Resource Library

This page contains information and links about statistical literacy. Some links are to textbooks, online articles, resources, and information about upcoming events.

This page discusses disadvantages of large datasets with regard to Simpson's Paradox.

This page has data sets used by UCLA statistics classes. The html files in the second column contain descriptions of a particular data set and a link to the data at the end of the file. There are also .dat and .dta files that contain just data, with no description.

The ICPSR provides access to a large repository of social science and political data. Data sets can be constructed and downloaded for use in most popular statistical packages.

This site describes what to do with data sets instead of simply presenting theory and methods as they appear in standard textbooks. It emphasizes statistical practices.

This site gives the outlines and shows the lessons for psychology 340/341: Advanced Statistical Methods.

This page is a collection of examples, demonstrations, and exercises that can be used to motivate a lecture, demonstrate an important point, or create a laboratory exercise for students. Topics include the following: Descriptives, Normal Distribution, Sampling Distributions, Probability, Chi-Square, t tests, Power, Correlation/Regression, One-way Anova, Multiple Comparisons, Factorial Anova, Repeated Measures, Multiple Regression, General Linear Model, Log Linear Models, and Distribution-Free Tests.

Each dataset in this collection includes description of the study, description of the data file, statistical topic covered, and reference. Topics addressed include: correlation, one-way ANOVA, Bonferroni multiple comparison procedure, regression (simple, multiple, and loglinear), chi-square, and the t-test.

This is an article published in the Journal of Statistics Education describing the ANOVA Visualization Tool and how it can be used in class.

This Java applet helps students visualize features and factors of one and two-way ANOVA tables together with representational models and model parameters.