Come learn with me techniques of prompt engineering (July 10 and 11). You are going to learn the basics of LLMs, Prompt Engineering methods, and how to do text generation and refinement via prompt engineering using R. Additionally, I created a new Groq AI Desktop App so that users can build intuition on how to do prompt-engineering for many different tasks without programming. Link in the comments.

Do you like Network Psychometrics and Exploratory Graph Analysis? Want to learn the basics of An Innovative Approach to Structural Validity, Item Analysis, and Dimension Analysis using networks? Come learn with me, from May 14th to May 17th. statisticalhorizons.com/semi… Researchers and applied professionals from many different fields, including psychology, sociology, education, political science, and the health sciences, have undoubtedly faced at least one of the following questions: How many factors (latent dimensions) are being assessed by my instrument (test, questionnaire, survey, etc.)? How stable is the dimensionality solution? How good are my items? Do they replicate into the same latent dimensions or not? How can I compare multiple dimensionality configurations (or solutions)? Is my instrument structurally valid? Are my items reliable, or do they merely seem reliable because we are using the common “cheating by repeating” approach? How can I answer all these questions if I have (intensive) longitudinal data instead of cross-sectional data? Too many important questions. Or maybe you’re just interested in text mining and want to analyze data from social media in order to estimate latent variables. Maybe you have heard a lot about network psychometrics and want to learn more about it. Or, being more creative, you just want to know what Russian trolls have to do with dimensionality assessment and reduction and item analysis. Again, exciting and important questions. If these are questions you’ve asked yourself, and you’ve been scratching your head staring at the dataset on the screen in front of you, this course is what you’ve been looking for. Seminar outline Exploratory Graph Analysis (EGA): what it is and how it works. A brief introduction to network models The Regularized Partial Correlations approach The Triangulated Maximally Filtered Graph approach Findings from recent simulation studies Guided hands-on activities in R with real and simulated datasets How to verify the fit of the EGA-estimated dimensionality structure to the data: The Entropy Fit Indices A brief introduction to information and quantum information metrics Shannon Entropy and Von Neumann’s Entropy Watanabe’s Total Correlation The Total Entropy Fit index The Generalized Total Entropy Fit Index for bifactor structures with multiple correlated general factors Comparing dimensionality solutions using the Total Entropy Fit Index Findings from recent simulation studies. Guided hands-on activities in R with real and simulated datasets How to estimate the stability of the dimensionality structure: the Bootstrap Exploratory Graph Analysis technique Estimating a “typical” structure using the bootstrap EGA technique Investigating the structural stability of the dimensionality structure estimated via EGA: introducing the structural consistency metric Investigating the item stability metric Sources of structural/item instability: wording effects, redundant items, items that don’t work well Configural Invariance using EGA Guided hands-on activities in R with real and simulated datasets How good are my items? The Network Loadings metric (akin to factor loadings) How can network loadings be computed? What do they mean? How do they compare to factor loadings? Guided hands-on activities in R with real and simulated datasets Invariance using the EGA approach Configural Invariance using structural consistency and item stability (recap) Metric invariance in the EGA approach Guided hands-on activities in R with real and simulated datasets Identifying redundant items using unique variable analysis Dynamic Exploratory Graph Analysis for (intensive) longitudinal data or for text data from social media Generalized local linear approximation Dynamic Exploratory Graph Analysis Network loadings and network scores for Dynamic EGA Recent simulation studies Guided hands-on activities in R with real and simulated datasets All topics will have simulated and real datasets that will be used during supervised hands-on activities.

Dive into the world of Network Psychometrics! Join us for an immersive live online workshop covering Exploratory Graph Analysis, Bootstrap EGA, Measurement Invariance, and Dynamic EGA for longitudinal/EMA/multivariate time series analysis and more!🗓️ May 14-17. Save your spot now! statisticalhorizons.com/semi… #NetworkPsychometrics #ExploratoryGraphAnalysis #StatisticsWorkshop @StatHorizons

Today at our @StatHorizons Workshop "Using Large Language Transformer Models for Research in R: A Short Course", Alex Christensen will show the structure of positive and negative "states" on Taylor Swift's lyrics, comparing two albums: Swift and Midnight. The emotions/mood states were computed using Zero-Shot classification with Transformers (cross-encoder/nli-roberta-base). Guess what? "Depression", "Anxiety", and "Rebellious" emotions/states were significantly different between the two albums (computed using EGA metric invariance with permutation tests).