Mike Frank and Ed Vul
Statistics and Visualization for Data Analysis and Inference
Mon Jan 26 thru Fri Jan 30, 1:00 – 3:00PM
Room 46-3189 M-R 1/26 1/29 and 46-3310 for F 1/30
Description:
A whirl-wind tour of the statistics used in behavioral science
research, covering topics including: data visualization, building your
own null-hypothesis distribution through permutation, useful
parametric distributions, the generalized linear model, and model-
based analyses more generally. Familiarity with Matlab, Octave, or R
will be useful, prior experience with statistics will be helpful but
is not essential. This course is intended to be a ground-up sketch of
a coherent, alternative perspective to the “null-hypothesis
significance testing” method for behavioral research (but don’t worry
if you don’t know what this means).
Course Outline:
Visualization. Creating a visualization to understand experimental
results. Simple univariate displays. Conventional multivariate
displays. The repertoire of visual variables. Introduction of examples
to be used throughout the course: simple behavioral experiments,
complex behavioral experiments, and eye-tracking.
Permutation. Understanding what would have happened by chance
through non-parametric tests, confidence bounds, and measures of
effect size. Discussion of null-hypothesis significance testing and
its limitations.
Distribution. Understanding the spread of data. Inferring parametric
forms (Binomial, Gaussian, Poisson, etc.) as a convenient way of
describing the structure of data. Effect size and Bayesian derivation
of tests for parametric distributions, inc. binomial test, t-test,
Cohens d, etc.
Models of Data 1: The Linear Model. What is a model of data. Basic
assumptions of the linear model. The standard and generalized linear
model and relationship to ANOVA. Bayesian derivation of the LM. Link
functions and logistic regression. Effect size in a linear model.
Introduction to multilevel models.
Models of Data 2: Bayesian Models. Constructing and testing more
complex models of data. Bayesian models as a tool for creating models
with complex task assumptions. Brief introduction to basic techniques
for Bayesian inference.