Presenter(s) Information

Giovanna BadiaFollow

Loading...

Media is loading
 

Start Date

3-12-2024 2:30 PM

End Date

3-12-2024 3:00 PM

Author(s) Bio

Giovanna Badia is the Assessment & Data Librarian at McGill University Library. She currently teaches workshops on finding Canadian datasets, reading regression results, organizing and critiquing studies for a literature review, and using charts to communicate findings and increase understanding of collected data. She authored 5 journal articles for the “Teaching Practicalities” column of College & Undergraduate Libraries from 2016-2019. She frequently uses count regression models to analyze library usage data.

Keywords

Statistical literacy, teaching, regression

Description of Proposal

Descriptive and inferential statistics are taught to students in many disciplines. More classroom time is often spent on the theory behind different statistical methods that investigate relationships between variables rather than on how to interpret the results obtained to answer the research question that started the process. While statistical software (such as R, Stata, and SPSS) has made it easier to undertake regression with any dataset, the output produced remains challenging to understand and explain to intended audiences. To address this issue, the author created a 90-minute workshop that teaches students how to read tables of descriptive statistics and linear regression results produced by statistical software. It focuses on tips for identifying and understanding what is important in these tables based on their purposes. The workshop has been taught each semester at the author’s institution since its creation in the Fall 2022 term, attracting a predominantly graduate student audience. Feedback has been positive thus far, with student requests for additional workshops on reading the results of different statistical models, such as logistic and count regression.

Through an explanation of the steps followed and resources used to create the workshop, this presentation will provide a practical example of how librarians can teach others how to read descriptive statistics and regression results using a research question and their experiences working with data to guide them. Lessons learned from promoting and delivering the workshop will also be presented.

What takeaways will attendees learn from your session?

Attendees will obtain a practical road map for creating a statistical literacy workshop for graduate students.

Share

COinS
 
Mar 12th, 2:30 PM Mar 12th, 3:00 PM

Teaching students to read regression results: A statistical literacy lesson plan for librarians

Descriptive and inferential statistics are taught to students in many disciplines. More classroom time is often spent on the theory behind different statistical methods that investigate relationships between variables rather than on how to interpret the results obtained to answer the research question that started the process. While statistical software (such as R, Stata, and SPSS) has made it easier to undertake regression with any dataset, the output produced remains challenging to understand and explain to intended audiences. To address this issue, the author created a 90-minute workshop that teaches students how to read tables of descriptive statistics and linear regression results produced by statistical software. It focuses on tips for identifying and understanding what is important in these tables based on their purposes. The workshop has been taught each semester at the author’s institution since its creation in the Fall 2022 term, attracting a predominantly graduate student audience. Feedback has been positive thus far, with student requests for additional workshops on reading the results of different statistical models, such as logistic and count regression.

Through an explanation of the steps followed and resources used to create the workshop, this presentation will provide a practical example of how librarians can teach others how to read descriptive statistics and regression results using a research question and their experiences working with data to guide them. Lessons learned from promoting and delivering the workshop will also be presented.