(Under development)


This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas. 

Academic Year: 


Course ID and Name: 

Section Number: 

Course Syllabus

Course Description: 

This seminar introduces fundamental methods for both qualitative and quantitative research in information studies. Additionally, the seminar introduces the student to established and emerging areas of scholarly research in Schools of Information to encourage her to identify a personal research agenda. The seminar is organized in two main parts: the first part introduces relevant research methods (quantitative and qualitative), whereas the second part overviews specific research directions currently active in the School of Information. The second part of the seminar will be covered by guest faculty who research in each of the covered areas.

1. Quantitative research methods (4 to 5 weeks)
a. Exploratory data analysis. How to visualize and summarize data to uncover its hidden structure. Elementary data mining techniques.
b. Experiment design. Why do experiments? Experimental control of programs; control of human/computer systems; using panels of human judges to evaluate system performance; blinding the panel to the source of answers. Spurious effects and biased sampling. Pilot experiments. Guidelines and tips.
c. Hypothesis testing and confidence intervals. The standard error of a statistic and sample size. Sampling distribution of the mean. Testing hypotheses about means. Confidence intervals. The difference between significance and meaning. Effect size. Should we care about significance tests when samples are enormous? Non-parametric vs. parametric hypothesis testing methods.
d. Performance Assessment. Common performance measures. “Gold standard” corpora. Testing on training sets. Cross-validation. Multiple testing problems. Variability, predictive power. So you got a significant result, but is it meaningful? Metrics such as predictive power that augment statistically significant differences in performance.
e. Generalization, the aim of science. The behavior of a system on a single dataset isn’t as interesting as what we can say about its behavior on a large class of datasets, or what we can say about equivalence classes of systems. Computer science has several strategies for moving from specific to general. One is to analytically classify problems by their complexity, but empirically there is much variance among problems in each analytical class. Another strategy is to develop corpora that are thought on empirical grounds to be representative of various classes of applied problems. The empirical generalization strategy and how it ties in with theoretical analysis.
2. Qualitative research methods (4 weeks)
a. Methodology
i. Phenomenology
ii. Ethnography
iii. Narrative
b. Methods
i. Interviews (to include the difference between qualitative interviewing and interviews for a quantitative study)
ii. Observations (empirical examination of people, texts, or processes)
iii. Fieldwork/fieldnotes
c. Analysis
i. Thematic
ii. Narrative
iii. Grounded
d. Tools for qualitative research: Qualtrix, LibQual, Atlas-TI, nvivo. 
3. Research dissemination (2 weeks) 
a. Writing papers, writing styles (deductive or inductive logic)
b. Reviewing papers
c. Responding to peer reviews
d. Academic presentations
4. Research ethics (1 week or less)
a. Protecting human research participants; Institutional Review Board (IRB)
5. Research topics in the School of Information Survey of active School Research (4 weeks)
a. Quantitative research in computational intelligence
b. Quantitative and qualitative research in digital arts
c. Quantitative and qualitative research in digital humanities
d. Philosophical analysis

Course Objective: 

Students will:
Become familiar with quantitative research methods, ranging from experiment design and data collection to building (simple) statistical models and hypothesis testing.
Become familiar with qualitative research, including focus groups, interviews (structured and unstructured), program evaluations, and a range of qualitative research methods, ranging from epistemology to ethnography.
Learn how to write good papers, review them, respond to reviews of own work, and give presentations at academic venues.
Be exposed to relevant research directions in the School of Information.
On guest speakers:
This seminar will take advantage of guest speakers from the School of Information as much as possible.  The goal is to have topics introduced to entering graduate students by experts on those topics.  In addition, this will provide you with an immediate introduction to the faculty of the School of Information and their research.
On diversity:
Like many courses in the School of Information, this seminar will incorporate diversity issues.  For instance, when seminar work, through projects or assignments, has a particular community as the subject of a research study, researchers will be careful to ensure that the results are made available to that community. 

Required Course Materials: 

This seminar does not follow any particular textbook. Specific readings from the research literature will be recommended by the instructor on each topic covered. 

Course Requirements: 

The set of four assignments will cover the following issues:
1. Recognizing good ideas:
a. Identify two papers that interest you from recent proceedings of a top conference. Write a synopsis of each paper together with a critical evaluation, including why you thought this work is interesting, what are its current limitations, how would you extend it, etc. Each document should be 2 to 3 pages double-spaced.
2. Generating ideas:
a. Select at least two papers from two top conferences in different areas, e.g., deception (qualitative) and natural language processing (quantitative). Propose a research direction that combines these works into something new. The resulting paper should be 3 to 5 pages double-spaced.
3. Criticizing ideas:
a. Review submissions of the second assignment. Write a critical review for two different submissions. Each review should be 4 – 500 words.
4. Communicating ideas:
a. Give an in-class presentation of your main project (see below). We will mock an actual conference presentation: each presentation should be approximately 15 minutes long with an additional 5 minutes for questions. 
Main project:
Choose and implement one research idea. The idea can be something that already interests you or something new. Try to choose an idea that spans multiple areas (credit will be given for cross-disciplinarity). Both qualitative and quantitative research directions are accepted. What is important is that you address all relevant aspects of the research cycle. For example, if your project work is quantitative, your project report should address: a) motivation (why is your work important?), b) experiment design (how did you get the data for your work?), c) modeling (what methods did you use to generalize from the data available?), and d) evaluation (how well does your model generalize?). The final project report should be 6 to 8 pages double-spaced.

Course Grading: 

Disputes about grades on a particular assignment will be entertained for two weeks from the day the assignment is graded, or 1 day after the end of classes, whichever is sooner. These will be resolved by re-grading the entire work. Note that this can result in a lower grade in the event that new mistakes are discovered.
No negotiations about individual students’ letter grades will be entertained once final grades are assigned, except as permitted by the policy stated above.
Collaboration Policy
Students are encouraged to work together, both in class / office hours and otherwise, to understand problems and general approaches for solutions. However, all assignments must be completed individually. Copying another person’s work (even if it comes from a website) is not permitted and will be treated as a case of academic dishonesty.
Late Policy
Projects are due electronically by the stated deadline. Permission for an extension must be granted by the instructor in advance of the deadline in order to receive full credit for a late submission. The first request by a given student is likely to be granted; the probability decreases with each subsequent request. 

Course Policies: 

Accessibility and Accommodations
It is the University’s goal that learning experiences be as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please let me know immediately so that we can discuss options. You are also welcome to contact Disability Resources (520-621-3268) to establish reasonable accommodations.
Academic Integrity
Students are expected to abide by The University of Arizona Code of Academic Integrity, see The guiding principle of academic integrity is that a student's submitted work must be the student's own.' If you have any questions regarding what is acceptable practice under this Code, please ask an Instructor.
Syllabus and Schedule
The information contained in the course syllabus, other than the grade and absence policies, may be subject to change with reasonable advance notice, as deemed appropriate by the instructor.
Incompletes are not generally approved except under exceptional circumstances in advance consultation with the instructor. The grade of I may be awarded only at the end of a term, when all but a minor portion of the course work has been satisfactorily completed. The grade of I is not to be awarded in place of a failing grade or when the student is expected to repeat the course; in such a case, a grade other than I must be assigned. Students should make arrangements with the instructor to receive an incomplete grade before the end of the term. If the incomplete is not removed by the instructor within one year the “I” grade will revert to a failing grade. See


College of Social and Behavioral Sciences