Previous Courses Offered
Fall Semester 2007
05-816: Applied Research Methods
Sara Kiesler
Carnegie Mellon University
Aug 27 - Dec 10
Mondays 2:30 - 5:00
Location: Room 201 at 300 S. Craig Street
Office: NSH3513
Email: kiesler@cs.cmu.edu
Course Description
The purpose of this course is to introduce hierarchical versions of linear
and logistic regression models. Hierarchical models are used when the units
of observation are grouped within clusters. In such clustered data observations
for the same cluster cannot be assumed to be mutually independent for given
covariate values as required by conventional linear and logistic regression.
Longitudinal or repeated measures data can also be thought of as clustered
data with measurement occasions clustered within subjects. This course will
focus on understanding the hierarchical (generalized) linear models and their
assumptions, as well as practical aspects of developing modes to address research
questions and interpreting the findings.
This course does not cover HCI professional methods such as usability testing
or design methods that are already covered in the Introduction to HCI Methods
and in various design courses. This course also does not cover advanced statistical
and computational analyses. The statistics covered will allow students to discriminate
among data types, to organize and prepare data for statistical analysis, to
assess data properties and correct for anomalies, to select appropriate analytical
techniques, to apply them using the JMP statistical package, and to choose
appropriate statistics for reporting and publication.
Each week, we discuss one important method. You’ll read at least one
article to prepare for the class session, including examples of how the method
has been used well. In the class itself, there will be reviews of readings,
lectures, guest speakers, demonstrations, and discussions and critiqued presentations
by students. Please bring a laptop to every class except when excused.
This is a twelve-hour credit course (12 hours of work are expected outside
of class).
PSYED 3486: Hierarchical Linear Modeling
Feifei Ye
School of Education
University of Pittsburgh
Thursdays 1:00 - 3:40
Location: TBA
Office: 5924 WWPH
Phone: 412-624-7233
Email: feifeiye@pitt.edu
Syllabus: PSYED_3486.pdf
Course Description
The purpose of this course is to introduce hierarchical versions of linear
and logistic regression models. Hierarchical models are used when the units
of observation are grouped within clusters. In such clustered data observations
for the same cluster cannot be assumed to be mutually independent for given
covariate values as required by conventional linear and logistic regression.
Longitudinal or repeated measures data can also be thought of as clustered
data with measurement occasions clustered within subjects. This course will
focus on understanding the hierarchical (generalized) linear models and their
assumptions, as well as practical aspects of developing modes to address research
questions and interpreting the findings.
Pre-requisites
PSYED 3410: Regression or an equivalent statistics course covering multiple
linear regression and logistic regression. It is also assumed that you have
some working knowledge of SAS.
05-834/05-434: Applied Machine Learning
Carolyn Penstein Rose
Human Computer Interaction Institute
Carnegie Mellon University
Tuesdays/Thursdays
1:30 - 2:50
Newell Simon Hall 1305
412-268-7130
Course Description
Machine Learning is concerned with computer programs that enable the behavior
of a computer to be learned from examples or experience rather than dictated
through rules written by hand. It has practical value in many application areas
of computer science such as on-line communities and digital libraries. This
class is meant to teach the practical side of machine learning for applications,
such as mining newsgroup data or building adaptive user interfaces. The emphasis
will be on learning the process of applying machine learning effectively to
a variety of problems rather than emphasizing an understanding of the theory
behind what makes machine learning work, although theory will be covered as
much as necessary to support thoughtful application. This course does not assume
any prior exposure to machine learning theory or practice. A major focus of
the course will be a semester long project that students can either select
from a provided list or design themselves.
Note: Online video versions of all lectures will also be available to students
enrolled in the course as optional supplementary materials
Psychology 2110: Topics in Social Psychology
- Intergroup Relations
John Levine
University of Pittsburgh
516 LRDC Bldg. (3939 O’Hara Street)
Meeting time: TBA
412-624-7462
jml@pitt.edu
Course Description
As humans, we have a strong tendency to categorize ourselves and others
on the basis of group membership. This categorization, in turn, has profound
consequences for our thoughts, feelings, and behaviors toward those who
share our ingroup identity and those who do not. This goal of this course
is to familiarize students with the large body of social psychological
theory and research on intergroup relations. Topics covered include the
impact of evolutionary factors and individual differences on intergroup
relations; perceptual, cognitive, and emotional determinants of stereotyping;
the role of language, communication, and social consensus in intergroup
relations; the causes and consequences of group identification; how targets
of discrimination/stigmatization cope with their predicament; social
interaction between sources and targets of prejudice; and interventions
for improving intergroup relations. Students are expected to read all
assigned material, participate actively in class discussions, and write
a review/theoretical paper on one of the topics covered in class.
