On selectivity of nonresponse in discrete-time multi-wave panel studies

The current paper deals with the issue of the detection of selective nonresponse in discrete-time, multi-wave panel studies. If groups in a population differ with respect to the chances that they will be (and remain) in a longitudinal sample, we speak of selective nonresponse. Ultimately, selective nonresponse may lead to a sample that is very different from the target population. We discuss ways to detect and quantify the amount of selectiveness by means of discrete-time Markov models. Then we proceed by addressing how a researcher may gain understanding of how to solve the problems caused by selective nonresponse, and the degree to which these solutions will be effective, by means of data on the nonresponse during a three-wave panel study involving 2800 young Dutch adults.     

The Importance of Experimental Control in Testing the Impact of Interviewer Continuity on Panel Survey Nonresponse

The conventional wisdom in survey research suggests that it is advisable to have the same interviewers return to the same respondents in order to maintain good response rates in longitudinal surveys. There has been, however, very little documented experimental research to support this. Work conducted by Campanelli and O'Muircheartaigh (1999) using a subsample of the British Household Panel Study (BHPS) at Wave 2 with experimental control of the allocation of respondents to interviewers showed no evidence of a positive continuity effect on nonresponse; more extensive analysis by Laurie et al. (1999) of the full BHPS sample using Waves 2 through 4 presents contradictory results. This paper extends the earlier analysis and shows that these differences in findings are due to the lack of experimental control for the inferences from the full BHPS sample in the Laurie et al. (1999) report rather than the shorter time frame considered in Campanelli and O'Muircheartaigh (1999). This paper also considers variation in interviewer continuity effects across areas through the use of multilevel statistical models.     

Issues in the Design and Analysis of Panel Studies: A Cursory Review

This paper offers a broad review of some aspects in the design and analysis of panel studies, chiefly of household panel surveys. Both the analytic benefits and the potential problems of panel surveys are briefly outlined, and selected methodological and operational issues, which crucially affect data quality are highlighted. These questions are then considered under four headings: (i) dynamic population and its implications for initial sampling and following rules; (ii) panel length and number of waves; (iii) tracking and tracing techniques, and other strategies for maintaining high participation rates; (iv) questionnaire design and strategies for collecting retrospective information. While no technical details are offered, there is some discussion of the possible drawbacks and advantages of the different approaches described.     

Interviewers,Interviewer Continuity,and Panel Survey Nonresponse

It is widely known that in practice, different interviewers have different response rates, though there has been no systematic examination of whether this is because of differences among interviewers or differences among those areas allocated to the interviewers (area effects), or both. Furthermore, the conventional wisdom in survey research suggests that it is advisable to have the same interviewers return to the same respondents in order to maintain good response rates in longitudinal surveys, though once again there has been very little documented experimental research to support this. This paper makes use of the interpenetrated sample design experiment in Wave 2 of the British Household Panel Study (BHPS) (i) to explore the effects of interviewers' background characteristics and years of experience on response rates, (ii) to identify and estimate the differential effects of interviewers on response rates and compare the magnitudes of area and interviewer effects, and (iii) to investigate the impact of interviewer continuity. The analysis is facilitated by the use of cross-classified multilevel modelling. The paper also looks at the issue of interviewer continuity qualitatively, through the impressions of the interviewers themselves.     

Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non-ignorable nonresponse?

Rising nonresponse rates in social surveys make the issue of nonresponse bias contentious. There are conflicting messages about the importance of high response rates and the hazards of low rates. Some articles (e.g. Groves and Peytcheva, 2008) suggest that the response rate is in general not a good predictor of survey quality. Equally, it is well known that nonresponse may induce bias and increase data collection costs. We go back in the history of the literature of nonresponse and suggest a possible reason to the notion that even a rather small nonresponse rate makes the quality of a survey debatable. We also explore the relationship between nonresponse rate and bias, assuming non-ignorable nonresponse and focusing on estimates of totals or means. We show that there is a ‘safe area’ enclosed by the response rate on the one hand and the correlation between the response propensity and the study variable on the other hand; in this area, (1) the response rate does not greatly affect the nonresponse bias, and (2) the nonresponse bias is small.     

Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ

Bayesian multiple imputation (MI) has become a highly useful paradigm for handling missing values in many settings. In this paper, I compare Bayesian MI with other methods – maximum likelihood, in particular—and point out some of its unique features. One key aspect of MI, the separation of the imputation phase from the analysis phase, can be advantageous in settings where the models underlying the two phases do not agree.     

Modeling Item Nonresponse in Questionnaires

The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.     

