Introductory Methods (SOAN*2120)
Code and section: SOAN*2120*01 and 02
Term: Fall 2025
Instructor: David Walters
Details
SOAN 2120: Introductory Research Methods
Department of Sociology and Anthropology
College of Social and Applied Human Sciences
University of Guelph
Fall/Winter 2025/2026
Instructor: David Walters
Email: dwalters@uoguelph.ca
Web Page: www.started.uoguelph.ca
Office: MacKinnon (MAC) 614
Office Hours: TBD
Time and Location (Blended): TBD
Course Prerequisites: ANTH 1150, SOC 1100, IDEV 1000 or SOC 1500 or permission by the instructor
There is a quantitative component to this course; however, this class is designed for students without a background in mathematics, statistics, or computer programming. The software used in this class is user-friendly, or will be made user-friendly through artificial intelligence (AI).
Learning Outcomes
By the end of this course, students should be able to:
Analyze and evaluate qualitative and quantitative research in sociology, anthropology, criminal justice and public policy, and other related fields of study.
Apply appropriate research methodologies to address contemporary, historical, social and global issues.
Develop and practice intellectual curiosity, data (qualitative and quantitative) analytic, problem-solving, critical thinking, decision-making and listening skills.
More specifically, students will learn how to solve problems and address social challenges across a variety fields and industries, within and outside academia. A key objective of this course is to help our students be work-ready when they complete their degree programs. This course will improve career-readiness through promoting skill development and skills awareness. Students will be taught how to communicate complex analyses in easy-to-understand formats and learn how to showcase their knowledge and skills to potential employers. The data analytic component is intended to provide students with opportunities that would otherwise be beyond their reach.
Calendar Description: A general introduction to the process of social research emphasizing research design, techniques of data collection, analysis and interpretation of research results.
Course Content
This course provides an overview of research methodology and the process of conducting social research in academic and non-academic settings. Students will learn skills necessary to become a competent researcher when working with qualitative, experiment and survey data. They will be exposed to a variety of topics relating to research methods including qualitative interviews, field research (also known as participant observation), ethical issues in social research, experimental designs, survey research, qualitative and quantitative sampling, measures of central tendency and dispersion, hypothesis testing, and inferential statistics. They will also be exposed to perspectives on Indigenous research and methodologies in relation to learning and knowledge creation in Canadian higher education institutions.
Through the use innovative and cutting-edge software students will learn how to analyze complex datasets to make a meaningful impact on the social world. They will be responsible for creating a research report using real data that contains information on a variety of social topics (e.g., crime, health, equity diversity, & inclusion). This project will provide them with an opportunity to demonstrate their writing and research related skills to create a journal article/policy report with practical real-life implications. Students will also gain experience using a variety of computer software packages for qualitative (NVivo) and quantitative (SPSS & R) data analyses as well as data visualization (Tableau). With respect to quantitative methods, students will be introduced to common statistical procedures and receive an introduction on how to use artificial intelligence (AI) and machine learning to assist them in data collection and statistical programming. As recent developments in computer software and AI have made advanced statistical analyses much more assessable to social science students, a strong background in mathematics or computer programming is not required for this course.