6:45\nWelcome and Introductions. Philosophy\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 9:00\nPsychometrics; Defining Constructs\n\n\n\n\n\n\nDay 2\n\n \n\n\n5:00 – 6:45\nIdentification & Scale setting\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:00\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n8:00 – 8:30\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n8:30 – 9:00\nMultiple-Group CFA – Testing for invariance\n\n\n\n\n\n\nDay 3\n\n \n\n\n5:00 – 6:45\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:00\nParcels and Parceling; Missing Data & Power\n\n\n8:00 – 8:30\nMultiple-Group SEM\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 Regression Models\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:30\nMediators\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\nModerators\n\n\n6:45 – 7:00\nSnack and Refreshment Break\n\n\n7:00 – 8:30\nInteractions Multi-Trait Multi-Method (MTMM) models\n\n\n8:30 – 9:00\nCatch up and Discussion\n\n\n\n\n\n\n\n  \n\nCourse Topics:\n\n\nAdvantages and disadvantages of SEM\, Latent vs measured variables\, Modeling with constructs\, Figural conventions\, Manifest vs latent test theory\, Selecting indicators\, Setting the scale\, Model fit indices\, Parceling\, Invariance testing\, Latent parameter testing\, Phantom constructs\, Sample size and power\, Missing data.\n\n\nCourse Description:\nEmbark on a comprehensive journey with our 5-day short course delving into SEM Foundations & Extended Applications. Immerse yourself in an intensive exploration of the principles of structural equation modeling (SEM) with expert instructors Dr. Little and Dr. Stickley. \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\nAPA Continuing Education Credits:\n \nThis course is NOT eligible for CE credits. You must attend a livestream or in-person event to qualify for APA continuing education credits. Please review our Summer Camp offerings for courses eligible for CE Credits. \n\nCourse Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe 5 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\nCourse 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\nCourse 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.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-structural-equation-modeling-a-comprehensive-seminar/
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,SEM Foundations & Extended Applications,The Craft of Structural Equation Modeling
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240116T170000
DTEND;TZID=America/Chicago:20240116T210000
DTSTAMP:20260405T182557
CREATED:20231129T062321Z
LAST-MODIFIED:20231217T222542Z
UID:7962-1705424400-1705438800@www.statscamp.org
SUMMARY:FREE Confirmatory Factor Analysis (CFA) Seminar
DESCRIPTION:RESCHEDULED / NEW DATE – JANUARY 16\, 2024\nLIVESTREAM / ASYNCHRONOUS (Link Expires In 48hrs) – 4hr Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: (CFA) Confirmatory Factor Analysis\n\nJoin us for an enlightening and intellectually stimulating afternoon as we delve into the fascinating world of Confirmatory Factor Analysis (CFA) at our free 4-hour seminar featuring renowned experts\, Dr. Todd D. Little and Zachary Stickley. This seminar promises to be a transformative experience for those seeking a comprehensive understanding of CFA\, a crucial statistical technique used in various fields such as psychology\, sociology\, and economics. Dr. Little\, a leading figure in the field of structural equation modeling and CFA\, will guide us through the foundational principles and practical applications of CFA\, while Zachary Stickley\, an emerging authority in statistical methodology\, will provide contemporary insights and real-world examples. Whether you are a seasoned researcher or just starting to explore the world of factor analysis\, this seminar offers a unique opportunity to expand your knowledge and engage with experts in the field. Don’t miss this chance to enhance your statistical expertise and network with fellow enthusiasts in a dynamic learning environment. \n\nSeminar Topics:\nAdvantages and disadvantages of SEM\, Latent vs measured variables\, Modeling with constructs\, Figural conventions\, Manifest vs latent test theory\, Selecting indicators\, Setting the scale\, Model fit indices\, Parceling\, Invariance testing\, Latent parameter testing\, Phantom constructs\, Sample size and power\, Missing data. \nSeminar Description:\nJoin us for an enlightening 4-hour session where experts demystify this essential statistical tool used across various disciplines. Dive deep into CFA principles\, applications\, and real-world insights in an engaging and informative atmosphere. \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\nAPA Continuing Education Credits:\n \nThis course is NOT eligible for CE credits. You must attend a livestream or in-person event to qualify. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe 4-hour training institute on Confirmatory Factor Analysis (CFA) will enable participants to: \n\nUnderstanding CFA Foundations\nModel Specification\nData Preparation\nModel Estimation\nModel Fit Assessment\nModel Modification\nInterpretation of Results\nModel Comparison\nAdvanced Topics\nHands-On Practice\nReporting and Communication\nTroubleshooting\nEthical Considerations\nSoftware Proficiency\nCritique and Review\n\nSeminar Prerequisites\nSeminar Prerequisites:\nNo formal prerequisites are required to attend this course; it’s open to all eager learners. However\, to maximize your experience\, having a basic understanding of statistics and familiarity with data analysis concepts will be beneficial. Don’t worry if you’re new to these topics; our expert instructors will guide you through the material\, ensuring everyone can grasp the foundations of Confirmatory Factor Analysis. \nSoftware and Computer Support\nSoftware and Computer Support:\nWe’ll guide you through the use of specialized software (e.g.\, R\, Mplus\, or SEM software) for CFA analysis\, providing hands-on training and troubleshooting assistance. You’ll gain the confidence to navigate the software environment effectively\, empowering you to apply CFA principles with ease in your research endeavors. \nSeminar Audience\nSeminar Audience:\nThis seminar is designed for researchers\, academics\, graduate students\, and professionals from various fields who want to enhance their statistical toolkit with the power of Confirmatory Factor Analysis. Whether you’re a seasoned statistician looking to sharpen your skills or a newcomer eager to explore the world of factor analysis\, this seminar welcomes all levels of expertise. Join us to engage with like-minded individuals\, expand your knowledge\, and gain practical insights into CFA that can elevate your research and decision-making processes. \nSeminar Files\nSeminar Files\nAll necessary materials and resources for the seminar will be conveniently provided through the Zoom chat link on the day of the virtual lecture. Rest assured\, you’ll have easy access to presentation slides\, handouts\, and any additional files needed to enrich your learning experience. Stay tuned for a seamless and accessible learning journey! \n\nPresentation slides\nSample datasets\nAdditional readings and resources\n\nSyllabus\n\n\n\nDay 1\n\n \n\n\n5:00 – 5:30 (CST)\nFoundations of CFA\n\n\n5:30 – 6:00 (CST)\nModel Specification\n\n\n6:00 – 6:45 (CST)\nData Preparation and Model Estimation\n\n\n6:45 – 7:00 (CST)\nSnack and Refreshment Break\n\n\n7:00 – 8:00 (CST)\nModel Fit Assessment and Interpretation\n\n\n8:00 – 8:30 (CST)\nHands-On Practice and Q&A\n\n\n8:30 – 9:00 (CST)\nCourse Conclusion and Resources\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/free-confirmatory-factor-analysis-seminar/
LOCATION:Livestream and/or Asynchronous (48hrs):\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 48hrs.
