This presentation will give an overview of the three main approaches to analyzing repeated measures analysis of variance: 1) Multivariate models, 2) traditional anova models, and 3) linear mixed models along with discussion of the advantages and disadvantages of each. The presentation includes...

Three approaches to understanding 3-way anova interactions will be presented: 1) a conceptual approach, 2) an anova approach and 3) a regression approach using dummy coding. The three approaches are illustrated through the use of a synthetic dataset with a significant 3-way interaction.

This presentation will show how to use of the new margins command, introduced in Stata 11, to explore interactions in regression and analysis of variance. The talk will cover three types of interactions: 1) categorical by categorical, 2) categorical by continuous, and 3) continuous by...

A review of issues and controversy surrounding F-ratio denominator degrees of freedom (DDF) in linear mixed models. The presentation will look at the history of denominator degrees of freedom as well survey their use in various statistical packages.

The creation and testing of interaction terms in regression models can be very cumbersome, even in Stata 8. We propose a simple wrapping command, -fitint-, that fits any generalised linear model and tests any twoway interactions, as well as all main effects. There is no need to use -xi- because...

Among various structures in Stata for cycling through lists (whether lists of variable names, numbers, or arbitrary strings) are foreach and forvalues, introduced in Stata 7 in 2001, and for, introduced in Stata 3.1 in 1992, and revised in 5.0 (1997) and 6.0 (1999). Typically, each member of the...

Stata's matrix language, Mata, highlighted in Bill Gould's Mata Matters columns in the Stata Journal, is very useful and powerful in its interactive mode. Stata users who write do-files or ado-files should gain an understanding of the Stata-Mata interface: how Mata may be called upon to do one...

The “fallacies of the wrong level” (e.g., ecological fallacy) are a concern in much of social-science research. When appropriate data are available, researchers frequently use intra-class correlations or ANOVAs to decide whether to use individual-level data or whether to aggregate and...

The Gini coefficient is widely used to measure inequality in the distribution of income, consumption, and other welfare proxies. Decomposing this measure can help you understand the determinants of inequality. In this presentation, I will use income data from Mexico to illustrate a user-written...

Survey data comes often as a plain table containing cryptic variable names, numbers, and letters. To make sense of the data, the researcher is given a questionnaire or a code book that contains a list of variable names, their description, and an interpretation of the values (either a number or a...