统计调查心理研究

The investigation process is a behavior one that is affected by the psychological factors. The psychological factors, to some extent, affect the progress of investigation and reality of results. In addition, the paper point out the meaning of research on investigation psychology and task concerned. Based on this the paper summarize the ten aspects on the research contents. In the end the paper create some usually ways suitable to the status of investigation psychology.     

论统计调查的多样性

党的十一届三中全会以来,随着经济改革和统计改革的深入开展,我国在统计调查方法的应用上也有不少改进,做得更加切合实际了。但是,同客观形势的需要比较,还有不少地方有待进一步改进和提高。值此党的十一届三中全会召开十周年之际,对统计调查问题作一回顾与前瞻,并对多种调查方法的结合运用提出一些意见,以进一步改进统计调查工作,看来是有必要的。党的十三大阐述了关于社会主义初级阶段的理论,提出了这一阶段党的基本路线和指导方针,这对于我国统计工作改革和现代化建设具有十分重要的意义。统计调查是统计认识过程的出发点,采取什么方式进行调查,如何搜集资料,如何进行观察,关系到统计事业的面貌,它是建设有中国特色的社会主义现代化统计的一个重要方面。     

多种统计调查方法的结合运用

经济体制改革的深入发展,给统计调查方法的应用带来了新的课题、新的要求。当前面临的问题是:1.随着经济体制改革的深入发展,专业管理部门的职能已向宏观控制调节方面发展,从条条管理,向按专业分工的全行业管理过渡,迫切需要掌握专业性全行业的统计资料。2.一些敏感性指标,对经济体制改革影响很大,各级领导极为关心。如工资总额的增减,基建投资额,物价指数,计划生育,粮食产量,耕地面积,经济效益,等等。反映收入     

城市统计调查的困境与出路

城市经济体制改革带来的巨大变化使传统的统计调查陷入了困境。统计工作在经济体制改革面前陷于被动。认识这些困境并寻找适应经济体制改革的出路,是当前统计界的一大迫切任务。统计调查的困境表现在哪些方面呢?     

论统计调查方法的结合运用

论统计调查方法的结合运用李金昌我国统计调查方法改革的目标模式是：建立一个以必要的周期性普查为基础,以经常性的抽样调查为主体,同时辅之以重点调查、科学推算和全面报表综合运用的统计调查方法体系①。这一目标模式既明确了各种统计调查方法的主辅地位。也强调了它...     

我们应选择怎样的统计调查模式

我们应选择怎样的统计调查模式石良平在我国十余年的统计改革与发展过程中,我们所要解决的问题不外乎以下三个方面：（ｌ）统计指标在能够正确反映我国社会经济科技发展状况的同时,全面向国际靠拢。这是一个统计国际化的过程。（２）统计调查由全面报表制度向普查与其他...     

随机化回答模型的探讨

随机化回答模型的探讨／赵俊康在社会调查中,打许多涉及被调查者隐私的问题,如储蓄、收入、吸毒、性关系等等,一般称为社会敏感问题。采用通常的方法对社会敏感问题进行调查,很难取得真实可靠的资料。为此,沃纳（ｗａｒｎｅｒ）于１９６５年设计出一种随机化回答模型...     

我国统计调查体系的现状及改革意见

一、评价统计调查体系科学性的原则统计调查是根据预定的目地和要求,设计出科学的调查方案,采用合理的调查方法,有组织有计划地对调查对象实施调查,搜集各种统计数据的过程。在统计工作的三个阶段中,统计调查是基础环节,能否得到丰富的、科学准确的统计数据,取决于统计调查工作是否科学、是否切合实际,因此,统计调查是开展统计分析研究、提供统计优质服务的必要的前提条件。统计调查作为整个社会经济统计工作的一个重要组成部分,与一般的社会经济调查有所不同,它主要是采用大量观察的方法对整个社会经济现象进行调查,反映社会经济现象的总体     

统计调查方法改革与统计效益

统计调查方法改革与统计效益龙华,王凡一、改革统计调查方法体系的紧迫性（一）现行统计调查方法体系存在的主要问题中国的统计调查方法体系是按照高度集中的计划经济管理体制和分级管理的要求建立起来的。长期以来,基本上依靠全面统计报表和层层汇总上报采集信息。改革...     

