Join "Automatic Item Generation & Validation: A Network Integrated Approach using #LLMs in #Rstats" on Aug. 20-21 with @hudsongolino.bsky.social. Learn how to use #AI to create and validate questionnaires and scales.

Back by popular demand! Build a systematic #AI assisted research workflow in “Claude-Powered Academic Research: From Ideation to Publication” w/ Jeffrey Dotson, Aug. 18-21. Use #Claude tools & #GitHub to support ideation, data wrangling, analysis, academic writing, & replicability.

Learn to use #AI agents in audit and correspondence #experiments without losing design control. In “AI-Enhanced Audit and Correspondence Experiments,” Aug. 18-21, Charles Crabtree covers signal control, treatment delivery, validation, and transparent documentation.

Join Simon Brewer on August 11-14 for "Spatial Analysis of Health Data"! Explore how geospatial #healthdata provide the tools to better understand & tackle public health challenges while addressing the issues that can arise while working with this type of data.

Analyze panel data in #Rstats while using #LLMs to support model design, estimation, and interpretation. Join @stephenvaisey.com in “Longitudinal Data Analysis Using R & LLMs”, Aug. 11-14, covering mixed models, fixed effects, between-within models, and dynamic panel models.

What research tasks are safe to speed up with AI—and which require human judgment? Join Charles Crabtree for “AI Agent-Driven Research Workflows,” Aug. 4-7, and learn how to use #AI agents responsibly through structured workflows, checks, and documentation.

Explore Monte Carlo simulations, a valuable tool for understanding, evaluating, comparing, & applying a wide array of statistical methodologies. Join @ashleynaimi.bsky.social Aug. 4-7 for "Design & Analysis of Simulation Studies" to learn to implement your own simulation using #Rstats.

Back by popular demand! “AI Tools for Data Analysis: From Chatbots to Coworkers” w/ Boris Nikolaev, Jul. 29-30. Build reliable #AI-assisted data analysis workflows and learn to clean data, run analyses, create visualizations, and evaluate outputs using today’s AI tools.

Join "Psychometrics" w/ @mattadiemer.bsky.social on July 28-31 to learn about #psychometrics and how to to understand, detect, and remediate bias in measures. Topics include exploratory factor analyses, confirmatory factor analyses, MIMIC models, invariance & more.

Looking for a thorough intro to #DAGs? Join "Directed Acyclic Graphs for Causal Inference" w/ Felix Elwert on July 28-31 to learn the essential elements for #causal reasoning with DAGs. We'll also use DAGs to discuss many important challenges in observational data analysis.

Images of text can be difficult to analyze at scale. In the next Machine Collaborators session, @stevendenney86.bsky.social discusses how OCR & LLMs can turn them into analyzable data. Join June 25 at 7pm ET (11pm UTC) for “From Pixels to Patterns: Vision-Language OCR and LLM-Based Text Analysis."

Join @brucedesmarais.bsky.social for "Machine Learning" July 21-24 to learn how #machinelearning can be used to uncover valuable insights, identify optimal model structures, perform variable selection, and deliver highly accurate predictions from data.

Transform how you design and analyze experiments in “Using AI to Build Better Experiments” with Charles Crabtree, July 14-17. Discover how to use #LLMs to create materials, augment studies, and interpret results.

Learn advanced structural equation modeling #SEM topics like instrumental variables, alternative estimation methods, multiple group models, & models for longitudinal data at your own pace. 4-week course "SEM: Part 2" On Demand with Paul Allison starts July 13.

In the next free Machine Collaborators session, Jie (Jason) Lian presents “Learning with AI,” on what it means for researchers to expand their knowledge boundaries while understanding the products, processes, and limits of AI-assisted workflows. Join June 11 at 7pm ET (11pm UTC).

Working with binary or ordinal outcomes in #SEM? "Structural Equation Modeling with Categorical Data" with @winterstat.bsky.social, July 7-10, covers key approaches to model specification, estimation, and interpretation for #categorical data, with examples in #Mplus and #Rstats.

