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!
LIVESTREAM / Asynchronous – 4-day Statistics Psychometric Training Online Course
Cutting-Edge Network Psychometric Training
This 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.
Syllabus:
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Day 1 |
Introduction and Fundamentals |
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9:00-9:30 |
Welcome and introductions |
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9:30-10:30 |
Introduction to Network Psychometrics: Overview of network psychometrics, comparison with traditional psychometric models, applications in psychology, neuroscience, education, and social science |
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10:30-11:30 |
Hands-on Exercise: Introduction to R and R Studio |
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11:30-12:30 |
Lunch break |
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12:30-1:30 |
Basic concepts in network analysis: Types of networks (undirected, directed, weighted, unweighted), Adjacency and correlation matrices |
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1:30-3:00 |
Network estimation methods: Thresholding techniques, Regularization techniques (e.g., LASSO and graphical LASSO) |
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Day 2 |
Network Analysis and Interpretation |
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9:00-11:00 |
Centrality Measures: Degree centrality, Betweenness centrality, Closeness centrality, Eigenvector centrality |
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11:00-11:30 |
Hands-on Exercise: Detecting communities in the network using ‘qgraph’ and ‘igraph’ R packages, Interpreting community structures in psychological data |
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11:30-12:30 |
Lunch break |
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12:30-2:00 |
Community 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 |
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2:00-3:00 |
Hands-on Exercise: Detecting communities in the network using ‘qgraph’ and ‘igraph’ R packages, Interpreting community structures in psychological data |
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Day 3 |
Advanced Topics in Network Psychometrics |
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9:00-10:30 |
Stability and Robustness Analysis: Bootstrapping and permutation tests, Assessing the stability of network parameters, visualizing stability results |
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10:30-11:30 |
Hands-on Exercise: Performing stability analysis using the R ‘bootnet’ package, Interpreting stability plots |
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11:30-12:30 |
Lunch break |
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12:30-1:30 |
Lunch break |
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1:30-2:30 |
Classical Item Analysis and Factor Analysis Using Networks: Network approaches to classical item analysis, Network-based factor analysis |
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2:30-3:00 |
Hands-on Exercise: Conducting item analysis and factor analysis using network models in R |
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Day 4 |
Applications and Case Studies |
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9:00-10:15 |
Network Psychometrics for Reliability Estimation: Network-based reliability estimation methods, Comparing network-based, classical, and structural equation modeling reliability estimates |
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10:15-11:30 |
Item Response Theory (IRT) and Network Models: Linking IRT with network psychometrics, Network-based IRT analysis |
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11:30-12:30 |
Lunch Break |
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12:30-1:30 |
Model Invariance and Structural Validation: Assessing model invariance cross-sectionally and longitudinally, Structural model validation using network models |
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1:30-3:00 |
Final 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. |
Course Topics:
- Introduction to Network Psychometrics
- Basic Concepts in Network Analysis
- Network Estimation Methods
- Centrality Measures
- Community Detection
- Stability and Robustness Analysis
- Classical Item Analysis Using Networks
- Network-based Factor Analysis
- Reliability Estimation with Network Models
- Item Response Theory (IRT) and Network Models
- Model Invariance Assessment (Cross-sectional and Longitudinal)
- Construct Validation Using Network Models
- Practical Applications and Case Studies
Psychometric Training Description:
This 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.
As 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.
Instructor: Larry Price, Ph.D.
Larry 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.
APA Continuing Education Credits:
Please 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.
Psychometric Training Course Includes:
Materials, downloads, recorded course video viewable for up to one year.