Estimation techniques that account systematically for nonresponse and at the same time succeed in delivering acceptable accuracy are much needed.
Estimation in Surveys with Nonresponse
Builds in the nonresponse feature of survey data collection as an integral part of the theory, both for point estimation and for variance estimation. Promotes weighting through calibration as a new and powerful technique for surveys with nonresponse. Highlights the analysis of nonresponse bias in estimates and methods to minimize this bias. Includes computational tools to help identify the best variables for calibration.
Discusses the use of imputation as a complement to weighting by calibration.
Estimation in surveys with nonresponse - WebSM
Contains guidelines for dealing with frame imperfections and coverage errors. Features worked examples throughout the text, using real data. Reviews "…useful…for the theoretically inclined survey statistician…" Technometrics , May "This impressive piece of work can be thoroughly recommended to anyone" Zentralblatt MATH Volume He is the author, or a co-author, of many articles on survey sampling theory and applications.
- Estimation in Surveys with Nonresponse | Wiley Online Books.
- Two Tales of Terror: Frankenstein and Dracula;
- Responsive Survey Designs for Reducing Nonresponse Bias.
Free Access. Groves reviews the popular models for assessing non-response bias in surveys. The techniques are summarized below:. Explanation: The researcher examines response rates from different subgroups e. Since these factors are often included as explanatory variables in research, different response rates among these subgroups may indicate non-response bias.
If differing response rates are found, they are either determined to be unimportant or corrected by weighting. Explanation: The researcher utilizes external databases or rich sampling frame information to append to respondent and non-respondent cases alike.
The researcher then compares these data to yield clues as to whether bias exists in the data collected in the survey based upon differences between responders and non-responders and the possible relationship of those differences to survey variables. Explanation: The researcher compares estimates from their own survey to those of a very high quality survey e. US Census to make comparisons of survey variables.
Major Weakness: The measurement of the survey variables being compared may be different between the surveys and there may be few meaningful variables available for comparison. Explanation: The researcher examines the respondents to the survey in subgroups created according to how much effort was utilized in interviewing them.
Those who were easiest to interview e. Theoretically those who were more difficult to interview more closely mimic the characteristics and qualities of non-respondents. Major Weakness: The technique has not been shown to produce meaningful information in assessing non-response bias.
Explanation: The researcher compares their un-weighted survey data to various weighting schemes to determine how much of an impact the different adjustments make on the survey variables. If you are already logged in, then you may need to update your profile to register your subscription.
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Weighting is one of the central steps in surveys. The typical weighting process involves three major stages. At the first stage, each unit is assigned a base weight, which is defined as the inverse of its inclusion probability. The base weights are then modified to account for unit nonresponse.
Handling Item Nonresponse in Surveys
At the last stage, the nonresponse-adjusted weights are further modified to ensure consistency between survey estimates and known population totals. When needed, the weights undergo a last modification through weight trimming or weight smoothing methods in order to improve the efficiency of survey estimates. This article provides an overview of the various stages involved in the typical weighting process used by national statistical offices.
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