CATEGORIES:Confirmatory Factor Analysis,Livestream
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20231016T150000
DTEND;TZID=America/Chicago:20231016T210000
DTSTAMP:20260405T182557
CREATED:20230908T190017Z
LAST-MODIFIED:20230908T192533Z
UID:7170-1697468400-1697490000@www.statscamp.org
SUMMARY:Mediation and Moderation
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: Mediation and Moderation Training Course\nGreat! You have an idea you’re interested in\, now what? You may even have a theory that X will predict Y\, but the more important question\, the question that we really what to know is\, why does X predict Y\, or when does X predict Y. Modeling these mechanisms of change are where Mediation and/or Moderation become your methodological hero! \nSeminar Topics:\nOur Mediation and Moderation seminar will teach you the fundamentals of framing the relationships between your observations\, including how to: \n\nEstimate\, test\, and interpret mediated (i.e.\, indirect) and moderated (i.e.\, interaction) effects using OLS regression.\nCombine mediation and moderation models to test conditional indirect effects.\nUse a macro called PROCESS for SPSS and SAS to test these models.\nApply the latest methods in moderation and mediation analysis using R software.\n\nSeminar Description:\nAre you ready to embark on a journey into the intricate world of understanding why and when certain variables predict specific outcomes? In this seminar\, we will delve deep into the realms of mediation and moderation analysis\, two powerful methodological tools that will unlock the secrets behind causal relationships. Whether you’re a seasoned researcher looking to refine your skills or a budding analyst seeking to grasp the fundamentals\, this course promises to elevate your research game and make you a methodological hero! \nWho Should Attend: \n\nResearchers from various fields interested in enhancing their quantitative research skills.\nGraduate students seeking to deepen their understanding of causal analysis.\nProfessionals looking to sharpen their data analysis techniques.\nAnyone curious about unlocking the mysteries behind predictive relationships in data.\n\nJoin us on this exciting journey as we unravel the “why” and “when” behind predictive relationships\, and become the methodological heroes of your research endeavors! \n\nInstructor: Mwarumba Mwavita\, Ph.D.\n \nMwarumba Mwavita is Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. In addition\, he is a Professor in the Research\, Evaluation\, Measurement and Statistics (REMS) program in the College of Education and Human Sciences at Oklahoma State University where he teachers the GLM sequence of courses that includes ANOVA\, Multiple Regression\, MANOVA\, and Multilevel Modeling. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC 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:\nAt the end of the course\, participants will be able to:\n\nCompute\, test\, and interpret mediation models in an OLS regression framework.\nEvaluate mediation models with four or more variables.\nCompute\, test\, interpret\, and graph moderation in an OLS regression framework.\nWork examples using the PROCESS Macro in conducting Mediation\, Mderation\, and Mediated-Moderated Models\nImplement moderation in a latent variable framework.\nImplement moderation in a multilevel framework.\nEvaluate advanced models that implement moderated mediators\, or mediated moderation.\n\nSeminar Prerequisites\nSeminar Audience:\nThis seminar will be helpful for researchers in any field—including psychology\, sociology\, education\, business\, human development\, political science\, public health\, communication—and others who want to learn how to apply the latest methods in moderation and mediation analysis using freely-available R software. Participants should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. \nSoftware and Computer Support\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in multiple linear regression\nIntermediate proficiency with at least once statistical software package (knowledge of SPSS or SAS are preferred\, but not required).\nAt least limited experience using syntax with statistical software.\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level seminar) with multivariate data analysis.\nAt least limited experience (e.g.\, graduate-level seminar) with investigating interactions (in r multiple linear regression or ANOVA)\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMediation and moderation\nAdvanced statistical techniques such as SEM\, MLM\, or logistic regression.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\n\nSoftware and Computer Support\nSoftware and Computer Support:\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.(statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nFall  Stats Camp 2023: Mediation Moderation\n\n\nMonday\nOctober 16\, 2023\n\n\n3:00-3:45\nWelcome and software review\n\n\n3:45-4:00\nRest Break\n\n\n4:00-5:30\nReview of regression\n\n\n5:30-6:30\nRest Break\n\n\n6:30-8:15\nIntroduction to mediation\n\n\n8:15-8:30\nRest Break\n\n\n8:30-9:00\nComputing\, testing and interpreting mediation in regression\n\n\nTuesday\nOctober 17\, 2023\n\n\n3:00-3:45\nMediation with four or more variables\n\n\n3:45-4:00\nRest Break\n\n\n4:00-5:30\nLatent variable mediation\n\n\n5:30-6:30\nRest Break\n\n\n6:30-8:15\nAdvanced topics in mediation: Multilevel modeling\n\n\n8:15-8:30\nRest Break\n\n\n8:30-9:00\nAdvanced topics in mediation: Longitudinal mediation\n\n\nWednesday\nOctober 18th\, 2023\n\n\n3:00-3:45\nIntroduction to moderation\n\n\n3:45-4:00\nRest Break\n\n\n4:00-5:30\nComputing moderation\n\n\n5:30-6:30\nRest Break\n\n\n6:30-8:15\nGraphing and interpreting moderation\n\n\n8:15-8:30\nRest Break\n\n\n8:30-9:00\nGraphing and interpreting moderation in regression / Individual Consultations\n\n\nThursday\nOctober 19th \, 2023\n\n\n3:00-3:45\nAdvanced topics in moderation: Multilevel modeling\, SEM\n\n\n3:45-4:00\nRest Break\n\n\n4:00-5:30\nAdvanced topics in Moderation: Longitudinal designs\n\n\n5:30-6:30\nRest Break\n\n\n6:30-8:15\nAdvance topics in moderation\n\n\n8:15-8:30\nRest Break\n\n\n8:30-9:00\nContinue Advanced topics / Individual 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/mediation-and-moderation-training/
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:Mediation and Moderation
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20231009T090000
DTEND;TZID=America/Chicago:20231012T150000
DTSTAMP:20260405T182557
CREATED:20220801T200438Z
LAST-MODIFIED:20230505T191848Z
UID:3584-1696842000-1697122800@www.statscamp.org
SUMMARY:Advanced Structural Equation Modeling: Bayesian SEM
DESCRIPTION:Sponsorship Opportunity:\nApply for $1150 scholarship and learn Bayesian SEM at Stats Camp. Instructor Mauricio Garnier Villarreal got awarded (co-PI) the grant “Scaling Bayesian Latent Variable Models to Big Education Data” from the United States Department of Education. We are able to sponsored 2 students with $1150 each for the registration to the Bayesian SEM course. \n\n\n\n\nTo apply for this sponsorship:\nPlease email the following information to the instructor at mgv@pm.me\n\n\n– CV\n– Explanation on how this course fits your professional development (maximum 300 words)\n– Statement of diversity\, how do you and\or your work is related to underrepresented groups\n\nLIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThe goal of the Bayesian SEM is to provide instruction in the application SEM from the Bayesian paradigm. It will cover the application of models commonly implemented in frequentist SEM\, and in models that are complicate or impossible to estimate in the frequentist paradigm. This seminar is design to portray the advantages of the Bayesian paradigm\, both philosophical and practical\, within the application of SEM. The material and examples will be provided in the open source platform R\, but if the students prefer to work with another program we will assist in the understanding and application. In addition\, students who sign up for this seminar can request topics to be included during the weeklong seminar. If we have the expertise\, we’ll gladly prepare and deliver a module on the topic! As with all Stats Camp seminars\, personal consultation time is available and ample support resources are provided on our web pages at statscamp.org. \nA perfect follow up for SEM Foundations or Bayesian Data Analysis! \nSeminar Topics:\n\nBayesian inference\nMCMC estimation algorithm\nPrior selection\nModel comparison\nCommomly applied SEM models: CFA\, SEM\, multiple group\, grorth curve\n\nSeminar Description:\nYou have a good to great understanding of structural equation modeling (SEM)\, maybe you have been doing traditional SEM for years\, but you’ve began a project where your observations are not normally distributed\, you have a small sample size\, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step! \nAdvanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM. \n\nInstructors: Mauricio Garnier-Villarreal\, Ph.D.