Survey Weights: A Step-by-Step Guide to Calculation


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Authors:
Richard Valliant and Jill A. Dever
Publisher: Stata Press
Copyright: 2018
ISBN-13: 978-1-59718-260-7
Pages: 183; paperback
Price: $48.00
Authors:
Richard Valliant and Jill A. Dever
Publisher: Stata Press
Copyright: 2018
ISBN-13: 978-1-59718-261-4
Pages: 183; eBook
Price: $42.00
Authors:
Richard Valliant and Jill A. Dever
Publisher: Stata Press
Copyright: 2018
ISBN-13: 978-1-59718-262-1
Pages: 183; eBook
Price: $38.00
Download the datasets used in this book

Comment from the Stata technical group

Creation and use of weights is fundamental to the analysis of survey data, but collection of survey data is rarely a problem-free process. Some individuals chosen for a sample will refuse to respond. Sampled individuals may not be from the target population. Groups may be under- or over-represented. Even so, we wish to analyze data from these samples and, thus, to have weights that account for these issues.

Survey Weights: A Step-by-Step Guide to Calculation, by Richard Valliant and Jill Dever, walks readers through the whys and hows of creating and adjusting survey weights. It includes examples of calculating and applying these weights using Stata. This book is a valuable resource for anyone who collects survey data and needs to create weights and for researchers who analyze survey data and need to better understand and utilize the weights that are included.

Valliant and Dever begin with a brief review of survey sampling theory and methods, including a discussion of purpose of weights and an overview of sampling techniques. Then, they explain each of the steps of creating weights. The first step is the creation of base weights (the inverse of selection probabilities). In the following steps, they discuss a number of adjustments that can be made to those base weights to account for problems such as nonresponse and units that might not be part of the population of interest. For instance, they demonstrate how to use propensity scores and boosting to account for nonresponse. They also explain how to reduce standard errors and correct coverage problems by using auxiliary data to adjust weights via poststratification and calibration. After covering these weighting adjustments, Valliant and Dever are careful to explain linearized and various replication-based variance estimators. Valliant and Dever also provide insight into working with nonprobability samples, combining samples using composite weighting, working with datasets that have multiple weights, and focusing on quality control when creating weights.

Throughout the book, Valliant and Dever demonstrate how to create, adjust, and apply survey weights using Stata. For instance, as they discuss the creation of base weights, the authors include Stata commands that select units and compute base weights for simple random samples, systematic samples, stratified samples, samples with probability proportional to size, and more. Later, when examining the use of calibration, they include examples that use svyset's new features for raking and general regression calibration. After reading this book, researchers will know how to use Stata's survey features and have an in-depth knowledge of weights, making them ready to create and use weights with their own survey datasets.

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