Statistical problems in multi-variate (etiological) surveys

A number of multi-variate etiological surveys are analyzed for recurrent sources of bias in balanced and purposive sampling designs. Three nonsampling components emerge that may dominate the total error of a sample survey estimate. Outstanding among these appear to be administrative consideration of cost and convenience which may actually determine a sampling procedure, especially by reliance on voluntary participation, proxy responses and case-finding methods that restrict the sample. Next is lack of comparability of population series that differ on some initial state (as to smoking). Finally, errors are caused by strong beliefs in what results should be. Extensive experience has now shown that it may not be possible to conduct a satisfactory etiological inquiry by use of surveys using nonrandom population samples.     

Sampling within households in household surveys

Summary.  The number of people to select within selected households has significant consequences for the conduct and output of household surveys. The operational and data quality implications of this choice are carefully considered in many surveys, but the effect on statistical efficiency is not well understood. The usual approach is to select all people in each selected household, where operational and data quality concerns make this feasible. If not, one person is usually selected from each selected household. We find that this strategy is not always justified, and we develop intermediate designs between these two extremes. Current practices were developed when household survey field procedures needed to be simple and robust; however, more complex designs are now feasible owing to the increasing use of computer-assisted interviewing. We develop more flexible designs by optimizing survey cost, based on a simple cost model, subject to a required variance for an estimator of population total. The innovation lies in the fact that household sample sizes are small integers, which creates challenges in both design and estimation. The new methods are evaluated empirically by using census and health survey data, showing considerable improvement over existing methods in some cases.     

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.     

Simulation of close-to-reality population data for household surveys with application to EU-SILC

Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).     

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     

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.     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

Variance estimation in complex survey sampling for generalized linear models

Summary.  Complex survey sampling is often used to sample a fraction of a large finite population. In general, the survey is conducted so that each unit (e.g. subject) in the sample has a different probability of being selected into the sample. For generalizability of the sample to the population, both the design and the probability of being selected into the sample must be incorporated in the analysis. In this paper we focus on non-standard regression models for complex survey data. In our motivating example, which is based on data from the Medical Expenditure Panel Survey, the outcome variable is the subject's 'total health care expenditures in the year 2002'. Previous analyses of medical cost data suggest that the variance is approximately equal to the mean raised to the power of 1.5, which is a non-standard variance function. Currently, the regression parameters for this model cannot be easily estimated in standard statistical software packages. We propose a simple two-step method to obtain consistent regression parameter and variance estimates; the method proposed can be implemented within any standard sample survey package. The approach is applicable to complex sample surveys with any number of stages.     

Voluntary versus involuntary sample surveys in the microcensus

The author compares aspects of voluntary and involuntary sample surveys in West Germany. "The German microcensus as a non-voluntary survey draws a random sample from the total population which includes persons that would also respond in a voluntary survey (respondents) and persons that would not respond (non-respondents). The population of a voluntary survey, however, includes only respondents. Hence, statistical inference from a voluntary sample survey is only valid for the total population, if the population of respondents does not differ from the total population. This null hypothesis must be rejected from the comparisons of data from the German microcensus of 1985, 1986 and 1987 and corresponding voluntary test sample surveys. The discrepancies are great in central demographic and socio-economic variables such as region of residence, community size, age, marital status, income and social security." (SUMMARY IN ENG)     

Missing data methods in official statistics in the United Kingdom: Some recent developments

In recent years an increase in nonresponse rates in major government and social surveys has been observed. It is thought that decreasing response rates and changes in nonresponse bias may affect, potentially severely, the quality of survey data. This paper discusses the problem of unit and item nonresponse in government surveys from an applied perspective and highlights some newer developments in this field with a focus on official statistics in the United Kingdom (UK). The main focus of the paper is on post-survey adjustment methods, in particular adjustment for item nonresponse. The use of various imputation and weighting methods is discussed in an example. The application also illustrates the close relationship between missing data and measurement error. JEL classification C42, C81     

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.     

Information on sexual behaviour when some data are missing

When tables contain missing values, statistical models that allow the non-response probability to be a function of the intended response have been proposed by several researchers. We investigate the properties of these methods in the context of a survey of the sexual behaviour of university students. Profile likelihoods can be computed, even when models are not identified and saturated profile likelihoods (making no assumptions about the non-response mechanism) are derived. Bayesian approaches are investigated and it is shown that their results may be highly sensitive to the prior specification. The proportion of responders answering 'yes' to the question 'have you ever had sexual intercourse?' was 73%. However, different assumptions about the non-responders gave proportions as low as 46% or as high as 83%. Our preferred estimate, derived from the response-saturated profile likelihood, is 67% with a 95% confidence interval of 58–74%. This is in line with other studies on response bias in the reports of young people's sexual behaviour that suggest that the respondents overrepresent the sexually active.     

