数量特征敏感问题问卷设计

目前,数量特征敏感问题调查主要采用随机化策略,该策略需使用随机化装置,从而需要在现场实施。提出一种问卷设计技术,该技术用无关问题替代随机化装置,因而不需要调查者亲临现场,不受调查规模及调查单位聚散的限制,使得调查更加方便、实用、经济。给出了相应的无偏估计量,推算出估计量的方差和方差的估计量并举例说明。     

Advances in estimation by the item sum technique using auxiliary information in complex surveys

To collect sensitive data, survey statisticians have designed many strategies to reduce nonresponse rates and social desirability response bias. In recent years, the item count technique has gained considerable popularity and credibility as an alternative mode of indirect questioning survey, and several variants of this technique have been proposed as new needs and challenges arise. The item sum technique (IST), which was introduced by Chaudhuri and Christofides (Indirect questioning in sample surveys, Springer-Verlag, Berlin, 2013) and Trappmann et al. (J Surv Stat Methodol 2:58–77, 2014), is one such variant, used to estimate the mean of a sensitive quantitative variable. In this approach, sampled units are asked to respond to a two-list of items containing a sensitive question related to the study variable and various innocuous, nonsensitive, questions. To the best of our knowledge, very few theoretical and applied papers have addressed the IST. In this article, therefore, we present certain methodological advances as a contribution to appraising the use of the IST in real-world surveys. In particular, we employ a generic sampling design to examine the problem of how to improve the estimates of the sensitive mean when auxiliary information on the population under study is available and is used at the design and estimation stages. A Horvitz–Thompson-type estimator and a calibration-type estimator are proposed and their efficiency is evaluated by means of an extensive simulation study. Using simulation experiments, we show that estimates obtained by the IST are nearly equivalent to those obtained using “true data” and that in general they outperform the estimates provided by a competitive randomized response method. Moreover, variance estimation may be considered satisfactory. These results open up new perspectives for academics, researchers and survey practitioners and could justify the use of the IST as a valid alternative to traditional direct questioning survey modes.     

A validation of a computer-assisted randomized response survey to estimate the prevalence of fraud in social security

Summary.  In the Netherlands, there is a research tradition that measures fraud against regulations by interviewing eligible individuals using a survey. In these studies the sensitive questions about fraud are posed by using a randomized response method. The paper describes the results of a Dutch study into the consequences of replacing home interviews by trained interviewers with Internet-delivered interviews in a survey on fraud in the area of disability benefits. Both surveys used computer-assisted self-interviews with randomized response questions. This study has three goals: first to present the research tradition that makes use of randomized response, second to compare the results of home interviews and the Internet survey and finally to introduce an adapted weighted logistic regression method to test the relationship between the probability of fraud and explanatory variables. The results show that there are no systematic differences between modes of interview, either for estimates of the prevalence of fraud or for the identification of associated variables. These outcomes result in the conclusion that the Internet survey is a useful and cost-effective instrument for measuring fraud in a population, and that it is unlikely that replacing home interviews with the Internet survey will result in a significant break with tradition.     

Calibration estimation in dual-frame surveys

Survey statisticians make use of auxiliary information to improve estimates. One important example is calibration estimation, which constructs new weights that match benchmark constraints on auxiliary variables while remaining “close” to the design weights. Multiple-frame surveys are increasingly used by statistical agencies and private organizations to reduce sampling costs and/or avoid frame undercoverage errors. Several ways of combining estimates derived from such frames have been proposed elsewhere; in this paper, we extend the calibration paradigm, previously used for single-frame surveys, to calculate the total value of a variable of interest in a dual-frame survey. Calibration is a general tool that allows to include auxiliary information from two frames. It also incorporates, as a special case, certain dual-frame estimators that have been proposed previously. The theoretical properties of our class of estimators are derived and discussed, and simulation studies conducted to compare the efficiency of the procedure, using different sets of auxiliary variables. Finally, the proposed methodology is applied to real data obtained from the Barometer of Culture of Andalusia survey.     