Looking to strengthen your research with #AI tools? Join @ethanbusby.bsky.social on July 7-10 for ""Using #LLMs for Social Science Research""! Learn the fundamentals of large language models (like #ChatGPT) and how to integrate them into your research.

Learn the basics of #Python in 4 days! Join @dalmaijer.bsky.social for "Introduction to Python for Data Analysis" on June 23-26 to explore how to use Python for data analysis, including handling datasets and writing full analyses.

How can agentic #AI help researchers share their work beyond the academy? Join @akoustov.bsky.social on Thurs., May 28 at 7pm ET (11pm UTC) as he presents “Using Agentic AI to Improve Your Public Engagement as a Researcher” in the next Machine Collaborators session. Learn more:

Join "Meta-Analysis" w/ David Wilson June 23-26 to learn to conduct all aspects of a meta-analysis. Search for studies to include, code & extract data, compute effect sizes, estimate models, explore publication selection bias, & report results w/ #Rstats, @statacorp.bsky.social, or #SPSS.

Join Gonzalo Vazquez-Bare on June 23-26 for a course on the latest developments in #DifferenceinDifferences. You will learn to apply classical and novel methods for estimating treatment effects and to evaluate the assumptions behind them.

Learn how interpretable #machinelearning can reveal hidden structure in data, improve predictive performance, & add transparency in @cynthiarudin.bsky.social Distinguished Speaker seminar, “Interpretable Machine Learning: Visualization, Sparse Models, & Neural Networks,” June 17.

Build reliable #AI-assisted data analysis workflows in “AI Tools for Data Analysis: From Chatbots to Coworkers” w/ Boris Nikolaev, June 15-16. Learn to clean data, run analyses, create visualizations, and evaluate outputs using today’s AI tools.

In his blog post, “How AI Can Help You Build Better Experiments,” Charles Crabtree explains why LLMs are becoming important tools for experimental research—and why validation, documentation, and human review still matter.

Are you a researcher burdened by costly and time-consuming data collection? Join @lakens.bsky.social for "Sample Size Justification" on June 9-12 to learn how to justify your sample size, make the most of your resources, and efficiently test your hypothesis.

Join the Machine Collaborators conversation Thurs., May 14, 7pm ET (11pm UTC) as Alexis Palmer presents “Silicon Sampling Under Different Sources of Uncertainty." Learn more:

Join Adam D. Rennhoff for "Interpretable Machine Learning", June 9-12. Grounded in familiar statistical ideas, you'll learn tools and workflows to interpret, explain, and communicate complex #MachineLearning models, with hands-on demos in #Rstats.

Join @hudsongolino.bsky.social for "LLM Text Generation in R: Practical Prompt Engineering" on June 3-4. In 8 hours (over 2 days), you'll learn to integrate #LLM models with #Rstats, engineer effective prompts, and apply LLMs to real research tasks and data analysis.

Learn how to design conjoint experiments, program surveys, analyze results, and communicate findings clearly in "Mastering Conjoint Analysis" with @andrew.heiss.phd, June 2-5.

Join "Causal Mediation Analysis" with Linda Valeri on June 2-5. Learn how to implement and interpret state-of-the-art #causalinference techniques, validate standard approaches, and apply regression methods for mediation with various outcomes.

Interested in getting started with #SEM? Join Paul Allison for "Structural Equation Modeling: Part 1" beginning June 1 for a 4-week self-paced introduction to this complex and wide-ranging methodology. No previous experience required. Part 2 coming in July!

In his new blog post, @brucedesmarais.bsky.social explains how the "black box problem" can affect generative #AI workflows through hidden errors, weak validation, and reproducibility challenges.

Leverage the power of statistical models to deliver clear & compelling insights w/ confidence in "Interpreting & Communicating Statistical Results w/ #Rstats" w/ @vincentab.bsky.social on May 28-29. Learn to illustrate your findings & create reproducible reports to convey results.

Gain a thorough intro to #causalinference in randomized field trials in "Experimental Methods" w/ Henry May on May 18-20. We'll cover key aspects of designing & conducting experiments that meet the requirements of many grant programs focused on intervention research.

