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Learn how Item Response Theory enhances measurement! Matthew Diemer’s seminar (June 3-6) covers IRT fundamentals and figures, the Graded Response Model, and practical applications in research. Watch the first hour now to see what IRT can do for you: youtu.be/B-oLR7XRVCU

Join Interactive Visual Dashboards Using R Shiny with Adam D. Rennhoff on April 30-May 2 to learn to create, customize, and deploy interactive dashboards online (without pricey software subscriptions) using the open-source #RShiny package for #Rstats.

Exploratory Factor Analysis (EFA) w/ Kristopher J. Preacher is now on demand! Starting May 5, learn the statistical theory of EFA & get hands-on experience factor analyzing data. See how EFA helps pinpoint underlying factors/dimensions explaining variability in observed variables.

How do we measure biological aging? Lauren Gaydosh’s "Analysis of Biological Aging" seminar (June 10-13) covers cutting-edge methods, incl. topics like the #geroscience hypothesis & biomarkers, DNA methylation, epigenetic clocks & more. Watch the 1st hour: youtu.be/ausJe_SstQs

Join Simon Brewer on May 7-9 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.

Join Analysis of Complex Survey Data w/ Brady West, May 8-10. 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.

Get started w/ Intensive Longitudinal Methods! Watch the 1st hr of Donald Hedeker’s course, then join us June 10-13 to analyze ecological momentary assessments (EMA) & daily diary data using mixed models, incl. advanced variance modeling techniques. bit.ly/ilm-sh

Join Stephen Vaisey on May 13-16 for Longitudinal Data Analysis Using Stata. Learn how to analyze #paneldata using mixed models, two period difference-in-differences, fixed-effects models, between-within models, and dynamic panel models.

In his latest SH blog post, The Statistical Foundations of Machine Learning, Bruce Desmarais shows how #machinelearning is a modern extension of #statistics, rooted in regression & probabilistic thinking & how they are more connected than you might think. bit.ly/ml5-sh

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 with Kristopher J. Preacher on May 14-16 & 19-21.

Join Causal Mediation Analysis with Linda Valeri on May 19-22. Learn how to implement and interpret state-of-the-art #causalinference techniques, validate standard approaches, and apply regression methods for mediation with various outcomes.

Join us May 28 for “Causal Inference for Multilevel Data” w/ Distinguished Speaker, Stephen Raudenbush. Learn how causal effects emerge in multilevel contexts & to estimate causal effects in randomized trials, observational studies, & natural experiments. bit.ly/dss-sr-sh

Transition from using #Stata to using #Rstats independently in only 8 hours (over 2 days)! Join R for Stata Users with Andrew Miles on May 29-30. Discover how R "thinks" and explore the differences in terminology, code syntax, data management, and default procedures.

Watch the first hour of Meta-Analysis with David Wilson, now on YouTube. Join the seminar June 24–27 to learn how to synthesize research findings, compute effect sizes, and explore heterogeneity across studies. youtu.be/YsNhBhtDTiA

Looking for a thorough intro to DAGs? Join Directed Acyclic Graphs for Causal Inference w/ Felix Elwert on June 3-6 to learn the essential elements for #causal reasoning with DAGs. We'll also use DAGs to discuss many important challenges in observational data analysis.

Join Bruce Desmarais for Machine Learning June 3-6 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.

Now on YouTube: The 1st hr of "SMART Designs for Developing Adaptive Interventions" w/ Nicholas Seewald. The July 8–11 course covers how to build, analyze, & apply sequential, multiple-assignment, randomized trials (SMARTs) to create dynamic interventions. bit.ly/smt-yt-sh

Join Item Response Theory (IRT) with Matthew Diemer on June 3-6 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.

Learn to analyze intensive #longitudinaldata by way of mixed models in Intensive Longitudinal Methods. Taught by Donald Hedeker on June 10-13, we'll cover the basic 2- and 3-level model, plus more extended uses of these models.

New to Python? "Introduction to Python for Data Analysis" with Edwin Dalmaijer (July 15-18) covers coding fundamentals, data handling, and essential #Python packages. Preview the 1st hour on YouTube: youtu.be/b_1mLKVV5xU