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For many household surveys in the United States, responses rates have been steadily declining for at least the past two decades. A similar decline in survey response can be observed in all wealthy countries. Efforts to raise response rates have used such strategies as monetary incentives or repeated attempts to contact sample members and obtain completed interviews, but these strategies increase the costs of surveys. This review addresses the core issues regarding survey nonresponse. It considers why response rates are declining and what that means for the accuracy of survey results. These trends are of particular concern for the social science community, which is heavily invested in obtaining information from household surveys. The evidence to date makes it apparent that current trends in nonresponse, if not arrested, threaten to undermine the potential of household surveys to elicit information that assists in understanding social and economic issues. The trends also threaten to weaken the validity of inferences drawn from estimates based on those surveys. High nonresponse rates create the potential or risk for bias in estimates and affect survey design, data collection, estimation, and analysis. The survey community is painfully aware of these trends and has responded aggressively to these threats. The interview modes employed by surveys in the public and private sectors have proliferated as new technologies and methods have emerged and matured. To the traditional trio of mail, telephone, and face-to-face surveys have been added interactive voice response (IVR), audio computer-assisted self-interviewing (ACASI), web surveys, and a number of hybrid methods. Similarly, a growing research agenda has emerged in the past decade or so focused on seeking solutions to various aspects of the problem of survey nonresponse; the potential solutions that have been considered range from better training and deployment of interviewers to more use of incentives, better use of the information collected in the data collection, and increased use of auxiliary information from other sources in survey design and data collection. Nonresponse in Social Science Surveys: A Research Agenda also documents the increased use of information collected in the survey process in nonresponse adjustment.

Investigates non-response bias in an inpatient experience survey with a low response rate by comparing sociodemographic characteristics and response behaviours of initial responders with responders to follow-up, and further explores the factors contributing to non-response. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

High response rates have traditionally been considered as one of the main indicators of survey quality.Obtaining high response rates is sometimes difficult and expensive, but clearly plays a beneficial role in terms of improving data quality.

This lecture, presented by Steven Rogelberg, discusses non-responses to organizational surveys.

Abstract Objective An important clinical question is how many patients with acute schizophrenia do not respond to antipsychotics despite being treated for adequate time and with an effective dose. However, up to date, the exact extent of the phenomenon remains unclear. Methods We calculated the nonresponse and nonremission percentages using individual patient data from 16 randomized controlled trials (RCTs). Six thousand two hundred twenty-one patients were assigned to one antipsychotic (amisulpride, flupenthixol, haloperidol, olanzapine, quetiapine, risperidone, or ziprasidone) at an adequate dose; the response was assessed at 4–6 weeks. As various definitions of nonresponse have been used in the literature, we applied 4 different cut-offs covering the whole range of percent Positive and Negative Syndrome Scale (PANSS)/Brief Psychiatric Rating Scale (BPRS) reduction (≤0%, <25%, <50%, <75%).For symptomatic remission, we used the definition proposed by Andreasen without employing the time criterion. Results The overall nonresponse for the cut-off of ≤0% PANSS/BPRS reduction was 19.8% (18.8%–20.8%); for the cut-off of <25% reduction it was 43% (41.7%–44.3%); for the cut-off of <50% reduction it was 66.5% (65.3%–67.8%); and for the cut-off of <75% reduction it was 87% (86%–87.9%). The overall percentage of no symptomatic remission was 66.9% (65.7%–68.1%). Earlier onset of illness, lower baseline severity and the antipsychotic used were significantly associated with higher nonresponse percentages. Conclusions Nonresponse and nonremission percentages were notably high. Nevertheless, the patients in our analysis could represent a negative selection since they came from short-term RCTs and could have been treated before study inclusion; thus, further response may not have been observed. Observational studies on this important question are needed.

