The effects of sampling on measures of association between variables based on sample means

Many studies that involve people's perceptions or behaviors focus on aggregate rather than individual responses. For example, variables describing public perceptions for some set of events may be represented as mean scores for each event. Event mean scores then become the unit of analysis for each variable. The variance of these mean scores for a variable is not only a function of the variation among the events themselves, but is also due to the variation among respondents and their possible responses. This is also the case for the covariances between variables based on event mean scores. In many contexts the variance and covariance components attributable to the sampling of respondents and their responses may be large; these components can be described as measurement error. In this paper we show how to estimate variances and covariances of aggregate variables that are free of these sources of measurement error. We also present a measure of reliability for the event means and examine the effect of the number of respondents on these spurious components. To illustrate how these estimates are computed, forty-two respondents were asked to rate forty events on seven risk perception variables. Computing the variances and covariances for these variables based on event means resulted in relatively large components attributable to measurement error. A demonstration is given of how this error is removed and the resulting effect on our estimates.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

Expected Practices in Background Checking: Review of the Human Resource Management Literature

In recent years we have seen a growing attention to the issue of background checks. Research on pre-employment inquires suggests that job candidates engage in extensive misrepresentation of academic and work credentials listed on resumes and job applications. An employer who fails to perform a thorough background check on a prospective employee may be vulnerable to the charges of negligent hiring or employment discrimination. Based on a review of the scientific and professional literature in human resource management, we defined expected management practices in background checking including the need to understand the job requirements, methods of background checks, the extensiveness of the background checks, the role of the application forms, and the use of interviews. Recent legal cases are also included to illustrate what practices are or are not defensible.

Urban travel alternatives: Models for individual and collective preferences

A method of estimating preference functions for alternative urban travel modes using non-metric scaling and conjoint measurement is introduced. The method treats travel alternatives as alternative collections of generic attributes and disaggregates preference orderings for alternative modes into components associated with the generic attributes. Preference functions are fitted for individual respondents and alternative methods of estimating collective preference functions for the group of respondents are examined. Particular attention is given to the error associated with aggregating individual responses. The methods are designed to be effective with relatively modest quantities of survey data.     

Robust testing procedures of process locations

In many manufacturing and service industries, the quality department of the organization works continuously to ensure that the mean or location of the process is close to the target value. In order to understand the process, it is necessary to provide numerical statements of the processes that are being investigated. That is why the researcher needs to check the validity of the hypotheses that are concerned with some physical phenomena. It is usually assumed that the collected data behave well. However, sometimes the data may contain outliers. The presence of one or more outliers might seriously distort the statistical inference. Since the sample mean is very sensitive to outliers, this research will use the smooth adaptive (SA) estimator to estimate the population mean. The SA estimator will be used to construct testing procedures, called smooth adaptive test (SA test), for testing various null hypotheses. A Monte Carlo study is used to simulate the values of the probability of a Type I error and the power of the SA test. This is accomplished by constructing confidence intervals of the process mean by using the SA estimator and bootstrap methods. The SA test will be compared with other tests such as the normal test, t test and a nonparametric statistical method, namely, the Wilcoxon signed-rank test. Also, the cases with and without outliers will be considered. For the right-skewed distributions, the SA test is the best choice. When the population is a right-skewed distribution with one outlier, the SA test controls the probability of a Type I error better than other tests and is recommended.     

The moderating effect of management behavior for Lean and process improvement

It is commonly agreed that the success of Lean management is not only determined by its technical practices, but also by the so-called soft practices such as behavior and actions of employees and management. Lean Management behavior is in itself paradoxical in nature as it incorporates technical aspects (e.g., fact-based management, analysis and adhering to the standard operating procedures for sake of efficiency) and social, follower-related aspects (e.g., promotion of employee responsibility to continuously improve their work processes). In this paper, we investigate the (moderating) effect of some important Lean related management actions on the relationship between Lean and the level of process improvement: i) envisioning and communicating the meaning of Lean, ii) setting goals and active steering on improvement performance metrics and ii) encouraging continuous improvement. Survey data of 178 responses from Dutch organizations, shows that these management actions have a positive effect on both Lean and the level of process improvement. In addition, active steering on performance improvement has a reinforcing effect on the relationship between Lean and process improvement. For respondents with a low level of steering on performance improvement Lean does not lead to process improvement, while it does for respondents with average and high levels of steering on performance improvement. The more management operates on performance improvement, the more Lean will result in a higher level of process improvement.     

