Uncertainty Estimation for Pseudo‐Bayesian Inference Under Complex Sampling

Social and economic studies are often implemented as complex survey designs. For example, multistage, unequal probability sampling designs utilised by federal statistical agencies are typically constructed to maximise the efficiency of the target domain level estimator (e.g. indexed by geographic area) within cost constraints for survey administration. Such designs may induce dependence between the sampled units; for example, with employment of a sampling step that selects geographically indexed clusters of units. A sampling‐weighted pseudo‐posterior distribution may be used to estimate the population model on the observed sample. The dependence induced between coclustered units inflates the scale of the resulting pseudo‐posterior covariance matrix that has been shown to induce under coverage of the credibility sets. By bridging results across Bayesian model misspecification and survey sampling, we demonstrate that the scale and shape of the asymptotic distributions are different between each of the pseudo‐maximum likelihood estimate (MLE), the pseudo‐posterior and the MLE under simple random sampling. Through insights from survey‐sampling variance estimation and recent advances in computational methods, we devise a correction applied as a simple and fast postprocessing step to Markov chain Monte Carlo draws of the pseudo‐posterior distribution. This adjustment projects the pseudo‐posterior covariance matrix such that the nominal coverage is approximately achieved. We make an application to the National Survey on Drug Use and Health as a motivating example and we demonstrate the efficacy of our scale and shape projection procedure on synthetic data on several common archetypes of survey designs.     

The Effect of using Household as a Sampling Unit

The effect of sampling people through households is considered. Results on design effects for two stage surveys are reviewed and applied to give design effects of household samples. The main factors that determine the design effect are identified for the designs in which one person, or all people, are selected from each selected household. Within household correlation is one factor. We show that the relationships between household size and the mean and variance within households are also important factors. Census and survey data are used to empirically compare the design effects for a range estimators, variables and designs.     

Quasi-longitudinal Designs in SEM state Space Modeling

The collection of longitudinal data over the full model time scope is often an appropriate way to estimate the dynamic state space model with time-varying parameters. Nevertheless, in many situations it is possible and preferable to collect and combine data from independent groups of subjects, each covering a shorter interval than the full dynamic model. Several quasi-longitudinal designs are discussed: overlapping designs (overlapping cohort design OCD and overlapping samples design OSD) as well as nonoverlapping designs up to the exclusively cross-sectional design. The use of the structural equation modeling (SEM) program Mx and continuous time state space modeling is recommended. Finally, a number of the quasi-longitudinal designs is empirically evaluated, comparing the results with those of the full-longitudinal design.     

Estimators for particulate sampling derived from a multinomial distribution

Counting the number of units is not always practical during the sampling of particulate materials: it is often much easier to sample a fixed volume or fixed mass of particles. Hence, a class of sampling designs is proposed which leads to samples that have approximately a constant mass or a constant volume. For these sampling designs, estimators were derived which are a ratio of arbitrary sample totals. A Taylor expansion was used to obtain a first-order approximation for the expected value and variance in the limit of a large batch-to-sample size ratio. Furthermore, a π -estimator for a ratio of batch totals was found by deriving expressions for the first- and second-order inclusion probabilities. Practical application of the π -estimator is limited because it requires inaccessible batch information. However, when the denominator of the estimated batch ratio is the batch size, the π -estimator becomes equal to a sample total divided by the sample size in the limit of a large sample-to-particle size ratio. As a consequence, the obtained sample ratio becomes an unbiased estimator for the corresponding batch ratio. Retaining unbiasedness, the Horvitz–Thompson estimator for the variance, which also contains inaccessible batch information, is replaced by an estimator containing sample information only. Practical application of this estimator is illustrated for the sampling of slag, produced during the production of steel.     

Spatially Balanced Sampling: A Review and A Reappraisal

Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design‐based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations.     

