Lectures: 5 sessions / 1 week, 2 hours / session
Interest in and experience with analyzing experimental data.
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.
Resampling. 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.
Distributions. 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, Cohen?s 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.
Gelman, Andrew, and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. 1st ed. Cambridge, UK: Cambridge University Press, 2006. ISBN: 9780521867061.
Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin. Bayesian Data Analysis. 2nd ed. New York, NY: Chapman & Hall, 2003. ISBN: 9781584883883.