Students interested in taking the course should contact the instructor
before enrolling.
Spring Semester 2007
Seminar on the Employment Relationship
Professor Denise Rousseau
Mini 4
Schedule: TBA
Course Description
Our focus is on how theory and research on the employment relationship
is evolving. Beginning with a look at how historical concepts (in theory
and practice) have cycled between economic and relational notions of
employee-employer relations, we will then focus on employment theory
and research in organizational behavior. We will examine psychological
contract research, perceived organizational support, models of social
and economic exchange in employment and related research. Lastly we will
examine emerging trends with regard to the blurry boundary between worker/manager/investor
in organizational theory, inter-organizational careers, and idiosyncratic
deals. Participants will be responsible for presenting readings in class,
participating in class discussions and writing one conceptual paper in
AMR fashion.
I anticipate this class will meet on Thursday mornings from 9 to noon,
but this is negotiable.
Computer Supported Collaborative Learning
Dr. Carolyn Rose and Dr. Susan Finger
05-899A Human Computer Interaction Institute / Language Technologies
Institute
Mondays and Wednesdays
1:30PM - 2:50PM
Porter Hall A19C
For more information: Contact Carolyn Rose at cprose@cs.cmu.edu
Course Description
The field of Computer Supported Collaborative Learning has as one of
its foundational goals to work towards understanding the pedagogical
and technological features that make on-line education in general, and
collaborative learning in particular, effective. If we can understand
the causal connections between interaction and learning, then we can
wield technology in ways that achieve maximal cognitive and social benefits
for on-line learners.
The purpose of this class is to expose students to the foundational theoretical
and methodological issues underlying previous work in collaborative learning,
to introduce students to the wide range of current approaches to collaborative
learning support that exist within the field of Computer Supported Collaborative
Learning, and to offer students a vision of where the field is going
through review of recent articles as well as hands on experience with
new technologies.
The field of Computer Supported Collaborative Learning is changing. Machine
learning and text processing technologies bring the potential to adapt
support offered to students to the specific needs that arise during their
group interactions. Whereas the state of the art in collaborative learning
support is primarily composed of static, one-size-fits-all approaches,
the ideal of adaptive collaboration support is now seen as within our
grasp. Nevertheless, important research questions must be addressed,
both on the technical side of extending and insightfully applying existing
technology that was originally developed for different purposes to this
new research area, and on the behavioral side of investigating the effect
of alternative strategies and approaches to responding to the events
that are detected using that technology.
The course will be structured primarily around group discussions of weekly
reading assignments as well as a major term project in which students
will work in small groups to design and prototype a form of adaptive
collaborative learning support.
Dynamic Network Analysis
Professor Kathleen Carley (kathleen.carley@cmu.edu)
17-700 Institute for Software Research International (ISRI)
Mondays
8:30AM - 11:20AM
Wean Hall 6423
Course Description
The study of networks is integral to numerous fields such as statistics,
sociology, organizational science, communication, computer science and
forensic science. Regardless of whether the term link analysis, social
network analysis, or “the new network science” is used, the
discussion hinges on the graph-theoretic based study of dynamic and ubiquitous
networks. Interest in networks has been growing rapidly among the public,
government officials, entrepreneurs, and scientists. General network
and dynamic network techniques can be and are being used to do a variety
of things such as improve computer searches, marketing, location of self
help groups, team design, and organizational performance evaluation.
Many companies, such as Friendster and VisualPath, are developing tools
to support the creation, maintenance and understanding of personal social
networks. Many see in the study of networks the possibility of a new
scientific field with general laws that could explain behavior in a dazzling
number of different areas from the most micro such as neural circuits
and gene interactions to the most macro such as entire ecologies of interacting
species or computers.
This graduate seminar, taught every other year, offers an overview and
evaluation of the theory and research on networks. This course provides
an in-depth understanding of networks and the associated data collection
and analysis techniques from a multi-disciplinary perspective. Questions
addressed include, but are not limited to: How do we conceptualize, measure,
compare and evaluate various types of networks? How do we evaluate the
impact of policies and technology on using these networks especially
given the fact that these networks are dynamic? What nodes, relations
and groups stand out in or are influential in a network? How do networks
emerge, evolve, change? What is the difference in analyzing networks
as complete graphs versus networks as emerging from a set of links? How
can data on networks be collected and what are the limits of these collection
techniques?