Note on umvsu-estimation under randomized response model

Considering a class ofs randomized response trials for eliciting sensitive information from a sample survey and a class of ordered sampling designs, a uniformly minimum variance unbiased estimator of population variance (of the sensitive character) has been obtained. This note indicates that a theorem (theorem 3.9) of Cassel, Sarndal and Wretman (1977) and the results in the present note can be extended to estimation of any symmetric function of population values in the field of direct response surveys and randomized response surveys respectively.     

Evaluation of adjustments for partial non-response bias in the US National Immunization Survey

Summary.  Many health surveys conduct an initial household interview to obtain demographic information and then request permission to obtain detailed information on health outcomes from the respondent's health care providers. A 'complete response' results when both the demographic information and the detailed health outcome data are obtained. A 'partial response' results when the initial interview is complete but, for one reason or another, the detailed health outcome information is not obtained. If 'complete responders' differ from 'partial responders' and the proportion of partial responders in the sample is at least moderately large, statistics that use only data from complete responders may be severely biased. We refer to bias that is attributable to these differences as 'partial non-response' bias. In health surveys it is customary to adjust survey estimates to account for potential differences by employing adjustment cells and weighting to reduce bias from partial response. Before making these adjustments, it is important to ask whether an adjustment is expected to increase or decrease bias from partial non-response. After making these adjustments, an equally important question is 'How well does the method of adjustment work to reduce partial non-response bias?'. The paper describes methods for answering these questions. Data from the US National Immunization Survey are used to illustrate the methods.     

Combining information from multiple surveys by using regression for efficient small domain estimation

Summary.  In sample surveys of finite populations, subpopulations for which the sample size is too small for estimation of adequate precision are referred to as small domains. Demand for small domain estimates has been growing in recent years among users of survey data. We explore the possibility of enhancing the precision of domain estimators by combining comparable information collected in multiple surveys of the same population. For this, we propose a regression method of estimation that is essentially an extended calibration procedure whereby comparable domain estimates from the various surveys are calibrated to each other. We show through analytic results and an empirical study that this method may greatly improve the precision of domain estimators for the variables that are common to these surveys, as these estimators make effective use of increased sample size for the common survey items. The design-based direct estimators proposed involve only domain-specific data on the variables of interest. This is in contrast with small domain (mostly small area) indirect estimators, based on a single survey, which incorporate through modelling data that are external to the targeted small domains. The approach proposed is also highly effective in handling the closely related problem of estimation for rare population characteristics.     

Embedded experiments in repeated and overlapping surveys

Summary.  Statistical agencies make changes to the data collection methodology of their surveys to improve the quality of the data collected or to improve the efficiency with which they are collected. For reasons of cost it may not be possible to estimate the effect of such a change on survey estimates or response rates reliably, without conducting an experiment that is embedded in the survey which involves enumerating some respondents by using the new method and some under the existing method. Embedded experiments are often designed for repeated and overlapping surveys; however, previous methods use sample data from only one occasion. The paper focuses on estimating the effect of a methodological change on estimates in the case of repeated surveys with overlapping samples from several occasions. Efficient design of an embedded experiment that covers more than one time point is also mentioned. All inference is unbiased over an assumed measurement model, the experimental design and the complex sample design. Other benefits of the approach proposed include the following: it exploits the correlation between the samples on each occasion to improve estimates of treatment effects; treatment effects are allowed to vary over time; it is robust against incorrectly rejecting the null hypothesis of no treatment effect; it allows a wide set of alternative experimental designs. This paper applies the methodology proposed to the Australian Labour Force Survey to measure the effect of replacing pen-and-paper interviewing with computer-assisted interviewing. This application considered alternative experimental designs in terms of their statistical efficiency and their risks to maintaining a consistent series. The approach proposed is significantly more efficient than using only 1 month of sample data in estimation.     