\n \n\n\n\nEsteban Montenegro\, Ph.D.\n \nI’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R\, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas\, I collaborate with several teams around the world on topics related to healthy aging\, Alzheimer Disease and other health related topics. \nRead More\n\n\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. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning and Bayes’ rule.\nDifferentiate between direct probability and indirect probability.\nUtilize Markov Chain Monte Carlo estimation using the Gibbs and Hamiltonian samplers.\nAnalyze Bayesian models in several software packages\, including JAGS\, STAN\, and blavaan.\nIdentify the properties of key distributions used in Bayesian analysis.\nConduct a confirmatory factor analysis using the Bayesian framework.\nImplement prior estimations in conducting CFA modeling.\nEvaluate model fit and compare CFA models.\nModel multiple group CFAs using the Bayesian framework.\nEvaluate latent regression models\, latent interactions\, quadratic effects\, and mediation in a Bayesian framework.\nUse Bayesian methods to evaluate non-normal continuous data.\nConduct SEM for categorical data using Bayesian Item Response Theory.\nAccount for missing data.\nEvaluate longitudinal models in a Bayesian framework.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nGeneral understanding of linear regression models\n\nNot required but advantageous: \n\nBasic understanding of Bayesian inference\nBasic understanding of SEM\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nWorking with R\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.Syllabus\n\n\n\n4-Day Schedule: Bayesian SEM\n\n\nDay 1\n\n\n\n9:00–10:45\nWelcome and Introductions.\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nBayesian reasoning and Baye’s rule : Direct probability vs indirect probability. Markov Chain Monte Carlo\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nKnow your distributions. Introduction to blavaan (R)\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nBayesian Confirmatory Factor Analysis\n\n\nDay 2\n\n\n\n9:00–10:45\nPriors: relevance\, choice\, strengths. CFA with cross-loadings\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nModel fit and model comparison\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMultiple-group CFA\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLatent regression models: latent interactions\, and mediation\n\n\nDay 3\n\n\n\n9:00–10:45\nLatent regression models: latent interactions\, and mediation\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data handling\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nSEM for categorical data\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLongitudinal CFA (Measurement Invariance)\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/advanced-structural-equation-modeling-bayesian-sem-seminar/
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:Advanced Structural Equation Modeling: Bayesian SEM Seminar,Livestream
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230921T090000
DTEND;TZID=America/Chicago:20230924T170000
DTSTAMP:20260405T182557
CREATED:20230428T200025Z
LAST-MODIFIED:20230428T212028Z
UID:6803-1695286800-1695574800@www.statscamp.org
SUMMARY:Analysis Retreat: Whitefish Montana 2023
DESCRIPTION:All-Inclusive In-Person – 4-day Statistics Training Retreat – September 2023\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nStatistics Training Retreat Overview:\n4 days of intensive data analysis\, statistics training\,  software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make your research “submission ready” with the guided assistance of our team of experts.\nStats Camp Analysis Retreat is a unique and powerful 4 day opportunity to have a highly cited International team of experts vet your research and increase the likelihood of peer review approval and publishing. Our statistics training team puts the advantage squarely in your corner with four days of intensive analysis\, software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make you research “submission ready.” Build solid confidence for tenure and promotion. Stats Camp Analysis Retreat is the positive action you can take NOW to get your career on the fast track. Due to the intense personal focus of the retreat\, seats are very limited and fill fast. \nAnalysis Retreat Includes:\n\n\nIn person one-on-one statistics training\n\nHotel lodging provided\n\nAll meals and daily beverages & snacks provided\n\nMaterials\, downloads\, and software access\n\n\n\n\n\nRetreat Description\nRetreat Description:\n\nTodd D. Little\, Ph.D. developed Stats Camps Analysis Retreats to address the need for specialized expertise that is often needed to move complex research projects to approval and publication. Dr. Little is a preeminent award-earning scholar who has published over 300 peer-reviewed works that have garnered 46\,611+ citations. \nMembers of the Stats Camp team are highly experienced and many have decades of experience in resolving complex analytical challenges with an understanding of best-practices procedures. You will have exclusive access to their expertise as they guide you to a final publication-ready product. \nExpert consulting at this level can quickly run into the tens of thousands of dollars. By bringing our team together with you at an exclusive and personalized all-inclusive Stats Camp Analysis Retreat\, we can offer this unprecedented access at a much more affordable price. \n\nWho Should Attend?\nWho Should Attend?\nIf you are in the pre-data analysis phase\, Stats Camp staff will engage with you to develop a testable research hypothesis\, a fully articulated analysis plan\, and ensure full understanding of the analysis model\, the nature of its results\, and their implications. \nIf you are in the analysis phase\, Stats Camp staff will work with you to learn the software syntax needed to analyze your data\, interpret the output and understand the implications of your findings. \nBreakout sessions will cover a vast array of techniques and topics. Some topics are predetermined while others will be prepared specifically to meet your needs and the needs of the other participants (a survey will be given prior to the event to develop this need list of topics to cover). \nWhat to expect\nWhat to expect:\nParticipants in our past annual Stats Camp Analysis Retreats were overwhelming positive about the individualized learning that took place as well as the opportunity to work with their own data – a practical aspect that makes learning easier. Because participants are enabled to ask questions and get immediate feedback\, learning the practical analytic skills is facilitated. Also\, participants learned about different topics tailored to their learning needs. \nParticipants uniformly exclaimed that the learning activities and interaction with the Stats Camp instructors contributed to publishing more articles and increased the quality of their research\, which has allowed them to target higher impact factor journals. Because quantitative methods are a critical gap in most fields\, the Stats Camp experience helped clarify the essential importance of quantitative methods. By learning the basics of applying these methods\, the knowledge is thereby carried over to other colleagues and students\, and ultimately helping to close the gap. \nThe Stats Camp Analysis Retreat is a perfect combination of learning and fun from the comfort of a beautiful lodge in an amazing destination\, networking in a collegial and social context. Having the stats camp in a retreat format\, gives you ample time\, motivation\, and access to incomparable expertise to propel your research forward. \nSchedule\nSchedule: Below is an example of our standard in-person schedule of events. All times are listed in local (MST) Mountain Standard Time.