IS4720 Quantitative Methods for Research

This course equips Information Science doctoral students with the quantitative methods necessary to support dissertation research, using real-world project data and case studies. Topics include: defining research objectives, formulating and testing hypotheses, designing experiments, developing analytic and simulation models, collecting data, analyzing data, validating models, using quantitative software tools, and presenting results in written and oral reports. Prerequisites: None.

Lecture Hours

3

Lab Hours

2

Course Learning Outcomes

In this course, we will cover the topic of quantitative research methods via detailed concepts from applied statistics, econometrics, quantitative research methods, decision sciences, and integrated risk management techniques. The course will include quantitative research methods in descriptive statistics, hypothesis testing, single- and multiple-variable parametric tests, nonparametric tests, multivariate regression, Monte Carlo risk simulation, stochastic forecasting, predictive modeling, and optimization. This course will be rigorous, with hands-on modeling practice and exercises. Throughout the course, you will also review several past Ph.D. dissertations and generic case studies that show how these advanced analytics are applied in research.

The IS-4720 Quantitative Research Method’s course goal is to provide the student with the tools and methodologies to help perform quantitative graduate-level research culminating in a doctoral dissertation. In this course, students will learn the fundamentals of analytical methods required for graduate research.

The course has the following Learning Outcomes:

• Understand the basics of descriptive and inferential statistics on single and multiple variables (the when and how to apply them).
• Understand types of research and data structures, parametric versus nonparametric methods, and how to design experiments and data collection methods.
• Understand proper relationship modeling approaches and predictive forecast methods.
• Understand data simulation and optimization techniques.