With nonignorable nonresponse, an effective method to construct valid estimators of population parameters is to use a covariate vector called instrument that can be excluded from the nonresponse propensity, but are associated with the response even when other covariates are conditioned. The existing work in this approach assumes such an instrument is given, which is frequently not the case in applications. In this paper, we investigate how to search for an instrument from a given set of covariates, based on a pseudo likelihood approach assuming a parametric distribution of response conditioned on covariates and a totally unspecified nonresponse propensity. We propose a method and show that it produces a consistent instrument selection as the sample size tends to infinity, under some regularity conditions. The proposed method is examined in a simulation study and illustrated in a real data example.

Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of adequately adjusting for observed covariates in some settings. To address these limitations, this article presents a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher. This method allows the researcher to balance very precisely the means of the observed covariates and other features of their marginal and joint distributions, such as variances and correlations and also, for example, the quantiles of interactions of pairs and triples of observed covariates, thus, balancing entire two- and three-way marginals. Since the weighting method is based on a well-defined convex optimization problem, duality theory provides insight into the behavior of the variance of the optimal weights in relation to the level of covariate balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights? Also, the weighting method runs in polynomial time so relatively large datasets can be handled quickly. An implementation of the method is provided in the new package sbw for R. This article shows some theoretical properties of the resulting weights and illustrates their use by analyzing both a dataset from the 2010 Chilean earthquake and a simulated example.

Response rates have been steadily declining over the last decades, making survey estimates vulnerable to nonresponse bias. To reduce the potential bias, two weighting approaches are commonly used in National Statistical Offices: the one-step and the two-step approaches. In this article, we focus on the one-step approach, whereby the design weights are modified in a single step with two simultaneous goals in mind: reduce the nonresponse bias and ensure the consistency between survey estimates and known population totals. In particular, we examine the properties of instrumental calibration, a special case of the one-step approach that has received a lot of attention in the literature in recent years. Despite the rich literature on the topic, there remain some important gaps that this article aims to fill. First, we give a set of sufficient conditions required for establishing the consistency of instrumental calibration estimators. Also, we show that the latter may suffer from a large bias when some of these conditions are violated. Results from a simulation study support our findings. Supplementary materials for this article are available online.

The National Health and Nutrition Examination Survey (NHANES) is a major program of the National Center for Health Statistics, designed to assess the health and nutritional status of adults and children in the United States. The analysis of NHANES dental caries data faces several challenges, including (1) the data were collected using a complex, multistage, stratified, unequal-probability sampling design; (2) the sample size of some primary sampling units (PSU), e.g., counties, is very small; (3) the measures of dental caries have complicated structure and correlation, and (4) there is a substantial percentage of nonresponses, which are expected not to be missing at random or non-ignorable. We propose a Bayesian hierarchical spatial model to address these analysis challenges. We develop a two-level Potts model that closely resembles the caries evolution process, and captures complicated spatial correlations between teeth and surfaces of the teeth. By adding Bayesian hierarchies to the Potts model, we account for the multistage survey sampling design, while also enabling information borrowing across PSUs for small-area estimation. We incorporate sampling weights by including them as a covariate in the model and adopt flexible B-splines to achieve robust inference. We account for non-ignorable missing outcomes and covariates using the selection model. We use data augmentation coupled with the noisy Monte Carlo algorithm to overcome the numerical difficulty caused by doubly-intractable normalizing constants and sample posteriors. Our analysis results show strong spatial associations between teeth and tooth surfaces, including that dental hygienic factors, such as fluorosis and sealant, reduce dental disease risks.

Respondents to panel surveys are commonly divided into continuous and noncontinuous groups based on their response patterns. In this study, we propose an estimator based on composite weights as an effective nonresponse adjustment method to reduce the bias of noncontinuous response groups. We derive the properties of the proposed estimator, such as its bias and mean squared error, and then compare its efficiency with that of alternative estimators. We present the results of simulations demonstrating that the proposed estimator exhibits less variance than the conventional method of directly using the response rate of a noncontinuous response group. It also exhibited a lower bias than that obtained using the response rate of the continuous response group. The composite weighting method used in the proposed estimator showed stable results in terms of minimizing extreme weights, indicating that it may be considered highly effective for noncontinuous response groups in panel surveys.