\nKey: All inclusive in Green \n\nThursday – 9/21/23\n\nTBD – Morning Nature Experience (if your arrive Wednesday)\n\nTransportation from the airport via Grouse Mountain Lodge Shuttle (For a Small Fee)\n\n\n\n12:00 – Group Lunch with some overview thoughts by Todd Little\n1:30 – Welcome and overview program and introductions\n2:30 – Common Session: Missing Data (Todd Little\, Zack Stickley)\n5:00 – Cocktail consultations and social networking\n\nHeavy appetizers and drink tickets\n\n\n~6:30 – Dinner at Lodge \n\n\nFriday – 9/22/23\n\nTBD – Morning Nature Experience\n8:00 – Group Breakfast with kick-off discussion.\n9:00 – Morning break-out sessions in two groups based on whether there is a clear analysis plan (group 1: already working on analyses – group 2: preparing for data analysis).\n\nFor both groups the goal is to answer questions like:\n\nWhat exactly is your research question?\nWhat are your testable research hypotheses?\nWhat analysis plan is best suited for you hypotheses?\nMake/review drawings of your model (observed/latent)\n\n\n\n\n12:00 – Group Lunch with small group discussions and networking\n1:30 – Afternoon break-out session with mini-lectures (conversations) on statistical models; choose from:\n\nTopics determined by you\n\n\n***During the day we will also have individual/small group consultation appointments to answer specific questions from you***\n5:00: Cocktail consultations and social networking\n\nHeavy appetizers and drinks\n\n\n~6:30 Dinner in Downtown Whitefish or surrounding area On Your Own or Dinner at Lodge with Group Provided.  – via lodge transportation or On Your Own\n\n\nSaturday – 9/23/23\n\nTBD: Morning Nature Experience\n8:00 – Group Breakfast with kick-off discussion\n9:00 – Morning break-out sessions introducing software:\n\nBasic SEM – CFA with Mplus or Intro lecture to R + Rstudio (based on needs survey)\nAdditional topics determined by you\n\n\n1200 – Group Lunch with small group discussions and networking\n1:30 Afternoon break-out session working on your own data (or work on exercises if you do not have data yet) based on level of experience:\n\nAdvanced Mplus or R\nAdditional topics determined by you\n\n\n***During the day we will also have individual/small group consultation appointments to answer specific questions from you.***\n5:00: Cocktail consultations and social networking\n\nHeavy appetizers and drink tickets\n\n\n~6:30 Dinner in Downtown Whitefish or surrounding area On Your Own or Dinner at Lodge with Group Provided.  – via lodge transportation or On Your Own\n\n\nSunday – 9/24/23\n\nTBD: Morning Nature Experience\n8:00 – Group Breakfast with wrap-up discussion\n9:00 – Continue to work on own data + get individual feedback on taking the next steps\n11:00 – Closing meeting with reflections (Todd Little)\n11:15 – Grab and go box lunches available\n12:00 – Group Lunch\n1:30 – Close of your unique Stats Camp Analysis Retreat experience\nTransportation to the airport via Grouse Mountain Lodge Shuttle\n\n  \n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample statistics training course materials. \n\n\nLooking for a LIVESTREAM event view our online statistics courses.
URL:https://www.statscamp.org/courses/analysis-retreat-whitefish-montana-2023/
LOCATION:Grouse Mountain Lodge – Whitefish\, Montana\, 2 Fairway Dr\, Whitefish\, MT\, 59937\, United States
CATEGORIES:Analysis Retreat
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230913T170000
DTEND;TZID=America/Chicago:20230913T210000
DTSTAMP:20260405T182557
CREATED:20230906T174630Z
LAST-MODIFIED:20231129T064856Z
UID:7163-1694624400-1694638800@www.statscamp.org
SUMMARY:FREE Confirmatory Factor Analysis (CFA) Seminar
DESCRIPTION:LIVESTREAM / ASYNCHRONOUS (Link Expires In 48hrs) – 4hr Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: (CFA) Confirmatory Factor Analysis\n\nJoin us for an enlightening and intellectually stimulating afternoon as we delve into the fascinating world of Confirmatory Factor Analysis (CFA) at our free 4-hour seminar featuring renowned experts\, Dr. Todd D. Little and Zachary Stickley. This seminar promises to be a transformative experience for those seeking a comprehensive understanding of CFA\, a crucial statistical technique used in various fields such as psychology\, sociology\, and economics. Dr. Little\, a leading figure in the field of structural equation modeling and CFA\, will guide us through the foundational principles and practical applications of CFA\, while Zachary Stickley\, an emerging authority in statistical methodology\, will provide contemporary insights and real-world examples. Whether you are a seasoned researcher or just starting to explore the world of factor analysis\, this seminar offers a unique opportunity to expand your knowledge and engage with experts in the field. Don’t miss this chance to enhance your statistical expertise and network with fellow enthusiasts in a dynamic learning environment. \n\nSeminar Topics:\n\nAdvantages and disadvantages of SEM\nLatent vs measured variables\nModeling with constructs\nFigural conventions\nManifest vs latent test theory\nSelecting indicators\nSetting the scale\nModel fit indices\nParceling\nInvariance testing\nLatent parameter testing\nPhantom constructs\nSample size and power\nMissing data\n\nSeminar Description:\nJoin us for an enlightening 4-hour session where experts demystify this essential statistical tool used across various disciplines. Dive deep into CFA principles\, applications\, and real-world insights in an engaging and informative atmosphere. \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\nAPA Continuing Education Credits:\n \nThis course is NOT eligible for CE credits. You must attend a livestream or in-person event to qualify. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe 4-hour training institute on Confirmatory Factor Analysis (CFA) will enable participants to: \n\nUnderstanding CFA Foundations\nModel Specification\nData Preparation\nModel Estimation\nModel Fit Assessment\nModel Modification\nInterpretation of Results\nModel Comparison\nAdvanced Topics\nHands-On Practice\nReporting and Communication\nTroubleshooting\nEthical Considerations\nSoftware Proficiency\nCritique and Review\n\nSeminar Prerequisites\nSeminar Prerequisites:\nNo formal prerequisites are required to attend this course; it’s open to all eager learners. However\, to maximize your experience\, having a basic understanding of statistics and familiarity with data analysis concepts will be beneficial. Don’t worry if you’re new to these topics; our expert instructors will guide you through the material\, ensuring everyone can grasp the foundations of Confirmatory Factor Analysis. \nSoftware and Computer Support\nSoftware and Computer Support:\nWe’ll guide you through the use of specialized software (e.g.