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.

Looking to strengthen your #causalinference skills? Join @noahgreifer.bsky.social on April 15-17 for "Causal Inference in R Using MatchIt and WeightIt" to gain the skills to apply these #Rstats packages to estimate and interpret treatment effects.

On April 14, Distinguished Speaker James Berger explores the history of objective #Bayesian and classical statistics, the p-value controversy, and multiplicity in “Objective Bayesian Analysis: History and Interfaces with Classical Statistics.”

"Causal Inference in Econometrics" with @nickchk.com is on demand! Join April 13-May 11 to learn the how & why of #econometric analysis of observational data using regression (how econometricians evaluate data) & identification (how they link theory to data) at your own pace.

Want to learn about @statacorp.bsky.social impressive data & model visualization capabilities? "Data Visualization Using Stata & LLMs" w/ @trentonmize.bsky.social (Apr 8-10) covers best practices & approaches 2 data exploration, model diagnostics/presentation & using #LLMs to enhance your workflow.

Introducing AI Horizons! Live, expert-led seminars on AI & LLMs for research, analytics, and data science. Build practical workflows + best practices to use these tools effectively and responsibly. Explore the schedule: aihorizons.io.

"Power Analysis & Sample Size Planning" w/ Christopher L. Aberson begins March 16! Join this 4-week on-demand seminar to learn practical power and sensitivity strategies to strengthen your approach to determining sample sizes across research designs.

Join Christopher L. Aberson on March 25-27 for "R for SPSS Users" to seamlessly transition from #SPSS to #Rstats! Gain insights from a long-time user of both programs, covering terminology, data import, visualizations, analyses, and more. Elevate your skills with practical solutions.

How are recent advances in #AI reshaping teaching and research? After leading his #MachineLearning seminar, @brucedesmarais.bsky.social shares his reflections in a new post on LinkedIn. Read it here:

Upgrade your marketing research! Join Jeffrey Dotson on March 25–27 for "AI-Enabled Marketing Research" and learn to use Google Gemini for #AI-driven qualitative analysis, intelligent survey design, and synthetic respondent methods.

Join @stephenvaisey.com on March 18–20 for "Longitudinal Data Analysis w/ #Stata and LLMs". Learn to analyze panel data using mixed models, two period difference-in-differences, fixed-effects models, between-within models, & dynamic panel models, w/ added #LLM support.

Distinguished Speaker James Berger presents "Objective Bayesian Analysis: History & Interfaces w/ Classical Statistics", April 14. Learn how objective #Bayesian and classical approaches diverged, why those differences matter, & what they mean for statistical evidence today.

How do researchers determine the sample size needed to address their research questions? Join "Power Analysis & Sample Size Planning" w/ Christopher L. Aberson to learn about power and sensitivity approaches for multiple research designs. This 4-week on-demand seminar starts March 16.

Join "Automatic Item Generation and Validation: A Network Integrated Approach using LLMs in #Rstats" on Mar. 11 & 13 with @hudsongolino.bsky.social to learn how to use artificial intelligence to create and validate questionnaires and scales!

Join @dmcneish.bsky.social for "Dynamic #StructuralEquationModeling", March 4-6! Explore DSEM, a cutting-edge framework that utilizes intensive longitudinal data to gain detailed insights into dynamic processes and variability within and between subjects.

Expand your social network analysis toolkit! Join John Skvoretz for "Social Networks: Statistical Approaches" starting Feb. 9. Learn how to test hypotheses and model network structure using #Rstats in this 4-week on-demand course.

Join "Machine Learning for Estimating Causal Effects" w/ @ashleynaimi.bsky.social on Feb. 25-27. Learn to use cutting-edge "double-robust" #machinelearning methods to estimate treatment effects, minimize biases, & avoid problems like the “curse of dimensionality.”

Join Boris Nikolaev on Feb. 19-20 for "Data Analysis with ChatGPT: A Practical Introduction"! Explore the strengths and limitations of #ChatGPT as a data analysis tool in real-world scenarios to work with complex data and create compelling visualizations.

Join “Multilevel & Mixed Models w/ #Stata & ChatGPT” w/ @stephenvaisey.com, Feb. 18-20 for an intensive intro to multilevel models. Learn the (real) difference between fixed & random effects, how to combine both in a single model, & how #ChatGPT can help design, estimate, and interpret your models.

Get Certified in Machine Learning! This 4-course certification builds from #ML foundations to advanced, applied methods, with a focus on responsible evaluation and practical use. Gain hands-on #machinelearning skills and real-world expertise. Contact us:

Learn to use natural language processing (NLP) techniques & large language transformer models (#LLMs) for research applications. Join "Text Classification with LLMs in #Rstats" w/ @hudsongolino.bsky.social on Feb. 11-13.

