A multilevel exploration of the role of interviewers in survey non-response

This paper illustrates the use of multilevel statistical modelling of cross-classified data to explore interviewers' influence on survey non-response. The results suggest that the variability in whole household refusal and non-contact rates is due more to the influence of interviewers than to the influence of areas. The results from separate logistic regression models are compared with the results from multinomial models using a polytomous dependent variable (refusals, non-contacts and responses). Using the cross-classified multilevel approach allows us to estimate correlations between refusals and non-contacts, suggesting that interviewers who are good at reducing whole household refusals are also good at reducing whole household non-contacts.     

The long-term effectiveness of refusal conversion procedures on longitudinal surveys

Summary.  Survey organizations often attempt to 'convert' sample members who refuse to take part in a survey. Persuasive techniques are used in an effort to change the refusers' minds and to agree to an interview. This is done to improve the response rate and, possibly, to reduce non-response bias. However, refusal conversion attempts are expensive and must be justified. Previous studies of the effects of refusal conversion attempts are few and have been restricted to cross-sectional surveys. The criteria for 'success' of a refusal conversion attempt are different in a longitudinal survey, where for many purposes the researcher requires complete data over multiple waves. The paper uses data from the British Household Panel Survey from 1994 to 2003 to assess the long-term effectiveness of refusal conversion procedures in terms of sample sizes, sample composition and data quality.     

我国住户调查一体化设计研究

住户调查一体化设计包括对各项住户调查的通盘考虑和与普查、行政记录的有机衔接。首先,在借鉴国际经验和考虑我国实际的基础上,提出我国住户调查一体化设计的两个基本要求;其次,构造出我国住户调查一体化设计的基础框架;最后,充分利用现行国家调查制度的渠道和机制,对住户调查项目按其调查内容特征和内在逻辑关系进行精简、整合,形成以劳动力调查和住户收支与生活状况调查为核心的住户调查体系,并给出以主样本为主体的我国住户调查的一体化设计思路。     

Estimation of population mean in presence of random non response in two-stage cluster sampling

The present investigation addresses the problem of estimating a finite population mean in two-phase cluster sampling in presence of random non response situations. Utilizing information on an auxiliary variable, regression type estimators has been proposed. Effective imputation techniques have been suggested to deal with the random non response situations. The properties of the proposed estimation strategies have been studied for different cases of random non response situations in practical surveys. The superiority of the suggested methodology over the natural sample mean estimator of population mean has been established through empirical studies carried over the data sets of natural population and artificially generated population.     

COMBINING HOUSEHOLD SURVEYS USING MASS IMPUTATION TO ESTIMATE POPULATION TOTALS

Pressure is often placed on statistical analysts to improve the accuracy of their population estimates. In response to this pressure, analysts have long exploited the potential to combine surveys in various ways. This paper develops a framework for combining surveys when data items from one of the surveys is mass imputed. The estimates from the surveys are combined using a composite estimator (CE). The CE accounts for the variability due to the imputation model and the surveys’ sampling schemes. Diagnostics for the validity of the imputation model are also discussed. We describe an application of combining the Australian Labour Force Survey and the National Aboriginal and Torres Strait Islander Health Survey to estimate employment characteristics about the Indigenous population. The findings suggest that combining these surveys is beneficial.     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

Refusal to Answer Specific Questions in a Survey: A Case Study

It is well known that non ignorable item non response may occur when the cause of the non response is the value of the latent variable of interest. In these cases, a refusal by a respondent to answer specific questions in a survey should be treated sometimes as a non ignorable item non response. The Rasch-Rasch model (RRM) is a new two-dimensional item response theory model for addressing non ignorable non response. This article demonstrates the use of the RRM on data from an Italian survey focused on assessment of healthcare workers’ knowledge about sudden infant death syndrome (that is, a context in which non response is presumed to be more likely among individuals with a low level of competence). We compare the performance of the RRM with other models within the Rasch model family that assume the unidimensionality of the latent trait. We conclude that this assumption should be considered unreliable for the data at hand, whereas the RRM provides a better fit of the data.     

Methodology of the National Survey of Sexual Attitudes and Lifestyles

"The preparation and fieldwork for the National Survey of Sexual Attitudes and Lifestyles was carried out between 1987-1991. The survey was a combination of a face-to-face interview and self-completion questionnaire and comprised 18,876 individuals aged 16-59 years in Great Britain. The response rate was similar to other sample surveys on less sensitive topics. Women were overrepresented but the distributions of other demographic characteristics were similar to other data sets. The external validity and internal consistency of the data were found to be adequate. The likely sources and effects of bias are discussed."     

Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys

Summary.  We analyse household unit non-response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non-respondents.     

Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data

Item non‐response in surveys occurs when some, but not all, variables are missing. Unadjusted estimators tend to exhibit some bias, called the non‐response bias, if the respondents differ from the non‐respondents with respect to the study variables. In this paper, we focus on item non‐response, which is usually treated by some form of single imputation. We examine the properties of doubly robust imputation procedures, which are those that lead to an estimator that remains consistent if either the outcome variable or the non‐response mechanism is adequately modelled. We establish the double robustness property of the imputed estimator of the finite population distribution function under random hot‐deck imputation within classes. We also discuss the links between our approach and that of Chambers and Dunstan. The results of a simulation study support our findings.     

Estimation bias of different design and analytical strategies in dual-frame telephone surveys: an empirical evaluation

Dual-frame survey designs have become increasingly popular in large-scale telephone surveys. This is due to the lack of coverage of the traditional landline survey design and the escalating use of cell phones in recent years. Several estimation strategies have been proposed and their properties have been discussed under ideal scenarios, including pseudo-maximum-likelihood estimation, single-frame estimation, and simple composite estimation [C.J. Skinner and J.N.K. Rao, Estimation in dual frame surveys with complex designs, J. Am. Statist. Assoc. 91 (1996), pp. 349–356; S.L. Lohr and J.N.K. Rao, Inference from dual frame surveys, J. Am. Statist. Assoc. 95 (2000), pp. 271–280]. In practice, estimation in dual-frame telephone surveys is vulnerable to biases and errors (e.g. inaccessibility, topic/mode salience, and measurement error). The investigation of the performance of popular dual-frame estimation methods is scarce in real and less ideal scenarios. Through an innovatively designed simulation study, we compare the estimation bias under different sampling designs with various estimation strategies. To reduce bias, different raking strategies are compared. Simulated scenarios incorporating sampling costs are examined for practical considerations. Overall, the cell phone-only design yields results with the least bias and variance. When accurate covariate information is available for post-stratification, raking estimates from the cell phone-any design also perform very well. We also provide SAS macros for this simulation evaluation upon request. Survey practitioners can fine-tune the parameters based on their prior knowledge of the target population and run the simulation under different scenarios to gain more insights into how to optimally design and analyse telephone surveys.     

Imputation of Household Survey Data Using Linear Mixed Models

Mixed models are regularly used in the analysis of clustered data, but are only recently being used for imputation of missing data. In household surveys where multiple people are selected from each household, imputation of missing values should preserve the structure pertaining to people within households and should not artificially change the apparent intracluster correlation (ICC). This paper focuses on the use of multilevel models for imputation of missing data in household surveys. In particular, the performance of a best linear unbiased predictor for both stochastic and deterministic imputation using a linear mixed model is compared to imputation based on a single level linear model, both with and without information about household respondents. In this paper an evaluation is carried out in the context of imputing hourly wage rate in the Household, Income and Labour Dynamics of Australia Survey. Nonresponse is generated under various assumptions about the missingness mechanism for persons and households, and with low, moderate and high intra‐household correlation to assess the benefits of the multilevel imputation model under different conditions. The mixed model and single level model with information about the household respondent lead to clear improvements when the ICC is moderate or high, and when there is informative missingness.     

子女数量对家庭收入的影响

基于2005年全国1%人口抽样调查数据,利用生育偏好和生育政策的城乡差异构造工具变量,实证分析子女数量对家庭收入的影响。结果显示,子女数量的增加显著抑制家庭收入的提升,生育二胎导致家庭收入平均下降20.8%。此外,这种负向影响还表现出显著的城乡差异,农村家庭生育二胎使得家庭收入显著下降8.8%,而城镇家庭收入则下降21.2%。进一步地,区分是否三代同住以及母亲职业类型家庭的异质性分析结果显示,在非三代同住家庭、母亲在机关企事业单位任领导职务或从事专业技术类职业的家庭中,子女数量对家庭收入均具有显著的负向影响。分析结论凸显了宏观层面人口结构调整和居民收入提升以及微观层面生育决策与家庭收入之间的双重权衡。     

Multilevel modelling of the number of property crimes: household and area effects

Summary.  This study examines household and area effects on the incidence of total property crimes and burglaries and thefts. It uses data from the 2000 British Crime Survey and the 1991 UK census small area statistics. Results are obtained from estimated random-effects multilevel models, with an assumed negative binomial distribution of the dependent variable. Both household and area characteristics, as well as selected interactions, explain a significant portion of the variation in property crimes. There are also a large number of significant between-area random variances and covariances of household characteristics. The estimated fixed and random effects may assist in advancing victimization theory. The methods have potential for developing a better understanding of factors that give rise to crime and so assist in framing crime prevention policy.     

