Indiana University Bloomington

Psychological and Brain Science P557
Representation Of Structure In Psychological Data

Contact: nosofsky@indiana.edu
Offered: Spring, 2016
Capacity: 15
Sequence: NA
Pre-Requisites: K300 or equivalent; calculus recommended but not required
Algebra Required: No matrix algebra used.
Calculus Required: Used for concepts.
Contact Person for Authorization: None.
Instructor: Robert Nosofsky
Days Per Week Offered: 2 days a week, 75 minutes per lecture. Computer lab is integrated with lectures.
Website: http://oncourse.iu.edu
Recommended follow-up classes: Advanced data mining courses
Syllabus: No Syllabus Avaliable
Keywords: multidimensional scaling, clustering, choice theory, signal detection theory, general recognition theory
Description: Theory and application of quantitative methods for representing patterns implicit in matrices of psychological proximity data. Emphasis will be given to the analysis of nxn matrices of data on the similarity or confusion between all pairs of n objects, using two-way and three-way multidimensional scaling, clustering techniques, choice theory, signal detection theory, and probabilistic scaling methods.
Books: Primary source readings
Substantive Orientation: Any (but examples are from psychological sciences).
Applied/Theoretical: Both theory and applications are emphasized
Software: SPSS
How Software is Used: Data analysis
Problem Sets: Once every two weeks
Data Analysis: As part of assignments.
Presentation: One final project and class presentation.
Exams: One final exam.
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