The Chicago guide to writing about multivariate analysis

Seven basic principles

Causality, statistical significance, and substantive significance

Five more technical principles

Creating effective tables

Creating effective charts

Choosing effective examples and analogies

Basic types of quantitative comparisons

Quantitative comparisons for multivariate models

The "Goldilocks problem" in multivariate regression

Choosing how to present statistical test results

Writing introductions, conclusions, and abstracts

Writing about data and methods

Writing about distributions and associations

Writing about multivariate models

Writing about interactions

Writing about event history analysis

Writing about hierarchical linear models / with Julie Phillips

Speaking about multivariate analyses

Writing for applied audiences.