理化检测实验室标准物质的期间核查

目的:保持理化检测实验室标准物质校准状态的置信度。方法:结合理化检测实验室标准物质期间核查的工作实际,就期间核查的程序、间隔、方法和结果评价等方面进行探讨。结果:对标准物质期间核查的程序、间隔、方法和结果评价等方面实施有效控制,是确保标准物质的稳定性和准确性的有力措施。结论:以GB/T27025-2008/ISO/IEC17025:2005《检测和校准实验室能力的通用要求》为依据,根据规定的程序和日程对理化检测实验室标准物质进行核查,以保持其校准状态的置信度,确保理化检测工作质量。     

高职物流专业技能抽查与行业企业需求紧密对接的探讨

物流管理专业技能抽查作为专业建设评价标准和教学质量考核的依据,对专业人才培养目标起着越来越重要的作用.笔者根据指导学生参加专业技能抽查的实际情况,总结了专业技能抽查现状及问题,分析了原因,提出开展专业技能抽查要紧密对接行业企业需求的建议和对策.     

Applicant Identity Congruence in Selection Decision Making: Implications for Alejandro and Consuela

A scenario study was conducted to determine the impact of identity congruence on selection decisions. Participants were provided identical resumes for either Alejandro or Consuela followed by information from reference checks. Hypotheses were developed to test the impact of finding out information that called into question the Latino/a identity of the applicants. Results were strongest for gender and, consistent with the study's hypotheses, participants favored Alejandro prior to the reference check information. The results also demonstrated the importance of conducting reference checks as the evaluations between the applicants were equalized once the additional information was provided. © 2014 Wiley Periodicals, Inc.     

Robustness checks and robustness tests in applied economics

A common exercise in empirical studies is a “robustness check”, where the researcher examines how certain “core” regression coefficient estimates behave when the regression specification is modified by adding or removing regressors. If the coefficients are plausible and robust, this is commonly interpreted as evidence of structural validity. Here, we study when and how one can infer structural validity from coefficient robustness and plausibility. As we show, there are numerous pitfalls, as commonly implemented robustness checks give neither necessary nor sufficient evidence for structural validity. Indeed, if not conducted properly, robustness checks can be completely uninformative or entirely misleading. We discuss how critical and non-critical core variables can be properly specified and how non-core variables for the comparison regression can be chosen to ensure that robustness checks are indeed structurally informative. We provide a straightforward new Hausman (1978) type test of robustness for the critical core coefficients, additional diagnostics that can help explain why robustness test rejection occurs, and a new estimator, the Feasible Optimally combined GLS (FOGLeSs) estimator, that makes relatively efficient use of the robustness check regressions. A new procedure for Matlab, testrob, embodies these methods.     

Bootstrapping the process of model selection: AN econometric example

If a researcher has mined the data (i.e. selected an empirical model based on a series of trial estimates), inferences based on the final set of results are in general incorrect. This note treats the entire data mining process as an estimator and shows how a bootstrapping technique may improve the quality of inference. The method is applied to an empirical example on the deterrent effects of capital punishment.     

Learning and fatigue during choice experiments: a comparison of online and mail survey modes

This study investigates the effect of survey mode on respondent learning and fatigue during repeated choice experiments. Stated preference data are obtained from an experiment concerning high‐speed Internet service conducted on samples of mail respondents and online respondents. We identify and estimate aspects of the error components for different subsets of the choice questions, for both mail and online respondents. Results show mail respondents answer questions consistently throughout a series of choice experiments, but the quality of the online respondents' answers declines. Therefore, while the online survey provides lower survey administration costs and reduced time between implementation and data analysis, such benefits come at the cost of less precise responses. Copyright © 2008 John Wiley & Sons, Ltd.     

Evaluating DSGE model forecasts of comovements

This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. We construct posterior predictive checks to evaluate conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. We find that the additional features incorporated into the Smets–Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.     

Evaluating estimation methods for ordinal data in structural equation modeling

This study examined the performance of two alternative estimation approaches in structural equation modeling for ordinal data under different levels of model misspecification, score skewness, sample size, and model size. Both approaches involve analyzing a polychoric correlation matrix as well as adjusting standard error estimates and model chi-squared, but one estimates model parameters with maximum likelihood and the other with robust weighted least-squared. Relative bias in parameter estimates and standard error estimates, Type I error rate, and empirical power of the model test, where appropriate, were evaluated through Monte Carlo simulations. These alternative approaches generally provided unbiased parameter estimates when the model was correctly specified. They also provided unbiased standard error estimates and adequate Type I error control in general unless sample size was small and the measured variables were moderately skewed. Differences between the methods in convergence problems and the evaluation criteria, especially under small sample and skewed variable conditions, were discussed.     