统计调查中的缺失数据及补救

Carrying out an additional investigation to non-response units is an important means to solve omission data problems in statistics investigation. Usually there exist two basic forms: one is repeating inquiries, the other is doing second sampling. However, even additional investigation is employed, non-response problems are unavoidable. Therefore, the adjustment of data is often needed. The paper mainly discusses the problems mentioned above.     

关于主观指标统计调查的几个问题

近年来,随着民意调查的推广,有关主观指标的统计调查,已逐步为社会各方面所接受。本文拟就主观指标的作用、原则及调查统计方法,作一些粗浅的研究。一社会经济现象是客观存在的。我们对客观存在的社会经济现象分别从规模、水平、速度、结构、比例和效益等方面来反映和描述,这就是通常所说的客观指标,同时,人们对客观存在的社会经济现象又各有其不同的主观感受,我们把人们对客观社会经济现象的意愿、态度、满意程度、期望和评价等主观感受用统计指标加以反映和描述,这就是主观指标。     

Response Inducing Techniques for Mail Surveys

A mail survey has been conducted to discover why certain hospital based health professionals did not reply to a previous mail survey. The results will assist in the design of future mail surveys for this group and may have a more general interest and applicability. Two return-inducing techniques have been tested. Other such techniques, reported in the literature but untested in this study, are listed. The observed responses times are reported and some suggestions regarding timing are made.     

网络访问固定样本调查的统计推断研究

如何解决网络访问固定样本调查的统计推断问题,是大数据背景下网络调查面临的严重挑战。针对此问题,提出将网络访问固定样本的调查样本与概率样本结合,利用倾向得分逆加权和加权组调整构造伪权数来估计目标总体,进一步采用基于有放回概率抽样的Vwr方法、基于广义回归估计的Vgreg方法与Jackknife方法来估计方差,并比较不同方法估计的效果。研究表明:无论概率样本的样本量较大还是较小,本研究所提出的总体均值估计方法效果较好,并且在方差估计中Jackknife方法的估计效果最好。     

Alternative Estimators for Randomized Response Techniques in Multi-Character Surveys

Tail estimates are developed for power law probability distributions with exponential tempering, using a conditional maximum likelihood approach based on the upper-order statistics. Tempered power law distributions are intermediate between heavy power-law tails and Laplace or exponential tails, and are sometimes called “semi-heavy” tailed distributions. The estimation method is demonstrated on simulated data from a tempered stable distribution, and for several data sets from geophysics and finance that show a power law probability tail with some tempering.     

Accounting for Response Misclassification and Covariate Measurement Error Using a Random Effects Logit Model

Often in longitudinal data arising out of epidemiologic studies, measurement error in covariates and/or classification errors in binary responses may be present. The goal of the present work is to develop a random effects logistic regression model that corrects for the classification errors in binary responses and/or measurement error in covariates. The analysis is carried out under a Bayesian set up. Simulation study reveals the effect of ignoring measurement error and/or classification errors on the estimates of the regression coefficients.     

Influential Observations in the Functional Measurement Error Model

In this work we propose Bayesian measures to quantify the influence of observations on the structural parameters of the simple measurement error model (MEM). Different influence measures, like those based on q-divergence between posterior distributions and Bayes risk, are studied to evaluate the influence. A strategy based on the perturbation function and MCMC samples is used to compute these measures. The samples from the posterior distributions are obtained by using the Metropolis-Hastings algorithm and assuming specific proper prior distributions. The results are illustrated with an application to a real example modeled with MEM in the literature.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

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.     

Simulation-Extrapolation via the Bezier Curve in Measurement Error Models

Simulation-extrapolation (SIMEX) is a method for correcting for bias in measurement error models, and parametric SIMEX estimates are often used. In this paper, we propose a nonparametric method for computing the SIMEX estimate via the Bezier curve, which is a popular smoothing technique in the computer graphics area. Comparisons are done for the bias of the limit values of parametric SIMEX estimates and the Bezier estimate in the various nonlinear measurement error models.     

EWMA Chart and Measurement Error

Measurement error is a usually met distortion factor in real-world applications that influences the outcome of a process. In this paper, we examine the effect of measurement error on the ability of the EWMA control chart to detect out-of-control situations. The model used is the one involving linear covariates. We investigate the ability of the EWMA chart in the case of a shift in mean. The effect of taking multiple measurements on each sampled unit and the case of linearly increasing variance are also examined. We prove that, in the case of measurement error, the performance of the chart regarding the mean is significantly affected.