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PIDLit Public Interest Data Literacy Initiatives

Lectures & In-Class Exercises ~ QUANTITATIVE Methods & Data

Developing Quantitative Data Analysis Skills to Assess Food Insecurity Data

Lectures + Applied Data Analysis Examples by Dr. Halley E.M. Riley

To prepare students to develop and answer research questions in the social sciences, Dr. Riley developed this lecture series on teaching best practices for conducting descriptive and inferential statistics using SPSS. Lectures cover: development of research questions, construct operationalization, data cleaning, and univariate and bivariate data analysis. Feeding America’s Map the Meal Gap data was used for the applied data analysis examples in the “Descriptive Statistics using SPSS” presentation; these data can be requested from Feeding America. Other data examples are from survey data collected specifically for the PIDLit Learning Lab. Screenshots (primarily from SPSS; some from SAS) and/or screen recordings are embedded in lecture slides.

“Developing Quantitative Data Analysis Skills to Assess Food Insecurity Data” © 2024 by Dr. Halley E.M. Riley (hriley@gsu.edu) is licensed under CC BY-NC-SA 4.0

Lectures & In-Class Exercises ~ QUALITATIVE Methods & Data

PIDLit Goes ‘Qual’ In! An Overview of Qualitative Research Methods

Mini-Methods Talk + In-Class Exercise by Dr. Mandy Swygart-Hobaugh

To prepare students to analyze Wholesome Wave Georgia’s Food For Health program evaluation data (focus group interviews) and the open-ended questions data from our two food insecurity surveys, the PIDLit course instructors included a curricular unit (one class session = 2.5 hours) devoted to introducing qualitative research and analytical coding, including this in-class exercise on coding an interview transcript.

"PIDLit Goes ‘Qual’ In! An Overview of Qualitative Research Methods" © 2024 by Dr. Mandy Swygart-Hobaugh (aswygarthobaugh@gsu.edu) is licensed under CC BY-NC-SA 4.0

INTERVIEW TRANSCRIPT - one interview but split into 4 Word files with a large right margin for hand-coding space. NOTE: The interviewee gave permission to share the transcript for teaching/learning purposes – you may reuse it for teaching/learning purposes only.

Lectures & In-Class Exercises ~ Research & Data Ethics

Research & Data Ethics

Lectures + In-Class Exercise by Dr. Halley E.M. Riley

To prepare students to conduct ethical research on food insecurity, two lectures on research and data ethics and in-class activity were developed. The lecture included two case studies: (1) the Tuskegee Syphilis Study, and (2) Wakefield et al.’s research connecting the MMR vaccine to Autism Spectrum Disorder.

The in-class exercise asked students to consider potential research scenarios and identify ethical concerns.

“Research & Data Ethics”  © 2024 by Dr. Halley E.M. Riley (hriley@gsu.edu) is licensed under CC BY-NC-SA 4.0

Out-of-Class Assignments

Data Dive Assignment - Using NVivo for Coding Qualitative Data

by Dr. Mandy Swygart-Hobaugh

To prepare students to analyze qualitative data for their final research projects, we included the following: (1) A curricular unit (one class session = 2.5 hours) devoted to introducing qualitative research and analytical coding, including an in-class exercise on coding an interview transcript; (2) Students watched two recorded NVivo trainings (GSU Library’s Research Data Services (RDS) NVivo 1 and NVivo 2 workshop recordings) and earned the NVivo Ready! Badge. (3) Students then completed this “data dive” assignment outside of class to apply the skills they learned by doing some initial exploratory analysis on the Wholesome Wave Georgia’s Food For Health program evaluation data (transcripts from two focus group interviews).

“Data Dive Assignment – Using NVivo for Coding Qualitative Data” © 2024 by Dr. Mandy Swygart-Hobaugh (aswygarthobaugh@gsu.edu) is licensed under CC BY-NC-SA 4.0