Quantitative Research Methods I

Catalog description

Quantitative research methods fall into two categories: exploration and testing. This first course in quantitative research methods focuses on the tools, techniques, and approaches of exploratory data analysis for the purpose of discovering patterns and conjecturing new explanations. Students will learn the fundamentals of data collection, cleaning, and management, visualization, and exploration. Topics include sampling considerations, missing data, data integrity, normalization, correlation analysis, principle component analysis, clustering techniques, standard charts and graphs, advanced visualization techniques to display as much of the data as possible, and data interpretation. Students will have guided reading of both classic and state-of-the-art articles. Cases will be used to illustrate these techniques and datasets will be used by students to in their hands-on projects to learn these techniques including: normality violations, multi-collinearity, and missing data.