Courses
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RSCH 8140 – Multivariate Analytical Methods (College of Education, Spring 2019) This course examines statistical procedures that have multiple independent and/or dependent variables, all correlated with one another to some degree. Emphases are placed on practical issues such as selecting the appropriate statistical analyses, using SPSS and R to screen and analyze data, interpreting output, presenting results, and applying these analyses in research areas. Students are trained to be critical consumers and novice producers of multivariate research. |
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RSCH 8150 – Structural Equation Modeling (College of Education, Spring 2018) The course is designed to apply general statistical modeling techniques to establish relationships among variables. Topics include regression models, path analysis models, exploratory and confirmatory factor analyses, latent variables, basic steps in structural equation modeling, multiple indicators and multiple causes (MIMIC) model, multi-group model, multilevel model, mixture model, structured mean model, second order factor model, latent variable growth model, and dynamic factor model. |
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GEOG 3000 – Topics in Geography – Introduction to Data Analytics: Tools, Techniques and Thinking (College of Liberal Arts and Sciences, Spring 2014) This course will be a baby step into the world of data science. Data science is starting to gain momentum in academia and industry alike, given the advent of massive user-data accumulation driven by the internet and mobile applications. The course’s goal is to equip students with a versatile skillset to independently carry out data-intensive projects in various scientific and business domains.The course will cover foundational aspects of conducting quantitative research, essential analytical techniques and their conceptual base, and best practices of reporting and displaying analytics through visualizations. To support this learning, multiple desktop and online software applications will be introduced as part of this course. |
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