基于并行数据的调查质量管理

计算机辅助调查方法(Computer-assisted Personal Interview,CAI)的应用产生了一类传统纸笔调查方法无法采集到的并行数据,该类数据对于调查实施的监控、调查质量的提高和调查方法的改进起到了非常重要的作用.文章着重分析并行数据的类型、采集方法及其在调查质量管理中的作用.     

网络调查和传统纸质调查的差异性研究

文章通过大学生消费问卷调查考察了网络调查与传统纸质调查在数据收集方面的差异性.结果表明,在单位无回答方面网络调查劣于纸质调查;但在项目无回答方面,明显优于纸质调查.对不同题目类型、行为问题和态度问题的数据分析结果表明,对两种调查方法来说.被访者对调查问卷的回答没有明显区别,即被访者对自身信息的揭露愿望,不受两种调查方法的影响.     

基于CAI模式的调查数据质量控制

计算机辅助调查(CAI)改变了传统纸笔调查的数据收集过程,将数据质量控制提前到调查之前和调查过程中.丰富的并行数据的采集和应用极大地扩展了质量控制的方法,提高了数据质量控制的时效性和有效性.文章着重分析CAI模式下质量控制的方法及其对数据质量改善的作用.     

网络调查的初步思考

与传统的调查方式相比,网络调查具有保密性和客观性、高效性与简单性特点。网络调查具有统计调查的性质,但又不是任何一种传统意义上的统计调查,可以将其归为“计算机调查”或“现代统计调查”。网络调查特别适合作电子商务个人消费市场研究调查、民意调查、敏感性问题调查和超前性问题调查。     

网上调查利弊谈

随着新一代信息传播媒体———国际互联网的出现,一种崭新的调查方式———网上调查产生。网上调查是在各种计算机网络上,通过电子邮件或其它形式访问预定调查对象,在网络上实施数据采集、传输、上报、交换等业务,自动进行处理和汇总的方法。作为一种新兴调查,网上调查既有传统调查无可比拟的优势,也存在一定的局限。一、网上调查之“利”与传统的调查方式比较,网上调查在组织实施、信息采集处理、调查效果等方面具有明显优势,正是这些优势使得网上调查得以产生、运用和发展。1.迅捷快速。在网上利用电子邮件进行问卷调查,通常几页纸的问卷…     

网络调查的误差及其处理

由于网络调查本身还有许多不完善的地方,所以调查结果受非抽样误差的影响很大.随着中国网络的普及,网络调查的非抽样误差有减少的趋势.可通过正确界定网络调查的适用范围、更好地与抽样技术相结合、科学设计调查方案、加强网络调查的安全、对估计的结果进行校正等途径来降低网络调查的误差,提高其可信度.     

网络调查的利与弊

世界已经进入信息时代。以国际互联网络为基础的信息技术正在改变着我们的生活方式、工作方式和商务方式。将互联网技术应用于统计调查工作已成为日益热门的研究话题。网络调查是指将网络技术和传统调查技术相结合，利用网络良好的交互界面和信息反馈速度快等优点而出现     

促进我国网络调查发展的对策与措施

随着人类进入网络经济时代．网络调查逐渐兴起和蓬勃发展，使人大有传统调查过时之感。不错，网络诵查具有传统调查所没有的许多优点．诸如提高调查的广泛性和即时管理性．提高调查的时效性，降低调查成本、减少调查费用．增加调查的趣味性和调查结果的客观性、可靠性，增加对“敏感性”问题回答的真实性等等。但是．网络调查也存在明显的不足。     

大数据背景下网络调查样本的随机森林倾向得分模型推断研究

随着大数据与互联网技术的迅猛发展,网络调查的应用越来越广泛。本文提出网络调查样本的随机森林倾向得分模型推断方法,通过构建若干棵分类决策树组成随机森林,对网络调查样本单元的倾向得分进行估计,从而实现对总体的推断。模拟分析和实证研究结果表明:基于随机森林倾向得分模型的总体均值估计的相对偏差、方差与均方误差均比基于Logistic倾向得分模型的总体均值估计的相对偏差、方差与均方误差小,提出的方法估计效果更好。     

