Summer Research Institute 2021

A student in a room with computers.

The UB Summer Research Institute trains researchers in a collegial, intellectually engaging and multidisciplinary atmosphere. Workshop participants learn easy to understand practical information about statistics and methods, developing skills and knowledge to confidently analyze their own data. Our courses are appropriate for a range of researchers, including graduate students from all disciplines, private and public sector researchers and policymakers, and college and university faculty. Anyone with research experience/training who is interested in gaining new skills, or brushing up on the latest analytic techniques, is welcome.

Sign up for the mailing list today! 

Important notifications and detailed information will be provided about registering, course information, and how the SRI synchronous online courses will be conducted. In the meantime, you can review the current classes and be sure to hold these dates and times in your calendar.

Online Modules for 2021

Prof. Presad Balkundi.

Session 1: Social Network Analysis, May 17-18
9am-12pm and 12:30-5pm (Eastern)

Professor Prasad Balkundi

Social networking is now a constant in our everyday lives and how it affects businesses and research has major implications. You will learn theoretical and methodological skills to independently conduct studies using social network analysis. Learn about network theories, their corresponding measures, and how to obtain data for network analysis. The training you will receive in social network analysis software (UCINET; NETDRAW) will enable you to learn analytic, empirical, and presentation options.

Prof. Erin Hatton.

Session 2: Qualitative Data Analysis, May 19-21
10am-12pm and 1-4pm (Eastern)

Professor Erin Hatton

Take a deep dive into familiarizing yourself with qualitative data analysis, focusing on in-depth interviews and content analysis. Learn how to develop thematic codes, apply them to qualitative data, and analytically interpret the results. You will conclude with an in-depth review about writing and presenting the results of such analyses.

Session 2 has been cancelled, but please sign up to get on the mailing list to be notified for future opportunities.

Prof. Craig Colder.

Session 3: Introduction to Structural Equation Modeling, May 24-26
9am-12pm and 1-3pm (Eastern)

Professor Craig Colder

You will be introduced to structural equation modeling (SEM) both with and without latent variables.  It is designed to provide the necessary skills to use SEM to analyze social science data with continuous variables.  We will primarily focus on the application of SEM as opposed to mathematical underpinnings.  We will cover introductory topics including path analysis, moderation and mediation, confirmatory factor analysis, and hybrid structural equation models (models that include causal paths between latent variables).  Special topics will include working with non-normally distributed variables and missing data, and power.  Upon finishing the course you will be able to estimate models using Mplus software to conduct research and interpret results.  This course will also prepare you for more advanced topics, including latent growth curve analysis, latent variable interactions, and latent mixture modeling.

Prof. Christopher Dennison.

Session 4: Building a Better Regression Model, May 27-28
8am-12pm and 1-4:30pm (Eastern)

Professor Christopher Dennison

Begin with an overview of regression models, focusing primarily on the interpretation of coefficients in multiple linear regression and then learn several analytic techniques to develop your skills. Specific topics will include testing coefficient differences within/across models, interaction terms, nonlinearity, and nonlinear interactions. You will finalize your training experience with an overview of dealing with missing data as well as regression models for count and categorical outcomes.

 

Session 4 has been cancelled, but please sign up to get on the mailing list to be notified for future opportunities.