Hello! In this repository you will find an HTML file and word document containing the R code and output for our manuscript (currently under review). I hope that by sharing this work, I can help clarify questions readers may have about the statistical methods utilized in our study. Additionally, I hope that sharing this code will make it useful to others who may be conducting similar analyses.

To view the code and output, please download the HTML file linked below and open it in a web browser (e.g., Chrome, Firefox, Safari):

• 📄 HTML

If Microsoft Word is preferred, please download the DOCX file linked below:

• 📎 MS Word

Juan Carlos Gonzalez, Ph.D. ¹, Daniel K. Feinberg, M.Ed.², Regan W. Stewart, Ph.D.³, John Young, Ph.D.⁴, Rosaura Orengo-Aguayo, Ph.D.³

1. University of California, San Francisco, Department of Psychiatry and Behavioral Sciences, San Francisco, CA
2. University of California, Santa Barbara, Department of Counseling, Clinical, and School Psychology, Santa Barbara, CA
3. Medical University of South Carolina, Department of Psychiatry and Behavioral Sciences, Charleston, SC
4. University of Mississippi, Department of Psychology, University, MS

Due to confidentiality requirements, I cannot share the dataset on which the present analyses were performed. However, please feel free to email me at [email protected] if you have any questions about my analyses and how they may apply to your work. Additionally, if you notice errors in my work, I would really appreciate your feedback.

Many thanks to the following people and organizations for their support and for allowing me the opportunity to learn from them as I worked on this project:

• John Young, Ph.D., and Andrew Maul, Ph.D., for kindly helping me with my many statistical questions.
• My co-authors on this project who have been so generous with their time and expertise.
• The Enhancing Diversity in Alcohol Research (EDAR) Program for the funding and support that made this project possible.
• The UCLA Advanced Research Computing Statistical Methods and Data Analytics Website for their excellent reference material on logit regression in R.

Of course, I must also acknowledge the debt of gratitude I owe to the many contributors who built and maintain the open source software that was vital to this project. Without their work, these analyses would not have been possible. Many thanks!