This workshop will demystify ANOVAs by framing them in the context of linear models with multiple predictors (i.e., multiple linear regression). The session will also introduce attendees to Directed Acyclical Graphs (DAGs) and demonstrate how to use them to infer causality in one’s model.

By the end of this session participants should be able to fit linear models with more than one predictor, check for collinearity between predictors, and interpret linear models using DAGs.

Embarking on the journey of writing your thesis or dissertation? This online workshop offers valuable tools and techniques to support your thesis writing process.

This session will introduce participants to the concept of P values and their role in hypothesis testing, highlighting that P values reflect the probability of observing the data under the null hypothesis, not the biological significance of the findings. The session will cover the computation of P values and delve into the nuances of one-sample t-tests.

By the end of the session, participants should be able to comprehend the meaning of P values, understand how hypothesis tests calculate P values, recognize when small P values indicate unlikely events under the null hypothesis, and explore the assumptions behind one-sample t-tests.

This workshop will introduce interaction terms in linear models along with random and fixed effects, including random and fixed intercepts and slopes, in the context of Hierarchical Linear Models (also known as Linear Mixed Models).

By the end of this session, participants should be able to fit (Hierarchical) Linear Models (HLMs) with interaction terms and interpret the output of the summary() function for Hierarchical Linear Models. Additionally, participants will be able to identify the limitations of (H)LMs.

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Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!