Nonresponse weighting adjustment using estimated response probability

To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.     

The effect of sociodemographic (mis)match between interviewers and respondents on unit and item nonresponse in Belgium

Interviewer characteristics affect nonresponse and measurement errors in face-to-face surveys. Some studies have shown that mismatched sociodemographic characteristics – for example gender – affect people's behavior when interacting with an interviewer at the door and during the survey interview, resulting in more nonresponse. We investigate the effect of sociodemographic (mis)matching on nonresponse in two successive rounds of the European Social Survey in Belgium. As such, we replicate the analyses of the effect of (mis)matching gender and age on unit nonresponse on the one hand, and of gender, age and education level (mis)matching on item nonresponse on the other hand. Recurring effects of sociodemographic (mis)match are found for both unit and item nonresponse.     

A fresh approach for intercession of nonresponse in multivariate longitudinal designs

The occurrence of nonresponse is very much plebeian in surveys, which troubles the analysis, and hence, an inappropriate inference is left out. To counterbalance the sour effects of the incompleteness, fresh imputation techniques have been proposed with the aid of multi-auxiliary variates for the estimation of population mean on successive waves. Properties of the proposed estimators have been elaborated, and they have been compared with the work of Priyanka et al. (2015). Detailed simulation study is carried out to substantiate the empirical and theoretical results. Several possible cases have been addressed in which nonresponse can occur.     

Adjustment for nonresponse with variables from different sources: Bias correction and precision loss,with application to the Swiss European Social Survey 2012

ABSTRACT

Adjustment for nonresponse should reduce the nonresponse bias without decreasing the precision of the estimates. Adjustment for nonresponses are commonly based on socio-demographic variables, although these variables may be poorly correlated with response propensities and with variables of interest. Such variables nevertheless have the advantage of being available for all sample units, whether or not they are participating in the survey. Alternatively, adjustment for nonresponse can be obtained from a follow-up survey aimed at sample units which did not participate in the survey and from which the variables are designed to be correlated with response propensities. However, information collected through these follow-up surveys is not available for people in the sample who participated neither in the survey nor in its nonresponse follow-up. These two sets of variables when used in a nonresponse model for the Swiss European Social Survey 2012 differ only slightly with regard to their effect on bias correction and on the precision of estimates. The variables from the follow-up are performing slightly better. In both cases, the adjustment for nonresponse performs poorly.     

Some efficient random imputation methods

Imputation methods that assign a selection of respondents’ values for missing i tern nonresponses give rise to an addd,tional source of sampling variation, which we term imputation varLance , We examine the effect of imputation variance on the precision of the mean, and propose four procedures for sampling the rEespondents that reduce this additional variance. Two of the procedures employ improved sample designs through selection of respc,ndents by sampling without replacement and by stratified sampl;lng. The other two increase the sample base by the use of multiple imputations.     

Survey nonresponse and the distribution of income

The paper examines the distributional implications of selective compliance in sample surveys, whereby households with different incomes are not equally likely to participate. Poverty and inequality measurement implications are discussed for monotonically decreasing and inverted-U compliance-income relationships. We demonstrate that the latent income effect on the probability of compliance can be estimated from information on response rates across geographic areas. On implementing the method on the Current Population Survey for the U.S. we find that the compliance probability falls monotonically as income rises. Correcting for nonresponse appreciably increases mean income and inequality, but has only a small impact on poverty incidence up to poverty lines common in the U.S. Martin Ravallion: Corresponding author.     

Factors that influence reading and comprehension of branching instructions in self–administered questionnaires

Summary: In this paper we examine the tendency for branching instructions to be ignored, misread, or otherwise not appropriately followed so that item nonresponse occurs for follow–up questions. The potential influence on branching errors of seven features of question complexity are examined, including high number of question words, high number of answer categories, last categories branch, all categories branch, write–in responses, location at the bottom of a page, and high distance between the answer box and branching instruction. A logistic regression analysis revealed that question complexity had a tendency to increase certain errors, but not others.* A more detailed version of this paper with additional analysis and discussion is available at http://www.sesrc.wsu.edu/dillman/. The opinions expressed here are those of the authors, not necessarily of the institutions where they presently work or the U.S. Census Bureau, which provided financial support for the collection of these data. We would like to thank Aref Dajani and Yves Thibaudeau for their advice on the analysis used in this paper.     

Semiparametric inference for estimating equations with nonignorably missing covariates

We consider statistical inference of unknown parameters in estimating equations (EEs) when some covariates have nonignorably missing values, which is quite common in practice but has rarely been discussed in the literature. When an instrument, a fully observed covariate vector that helps identifying parameters under nonignorable missingness, is available, the conditional distribution of the missing covariates given other covariates can be estimated by the pseudolikelihood method of Zhao and Shao [(2015), ‘Semiparametric pseudo likelihoods in generalised linear models with nonignorable missing data’, Journal of the American Statistical Association, 110, 1577–1590)] and be used to construct unbiased EEs. These modified EEs then constitute a basis for valid inference by empirical likelihood. Our method is applicable to a wide range of EEs used in practice. It is semiparametric since no parametric model for the propensity of missing covariate data is assumed. Asymptotic properties of the proposed estimator and the empirical likelihood ratio test statistic are derived. Some simulation results and a real data analysis are presented for illustration.     

Research Note: The LIVITY study: research challenges and strategies for engaging with the black caribbean community in a study of HIV infection

There is an increasing incidence of new HIV‐1 diagnoses among black Caribbeans in the UK, but there has been limited research in this area. The LIVITY study is the first in‐depth epidemiological and behavioural study to examine the impact of HIV among black Caribbeans in the UK. The UK black Caribbean community has traditionally been regarded as less likely to participate in clinical research and surveys. We identified three major challenges to recruitment to the LIVITY study: general distrust of the research process; the considerable stigma surrounding HIV in the black Caribbean community and the sensitivity of obtaining detailed sexual histories. The strategies devised to help overcome these barriers to optimise recruitment included: the establishment of a black Caribbean Community Advisory Group to facilitate communication between the community and the researchers and assist in questionnaire design to improve acceptability to the participants; use of healthcare practitioners as gatekeepers; extensive piloting and modification of the questionnaire among black Caribbean patients; reassurances of confidentiality during recruitment; and ethnic matching between interviewer and study participants. Another challenge was the high rate of loss to follow‐up among eligible patients, potentially compromising the study’s generalisability. Nevertheless, use of the above strategies resulted in a satisfactory enrolment rate of 57%, who were representative of all eligible patients.