Time Schedule:
Jacob O Wobbrock
INSC 599
Seattle Campus
Readings, design projects, or research under faculty supervision. Prerequisite: permission of instructor and Ph.D. program chair.
Class description
Independent Study: Practical Statistics for Human-Computer Interaction This course gives HCI students in any department (e.g., iSchool, CSE, HCDE, Design, Psych) the ability to understand essential statistical concepts and analyze experiment data correctly using current tools SAS JMP and IBM SPSS. The class is neither fully theoretical nor fully tools-based, but a hybrid of the two with a pragmatic bent towards getting HCI students up and running with properly understood and executed statistical analyses.
Student learning goals
Students will be able to understand and explain essential concepts from statistical inference.
Students will be able to recognize, choose, and perform a variety of common statistical tests and techniques.
Students will be able to use current statistical tools SAS JMP and IBM SPSS.
Students will understand, at a non-theoretical pragmatic level, advanced concepts like nested effects, random effects, mixed-effects models, REML analyses, longitudinal analyses, covariance structures, and nonparametric analyses, and importantly, when and why to use them.
General method of instruction
Independent study with self-administered and self-graded modules.
Recommended preparation
Familiarity with the field of HCI helps but is not essential. Understanding of basic statistical concepts like mean, median, variance, standard deviation, and similar.
Class assignments and grading
10 modules meant to be done roughly 1/week get progressively harder.
All modules must be thoroughly done and carefully self-graded, and turned in, graded, in hardcopy at the end of the quarter for a CR grade.
