Quantitative Research Methods
In this course, students will harness statistical analysis to tackle real-world questions. It is designed for undergrads and grads with introductory statistical knowledge. The curriculum covers techniques such as correlation, ANOVA, and regression but emphasizes the derivation of meaning for applied audiences using cross-sectional, nested, and time-series data. The hands-on experience extends to data cleaning, analysis of missing data, variable transformation, and other data management tasks facilitated by statistical software. Initially, students work on provided data. Later, they collaborate in teams finding data to complete a significant project suitable for publication. This practical approach equips students with the skills to analyze and interpret complex data, contributing to informed decision-making in social sciences.
Topics
- Describe how and why statistics are used in social policy research.
- Illustrate the relationships between research questions, theory, data, and methods including: Select and carry out methods based on research questions, theory and available data.
- Evaluate and describe the strengths and weaknesses of chosen methods.
- Find multiple sources of data and manage selected data including data cleaning, manipulation, and merging multiple datasets.
- Employ a full range of statistical techniques including: Univariate and descriptive statistics including distribution, spread, and skewness; Bivariate analysis including t-tests, scatterplots, crosstabs, and correlations; Multivariate analysis including ANOVA, OLS regression, and logistic regression with exposure to introductory spatial analyses, data visualization, time series regression, and multilevel regression.
- Employ issues of reliability, precision, and competently discuss difference between causation and correlation.
Official roster learning outcomes
Prerequisites & course information
background equivalent to: ILRST 2100 or STSCI 2100 or PUBPOL 2100 or MATH 4720.
Sources
- Course catalogue ↗2026-2027