\, R\, Mplus\, or SEM software) for CFA analysis\, providing hands-on training and troubleshooting assistance. You’ll gain the confidence to navigate the software environment effectively\, empowering you to apply CFA principles with ease in your research endeavors. \nSeminar Audience\nSeminar Audience:\nThis seminar is designed for researchers\, academics\, graduate students\, and professionals from various fields who want to enhance their statistical toolkit with the power of Confirmatory Factor Analysis. Whether you’re a seasoned statistician looking to sharpen your skills or a newcomer eager to explore the world of factor analysis\, this seminar welcomes all levels of expertise. Join us to engage with like-minded individuals\, expand your knowledge\, and gain practical insights into CFA that can elevate your research and decision-making processes. \nSeminar Files\nSeminar Files\nAll necessary materials and resources for the seminar will be conveniently provided through the Zoom chat link on the day of the virtual lecture. Rest assured\, you’ll have easy access to presentation slides\, handouts\, and any additional files needed to enrich your learning experience. Stay tuned for a seamless and accessible learning journey! \n\nPresentation slides\nSample datasets\nAdditional readings and resources\n\nSyllabus\n\n\n\nDay 1\n\n \n\n\n5:00 – 5:30 (CST)\nFoundations of CFA\n\n\n5:30 – 6:00 (CST)\nModel Specification\n\n\n6:00 – 6:45 (CST)\nData Preparation and Model Estimation\n\n\n6:45 – 7:00 (CST)\nSnack and Refreshment Break\n\n\n7:00 – 8:00 (CST)\nModel Fit Assessment and Interpretation\n\n\n8:00 – 8:30 (CST)\nHands-On Practice and Q&A\n\n\n8:30 – 9:00 (CST)\nCourse Conclusion and Resources\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/free-confirmatory-factor-analysis-cfa-seminar/
LOCATION:Livestream and/or Asynchronous (48hrs):\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 48hrs.
CATEGORIES:Confirmatory Factor Analysis,Livestream
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230626T170000
DTEND;TZID=America/Chicago:20230630T210000
DTSTAMP:20260405T182557
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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DTSTART;TZID=America/Denver:20230612T090000
DTEND;TZID=America/Denver:20230616T170000
DTSTAMP:20260405T182557
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:20230310T090000
DTEND;TZID=America/Chicago:20230313T150000
DTSTAMP:20260405T182557
CREATED:20220701T020243Z
LAST-MODIFIED:20230309T065011Z
UID:2523-1678438800-1678719600@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling (LSEM)
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nA comprehensive 4-day Stats Camp seminar on Longitudinal Structural Equation Modeling. \nSeminar Topics:\n\nDesign and measurement considerations in longitudinal research\nBuilding and evaluating a longitudinal SEM\nLatent Panel SEMs\nEvaluating longitudinal measurement invariance\nMultiple group longitudinal SEM\nLatent Mediation SEM\nLatent growth curve analysis\nAdditional considerations for longitudinal modeling such as missing data and parceling\n\nSeminar Description:\nThis camp is an advanced intensive short course in the analysis of longitudinal data using SEM. The course includes a series of live lectures along with time for individual and group consultation time to provide participants with the tools needed to use of SEM for the analysis of longitudinal data. If 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 course is for you. Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the course. \nParticipants will receive a link to the course materials by the first day that includes lecture slides\, software example scripts (in Mplus\, lavaan\, and LISREL)\, relevant supporting documentation\, and recommended readings. Participants will receive a link to the course video recording at the end of the camp. \nInstructor: Todd 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\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary\, Ph.D. is a senior research scientist at Yhat Enterprises LLC. where he pursues his research interests in measurement design\, applied latent variable modeling\, and modern approaches to missing data. Dr. Stickley has also served as an instructor and coordinator for the Stats Camp Foundation since first joining the team as a graduate student in 2018. He received his Ph.D. in Educational Psychology from College of Education at Texas Tech University with a focus on research methodology\, measurement design\, and statistical modeling. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 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:\nThis comprehensive 4-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:\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 SEM.\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.Syllabus\n\n\n\nDay 1\n \n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nReview of Foundations of SEM\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:00\nParcels and Parceling\n\n\nDay 2\n\n\n\n9:00-10:45\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Panel Models: Basics\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:00\nMultiple-group Longitudinal Panel Models: CFA\, SEM\, & RI-CLPM\n\n\nDay 3\n \n\n\n9:00-10:45\nMixture Modeling\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nMixture Modeling\n\n\n12:30-1:30\nLunch Break & Individual Consultations\n\n\n1:30-2:30\nLongitudinal Mediation\n\n\n2:30-3:00\nIndividual Consultations\n\n\nDay 4\n\n\n\n9:00-10:45\nLatent Growth Curve Modeling: Basics & Multivariate and Multiple Groups\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nMissing Data: Planned and Unplanned\n\n\n12:30-1:30\nLunch Break & Individual Consultations\n\n\n1:30-2:30\nLongitudinal Moderation\n\n\n2:30-3:00\nWrap-up then Individual 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/
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:Longitudinal SEM,Longitudinal Structural Equation Modeling,Winter Camp
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DTSTART;TZID=America/Chicago:20230217T090000
DTEND;TZID=America/Chicago:20230220T150000
DTSTAMP:20260405T182557
CREATED:20160316T175245Z
LAST-MODIFIED:20230228T013650Z
UID:618-1676624400-1676905200@www.statscamp.org
SUMMARY:Latent Profile Analysis
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\n\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent profile analysis (aka\, latent class cluster analysis)\, and modeling predictors and outcomes of latent profile membership. Hands-on practice with Mplus is provided. \nSeminar Topics:\nLatent Profile Analysis (LPA) steps including research questions appropriate for latent profile analysis\, profile (class) enumeration and assessing profile model results (classification quality\, profile homogeneity and separation)\, predicting profile membership with other variables and profile membership predicting outcomes. Practice analyses will be completed to build comfort with syntax and reading of output. We will also cover how to interpret and present the results to maximize audience understanding. \nSeminar Description:\nThis four-day camp is an intensive short seminar in the fundamentals of latent profile analysis (LPA).\nLPA is a type of latent variable model-based finite mixture models that express the overall distribution of one or more continuous variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of continuous\, manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent profiles\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic latent profile analysis to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA)\, including model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership as well as distal outcomes of latent profile membership will be presented. The implementation of these models in the most recent version of the Mplus software will be demonstrated and practiced throughout the seminar. \nInstructor: Whitney Moore\, Ph.D.\n \nDr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM\, Longitudinal SEM\, and MLM). Whitney has taught graduate courses in research design\, introduction to statistics\, ANOVA\, SEM\, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport\, exercise\, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs\, finite mixture modeling\, plus mediation and moderation in SEM. