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DEPENd/DNPL MLM Workshop 2021:

The goal of this workshop is to provide an overview of multilevel modeling and to consider its application to behavioral data, particularly from cognitive and decision-making tasks. More information on accessing training information and materials can be found here:

Structural Equation Modeling (SEM):

Previously taught at Penn State University by Dr. Michael Hallquist, PSY 597 – Structural Equation Modeling is a graduate course intended to provide an applied introduction to structural equation modeling (SEM) in the social sciences. Lectures from the Spring 2019 session cover fundamental topics in measurement and structural models and aim to develop the knowledge to critique applications of SEM in the research literature. Dr. Hallquist’s SEM website can be accessed here:

R Bootcamp:

Previously organized by Dr. Hallquist and colleagues at Penn State, the R bootcamp provided an intensive immersion in fundamentals of R intended for incoming graduate students in the social and behavioral sciences. People interested in getting more experience in fundamentals of data science in R may find these materials helpful.


    • Slow R: basic introduction to R

    • Multilevel Models and Interactions

    • Structural equation modeling

    • Dynamical systems analysis using dynr

    • Data wrangling

    • Data visualization

    • R-eproducible Science

    • iPRACTISE data wrangling and visualization

    • Basic data analyses

    • Resources from previous years

      • Best practices in R programming

    • Correlation and regression in R

    • ANOVA and categorical data

    • Exploratory factor analysis

    • Parallel computing and big data


More information on the topics listed above can be found here:

How Good Programming Practices Support Scientific Reproducibility:

The goal here is to convey some of the data science lessons Dr. Hallquist has learned over the past 10 years and provide an approximate framework for solving data management and analysis problems that extend beyond the basics of the data wrangling and programming. Access this bootcamp here:

Learn Programming in R, Python, and More

Our lab uses a number of programming languages to process, manage, and analyze data. Training in these languages, including Python and R, is not a major focus in most undergraduate psychology programs. We are fortunate to have support from DataCamp to provide access to their helpful online courses via an Academic subscription. DataCamp is a great resource for learning programming languages and data science! Check it out.