Join Distinguished Speaker Cynthia Rudin, June 17, for “Interpretable Machine Learning: Visualization, Sparse Models, & Neural Networks.” Learn how interpretable #machinelearning can visualize complex data, build sparse models for tabular data, & clarify computer vision predictions.

Join us for Machine Collaborators, a free conversation series on how researchers are using #AI. On Thurs., April 30 at 7pm ET (11pm UTC), @mbosley.bsky.social presents “Principles of Agentic Research,” where he’ll discuss working with AI agents. Learn more:

Join @brucedesmarais.bsky.social for "Advanced Machine Learning & Applied AI Workflows", May 13-15. Go beyond prediction—address causal inference, missing data, LLMs, and more while building stronger #AI workflows with advanced #machinelearning methods in #Rstats.

Want a smoother coding and collaboration workflow? Join @aarongullickson.bsky.social for the 1-day seminar “Using Version Control for Data Analysis through GitHub” on April 22. Learn the basics of #git, collaboration, and common troubleshooting steps.

Looking for more sophisticated methods to handle your complex designs and modeling objectives? Learn cutting edge "Multilevel Structural Equation Modeling" (MSEM) and other multilevel modeling extensions w/ @krispreacher.bsky.social on May 13-15 & 18-20.

We’re excited to co-sponsor Machine Collaborators, a free conversation series led by Charles Crabtree on how researchers are using #AI in their work. Join the 1st session Thur, April 16 at 7pm ET (11pm UTC) as @brhkim.bsky.social discusses accelerating quantitative research w/ AI agents. Learn more:

Join "Analysis of Complex Survey Data" w/ @bradytwest.bsky.social, May 6-8. Get a thorough intro to techniques that account for multistage sampling designs' unique weighting, stratification, & cluster sampling properties, as well as practical, hands-on experience w/ #Stata & #Rstats.

Looking to improve your #Rstats skills? Join Andrew Miles, May 6-8 for "Essential R Skills for Intermediate Users." This hands-on course will help you write cleaner code, debug, create functions, & streamline your research—plus learn practical ways to use LLMs for common R tasks.

Join Donald Hedeker for "Multilevel Modeling of Categorical Outcomes" starting May 4. You'll learn how to analyze non-normal outcomes for MLM, including dichotomous, ordinal, count, & nominal outcomes for both clustered & longitudinal data in this 4-week on-demand seminar.

Turn sports data into clear, actionable insights in "Sports Analytics Using R" with @barts78.bsky.social, April 28-30. Gain hands-on experience cleaning, visualizing, and presenting data in #Rstats while building interactive dashboards that coaches and analysts can use.

Join "Item Response Theory" (IRT) with @mattadiemer.bsky.social on April 29-May 1 to learn how to interpret, analyze, and depict IRT analyses. This hands-on introduction will teach you how to develop and improve measures using #RStats and #MPlus.

Join "Causal Inference in R Using MatchIt and WeightIt" with @noahgreifer.bsky.social on April 15-17. Gain hands-on #causalinference experience in #Rstats and learn matching and weighting methods for estimating and interpreting treatment effects.

Data analysts: want to improve your research/coding workflow & simplify research collaboration using git? Join "Using Version Control for Data Analysis through GitHub" w/ @aarongullickson.bsky.social April 22. Topics include: basic #git workflow, collaborating w/ others, & dealing w/ complications.

Join "Scale Construction & Development" w/ Deborah Bandalos on April 22-24 to learn how to construct scales that produce accurate & reliable measurements. This seminar will take you from current best practices to exploratory factor analysis & confirmatory factor analysis.

Looking for a rigorous approach to understanding expert opinion? Join "Consensus Development using the Delphi Method” with @seangrant.bsky.social, Apr. 15–17. Learn how to plan, implement, and report Delphi studies—plus how to critically appraise their results.

Get certified in #causalinference. Across 4 seminars, you’ll learn how to think more clearly about design, estimation, assumptions, and interpretation in applied settings. Strengthen your research toolkit and earn a credential in the process.