房地产价格区域间联动与泡沫的空间扩散 ——基于2000－2005年中国35个大中城市面板数据的实证检验

 利用中国35个大中城市2000－2005年间的面板数据，本文首先对房地产价格中的泡沫成分进行测度；然后构建泡沫自回归模型，并对其残差进行CSD检验，结果显示不同城市间房地产泡沫的演化过程相互影响。与前人研究不同，以房地产泡沫为研究对象，不仅可以确认中国不同城市间房地产价格存在联动性，而且可以证明消费者的适应性预期是其中重要的传导机制之一。     

我国通货膨胀预期的微观基础研究

 通货膨胀预期的微观基础是一个重要的理论问题和实践问题,但是至今为止相关的研究还非常少。本文采用中国人民银行的《居民储蓄问卷调查系统》数据库,从经济主体的经济特征和人口统计特征两方面详细研究哪些因素会显著影响居民通胀预期。研究结果表明：人们对未来家庭经济状况的信心、风险厌恶程度和金融参与及熟悉度、家庭所处阶层、人们对现阶段经济运行状况的满意程度以及最近家庭所处的经济环境都是形成异质性通胀预期的重要原因。研究结果对于制定更加科学的货币政策和加强通胀预期管理具有重要的参考价值。     

The pseudo‐GEE approach to the analysis of longitudinal surveys

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. Longitudinal data are often analyzed through the generalized estimating equations (GEE) approach. The vast majority of existing literature on the GEE method; however, is developed under non‐survey settings and are inappropriate for data collected through complex sampling designs. In this paper the authors develop a pseudo‐GEE approach for the analysis of survey data. They show that survey weights must and can be appropriately accounted in the GEE method under a joint randomization framework. The consistency of the resulting pseudo‐GEE estimators is established under the proposed framework. Linearization variance estimators are developed for the pseudo‐GEE estimators when the finite population sampling fractions are small or negligible, a scenario often held for large‐scale surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study using data from the National Longitudinal Survey of Children and Youth. The results show that the pseudo‐GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous and binary responses. The Canadian Journal of Statistics 38: 540–554; 2010 © 2010 Statistical Society of Canada     

Randomized response techniques: An application to the Botswana AIDS impact survey

The performance of the sampling strategy used in the Botswana Aids Impact Survey II (BAISII) has been studied in detail under a randomized response technique. We have shown that alternative strategies based on the Rao–Harley–Cochran (RHC) sampling scheme for the selection of first stage units perform much better than other strategies. In particular, the combination RHC for the selection of first stage units (fsu's) and systematic sampling for the selection of second stage units (ssu's) perform the best when the sample size is small where as the RHC and SRSWOR perform the best when the sample size is large. In view of the present findings it is recommended that the BAISII survey should be studied in more detail incorporating more indicators and increased sample sizes. This is because the BAISII survey design is extensively in use for large scales surveys in Southern African countries.     

A REGRESSION APPROACH TO THE ESTIMATION OF THE FINITE POPULATION MEAN IN THE PRESENCE OF NON‐RESPONSE

This paper presents various estimators for estimating the population mean of the study variable y using information on the auxiliary variable x in the presence of non‐response. Properties of the suggested estimators are studied and compared with those of existing estimators. It is shown that the estimators suggested in this paper are among the best of all the estimators considered. An empirical study is carried out to demonstrate the performance of the suggested estimators and of others, and it is found that the empirical results support the theoretical study.     

Estimation of population mean on the most recent occasion under non response in h-occasion successive sampling using several auxiliary variables

In this article, general estimation procedures of population mean on the most recent occasion in the presence of non response on all occasions have been deduced in h-occasion successive sampling. In the estimation procedures, regression type estimators have been suggested with the aid of stable and mutually independent several auxiliary variables which are readily available only on the most recent occasion. To check the efficiency of the proposed estimators, they have been compared with similar estimator in absence of non response. Properties of the suggested estimation procedures have been studied and their empirical studies are carried out to validate the theoretical results. Suitable recommendations have been made.