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course provides 16 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. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 4-day statistics training institute on Latent Profile Analysis will enable participants to:\n\nAcquire understanding of latent profile analysis techniques as applied in the social and behavioral sciences.\nDevelop an appreciation for the research questions and data best suited for latent profile analysis models and the common pitfalls leading to the misuse of mixture models.\nGain detailed knowledge of current “best practices” for mixture model specification\, estimation\, selection\, evaluation\, comparison\, interpretation\, and presentation.\nUnderstand how latent profile variables may be integrated into a larger (latent) variable system.\nBecome acquainted with a variety of mixture modeling extensions.\nBecome proficient in the use of Mplus for analysis of mixture models.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies with continuous variables using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent profiles) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, latent profile analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should have a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software.\nNote: We will also make use of Excel to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \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\n\n\n\n\nDay 1\n \n\n\n9:00-9:30\nWelcome and introductions plus Zoom Orientation\n\n\n9:30-10:30\nOverview of mixture modeling in a general latent variable framework\n\n\n10:30-10:45\nSnack and refreshment break\n\n\n10:45-12:15\nOverview of mixture modeling in a general latent variable framework\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nOverview of LPA steps\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIntroduction to Mplus syntax introduction with Latent Profile Analysis (LPA) example\n\n\n5:30~7:30\nSocial “hour” reception for all Stats Campers\n\n\nDay 2\n\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLPA class enumeration across variance-covariance structures introduction\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nSyntax and interpretation of output for LPA enumeration across variance-covariance structures\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nIndividual consultation & Practice of LPA enumeration process\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation & Review of multinomial logistic regression\n\n\nDay 3\n\n \n\n\n9:00-9:30\nReview of LPA enumeration process and decision-making\n\n\n9:30-10:45\nExamination of Profile homogeneity and separation\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nIntroduction to latent class regression (LCR) with inclusion of predictive covariates\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nLCR continued with inclusion of distal outcomes\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation\n\n\nDay 4\n\n \n\n\n9:00-9:30\nInformation Coming Soon…\n\n\n9:30-10:45\nInformation Coming Soon…\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nInformation Coming Soon…\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nInformation Coming Soon…\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation\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/latent-profile-analysis/
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:Latent Profile Analysis,Winter Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230216T090000
DTEND;TZID=America/Chicago:20230219T150000
DTSTAMP:20260405T182557
CREATED:20220816T021530Z
LAST-MODIFIED:20230228T013607Z
UID:4246-1676538000-1676818800@www.statscamp.org
SUMMARY:Psychometrics
DESCRIPTION:This course is currently in session. If you register now you will get instant access to the remainder of the livestream online discussion. The links to watch the asynchronous video will be provided on February 21st. \nLIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 4-day course in the application of psychometrics. Participants should be proficient specific to the material covered in a two-semester graduate-level social science statistics course sequence. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nScaling\, scaling models & scale development – stimulus\, response and subject centered\nValidity – conceptual and statistical aspects necessary for evidential arguments\nIntroduction to Factor analysis – traditional\, IRT and SEM-based approaches/connections\nReliability – classical and modern approaches to estimation of score reliability\nIntroduction to Item Response Theory\n\nSeminar Description:\nPsychometrics is defined as the science of evaluating the characteristics of tests or other devices designed to measure psychological attributes of people. Tests are broadly defined as devices for measuring ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. Application of psychometrics to psychology and social/behavioral science constitutes an organized effort to (a) properly use theory-based measurement procedures for the development of tests and other measurement instruments for inter- and intraindividual research designs and (b) incorporate current best practices for applying measurement theory\, item/scale development\, reliability estimation (classical and modern)\, factor analysis/IRT and establishing statistical evidence of score validity through a unified approach. advance knowledge in psychological and sensory processes. 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 provides 22 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 psychometrics as applied to social and behavioral sciences.\nDevelop a clear understanding of the conceptual and theoretical basis of measurement theories\, models\, and statistical concepts specific to psychometrics.\nAcquire knowledge of how to properly apply psychometric techniques such as scale development\, item analysis/refinement\, score reliability and statistical validity.\nGain knowledge of how to apply factor analysis using traditional and structural equation modeling approaches related to test and scale development and evaluation.\nGain knowledge of how to apply generalizability theory for estimating variance components and score reliability when classical test theory model is inadequate.\nAcquire basic knowledge of how and why to apply item response theory for scaling test data and test development and evaluation.\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:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. Students should have access to IBM SPSS\, version 21.0 or higher and Mplus\, version 7.1 or higher and R. \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\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:15\nMeasurement & statistical concepts\n\n\n10:15-10:30\nBreak\n\n\n10:30-11:30\nScaling and scaling models – achievement\, ability\, attitude & perception\n\n\n11:30-12:30\nLunch break\n\n\n12:30-1:30\nTechniques for item and test development\, evaluation & refinement\n\n\n1:30-3:00\nValidity – criterion\, content & construct considerations\n\n\n3:15-5:00\nStatistical aspects of the score validation process\n\n\nDay 2\n\n\n\n9:00-10:00\nFactor analysis – foundations\, types and estimating factor models using exploratory and confirmatory approaches – part 1\n\n\n10:00-11:30\nFactor analysis – a unified model for test theory and application\, estimating factor models using structural equation modeling – part 2\n\n\n11:30-12:30\nLunch break\n\n\n12:30-1:30\nComputer exercises – Common Factor Analysis using traditional algorithms for applied factor analysis – exploratory and confirmatory strategies in test development\n\n\n1:30-3:30\nHigher-order\, Bifactor\, and multidimensionality with computer exercises\n\n\nDay 3\n\n\n\n9:00-10:00\nReliability of test scores – foundations/application of classical test theory; Using/applying structural equation modeling and IRT for score reliability estimation; Rater reliability models\n\n\n10:00-11:00\nContemporary approaches to reliability estimation (factor analysis & IRT)\n\n\n11:00-12:00\nIntroduction to Item Response Theory – theory and applications for applied psychometrics; Relationship to structural equation modeling\n\n\n12:00-1:00\nLunch break\n\n\n1:00-3:00\nComputer exercises – Item Response Theory & Factor Analysis for scale construction and refinement\n\n\n3:15-5:00 *\nIndividual Consultations (optional)\n\n\nDay 4\n\n\n\n9:00-10:30\nIntroduction to Measurement Invariance/Differential Item & Test Functioning – example programs for analyses and interpretation\n\n\n10:30-10:45\nBreak\n\n\n10:45-12:00\nIntroduction to generalizability theory – G-studies and D-studies\n\n\n12:00-1:00\nLunch Break\n\n\n1:00-2:00\nGeneralizability – estimating reliability of rater data\n\n\n2:00-3:00\nNormative scores – development and use\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-training/