On modeling panels of time series*

This paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a retail store. A panel of time series concerns the case where the cross‐sectional dimension and the time dimension are large. Often, there is no a priori reason to select a few series or to aggregate the series over the cross‐sectional dimension. The use of, for example, a vector autoregression or other types of multivariate models then becomes cumbersome. Panel models and associated estimation techniques are more useful. Due to the large time dimension, one should however incorporate the time‐series features. And, the models should not have too many parameters to facilitate interpretation. This paper discusses representation, estimation and inference of relevant models and discusses recently proposed modeling approaches that explicitly aim to meet these requirements. The paper concludes with some reflections on the usefulness of large data sets. These concern sample selection issues and the notion that more detail also requires more complex models.     

某公共建筑加层改造设计探析

通过对某公共建筑改造过程工程实例,阐述框架结构房屋改变使用功能后又加层改造的,采用多种加固措施对房屋进行加固改造,改善施工条件,加强房屋抗震性能,不仅满足了结构的受力要求,而且避免了加固期间停止营业带来的经济损失,节约了加固的费用,取得了良好的技术和经济效益。     

社会治理支出与经济增长：抑制还是促进？

本文基于1990—2012时间序列数据,对社会治理支出与经济增长进行 Johansen 协整和 Granger 因果检验,并进行了脉冲响应分析和方差分解。为了弥补时间序列数据只包含时间和指标两维信息的缺陷,进一步基于2000—2012年中国31省域面板数据,对二者进行了面板数据单位根检验、协整检验和固定效应变系数模型估计分析。研究结果表明,社会治理支出会促进经济增长,而经济增长对社会治理支出促进作用十分有限,各省域社会治理支出对经济增长促进作用不尽相同。     

Nonresponse Bias and Stimulus Effects in the Dutch National Election Study

Bias is a much-debated issue in survey research. Answer effects (respondents claim to have behaved differently than they did in reality), nonresponse bias (nonrespondents differ on important variables from the respondents) and stimulus effects (by participating in a previous wave of a study, respondents change their behavior or attitude) can seriously distort the results of survey research. By using data from the 1998 Dutch National Election Study the authors show that the results of election research can indeed be affected by bias. Not only are significant effects found in the distribution of political attitude and voting behavior variables as a result of both nonresponse bias and stimulus effects, it is also shown that relations between variables change as a result of bias.     

A Middle Class Image of Society

We studied undercoverage and nonresponse in a telephone survey among the population of the City ofGroningen, the Netherlands. The original sample, drawn from the municipal population register,contained 7000 individuals. For 37 percent of them, the telephone company was unable to produce a validtelephone number. Of those with a known telephone number, 49 percent did not answer the telephone orrefused to cooperate. As a result, the final respondents comprised merely 32 percent of the originalsample. To study distributional bias, we used individual-level data compiled from municipal records asour benchmark. Bivariate as well as multivariate analyses showed the undercoverage to be stronglyrelated to all sociodemographic variables studied, except gender. Nonresponse was related to age, countryof origin, marital status, and household size and composition, but not to gender, unemployment, socialassistance benefit, and education. Both undercoverage and nonresponse contributed to a strong middleclass bias in the final data set: middle-aged and older, economically secure people, of Dutch origin andliving with others in a household are overrepresented, while persons in disadvantaged and marginalpositions, such as the young, people of foreign stock, the unemployed, persons depending on publicincome support and singles are underrepresented.     

点支承玻璃幕墙的设计与施工技术

文章介绍了点支承玻璃幕墙的设计与施工技术及其施工过程中的质量控制措施。     

试议PLC在智能型铁路信号电源屏中的网络设计

本文首先阐述了PLC组网的几种主要形式,继而对S7-200系列PLC在智能型铁路信号电源屏中的网络程序设计展开了介绍,同时详细地分析了PLC的自由口通信与组网情况。     

Bayesian inference in time series models using kernel quasi likelihoods

The paper takes up Bayesian inference in time series models when essentially nothing is known about the distribution of the dependent variable given past realizations or other covariates. It proposes the use of kernel quasi likelihoods upon which formal inference can be based. Gibbs sampling with data augmentation is used to perform the computations related to numerical Bayesian analysis of the model. The method is illustrated with artificial and real data sets.     

湛江市公共建筑空调设计现状及发展研究

本文对广东省湛江市公共建筑的空调设计现状作了综述,并对未来的发展,特别是节能环保作了探讨。     

谈影响汽车仪表板支架质量的相关因素

仪表板支架总成不仅是仪表板系统的内部骨架,还是仪表板系统与白车身总成的连接支架,起到支撑仪表板系统的作用,为仪表板系统安装提供部件,以增加整个车身的强度.文章对某公司不同车型仪表板支架进行比较分析,概述了影响汽车仪表板支架质量的相关因素,从而获得影响汽车仪表板支架质量的相关信息,为后续仪表板支架的设计、制造及质量控制提供参考