Design-based or Prediction-based Inference? Stratified Random vs Stratified Balanced Sampling

Early survey statisticians faced a puzzling choice between randomized sampling and purposive selection but, by the early 1950s, Neyman's design-based or randomization approach had become generally accepted as standard. It remained virtually unchallenged until the early 1970s, when Royall and his co-authors produced an alternative approach based on statistical modelling. This revived the old idea of purposive selection, under the new name of “balanced sampling”. Suppose that the sampling strategy to be used for a particular survey is required to involve both a stratified sampling design and the classical ratio estimator, but that, within each stratum, a choice is allowed between simple random sampling and simple balanced sampling; then which should the survey statistician choose? The balanced sampling strategy appears preferable in terms of robustness and efficiency, but the randomized design has certain countervailing advantages. These include the simplicity of the selection process and an established public acceptance that randomization is “fair”. It transpires that nearly all the advantages of both schemes can be secured if simple random samples are selected within each stratum and a generalized regression estimator is used instead of the classical ratio estimator.     

Sampling-based Randomised Designs for Causal Inference under the Potential Outcomes Framework

We establish the inferential properties of the mean-difference estimator for the average treatment effect in randomised experiments where each unit in a population is randomised to one of two treatments and then units within treatment groups are randomly sampled. The properties of this estimator are well understood in the experimental design scenario where first units are randomly sampled and then treatment is randomly assigned but not for the aforementioned scenario where the sampling and treatment assignment stages are reversed. We find that the inferential properties of the mean-difference estimator under this experimental design scenario are identical to those under the more common sample-first-randomise-second design. This finding will bring some clarifications about sampling-based randomised designs for causal inference, particularly for settings where there is a finite super-population. Finally, we explore to what extent pre-treatment measurements can be used to improve upon the mean-difference estimator for this randomise-first-sample-second design. Unfortunately, we find that pre-treatment measurements are often unhelpful in improving the precision of average treatment effect estimators under this design, unless a large number of pre-treatment measurements that are highly associative with the post-treatment measurements can be obtained. We confirm these results using a simulation study based on a real experiment in nanomaterials.     

Longitudinal Surveys and the Study of Occupational Mobility: Panel and Retrospective Design in Comparison

The paper discusses the extent to which longitudinal surveys are able to expose occupational mobility and the dynamics of these processes. It compares two survey designs: the repeated panel design and the retrospective life history design. This comparison details the strength and the weaknesses of the two designs. The paper particularly calls attention to the unique features of the two designs with respect to the exploration of occupational mobility – pointing to three general problems of quantitative social research: item-nonresponse, design effects of single and multi-measurement occasion, and the strong assumption that researchers and respondents share the same definition of concepts. The main conclusion of the paper is that both designs offer unique opportunities to study social change, yet the differences between the two should be kept in mind when choosing the data set for particular research questions.     

ASSESSING THE SAMPLES OF PRIOR ORGANIZATIONAL RESEARCH

The sampling designs used in organizational research have been less than consistent across different reported studies. In this analysis we examine the reported relationships among several key organizational variables in ten separate previously published studies to determine the degree to which major differences in sample designs have influenced findings. We isolate that portion of reported associations due to unique characteristics of the samples and report the association these ‘sample design effects’ have with particular sample designs. Results indicate homogeneous samples of organizations inflate reported relationships yet leave significant sources of variation uncontrolled in sample selection. an alternative to sampling of homogeneous organizations is suggested by the fact that larger and probabilitistically selected samples are also associated with larger reported relationships among organizational variables.     

The place attachment experience regarding the disabled people: the typology of coffee shops

This study inquires the possible creation of a sense of a place in coffee houses for the disabled people as public places. The physical characteristics of a space are the main factors allowing all people including the disabled ones as well to perceive that place properly and within the “person, place and interaction process” framework. The place attachment, which can be defined as the main emotional bond between space and the users, is heavily influenced by the individualistic experiences and reactions whether they are ordinary or differently abled. In order to initiate a place attachment, spaces should be designed in a way that they respect the occupant’s perception, feelings, memories, senses and their physical abilities to enable them to develop a sense of belonging and ownership. Therefore, creating designs which foster people’s construction process of sense of a place that comprises the concept of identity and sense of belonging, is among the essential issues for realizing place attachment in architecture and design of public spaces. In our contemporary world, there are settings that possess these qualities; contrariwise there are more architectural and urban spaces which ignore these qualities and their benefits for people, especially for the disabled ones. Relatedly, a building should offer people enough information for perceiving it and with its physical features should also support the experiences that occur in it. There is a strong link between memorable events and the space where those events occur. An environment which provides outstanding and meaningful ambiance by its architectural characteristics can establish a sense of a place. Disabled people often have less opportunities to experience the spaces properly compared to others because their limitations are not taken into consideration when designing the public spaces. Therefore, the design process of public spaces should involve the basic needs of disabled people. In order to propose a typology for traditional coffee houses, this research aims to define and discuss the place attachment experience of disabled people in public spaces. The study begins with an overview of identifying different types of disabilities and special needs, and an introductory discussion of how space attachment can help to meet those needs. Therefore, the physical, social and typological aspects of traditional coffee houses were evaluated in terms of location, space organization and functions by considering the profiles of disabled users. Methodologically, this is a case study, which has a mixed research design including qualitative and quantitative research instruments.     

