Course content
This course is the first in our multi-course research methods sequence for undergraduate students. The aim of the course is to introduce students to research design and methods for analyzing and visualizing quantitative data.
Included in the course is the 1st year project. Drawing on learnings from the course, students will be asked to formulate a research question, operationalize theoretical concepts from the 1st year Bsc Soc syllabus, select appropriate data, apply relevant quantitative methods, and reflect critically on their findings.
Readings and practical exercises will introduce students to the research process and quantitative data analysis. The first part of the course focuses on developing a research question and an appropriate quantitative research design to answer the question. Students are also introduced to the basics of the R statistical language and how to use R to collect, clean, reshape, and aggregate data, and describe relationships between variables.
In the second part of the course, students will (1) obtain an understanding of basic statistical methods, (2) learn how to use quantitative research designs to evaluate economic and social processes in organizations and society, and (3) be enabled to apply quantitative research methods for their own research projects.
The topics that we will cover in this course include selecting research questions and appropriate quantitative research designs, the data-generating process, and its implications for analyses and answering the research question. Moreover, the course will cover the operationalization of concepts, measurement, sampling and probability distributions, descriptive statistics, measures of central tendency, uncertainty, hypothesis testing and inference, bivariate, and multivariate linear regression analysis, how to differentiate correlation from causation, and sampling bias.
The approach throughout the course is hands-on and data-driven. Students learn how to analyze data using practical exercises with real-world data in R, the statistical programming language that will be used for exercises and assignments. Finally, the course will guide students on how to document the research process and report results from quantitative analysis in an accessible and transparent manner.
See course description in course catalogue