%texentities; ]]> ]]> ]> Data Analysis for LIS Research LIS 450 Graduate School of Library and Information Science Spring 2000
Section DA Thursday, 12–2:50 PM Room 111, Speech and Hearing Building
David Dubin LIS 222 Tuesdays, 1–3 PM 217–244–3275 (217–BIG–EARL) dubin@alexia.lis.uiuc.edu http://www.lis.uiuc.edu/˜dubin

This document is Copyright © 2000 by David Dubin and the Trustees of the University of Illinois. In addition to this syllabus, this course is governed by the rules and guidelines set forth in the document A Handbook for Graduate Students and Advisers which students receive upon admission to the program. Students should also consult, and take to heart, the Professional Guidelines and Codes of Ethics for Library and Information Science Professionals available from the GSLIS main office.

This syllabus is provided to UIUC students as part of the materials for a particular class. However, it may be copied, redistributed, and modified under the terms of the OpenContent License (Version 1.0). The text of that license is available on the Worldwide Web at www.opencontent.org. Resources that are linked to or referenced from within this syllabus (e.g., readings, outlines, discussions) are not covered by the OpenContent License, unless specifically labeled as such.

 Hartwig, F. and Dearing, B. E. Exploratory Data Analysis Sage Publications 1979 Jacoby, W. G. Data Theory and Dimensional Analysis Sage Publications 1991 Knoke, D. and Kuklinski, J. H. Network Analysis Sage Publications 1982 Dubin, D. (instructor) Reading Packet for LIS450-DA Campus Publishing Services 2000 Scope and Objectives

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. This Syllabus

The official syllabus for this course is the SGML version that is linked off the class web page. Expressions of the syllabus in other formats are derived from the SGML version. The current SGML version should be consulted to resolve any inconsistencies among other renditions.

 Basis for Evaluation

Students are responsible for their performance in meeting their own educational goals and those of the course; instructors are responsible for providing guidance, expertise, and support to help students reach those goals. Students are expected to participate in class exercises and discussions. Satisfactory work will receive a grade in the C range, good work will receive a grade in the B range, and superior work will receive a grade in the A range.

Final grades will be calculated as follows: Annotated Bibliography 30% Class Presentation 20% Term Project: 40% Class Participation: 10% Annotated Bibliography

Write a one-page overview of the data set you have chosen for the focus of assignments in this class. Include a description of the data's origin, and the type of data it represents (e.g., survey data, word frequency data, etc.). Outline the types of inferences a researcher might wish to draw from the data, and the inferential tools or methods that one would apply.

Review the literature of recommendations for analysis of the type of data you have selected. Include tests on the assumptions underlying the inferential methods described in the overview. Include whatever recommendations are relevant to approaching your data with honesty and skepticism.

The overview and annotated bibliography are to be prepared in a structured, plain text format that will be specified by the instructor. This assignment will be submitted as a machine-readable file. Class Presentation

Schedule a class presentation relating to the data set you have chosen and its analysis. Presentations will take place during one of the final three class meetings prior to the course wrap-up and evaluation. Assign one or two readings from your annotated bibliography to the class no later than three weeks before your presentation. Place a copy of the readings on reserve no later than two weeks before your presentation. Term Project

Analyze your data set according to the recommendations of the literature that you have reviewed. Prepare a research paper (approximately 20 pages in paper form) that reports the results of your analysis. Include whatever graphs or other visualizations of the data are illustrative of your findings. Document the paper with appropriate references.

Renditions of the term project can take whatever form (paper or electronic) are most suitable for conveying the results of the analysis. However, the format in which the project is authored must be expressive enough for academic writing. Class Participation

The class participation grade is based on consistent attendance, contribution to in-class and/or online discussions, and providing assistance to classmates outside of class. Please alert the instructor if a classmate has been of help to you outside of class.

Semester Outline Introduction January 20 Overview of the class Syllabus Exploratory Data Analysis 1 January 27 Displays of distributions, skewness, outliers. Hartwig and Dearing ch. 1–2; McNeil ch. 1–2 Exploratory Data Analysis 2 February 3 Relations and transformations. Hartwig and Dearing, ch. 3–4, McNeil ch. 3 Exploratory Data Analysis 3 February 10 Normality, transformations, multiple comparisons Lunn and McNeil, ch. 2, 3, and 6; Hartwig and Dearing ch. 5–6 Measurement Theory February 17 Scales, invariance, appropriate statistics, order, additive structure. Stevens, 1959; Michell, 1986 Elicitation and Pre-Analysis February 24 Knowledge elicitation tools, pre-analysis, imputation Dubin, Kwasnik, and Tangmanee, 1996; Banks and Parmigiani, 1992; Levy and Lemeshow ch. 13 Dimensional Analysis 1 March 2 data theory, measurement Jacoby, ch. 1–3 Dimensional Analysis 2 March 9 dimensionality, scaling methods Jacoby ch. 4–7 Spring Break March 16 Network Analysis March 23 Network models: data collection and analysis Knoke and Kuklinski Scaling and Clustering March 30 scaling vs. clustering Kruskal, 1977 Visualization April 6 Projection pursuit, VIRIs, dotplot analysis Swayne, Cook, and Buja, 1998; Church and Helfman, 1993; Korfhage, ch. 7 Presentations April 13 Student-assigned readings Presentations April 20 Student-assigned readings Presentations April 27 Student-assigned readings Wrap-up and Evaluation May 4 Final Projects Due May 11 at 5 PM. Reading Assignments