Testing for Distortions in Performance Measures: An Application to Residual Income‐Based Measures like Economic Value Added

Distorted performance measures in compensation contracts elicit suboptimal behavioral responses that may even prove to be dysfunctional (gaming). This paper applies the empirical test developed by Courty and Marschke (Review of Economics and Statistics, 90, 428–441) to detect whether the widely used class of residual income‐based performance measures—such as economic value added (EVA)—is distorted, leading to unintended agent behavior. The paper uses a difference‐in‐differences approach to account for changes in economic circumstances and the self‐selection of firms using EVA. Our findings indicate that EVA is a distorted performance measure that elicits the gaming response.     

实验室认可中的期间核查

文章对衡器、砝码的期间核查、核查标准及测量过程控制作了介绍与分析。     

Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains

Anchored in the resource‐based view theory, the objective of this research is to empirically analyse the behavioural factors affecting the green supply chain management (GCSM) performance in a fast‐growing emerging economy by taking an empirical data set of 101 responses from personnel in the mining sector. Behavioural factors in green supply chains are still a critical challenge—not yet a well‐explored academic subject—when the focus is on the mining industry of emerging economies like India; the lack of studies in this field could be a factor preventing the Indian mining industry becoming more green. In terms of methodology, original survey data were processed through AMOS 4.0, adopted for assessing the causal connection among the six constructs, that is, top management support, teamwork, workplace culture, resistance to change, green innovation, and green motivation. We further explore the input from the human side of GCSM by highlighting that top management support and green motivation are the most crucial behavioural factors that influence GCSM in the Indian mining sector. The study will be helpful for mining companies because it will enable them to identify the areas that require their attention for enhancing GCSM performance related to behavioural aspects.     

The effect of questionnaire length on survey response

Survey textbooks suggest that long questionnaires should be avoided, and a careful reading of the available empirical evidence confirms the negative effects of substantial length on both response rates and the quality of those responses which are obtained. Data is presented from a lengthy survey in Britain in 1987. Analysis of reasons for nonresponse to this survey suggest that length may indeed have been a significant disincentive to respond for many. However, no effect of length was found on item quality as measured by the number of responses given to open-ended questions. Unexpectedly, the variance in number of responses was greater when the questions were asked later in the questionnaire. The results are interpreted as resulting from the greater power that respondents gain as the survey proceeds.     

Proposition of a structural model for business value creation based on circular business models,innovation, and resource recovery in the pet industry

Based on the assumptions of circular economy, resource recovery, and innovation, the aim of this research is to analyze the relationship between innovation constructs, resource recovery practices, Circular Economy Business Models (CEBMs), and value creation in a fast-growing emerging economy. A set of empirical data from 443 companies in the pet industry were used. In terms of methodology, the study was processed using the SPSS software, using Structural Equation Modeling, with manipulation of primary data. CEBMs demand greater engagement, internalization of initiatives, and synergy with the operating ecosystem of the pet industry. Innovation is present but has the potential for expansion through adherence to the innovation ecosystem and its technological package, partnerships, and relationships so that companies can scale up and create value with the support of resource recovery practices. The results of this study can be useful for companies in the pet industry, as it allows the identification of areas and practices that require their attention to improve organizational performance and create value for the business based on initiatives supported by the assumptions of circular economy and sustainability.     

Determinants of social desirability bias in sensitive surveys: a literature review

Survey questions asking about taboo topics such as sexual activities, illegal behaviour such as social fraud, or unsocial attitudes such as racism, often generate inaccurate survey estimates which are distorted by social desirability bias. Due to self-presentation concerns, survey respondents underreport socially undesirable activities and overreport socially desirable ones. This article reviews theoretical explanations of socially motivated misreporting in sensitive surveys and provides an overview of the empirical evidence on the effectiveness of specific survey methods designed to encourage the respondents to answer more honestly. Besides psychological aspects, like a stable need for social approval and the preference for not getting involved into embarrassing social interactions, aspects of the survey design, the interviewer’s characteristics and the survey situation determine the occurrence and the degree of social desirability bias. The review shows that survey designers could generate more valid data by selecting appropriate data collection strategies that reduce respondents’ discomfort when answering to a sensitive question.