Are you a graduate student who is working on your thesis or dissertation? 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!

This session will address the advantages of box plots over bar charts for displaying the spread and variability in data. Participants will learn how box plots can be used to compare multiple samples, the impact of sample size on data representation, and the efficient identification of outliers.

By the end of the session, participants should be able to create and interpret box plots, appreciate their usefulness in comparing multiple samples, understand the implications of sample size, and identify outliers and median confidence intervals through notches in box plots.

This workshop will introduce Generalized Linear Models (GLMs), which allow one to model non-Gaussian (i.e., non-normal) data.

By the end of this session, participants will be familiar with the three parts of GLMs (family of distribution, linear predictor, and link function) and will be able to decide what family of distributions and link function to choose for their data. They will also be able to interpret the output of the summary() function and diagnostic plots for (H)GLMs and recognize the limitations of (H)GLMs.

Are you planning to publish your research in an open access journal or book? Do you want to avoid falling prey to predatory publishers who charge exorbitant fees, provide poor quality control, and damage your reputation? If so, this workshop is for you!

This session will introduce participants to the various types of t-tests, including one-sample, two-sample, paired, and one-sided tests. Attendees will learn about the appropriate applications for each type and the visualization techniques that can enhance the interpretation of t-test results.

By the end of the session, participants should be able to apply and visually represent different t-tests, interpret their results, understand the implications of multiple testing corrections, and select the appropriate test for their data.

Ever found it challenging to answer the critical “So what” question in your research? Struggle no more!

If you're interested in community-based and health-related programs, join us for an engaging and informative workshop on program evaluation.

If you're interested in community-based and health-related programs, join us for an engaging and informative workshop on program evaluation.

This workshop will introduce Generalized Additive Models (GAMs), which allow one to fit models that are complex and nonlinear but easily interpretable, unlike many “black-box” machine learning models.

By the end of this session, participants will be able to fit GAMs in R using the mgcv package and understand the advantages of GAMs over GLMs and LMs.

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests. Attendees will learn to compare the adaptability of these tests with different data distributions and to visualize their operation.

By the end of the session, participants should be able to choose and apply the appropriate non-parametric tests for their data, visualize the operation of these tests, and understand the challenges of multiple testing with non-parametric methods.

Unlock the secrets of crafting impactful literature reviews in this interactive online workshop for graduate students. Discover how to synthesize existing research succinctly while maintaining your own critical perspective and voice.

Join our online workshop for a comprehensive overview of research proposal writing, applicable across various disciplines.

This workshop will show how to interpret GAMs and how to use GAMs to make publication-level figures.

By the end of this session, participants should be able to interpret GAMs and the output of the summary() function, predict from GAMs, and make figures using GAMs.

This session will introduce participants to the Analysis of Variance (ANOVA), a statistical method used for comparing the means of three or more groups. The concept of blocking will also be introduced to reduce noise and isolate sources of variation.

By the end of the session, participants should be able to use ANOVA for multi-treatment analysis, implement blocking in experimental design, calculate the F statistic for assessing significance, and appreciate how blocking can improve the efficiency of a study.