A Destructive Sampling Method Designed for Outsourcing Situations Involving High Quality Production Processes

In outsourcing situations involving manufacturing and assembly, the sampling of units produced is important since in many situations not all of the units can be tested. Destructive sampling, which commonly occurs in the assembly and manufacturing industry, is a form of sampling where all units produced cannot be tested since the parts are destructively tested. In this situation, sampling techniques are used to determine if an entire lot should be accepted or rejected based on the sampling results. The traditional sampling techniques include single or classical sampling, double sampling, multiple sampling, skip-lot sampling, chain sampling and MIL-STD-105E. The purpose of this paper is to develop a sophisticated technique that monitors quality at a lower cost than traditional methods yet has similar monitoring characteristics in situations where quality is high and tests are destructive. The proposed technique, Destructive Sampling Method for High Quality production processes (DSM-HQ), is based on a cost function, which balances the costs of sampling versus the costs of finding a defect on the field. DSM-HQ assumes to have a Poisson process defect pattern and uses an Empirical Bayesian analysis to allow the researcher to include prior knowledge.     

Successive Sampling to Estimate Quantiles with P-Auxiliary Variables

The successive sampling is a known technique that can be used in longitudinal surveys to estimate population parameters and measurements of difference or change of a study variable. The paper discusses the estimation of quantiles for the current occasion based on sampling in two successive occasions and using p-auxiliary variables obtained of the previous occasion. A multivariate ratio estimator from the matched portion is used to provide the optimum estimate of a quantile by weighting the estimates inversely to derived optimum weights. Its properties are studied under large–sample approximation and the expressions of the variances are established. The behavior of these asymptotic variances is analyzed on the basis of data from natural populations. A simulation study is also used to measure the precision of the proposed estimator.     

Polygonal designs with blocks of size k≤10

The use of polygonal designs is motivated by the desire to avoid the selection of contiguous units in a sample from an ordered finite population. However very little is known about polygonal designs that have block size 5 or more. We present new polygonal designs with blocks of sizes 5 through 10, including the first designs with block sizes 9 and 10. For block sizes 5 through 7, we identify, with one possible exception, all values for the number of varieties for which a polygonal design exists. Received January 2001     

THE ADMINISTRATIVE COMPONENT OF ORGANIZATIONS AND THE RATCHET EFFECT: A CRITIQUE OF CROSS-SECTIONAL STUDIES

When growth and decline are examined with regard to the administrative component of organizations, an apparent dichotomy exists between crosssectional designs and within organization longitudinal studies. The results of longitudinal studes indicate the existence of a ratcheting effect of the administtrative component which cannot be explanined by the size construct alone. Therefore, an analysis of the ratchet effect is presented and a conceptual model of factors influencing the growth and decline of the administrative component is propsed.     

The Method of Purging Applied to Repeated Cross-Sectional Data

In cross-sectional survey research, it is quite common to estimate the(standardized) effect of independent variable(s) on a dependent variable. However, if repeated cross-sectional data are available, much is to be gained if the consequences of these effects on longitudinal social change are considered.To assess these consequences, we describe a type of simulation in whichlongitudinal shifts in the independent variable's distribution, and longitudinal variation in effect on the dependent variable are `purged' from the data. Although the method of purging is known for many years, we add new practical features by relating the method to logistic and linear regression analysis. Because both logistic and linear regression analysis can be found in all majorstatistical packages, the method of purging is made available to a wider group of social scientists. With the use of repeated cross-sectional data, gathered in the Netherlands between 1970 and 1998, the new practical features of the purging method are shown, using the SPSS package.     

