Minimum breakdown designs in blocks of size two

In scientific investigations, there are many situations where each two experimental units have to be grouped into a block of size two. For planning such experiments, the variance-based optimality criteria like A-, D- and E-criterion are typically employed to choose efficient designs, if the estimation efficiency of treatment contrasts is primarily concerned. Alternatively, if there are observations which tend to become lost during the experimental period, the robustness criteria against the unavailability of data should be strongly recommended for selecting the planning scheme. In this study, a new criterion, called minimum breakdown criterion, is proposed to quantify the robustness of designs in blocks of size two. Based on the proposed criterion, a new class of robust designs, called minimum breakdown designs, is defined. When various numbers of blocks are missing, the minimum breakdown designs provide the highest probabilities that all the treatment contrasts are estimable. An exhaustive search procedure is proposed to generate such designs. In addition, two classes of uniformly minimum breakdown designs are theoretically verified.     

Acceptance Sampling Plans from Truncated Life Tests Based on the Birnbaum–Saunders Distribution for Percentiles

Time to failure due to fatigue is one of the common quality characteristics in material engineering applications. In this article, acceptance sampling plans are developed for the Birnbaum–Saunders distribution percentiles when the life test is truncated at a pre-specified time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given customer's risk. The operating characteristic values (and curves) of the sampling plans as well as the producer's risk are presented. The R package named spbsq is developed to implement the developed sampling plans. Two examples with real data sets are also given as illustration.     

Customer negative events and employee service sabotage: The roles of employee hostility,personality and group affective tone

Abstract

In the service industries, customer negative events towards a service provider, such as unreasonable demands or low-quality interpersonal treatment, might trigger service sabotage behaviours by the employee in response. Mitigating the problems associated with customer negative events is therefore an important issue for both practitioners and researchers. In the present study, we incorporate the perspectives of affective events theory into our research framework to clarify the mechanisms and boundary conditions of the customer negative event–service sabotage relationship in the context of face-to-face service. Specifically, we theorize and examine whether customer negative events lead to employee service sabotage through emotional reactions of the service worker (i.e. state hostility) and whether their personality traits (i.e. extraversion and neuroticism) and the work unit context (i.e. group affective tone) moderate this process. The sample was composed of 195 hairstylists and 61 managers from 61 hair salons in Taiwan. The results of hierarchical linear modelling showed that hairstylists' state hostility mediated the negative event–sabotage relationship. In addition, hairstylists' neuroticism and the affective tone of the unit moderated the relationship between negative events and state hostility, which in turn predicted service sabotage. The theoretical and practical implications of the findings are also discussed.     

Teaching Note—Creating an Integrative Research Learning Environment for BSW and MSW Students

ABSTRACT

Research courses are often the least popular among BSW and MSW students because the connection between social work practice and research is not always evident. This teaching note introduces the structure of the Social Work integrative Research Lab (SWiRL), which was implemented in a social work program without a doctoral program at a large public university. SWiRL offers students opportunities to engage meaningfully in a variety of social work research projects using a framework of nested mentoring. Students gain hands-on research experience while they develop confidence, leadership, and mentoring skills within this structure. The authors discuss recommendations for other social work programs that do not have doctoral education or an established research environment.     

Regression model selection—a residual likelihood approach

Summary. We obtain the residual information criterion RIC, a selection criterion based on the residual log-likelihood, for regression models including classical regression models, Box–Cox transformation models, weighted regression models and regression models with autoregressive moving average errors. We show that RIC is a consistent criterion, and that simulation studies for each of the four models indicate that RIC provides better model order choices than the Akaike information criterion, corrected Akaike information criterion, final prediction error, C _p and R _adj², except when the sample size is small and the signal-to-noise ratio is weak. In this case, none of the criteria performs well. Monte Carlo results also show that RIC is superior to the consistent Bayesian information criterion BIC when the signal-to-noise ratio is not weak, and it is comparable with BIC when the signal-to-noise ratio is weak and the sample size is large.     

Model selection criteria for dual-inflated data

ABSTRACT

Inflated data are prevalent in many situations and a variety of inflated models with extensions have been derived to fit data with excessive counts of some particular responses. The family of information criteria (IC) has been used to compare the fit of models for selection purposes. Yet despite the common use in statistical applications, there are not too many studies evaluating the performance of IC in inflated models. In this study, we studied the performance of IC for data with dual-inflated data. The new zero- and K-inflated Poisson (ZKIP) regression model and conventional inflated models including Poisson regression and zero-inflated Poisson (ZIP) regression were fitted for dual-inflated data and the performance of IC were compared. The effect of sample sizes and the proportions of inflated observations towards selection performance were also examined. The results suggest that the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) are more accurate than the Akaike information criterion (AIC) in terms of model selection when the true model is simple (i.e. Poisson regression (POI)). For more complex models, such as ZIP and ZKIP, the AIC was consistently better than the BIC and CAIC, although it did not reach high levels of accuracy when sample size and the proportion of zero observations were small. The AIC tended to over-fit the data for the POI, whereas the BIC and CAIC tended to under-parameterize the data for ZIP and ZKIP. Therefore, it is desirable to study other model selection criteria for dual-inflated data with small sample size.     

大学生学习环境乐趣量表编制:方法与原则

本研究依据工作场所乐趣的相关理论,研拟讨论问题,透过大学生深入访谈,厘清大学生学习环境乐趣量表的各个因素与问题,并针对大学生问卷调查进行探索性因素分析与二阶验证性因素分析.研究结果表明,大学生学习环境乐趣量表可分三大因素:同侪互动、环境乐趣营造、学习气氛,量表具有良好的结构效度和效标关联效度,各项指针显示具有较好的稳定性和一致性.     

Inference of δ = P(X < Y) for Burr XII distributions with record samples

Let X and Y follow independent Burr type XII distributions, which share a common inner shape parameter. The maximum likelihood estimator of the parameter δ = P(X < Y) is studied based on record samples. The existence and uniqueness of the maximum likelihood estimator of δ based on record samples are established. When the inner shape parameter is known, an exact confidence interval of δ is derived; otherwise, the Fisher information matrix and two bootstrap methods are used to obtain three approximate confidence intervals of δ. The performances of the proposed methods are evaluated via Monte Carlo simulation. Two examples are provided for illustration.     

Social Diversity,Institutions and Trust: A Cross-National Analysis

This cross-national investigation examines hypotheses derived from two major alternative perspectives on the determinants of trust in contemporary societies. Is a society’s level of generalized trust a function of its ethnic composition, or of its type of governance and political system? The argument that social diversity (ethnic, linguistic, and religious) leads to lower levels of trust, at least in the short run, is assessed with cross-national data (N = 98). Two hypotheses derived from this perspective are not confirmed. The alternative rational governance argument, which holds that trust is a function of rational governance, stable democracy, and civil rights is also assessed. Three hypotheses derived from this political perspective reveal mixed results. The findings highlight the complex interplay of multiple factors in shaping a society’s overall level of generalized trust.     

Minimum winning versus oversized coalitions in public finance: the role of uncertainty

This paper extends Persson et al.’s (J Polit Econ 108:1121–1161, 2000) simple legislature in the context of public finance with certainty to uncertainty. In our uncertain world, oversized coalitions (OSCs) as well as minimum winning coalitions (MWCs) may arise in equilibrium, and the agenda setter’s proposed policy may fail to receive a majority support. This is in marked contrast to the certain world, in which only MWCs can arise in equilibrium and the agenda setter’s proposal never fails to pass. When OSCs arise, we show that both public good provision and redistribution are likely to achieve their first-best solution, even if the legislature is simple.