Evaluating #AI in practice? Join Distinguished Speaker Kosuke Imai on Feb. 10 for "Evaluating AI and #MachineLearning Algorithms for #causalinference". Learn frameworks to assess algorithmic impact and improve decisions.

Already have a background in social network analysis & looking to expand your knowledge? Join John Skvoretz for "Social Networks: Statistical Approaches" starting Feb. 9 to learn statistical methods for analyzing social network data using #Rstats in this 4-week on-demand course.

Join @brucedesmarais.bsky.social for "Machine Learning" Feb. 5-7 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.

Join @hudsongolino.bsky.social for "LLM Text Generation in R: Practical Prompt Engineering" on Jan. 28-29. 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.

Transition from using #Stata to using #Rstats independently in only 8 hours (over 2 days)! Join "R for Stata Users" with Andrew Miles on Jan. 22-23. Discover how R "thinks" and explore the differences in terminology, code syntax, data management, and default procedures.

Learn to uncover hidden structures & patterns in your research using "Latent Class Analysis" (#LCA) w/ Drs. Lanza & Bray on Jan. 22-24. You'll learn to apply LCA to inform theory, guide interventions, contribute to evidence-based decision-making, & extract new insights from data.

Transform how you design and analyze experiments in “Using AI to Build Better Experiments,” with Charles Crabtree on Jan. 14-16. Discover how to use #LLMs to create materials, augment studies, and interpret results.

Using #AI to write #machinelearning code? It can speed up your coding workflow, but you can’t always trust the accuracy or rigor of AI-generated code. Read @brucedesmarais.bsky.social's SH blog post for 5 checks to improve accuracy, evaluation, and interpretation.

Join Kenneth A. Bollen for "How to Choose a Model for Longitudinal Data", January 14-16. Gain invaluable insights and practical skills for comparing, selecting, and fitting the best #longitudinal models for repeated measures.

Learn to use structural equation modeling (SEM) to estimate dynamic panel data models w/ Paul Allison. "Longitudinal Data Analysis Using #SEM" begins Jan. 12. This 4-week on-demand seminar introduces maximum likelihood with SEM software (ML-SEM) using #Mplus, #Stata, SAS, and #Rstats.

Looking for a comprehensive way to manage, manipulate, analyze, & visualize your data in #Rstats? Join "Data Wrangling with the Tidyverse" with @kjhealy.co, January 7-9, to get hands-on experience w/ R's #tidyverse tools to accomplish everyday statistical analysis tasks.

Looking for a practical intro to designing effective web surveys & working with online respondents? Join "Designing Effective Online Surveys", taught by Andrew Miles on Jan. 7-9, for actionable design strategies, plus advantages & disadvantages of online samples.

Join “Applied Bayesian Data Analysis: A Second Course,” Dec. 10-12 with Roy Levy. Strengthen your ability to build, estimate, and interpret #Bayesian models for complex applied research.

See how @kjhealy.co builds trustworthy visualizations at #positconf2025 —then level up your own skills in his course on "Data Wrangling with the #Tidyverse", Jan. 7–9.

Join “Latent Growth Curve Modeling,” Dec. 10-12 with @dmcneish.bsky.social. Learn how to model change over time, understand growth factors, and apply growth models in your research.

Struggling with missing data? Join Paul Allison for "Missing Data Using R", a 4-week on-demand seminar starting Nov. 17. You'll learn maximum likelihood & multiple imputation—two of the most powerful #missingdata solutions using #Rstats.

Join Roy Levy on Dec. 10-12 for "Applied Bayesian Data Analysis: A Second Course" to take your #Bayesian skills to the next level! Master key skills in advanced modeling frameworks, including factor analysis, structural equation models, and multilevel models.

Check out the Data Science at Home podcast for insights on AI, machine learning, and tech innovation. Listen to the latest episode here.

Learn to model change or growth in #longitudinal data using "Latent Growth Curve Modeling" with @dmcneish.bsky.social, December 10-12. You'll walk away with the ability to confidently apply and interpret these methods to answer your unique research questions.

Distinguished Speaker Kosuke Imai presents "Evaluating AI & Machine Learning Algorithms for #causalinference", Feb. 10. Learn to assess #AI & #machinelearning practically—from heterogeneous treatment effects, individualized treatment rules, & human–AI collaboration.

Distinguished Speaker Kosuke Imai presents "Evaluating AI & Machine Learning Algorithms for #causalinference", Feb. 10. Learn to assess #AI & #machinelearning practically—from heterogeneous treatment effects, individualized treatment rules, & human–AI collaboration.