Principles of Workflow in Data Analysis

Copy the text below to embed this resource

2010-09-03 (Creation date: 2010-09-03)
Main contributor
J. Scott Long
The workflow of data analysis encompasses the entire process of scientific research: Planning, documenting, and organizing your work; creating, labeling, naming, and verifying variables; performing and presenting statistical analyses; preserving your work; and (perhaps, most important) producing replicable results. Most of our work in statistics classes focuses on estimating and interpreting models. In most “real world” research projects, these activities involve less than 10% of the total work. Professor Long’s talk is about the other 90% of the work. An efficient workflow saves time, introduces greater reliability into the steps of the analysis, and generates replicable results. A recent entry on a blog discussing Professor Long’s recent book, The Workflow of Data Analysis Using Stata, claimed: “The publication of [this book] may even reduce Indiana’s comparative advantage of producing hotshot quant PhDs now that grad students elsewhere can vicariously benefit from this important aspect of the training there.” Can you afford to miss this talk?
IU Workshop in Methods
Workshop in Methods
Social Science Research Commons


Dr. Long is Distinguished Professor and Chancellor's Professor of Sociology and Statistics at Indiana University.

Access Restrictions

This item is accessible by: the public.