Year: 2007
Type: article
Abstract: Gelman's article "Struggles with survey weighting and regression modeling" addresses the question of what approach analysts should use to produce estimates (and associated estimates of variability) based on sample survey data.Gelman starts by asserting that survey weighting is a "mess."While we agree that incorporation of the survey design for regression remains challenging, with important open questions, many recent contributions to the literature have greatly clarified the situation.Examples include relatively recent contributions by Pfeffermann and Sverchkov (1999), Graubard and Korn (2002) and Little (2004).Gelman's paper is a very welcome addition to that literature.There are some understandable reasons for the current lack of resolution.First, U.S. federal statistical agencies have been historically limited by their mission statements to producing statistical summaries, primarily means, percentages, ratios and cross-classified tables of counts.This is one explanation for why Cochran (1977) and Kish (1965) devote the great majority of their classical texts to these estimates.As a result, the job of using regression and other more complex models to learn about any causal structure underlying these summary statistics was generally left to sister policy agencies and outside users.However, things are changing.The federal statistical system (whether it likes it or not) is becoming more involved with complex modeling.This includes small-area estimation (e.g., unemployment estimates and census net undercoverage estimates) and research into models combining information from surveys with administrative data.(There will also likely be increased demands to use data mining procedures on federal statistical data.)This relatively new development has
Source: Statistical Science
Authors: Robert M. Bell, Michael L. Cohen
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2055014077