Catalog Course Description:
Introduces research designs and methods of quantitative analysis for various problems in information systems, management of information resources, and scholarly and professional communication.  Presents statistical techniques through packaged computer programs.

Pre-requisites and Co-requisites:
Doctoral student status

Curriculum Role:
This course introduces students to analysis of quantitative data using multivariate techniques.  The course is required for all doctoral students.  Full-time doctoral students normally take the course in the third term of their doctoral program; part-time students typically take the course in the first or second year of their doctoral program.

Course Rationale:
This course is offered to give doctoral students a solid basis in quantitative methods, especially techniques of multivariate data analysis.  This course will prepare the students for understanding others’ academic articles and carrying out analyses in their own research.

Course Outcomes:
Upon successful completion of this course, a student will be able to:
• Understand measurement scales, and use appropriate and non parametric summary statistics
• Understand research design in terms of sampling, control and experimental groups, independent vs. dependent variables, causal models in experimental and non-experimental studies, factored designs, and significance levels
• Understand assumptions and appropriate use of multivariate analysis techniques, involving correlation, regression, principal component analysis, factor analysis, discriminant analysis, MANOVA, multidimensional scaling, and clustering
• Apply a standard computer statistical package to data in information studies

Course Content:
Principal topics and the approximate number of weeks devoted to each are:
• Research designs (2)
• Principal component analysis (1)
• Factor analysis (1)
• Cluster analysis (1)
• Discriminant analysis (1)
• Logistic regression (1)
• MANOVA (1)
• Structural models (1)
• Multidimensional scaling (1)

Presentation:
Note: Presentation method may vary somewhat from section to section.
Lectures, readings, and discussion of research applications. 

Assessment:
Note: Assessment method may vary somewhat from section to section.
Exercises in statistical analysis.  Written test (take home).