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3 Mitigating the Consequences of Nonresponse
Pages 51-60

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From page 51...
... Survey researchers are focusing less attention on increasing overall rates and are increasingly focusing on understanding the causes and correlates of nonresponse and making adjustments based on that understanding. In this chapter, we explore some of the ways in which survey methodologists and managers are responding to the growing problem of survey nonresponse.
From page 52...
... NONRESPONSE WEIGHTING ADJUSTMENT METHODS1 The need for nonresponse adjustment arises because probability samples, in which all units have a known, positive probability of selection, require complete responses. Without other non-sampling errors, estimators for probability samples are approximately design-unbiased, consistent, and measurable.
From page 53...
... MAR implies that the probability of response does not depend on the y-variable once we control for a vector of known x-variables. Weighting class adjustment schemes that define subgroups using the auxiliary data, assuming that the sample units within the subgroups (h = 1,...,H)
From page 54...
... Models constructed to meet stage 1 are called response propensity stratification, and those designed to meet stage 2 are referred to as predicted mean stratification. The classes themselves are sometimes formed by subject matter experts, based on information on the key survey outcomes.
From page 55...
... In this method a response indicator is regressed on a set of independent variables, such as those used to define weighting class cells. A predicted value derived from the regression equation is called the propensity score, which is simply an estimated response probability (Rosenbaum and Rubin, 1983)
From page 56...
... As a result, it has been heavily studied for use in countries with population registers or when the sampling frame is rich in auxiliary data, such as in establishment surveys. Mixture Models Selection models can be thought of as expressing the joint distribution of the outcome and "missingness" as the product of the distribution of the missing data mechanism conditional on the outcome variable and on the marginal distribution of the outcome variable.
From page 57...
... Observations About Weighting Adjustment Approaches All of these weighting adjustment schemes depend very heavily on the availability of auxiliary data that are highly correlated with either the response propensities or the key outcomes. Without these types of data, the adjustments are ineffective in reducing nonresponse bias.
From page 58...
... The second option is to use paradata in adjusting base weights of the respondents. A third use of paradata is to use the data to better understand the survey participation phenomenon so that future surveys may reduce nonresponse, but this use does not result in reducing nonresponse bias for the survey at hand.
From page 59...
... Psychologists, in collaboration with survey methodologists, could aid in understanding the factors that drive errors in interviewer observation, how training could improve ratings, how much error can be tolerated, and what the quality is relative to other sources. Finally, collaboration with fieldwork staff could help identify the costs associated with paradata collection as well as cheaper alternatives, the risks in interviewer multitasking, the appropriate level of observation, and ethical and legal matters issues that need to be resolved.
From page 60...
... should be addressed, particularly in terms of the possible interaction with nonresponse. Recommendation 3-3: Research is needed on the impact that reduc tion of survey nonresponse would have on other error sources, such as measurement error.


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