Analysis of inaccurate data with mixture measurement error models

Measurement error, the difference between a measured (observed) value of quantity and its true value, is perceived as a possible source of estimation bias in many surveys. To correct for such bias, a validation sample can be used in addition to the original sample for adjustment of measurement error. Depending on the type of validation sample, we can either use the internal calibration approach or the external calibration approach. Motivated by Korean Longitudinal Study of Aging (KLoSA), we propose a novel application of fractional imputation to correct for measurement error in the analysis of survey data. The proposed method is to create imputed values of the unobserved true variables, which are mis-measured in the main study, by using validation subsample. Furthermore, the proposed method can be directly applicable when the measurement error model is a mixture distribution. Variance estimation using Taylor linearization is developed. Results from a limited simulation study are also presented.     

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.     

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。     

国际比较项目中的中国PPP偏差问题

2014年世界银行公布的2011年轮全球国际比较项目（ICP）测算结果与发展中国家真实经济规模存在偏差,引发了发展中国家购买力平价是否被系统性低估的疑问。为此,基于ICP中中国部分的测算结果,立足于国际比较项目支出法测算的角度,对中国PPP偏差问题进行了分析。分析认为,从理论上看,ICP要求参与国提供GDP支出项数据与规格品价格数据在大国内部受到 了市场分割的影响,识别这种分割程度即空间关联性是ICP数据调查的前提。基于空间计量模型的实证结果表明：①在ICP的第七轮与第八轮调查期内,中国内部市场分割导致了价格水平存在时空两个方面的差异,影响了中国PPP计算的准确性;②市场分割导致的中国各地区的价差在短期内呈现随机性,只有长期才有可能出现趋同性。但2017年轮ICP调查周期由6年缩短为3年,使得这种趋同性难以出现在ICP活动的调查期内;③虽然ICP2017年轮采用滚动基期计算PPP,仍无法涵盖中国分割市场各自支出项目在空间上的不同作用力,这会使得支出法角度测算PPP时依然不能完全消除它的偏差,需要根据不同规格品的归属赋予不同权重才能较好地提高PPP测算的准确性。     

Appraisal of the factors influencing changes in the total number and composition of births by mother's age

The causes of the temporary and unexpected increase in fertility in Poland that occurred at the beginning of the 1980s are reviewed. Among the possible causes considered are a change in the number of women of reproductive age, changes in the size of the female population in selected areas due to migration, and actual changes in age-specific fertility.     

New statistical needs concerning the estimation of the demographic changes taking place in Poland

The authors examine the increased need for survey data in Poland to keep track of the rapid economic, social, and demographic changes that are occurring in the country. They discuss the type of surveys that are needed and such topics as data compatibility, sampling, frequency, and organization of data collection through surveys. (ANNOTATION)     

On the planning and design of sample surveys

Surveys rely on structured questions used to map out reality, using sample observations from a population frame, into data that can be statistically analyzed. This paper focuses on the planning and design of surveys, making a distinction between individual surveys, household surveys and establishment surveys. Knowledge from cognitive science is used to provide guidelines on questionnaire design. Non-standard, but simple, statistical methods are described for analyzing survey results. The paper is based on experience gained by conducting over 150 customer satisfaction surveys in Europe, America and the Far East.     

Sample size determination for the parallel model in a survey with sensitive questions

Recently, a new non-randomized parallel design is proposed by Tian (2013) for surveys with sensitive topics. However, the sample size formulae associated with testing hypotheses for the parallel model are not yet available. As a crucial component in surveys, the sample size formulae with the parallel design are developed in this paper by using the power analysis method for both the one- and two-sample problems. We consider both the one- and two-sample problems. The asymptotic power functions and the corresponding sample size formulae for both the one- and two-sided tests based on the large-sample normal approximation are derived. The performance is assessed through comparing the asymptotic power with the exact power and reporting the ratio of the sample sizes with the parallel model and the design of direct questioning. We numerically compare the sample sizes needed for the parallel design with those required for the crosswise and triangular models. Two theoretical justifications are also provided. An example from a survey on ‘sexual practices’ in San Francisco, Las Vegas and Portland is used to illustrate the proposed methods.