Multinomial distributions applied to random sampling of particulate materials

When sampling a batch consisting of particulate material, the distribution of a sample estimator can be characterized using knowledge about the sample drawing process. With Bernoulli sampling, the number of particles in the sample is binomially distributed. Because this is rarely realized in practice, we propose a sampling design in which the possible samples have a nearly equal mass. Expected values and variances of the sample estimator are calculated. It is shown that the sample estimator becomes identical to the Horvitz–Thompson estimator in the case of a large batch-to-sample mass ratio and a large sample mass. Simulations and experiments were performed to test the theory. Simulations confirm that the round-off error due to the discrete nature of particles is negligible for large sample sizes. Sampling experiments were carried out with a mixture of PolyPropylene (PP) and PolyTetraFluorEthylene (PTFE) spheres suspended in a viscous medium. The measured and theoretical variations are in good agreement.     

A Guide to Exact Simulation

Markov Chain Monte Carlo (MCMC) methods are used to sample from complicated multivariate distributions with normalizing constants that may not be computable in practice and from which direct sampling is not feasible. A fundamental problem is to determine convergence of the chains. Propp & Wilson (1996) devised a Markov chain algorithm called Coupling From The Past (CFTP) that solves this problem, as it produces exact samples from the target distribution and determines automatically how long it needs to run. Exact sampling by CFTP and other methods is currently a thriving research topic. This paper gives a review of some of these ideas, with emphasis on the CFTP algorithm. The concepts of coupling and monotone CFTP are introduced, and results on the running time of the algorithm presented. The interruptible method of Fill (1998) and the method of Murdoch & Green (1998) for exact sampling for continuous distributions are presented. Novel simulation experiments are reported for exact sampling from the Ising model in the setting of Bayesian image restoration, and the results are compared to standard MCMC. The results show that CFTP works at least as well as standard MCMC, with convergence monitored by the method of Raftery & Lewis (1992, 1996).     

New results on quaternary codes and their Gray map images for constructing uniform designs

The research of developing efficient methodologies for constructing optimal experimental designs has been very active in the last decade. Uniform design is one of the most popular approaches, carried out by filling up experimental points in a determinately uniform fashion. Applications of coding theory in experimental design are interesting and promising. Quaternary codes and their binary Gray map images attracted much attention from those researching design of experiments in recent years. The present paper aims at exploring new results for constructing uniform designs based on quaternary codes and their binary Gray map images. This paper studies the optimality of quaternary designs and their two and three binary Gray map image designs in terms of the uniformity criteria measured by: the Lee, wrap-around, symmetric, centered and mixture discrepancies. Strong relationships between quaternary designs and their two and three binary Gray map image designs are obtained, which can be used for efficiently constructing two-level designs from four-level designs and vice versa. The significance of this work is evaluated by comparing our results to the existing literature.     

双侧限“σ”法抽样方案的设计（均值）——关于GB／T8054—2008表2的设计建议

文章利用文献[1]中的理论公式和数据表，给出了以均值为质量指标的双侧限“σ”法抽样方案表。该抽样方案表的设计是对GB／T8054—2008表2的改进，不仅符合设计目标，而且更便于应用。     

Immigration,socio-economic conditions and crime: a cross-sectional versus cross-sectional time-series perspective

This study purpose is to verify if there is an association between foreign immigration and crime. In doing this, the study investigates also some satellite aspects revolving around this possible association: the range of offences affected by immigration, the relationship between immigrant and native crime, and whether the immigration impact on crime is direct or indirect. The present study has addressed these issues by both a cross-sectional and a longitudinal analysis, the latter including an instrument. The study is based on data of the Italian provinces. Italy represents a critical case for studying the migration–crime relationship, because in this country the rise in foreign immigration has been sudden and its pace feverish. The cross-sectional analysis findings show that crime intensities are affected by time-invariant factors and marginally by immigration. On the contrary, the longitudinal analysis shows that variations in immigration had a positive impact on both the most serious and the most common offences, on property crimes as well as on crimes of violence. There is no evidence of indirect effects of immigration on crime or of a link with native crime. In contrast to previous literature regarding the U.S., Canada, and Australia, these results suggest that a spiralling immigration can affect crime. In terms of methods, these findings show that the standard synchronic analysis models can be biased by non-observed factors and that therefore cross-sectional time-series models can offer significant advantages.