Psychology 3110: Natural Groups
Dr. Richard Moreland
Department of Psychology
University of Pittsburgh
Thursdays 2:00PM - 5:00PM
Room 127 Cathedral of Learning (Irish Room)
Course Description
Although much has been learned from experimental research on artificial
groups in laboratory settings, valuable field work on various kinds of
natural groups has also been done. This course will examine such work,
focusing on how natural groups can be studied and what has been learned
about them. We will examine many kinds of groups, reading and discussing
recent work on their composition, structure, dynamics, performance, and
ecology.
Class sessions will be held on Thursday afternoons, from 2:00 to 5:00,
in Room 127 of the Cathedral of Learning (Irish Room). The course will
have a seminar format: No one is assumed to be an expert and everyone
is expected to help understand and appreciate the material. Grades in
the course will depend on weekly class participation and on a single term
paper that must be presented and discussed in class at the end of the
semester. Several options for this paper are available to students.
At each class session, selected students will present the assigned
readings and guide our discussion of them. About 30 minutes can be devoted
to each reading. There is more to a good presentation than just summarizing
a paper and answering questions about it, although these are important
activities that require preparation and skill. There are at least three
other important goals, namely (a) linking the paper to earlier and later
work on similar groups; (b) integrating the paper with other readings
from the course; and (c) identifying what the paper reveals or implies
about small groups. Almost all of the readings for this course are narrow,
empirical papers, so providing a broader context for every reading will
be critical.
For more information email cslewis@pitt.edu
Computer-Mediated Communication
Susan Fussell
05-417/05-817 (12 units)
Tuesdays & Thursdays 1:30PM - 2:50PM
NSH 1305
Prerequisites: None
Course Description
This course examines how different types of computer-mediated communication
(CMC) technologies affect interpersonal communication. Among the topics
we will consider are:
- Classic CMC tools like e-mail, telephony, IM
- Video and audio conferencing
- Blogs, d-lists and newsgroups
- Tools to support gesture and eye gaze
- Socio-cultural dimensions of CMC
Students will be expected to post to weekly discussion lists, to do
a research project on a specific aspect of CMC, and to present a talk
on their project to the class. The course is appropriate for graduate
students and advanced undergraduates in HCI, LTI, Psychology, English
and related fields. For more information email sfussell@andrew.cmu.edu
Research Methods - Skills for Field Research
Professor Paul S. Goodman (pg14@andrew.cmu.edu)
47-894 Tepper School of Business
Mini 3, 2007
First class meeting: Friday, January 19th
12:30PM - 3:30PM
Posner 318
Course Description
This course is designed to build your data collecting skills in doing
organizational field research. The focus on skills means that we need
to practice. There will be, of course, readings, but the focus of this
course is on skill development. If we are considering interviewing, an
important data collection method in fieldwork, there will be readings,
but the emphasis will be on doing -- practicing interviewing.
This emphasis on the development of skills has some implications for
the student, professor, and the structure of the class. In the traditional
Ph.D. class, there is a set of readings that are reviewed before class
and then discussed in class. In this course, there will be a set of learning
tasks, some of which will be realized in class, while others will be out-of-class
projects. Therefore, it is better to think of this class in terms of total
learning time.
Spring Semester 2006
Small Groups (Psychology 2155)
Instructors: John Levine and Richard Moreland
Tuesday 3:00-5:30
4125 Sennott Square (210 S. Bouquet St.) University of Pittsburgh
Course description
This graduate seminar, taught every other year, offers an overview and evaluation of
theory and research on small group behavior. Topics covered include: group composition;
group structure (e.g., status, roles, norms); group dynamics (e.g., power, negotiation,
majority/minority influence); group performance (e.g., leadership, productivity, decision making);
and group ecology (physical, social, and temporal environments).
Readings emphasize work done by social psychologists and organizational scientists.
Class size is limited to 12 students. Instructor permission is required.
Students interested in taking the course should contact John Levine at jml@pitt.edu.
The first class meeting will be held on January 10.
Conflict and Negotiation Research (47-893)
Mini 3, 2006 (January 18 - March 6)
Instructor: Professor Laurie R. Weingart
Days and Time: Mondays & Wednesdays 1:30-3:20
Location: Posner 261, Tepper School of Business, CMU
Course description
This graduate seminar will expose you to theories and research
on conflict and negotiation in the field of organizational behavior.
We will focus on interpersonal, team, and intergroup conflict and the processes
underlying negotiation. This course will draw heavily on research from fundamental
disciplines that influence organizational behavior, including psychology, and communication in particular.
Please contact Laurie Weingart (weingart@cmu.edu) for more information.
