Sociology Summer Research Institute

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.

Modules

Module 1: Qualitative Data Analysis, May 20-22, Professor Erin Hatton

This course will familiarize students with qualitative data analysis, focusing on in-depth interviews and content analysis. Students will learn how to develop thematic codes, apply them to qualitative data, and analytically interpret the results. The course will conclude with discussion about writing and presenting the results of such analyses.

Module 2: Spatial/GIS Analysis, May 20-22, Professor Kevin Smiley  

This course will introduce students to the utility of spatial data and Geographic Information Systems (GIS) with a focus on teaching applied spatial analysis skills. Students will learn about mapping software including ArcGIS, creating maps for presentations, transforming mapping data, and spatial statistical tools.

Module 3: Applied Survey Research Design, May 28-30, Professor Jessica Su

This course will equip students with survey research skills such as designing a survey, collecting meaningful data, and analyzing the results. Specific topics will include questionnaire design, sampling techniques, survey implementation using Google Forms, and basic analysis of survey data using Microsoft Excel. 

Module 4: Building a Better Regression Model, June 3-5, Professor Christopher Dennison,

This course will begin with an overview of regression models, focusing on coefficients in multiple linear regression. Students will then learn several analytic techniques to further develop their skills. Specific topics will include testing coefficient differences across models; interaction terms; nonlinearity; and nonlinear interactions. The course will conclude with discussion about regression models for count and categorical outcomes.

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