网络调查方法的质量评价及减少调查误差的措施研究

随着人类进入信息时代,越来越多的人将网络作为一种获得信息,交流信息的手段,网络调查也逐渐兴起.网络调查又称联机调查,它是指在各种计算机上通过互联网以电子邮件或其他形式对调查对象进行统计调查,并在网络上实施数据采集、传输、上报、交换等业务,对数据进行自动处理和汇总.网络调查是现代网络技术和传统调查技术相结合的产物,而且它是针对具有高度的信息收集能力的网络用户群体而产生的调查方式.     

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.     

Ratio and product methods of estimation of population mean in the presence of correlated measurement errors

The properties of the estimators of population mean arising from the ratio and product methods of estimation in the context of sample surveys have been analyzed in this paper when the observations on both the study and auxiliary variables are contaminated with measurement errors. The measurement errors in both the variables are also correlated. The properties of the ratio and product estimators along with the sample mean under the influence of measurement errors are derived and studied. The properties of the estimators in finite samples are studied through Monte-Carlo simulation and its findings are reported.     

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.     

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.     

大数据背景下网络调查样本的建模推断问题研究——以广义Boosted模型的倾向得分推断为例

随着大数据和网络的不断发展,网络调查越来越广泛,大部分网络调查样本属于非概率样本,难以采用传统的抽样推断理论进行推断,如何解决网络调查样本的推断问题是大数据背景下网络调查发展的迫切需求。本文首次从建模的角度提出了解决该问题的基本思路：一是入样概率的建模推断,可以考虑构建基于机器学习与变量选择的倾向得分模型来估计入样概率推断总体;二是目标变量的建模推断,可以考虑直接对目标变量建立参数、非参数或半参数超总体模型进行估计;三是入样概率与目标变量的双重建模推断,可以考虑进行倾向得分模型与超总体模型的加权估计与混合推断。最后,以基于广义Boosted模型的入样概率建模推断为例演示了具体解决方法。     

Optimal Prediction in Stochastic Regression Models with Application to the Analysis of Repeated Surveys

Several results relating to the optimal prediction of regression coefficients and random variables under a general linear model with stochastic coefficients are presented. These results are then applied to the analysis of repeated sample surveys over time. In particular, if the finite population can be modelled by a superpopulation model, a fully efficient method for the analysis of repeated surveys is proposed.     

Combining information from multiple surveys for assessing health disparities

Despite advances in public health practice and medical technology, the disparities in health among the various racial/ethnic and socioeconomic groups remain a concern which has prompted the Department of Human and Health Services to designate the elimination of disparities in health as an overarching goal of Healthy People 2010. To assess the progress towards this goal, suitable measures are needed at the population level that can be tracked over time; Statistical inferential procedures have to be developed for these population level measures; and the data sources have to be identified to allow for such inferences to be conducted. Popular data sources for health disparities research are large surveys such the National Health and Interview Survey (NHIS) or the Behavior Risk Factor Surveillance System (BRFSS). The self-report disease status collected in these surveys may be inaccurate and the errors may be correlated with variables used in defining the groups. This article uses the National Health and Nutritional Examination Survey (NHANES) 99-00 to assess the extent of error in the self-report disease status; uses a Bayesian framework develop corrections for the self-report disease status in the National Health Interview Survey (NHIS) 99-00; and compares inferences about various measures of health disparities, with and without correcting for measurement error. The methodology is illustrated using the disease outcome hypertension, a common risk factor for cardiovascular disease. JEL classification C1 (C11, C13, C15), C4 (C42) and I3 (I31, I38)     

A mail survey design for sensitive character without using randomization device

In the present study, we develop theory for Takahasi and Sakasegawa's (1977) suggest ion and propose a new randomized response model suitable for mail surveys. The proposed model based estimator is unbiased and is always more efficient than the estimator based on Takahasi and Sakasegawa model.