590DA
From GSLISWiki
[edit] LIS590-DA Data Analysis for LIS Research
- Previously Taught
- Spring 2002
- Spring 2000
- Instructor
- David Dubin
- Has Taken
[edit] Course Description
This class is a survey of data analysis issues, tools, and techniques for research in Library and Information Science. Students will locate and work with a data set of their choice, review the literature of recommended analysis methods, and prepare an analysis appropriate to the data set they have chosen.
- Objectives
- Survey techniques for data collection, elicitation, analysis, and visualization.
- Review assumptions underlying inferential analysis methods.
- Develop research strategies for honest and skeptical data analysis.
Prerequisites: This class works best as a complement to a traditional statistics class. We will focus on issues that your statistics professor won't have time to cover: topics that build the bridge between a research problem you have chosen to investigate and the justification for particular quantitative methods. It would help if you took statistics first.
In earlier semesters I built the reading list from a number of Sage monographs and a packet of readings. The next time this class is offered, it's likely I will make the following changes:
- Adopt one main text instead of a bunch of small ones. Currently the most promising candidate is Maindonald and Braun's Data Analysis and Graphics Using R (Cambridge Univ. Press, 2003).
- Keep at least one reading from earlier offerings, because it's indispensable: Michel, J. (1986). Measurement scales and statistics: A clash of paradigms. Psychological Bulletin, 100(3):398-407.
- Make case studies the focus of class discussions. But instead of trying to hold up exemplary research designs for bland edification, we will instead make a detailed and thoughtful study of projects where the relationship between the quantitative procedure and the research question is intriguing or problematic for one reason or another.
The main written assignments, though, will still be focused on a particular data set that the student picks out for himself or herself (as in previous semesters).