Exploring spatial dependence in area-level random effect model for disaggregate-level crop yield estimation

This paper describes an application of small area estimation (SAE) techniques under area-level spatial random effect models when only area (or district or aggregated) level data are available. In particular, the SAE approach is applied to produce district-level model-based estimates of crop yield for paddy in the state of Uttar Pradesh in India using the data on crop-cutting experiments supervised under the Improvement of Crop Statistics scheme and the secondary data from the Population Census. The diagnostic measures are illustrated to examine the model assumptions as well as reliability and validity of the generated model-based small area estimates. The results show a considerable gain in precision in model-based estimates produced applying SAE. Furthermore, the model-based estimates obtained by exploiting spatial information are more efficient than the one obtained by ignoring this information. However, both of these model-based estimates are more efficient than the direct survey estimate. In many districts, there is no survey data and therefore it is not possible to produce direct survey estimates for these districts. The model-based estimates generated using SAE are still reliable for such districts. These estimates produced by using SAE will provide invaluable information to policy-analysts and decision-makers.     

Surveying migrant households: a comparison of census-based, snowball and intercept point surveys

Summary.  Few representative surveys of households of migrants exist, limiting our ability to study the effects of international migration on sending families. We report the results of an experiment that was designed to compare the performance of three alternative survey methods in collecting data from Japanese–Brazilian families, many of whom send migrants to Japan. The three surveys that were conducted were households selected randomly from a door-to-door listing using the Brazilian census to select census blocks, a snowball survey using Nikkei community groups to select the seeds and an intercept point survey that was collected at Nikkei community gatherings, ethnic grocery stores, sports clubs and other locations where family members of migrants are likely to congregate. We analyse how closely well-designed snowball and intercept point surveys can approach the much more expensive census-based method in terms of giving information on the characteristics of migrants, the level of remittances received and the incidence and determinants of return migration.     

Optimal allocation of sample sizes between regular banding and radio-tagging for estimating annual survival and emigration rates

Many authors have shown that a combined analysis of data from two or more types of recapture survey brings advantages, such as the ability to provide more information about parameters of interest. For example, a combined analysis of annual resighting and monthly radio-telemetry data allows separate estimates of true survival and emigration rates, whereas only apparent survival can be estimated from the resighting data alone. For studies involving more than one type of survey, biologists should consider how to allocate the total budget to the surveys related to the different types of marks so that they will gain optimal information from the surveys. For example, since radio tags and subsequent monitoring are very costly, while leg bands are cheap, the biologists should try to balance costs with information obtained in deciding how many animals should receive radios. Given a total budget and specific costs, it is possible to determine the allocation of sample sizes to different types of marks in order to minimize the variance of parameters of interest, such as annual survival and emigration rates. In this paper, we propose a cost function for a study where all birds receive leg bands and a subset receives radio tags and all new releases occur at the start of the study. Using this cost function, we obtain the allocation of sample sizes to the two survey types that minimizes the standard error of survival rate estimates or, alternatively, the standard error of emigration rates. Given the proposed costs, we show that for high resighting probability, e.g. 0.6, tagging roughly 10-40% of birds with radios will give survival estimates with standard errors within the minimum range. Lower resighting rates will require a higher percentage of radioed birds. In addition, the proposed costs require tagging the maximum possible percentage of radioed birds to minimize the standard error of emigration estimates.     

Survey non-response and the duration of unemployment

Summary.  Social surveys are often used to estimate unemployment duration distributions. Survey non-response may then cause a bias. We study this by using a data set that combines survey information of individual workers with administrative records of the same workers. The latter provide information on durations of unemployment and personal characteristics of all survey respondents and non-respondents. We develop a method to distinguish empirically between two explanations for a bias in results based on only survey data: selectivity due to related unobserved determinants of durations of unemployment and non-response and a causal effect of a job exit on non-response. The latter may occur even in fully homogeneous populations. The methodology exploits variation in the timing of the duration outcome relative to the survey moment. The results show evidence for both explanations. We discuss implications for standard methods to deal with non-response bias.     

The Stabilisation of Model Parameter Estimates from Repeated Surveys with Rare Observations

In many surveys, the domains of study are small and the samples that carry information on a domain can be very small indeed. If the survey is conducted repeatedly there is often a high degree of overlap in samples over time. We show how to use the richness of information over time to compensate for the paucity of cross‐sectional information. We propose a model‐based estimator of the population total which makes use of stabilised parameter estimates that combine information from different survey periods that are adjacent in time. The motivating example for this research was the ProdCom survey as implemented in the UK.     

