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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
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