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:Psychometrics
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221208T090000
DTEND;TZID=America/Chicago:20221211T150000
DTSTAMP:20260405T182557
CREATED:20220908T212013Z
LAST-MODIFIED:20221209T185246Z
UID:4462-1670490000-1670770800@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling (LSEM)
DESCRIPTION:THIS COURSE IS CURRENTLY IN SESSION \nIF YOU PURCHASE NOW YOU GET INSTANT ACCESS TO THE REMAINING LIVE INSTRUCTION AS WELL AS THE ASYNCHRONOUS VIDEO TO REVIEW ON YOUR OWN FOR 1 FULL YEAR.\nNEXT LIVESTREAM LSEM COURSE STARTS – FEBRUARY 17 – 20\, 2023 – ENROLL NOW\, CLICK HERE \nLIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThis camp is an advanced intensive short seminar in the analysis of longitudinal data using SEM. The 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. \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\nMultilevel and multiple group SEM\nUsing Phantom Constructs\nMultiple group modeling\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\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC 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:\nThis comprehensive 4-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 4-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.Syllabus\n\n\n\nDay 1\n \n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nReview of Foundations of SEM\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:00\nParcels and Parceling\n\n\nDay 2\n\n\n\n9:00-10:45\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Panel Models: Basics\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:00\nMultiple-group Longitudinal Panel Models: CFA\, SEM\, & RI-CLPM\n\n\nDay 3\n \n\n\n9:00-10:45\nMixture Modeling\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nMixture Modeling\n\n\n12:30-1:30\nLunch Break & Individual Consultations\n\n\n1:30-2:30\nLongitudinal Mediation\n\n\n2:30-3:00\nIndividual Consultations\n\n\nDay 4\n\n\n\n9:00-10:45\nLatent Growth Curve Modeling: Basics & Multivariate and Multiple Groups\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nMissing Data: Planned and Unplanned\n\n\n12:30-1:30\nLunch Break & Individual Consultations\n\n\n1:30-2:30\nLongitudinal Moderation\n\n\n2:30-3:00\nWrap-up then Individual 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/
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:Longitudinal SEM,Longitudinal Structural Equation Modeling
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221007T090000
DTEND;TZID=America/Chicago:20221009T170000
DTSTAMP:20260405T182557
CREATED:20220729T193714Z
LAST-MODIFIED:20220921T035221Z
UID:3568-1665133200-1665334800@www.statscamp.org
SUMMARY:Mediation and Moderation
DESCRIPTION:LIVE STREAM – 3-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: Mediation and Moderation Training Course\nMediation and moderation analyses are commonly used in many instances. In many scientific fields research questions have become more complex. Researchers are no longer simply interested in if one variable (X) is related to another (Y). Instead\, research questions such as: “Why is X related to Y?” and “When is X related to Y?” abound. This course addresses methods to test why two variables are related (mediation) and when two variables are related (moderation). \nSeminar Topics:\n\nClassic and contemporary approaches of Mediation and Moderation\nEstimating mediation effects\nEstimating Moderation\nPath analysis\nIndirect and direct effects\nTesting intervening variable effects\nProbing and plotting interactions\nCombining moderation and mediation\n\nSeminar Description:\nThis Mediation and Moderation course will be helpful for researchers in any field—including psychology\, sociology\, education\, business\, human development\, political science\, public health\, communication—and others who want to learn how to apply the latest methods in moderation and mediation analysis using SPSS with PROCESS MACRO. Participants should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. \n\nInstructor: Mwarumba Mwavita\, Ph.D.\n \nMwarumba Mwavita is Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. In addition\, he is a Professor in the Research\, Evaluation\, Measurement and Statistics (REMS) program in the College of Education and Human Sciences at Oklahoma State University where he teachers the GLM sequence of courses that includes ANOVA\, Multiple Regression\, MANOVA\, and Multilevel Modeling. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC 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:\nAt the end of the course\, participants will be able to:\n\nEstimate\, test\, and interpret mediated (i.e.\, indirect) effects using PROCESS macro in SPSS\nEstimate\, test\, and interpret moderated (i.e.\, interaction) effects using PROCESS macro in SPSS  and other advanced techniques\nCombine mediation and moderation models to test conditional indirect effects.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired:\n·       Basic proficiency in multiple linear regression\n\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\n\nNot required but advantageous for Mediation and Moderation training: \n\nAt least limited experience (e.g.\, graduate-level course) path analysis or Structural Equation Modeling training.\n\nSoftware and Computer Support\nSoftware and Computer Support:\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.(statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n Mediation and Moderation Training Course\n\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nReview of Multiple Regression and Path analysis\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nIntroduction to Mediation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-15:00\nMediation using PROCESS macro with SPSS\n\n\n15:00-15:15\nSnack and refreshments break\n\n\n15:15-17:00\nSerial and Parallel Mediation\n\n\nDay 2\n\n\n\n9:00-10:45\nComputing\, testing and Interpreting Mediation using PROCESS\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nIntroduction to Moderation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-15:00\nCategorical Moderators Using PROCESS\n\n\n15:00-15:15\nSnack and refreshments break\n\n\n15:15-17:00\nContinuous Moderators and Multiple Moderators\n\n\nDay 3\n\n\n\n9:00-10:45\nGraphing and Interpreting Moderation in PROCESS\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nCombining mediation and moderation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-17:00\nTesting and interpreting conditional Indirect effects \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/mediation-and-moderation-fall/
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:Mediation and Moderation,UCLA Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20220922T090000
DTEND;TZID=America/Denver:20220925T170000
DTSTAMP:20260405T182557
CREATED:20220707T002711Z
LAST-MODIFIED:20221018T214009Z
UID:3093-1663837200-1664125200@www.statscamp.org
SUMMARY:Analysis Retreat: Whitefish Montana