Some New Results on the Multinomial Randomized Response Model

ABSTRACT

The randomized response technique is an effective survey method designed to elicit sensitive information while ensuring the privacy of the respondents. In this article, we present some new results on the randomization response model in situations wherein one or two response variables are assumed to follow a multinomial distribution. For a single sensitive question, we use the well-known Hopkins randomization device to derive estimates, both under the assumption of truthful and untruthful responses, and present a technique for making pairwise comparisons. When there are two sensitive questions of interest, we derive a Pearson product moment correlation estimator based on the multinomial model assumption. This estimator may be used to quantify the linear relationship between two variables when multinomial response data are observed according to a randomized-response protocol.     

Small area estimation with auxiliary survey data

Large governmental surveys typically provide accurate national statistics. To decrease the mean squared error of estimates for small areas, i.e., domains in which the sample size is small, auxiliary variables from administrative records are often used as covariates in a mixed linear model. It is generally assumed that the auxiliary information is available for every small area. In many cases, though, such information is available for only some of the small areas, either from another survey or from a previous administration of the same survey. The authors propose and study small area estimators that use multivariate models to combine information from several surveys. They discuss computational algorithms, and a simulation study indicates that if quantities in the different surveys are sufficiently correlated, substantial gains in efficiency can be achieved.     

Logistic regression analysis of randomized response data with missing covariates

Randomized response is an interview technique designed to eliminate response bias when sensitive questions are asked. In this paper, we present a logistic regression model on randomized response data when the covariates on some subjects are missing at random. In particular, we propose Horvitz and Thompson (1952)-type weighted estimators by using different estimates of the selection probabilities. We present large sample theory for the proposed estimators and show that they are more efficient than the estimator using the true selection probabilities. Simulation results support theoretical analysis. We also illustrate the approach using data from a survey of cable TV.     

The sensitivity of estimates of the change in population behaviour to realistic changes in bias in repeated surveys

Summary.  The first British National Survey of Sexual Attitudes and Lifestyles (NATSAL) was conducted in 1990–1991 and the second in 1999–2001. When surveys are repeated, the changes in population parameters are of interest and are generally estimated from a comparison of the data between surveys. However, since all surveys may be subject to bias, such comparisons may partly reflect a change in bias. Typically limited external data are available to estimate the change in bias directly. However, one approach, which is often possible, is to define in each survey a sample of participants who are eligible for both surveys, and then to compare the reporting of selected events that occurred before the earlier survey time point. A difference in reporting suggests a change in overall survey bias between time points, although other explanations are possible. In NATSAL, changes in bias are likely to be similar for groups of sexual experiences. The grouping of experiences allows the information that is derived from the selected events to be incorporated into inference concerning population changes in other sexual experiences. We use generalized estimating equations, which incorporate weighting for differential probabilities of sampling and non-response in a relatively straightforward manner. The results, combined with estimates of the change in reporting, are used to derive minimum established population changes, based on NATSAL data. For some key population parameters, the change in reporting is seen to be consistent with a change in bias alone. Recommendations are made for the design of future surveys.     

Randomized response model in a matched pair study

The development of randomized response models for personal interview surveys has attracted much attention since the pioneering work of Warner [1965. Randomized response: a survey technique for eliminating evasive answer bias. J. Amer. Statist. Assoc. 60, 63–69]. Several randomized response models have been developed by researchers for collecting data on both qualitative and the quantitative variables, but none of these models discuss matched pair data. In this paper, we develop a new randomized response model and study its application to an important political question.     

The Use of Balanced Incomplete Block Designs in Designing Randomized Response Surveys

This paper investigates the block total response method proposed by Raghavarao and Federer for providing accurate estimates of the base rates of sensitive characteristics during surveys. It determines the best balanced incomplete block design to use to estimate the base rates for three, four, five and six sensitive attributes respectively, given a maximum total number of 13 questions. The estimates obtained from this method have smaller variance than estimates obtained using the similar, but more popular, unmatched count technique.     

Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi‐parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.