DESCRIPTION:All-Inclusive In-Person – 4-day Statistics Training Retreat\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nStatistics Training Retreat Overview:\n4 days of intensive data analysis\, statistics training\,  software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make your research “submission ready” with the guided assistance of our team of experts.\nStats Camp Analysis Retreat is a unique and powerful 4 day opportunity to have a highly cited International team of experts vet your research and increase the likelihood of peer review approval and publishing. Our statistics training team puts the advantage squarely in your corner with four days of intensive analysis\, software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make you research “submission ready.” Build solid confidence for tenure and promotion. Stats Camp Analysis Retreat is the positive action you can take NOW to get your career on the fast track. Due to the intense personal focus of the retreat\, seats are very limited and fill fast. \nAnalysis Retreat Includes:\n\n\nIn person one-on-one statistics training\n\nHotel lodging provided\n\nAll meals and daily beverages & snacks provided\n\nMaterials\, downloads\, and software access\n\n\n\n\n\nRetreat Description\nRetreat Description:\n\nTodd D. Little\, Ph.D. developed Stats Camps Analysis Retreats to address the need for specialized expertise that is often needed to move complex research projects to approval and publication. Dr. Little is a preeminent award-earning scholar who has published over 300 peer-reviewed works that have garnered 46\,611+ citations. \nMembers of the Stats Camp team are highly experienced and many have decades of experience in resolving complex analytical challenges with an understanding of best-practices procedures. You will have exclusive access to their expertise as they guide you to a final publication-ready product. \nExpert consulting at this level can quickly run into the tens of thousands of dollars. By bringing our team together with you at an exclusive and personalized all-inclusive Stats Camp Analysis Retreat\, we can offer this unprecedented access at a much more affordable price. \n\nWho Should Attend?\nWho Should Attend?\nIf you are in the pre-data analysis phase\, Stats Camp staff will engage with you to develop a testable research hypothesis\, a fully articulated analysis plan\, and ensure full understanding of the analysis model\, the nature of its results\, and their implications. \nIf you are in the analysis phase\, Stats Camp staff will work with you to learn the software syntax needed to analyze your data\, interpret the output and understand the implications of your findings. \nBreakout sessions will cover a vast array of techniques and topics. Some topics are predetermined while others will be prepared specifically to meet your needs and the needs of the other participants (a survey will be given prior to the event to develop this need list of topics to cover). \nWhat to expect\nWhat to expect:\nParticipants in our past annual Stats Camp Analysis Retreats were overwhelming positive about the individualized learning that took place as well as the opportunity to work with their own data – a practical aspect that makes learning easier. Because participants are enabled to ask questions and get immediate feedback\, learning the practical analytic skills is facilitated. Also\, participants learned about different topics tailored to their learning needs. \nParticipants uniformly exclaimed that the learning activities and interaction with the Stats Camp instructors contributed to publishing more articles and increased the quality of their research\, which has allowed them to target higher impact factor journals. Because quantitative methods are a critical gap in most fields\, the Stats Camp experience helped clarify the essential importance of quantitative methods. By learning the basics of applying these methods\, the knowledge is thereby carried over to other colleagues and students\, and ultimately helping to close the gap. \nThe Stats Camp Analysis Retreat is a perfect combination of learning and fun from the comfort of a beautiful lodge in an amazing destination\, networking in a collegial and social context. Having the stats camp in a retreat format\, gives you ample time\, motivation\, and access to incomparable expertise to propel your research forward. \nSchedule\nSchedule: Below is an example of our standard in-person schedule of events. All times are listed in local (MST) Mountain Standard Time.\nKey: All inclusive in Green \n\nThursday\n\nTBD – Morning Nature Experience (if your arrive Wednesday)\n\nTransportation from the airport via Grouse Mountain Lodge Shuttle (For a Small Fee)\n\n\n\n12:00 – Group Lunch with some overview thoughts by Todd Little\n1:30 – Welcome and overview program and introductions\n2:30 – Common Session: Missing Data (Todd Little\, Zack Stickley)\n5:00 – Cocktail consultations and social networking\n\nHeavy appetizers and drink tickets\n\n\n~6:30 – Dinner at Lodge \n\n\nFriday\n\nTBD – Morning Nature Experience\n8:00 – Group Breakfast with kick-off discussion.\n9:00 – Morning break-out sessions in two groups based on whether there is a clear analysis plan (group 1: already working on analyses – group 2: preparing for data analysis).\n\nFor both groups the goal is to answer questions like:\n\nWhat exactly is your research question?\nWhat are your testable research hypotheses?\nWhat analysis plan is best suited for you hypotheses?\nMake/review drawings of your model (observed/latent)\n\n\n\n\n12:00 – Group Lunch with small group discussions and networking\n1:30 – Afternoon break-out session with mini-lectures (conversations) on statistical models; choose from:\n\nTopics determined by you\n\n\n***During the day we will also have individual/small group consultation appointments to answer specific questions from you***\n5:00: Cocktail consultations and social networking\n\nHeavy appetizers and drinks\n\n\n~6:30 Dinner in Downtown Whitefish or surrounding area On Your Own or Dinner at Lodge with Group Provided.  – via lodge transportation or On Your Own\n\n\nSaturday\n\nTBD: Morning Nature Experience\n8:00 – Group Breakfast with kick-off discussion\n9:00 – Morning break-out sessions introducing software:\n\nBasic SEM – CFA with Mplus or Intro lecture to R + Rstudio (based on needs survey)\nAdditional topics determined by you\n\n\n1200 – Group Lunch with small group discussions and networking\n1:30 Afternoon break-out session working on your own data (or work on exercises if you do not have data yet) based on level of experience:\n\nAdvanced Mplus or R\nAdditional topics determined by you\n\n\n***During the day we will also have individual/small group consultation appointments to answer specific questions from you.***\n5:00: Cocktail consultations and social networking\n\nHeavy appetizers and drink tickets\n\n\n~6:30 Dinner in Downtown Whitefish or surrounding area On Your Own or Dinner at Lodge with Group Provided.  – via lodge transportation or On Your Own\n\n\nSunday\n\nTBD: Morning Nature Experience\n8:00 – Group Breakfast with wrap-up discussion\n9:00 – Continue to work on own data + get individual feedback on taking the next steps\n11:00 – Closing meeting with reflections (Todd Little)\n11:15 – Grab and go box lunches available\n12:00 – Group Lunch\n1:30 – Close of your unique Stats Camp Analysis Retreat experience\nTransportation to the airport via Grouse Mountain Lodge Shuttle\n\n  \n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample statistics training course materials. \n\n\nLooking for a LIVESTREAM event view our online statistics courses.
URL:https://www.statscamp.org/courses/analysis-retreat-whitefish-montana/
LOCATION:Grouse Mountain Lodge – Whitefish\, Montana\, 2 Fairway Dr\, Whitefish\, MT\, 59937\, United States
CATEGORIES:Analysis Retreat
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/07/analysis-retreat.jpg
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