Setting aside mood-biased thoughts and judgements: theory-based bias correction / Apartar las opiniones y los pensamientos sesgados por el estado de ánimo: la corrección del sesgo basada en teoría

Abstract

When people perceive their thoughts and judgements as unduly affected by some biasing factor (in themselves or in the judgement setting), they often attempt to avoid or remove those biases. Theories describe different psychological mechanisms guiding these efforts. We review the primary theories of bias correction and focus on the use of naive theories of bias in attempts to form accurate assessments of judgement targets. One distinguishing feature of the theory-based correction approach is its ability to deal with corrections for contrastive biases (i.e., biases that lead judgements in a direction opposite to the meaning of the biasing factor). Another is that any perceived biasing factor can result in corrections, even when those factors are not easily mapped into the kinds of mental representation mechanisms that form the heart of alternative approaches. We use corrections for mood-based biases to illustrate the utility of the theory-based correction approach, even though many mood-based biases can be conceptualized in ways that fit alternative approaches to bias correction. Implications for correction of many potential types of biases are discussed.     

External environmental change and transparency in grassroots organizations in China

As the nonprofit sector emerges in China, so too do the concerns about nonprofit transparency. In response to calls for increased nonprofit information disclosure and the lack of related research, this study examines changes in transparency of Chinese grassroots nonprofit organizations. Using the first national Survey of Transparency on Grassroots Organizations (2013–2015), this study finds that Chinese grassroots nonprofit transparency has increased in the past few years. Applying resource dependence theory in China‐specific social contexts, this study highlights the importance of external resource to nonprofit transparency. These findings contribute empirical evidence to the nascent literature on nonprofit transparency and provide implications for enhancing nonprofit transparency in China and beyond.     

Regression Diagnostic under Model Misspecification

We propose two novel diagnostic measures for the detection of influential observations for regression parameters in linear regression. Traditional diagnostic statistics focus on the effect of deletion of data points either on parameter estimates, or on predicted values. A data point is regarded as influential by the new methods if its inclusion determines a significantly different likelihood function for the parameter of interest. The concerned likelihood function is asymptotically valid for practically all underlying distributions whose second moments exist.     

Health Service Utilization and Expenditure of the Elderly in China

Using data from the China Health and Retirement Longitudinal Survey, we employed counterfactual propensity score matching to examine the association between income and health service utilization and expenditure of the elderly in China. The results demonstrated that income did not differentiate whether the poor elderly in China visited a doctor when suffering an illness, but their expenditure on medical treatment was significantly associated with income. Subgroup analysis further indicated that the medical demands of female, older, and rural elderly were more likely to be reduced by their income compared with their counterparts.     

Needs assessment: an Internet information system for Taiwan's professionals working with adult survivors of childhood sexual abuse

The problem of childhood sexual abuse (CSA) continues as a serious public health issue in Taiwan. However, the current service delivery systems in Taiwan are fragmented and fraught with a variety of problems, similar to those reported in Western society. Because of the sensitive nature of CSA, the development of an internet information system may be helpful in treatment efforts. The Delphi method was used to collect data from 15 professionals in the field of CSA in order to assess the need for such an information system. The needed resources, limitations, and implications of the study are discussed.     

Impact of internal migration on the economic well-being of local workers and school dropouts among children in Vietnam

This study examines the impact of migration on local workers and high school dropouts of local students. A conventional approach without addressing the endogeneity issue may lead to biased results. Our endogeneity-corrected estimates show a positive impact of migration on the monthly wages of local workers. A higher share of migrants increases the probability of local workers with employment contracts, social insurance, and holiday pay. Heterogeneity analysis shows that local workers, no matter their educational level, work experience, or occupation sectors, benefit from the inflow of migrants. Finally, migration inflow lowers the probability of high school dropouts among local students. Our findings resulting from rigorous statistical approaches suggest that internal migration produces positive impacts on the economic well-being of local residents, refuting the argument about the adverse effects associated with internal migration.     

A model selection criterion for clustered survival analysis with informative cluster size

We propose a model selection criterion for correlated survival data when the cluster size is informative to the outcome. This approach, called Resampling Cluster Survival Information Criterion (RCSIC), uses the Cox proportional hazards model that is weighted with the inverse of the cluster size. The RCSIC based on the within-cluster resampling idea takes into account the possible variability of the within-cluster subsampling and the possible informativeness of cluster sizes. The RCSIC allows for easy execution for the within-cluster resampling idea without a large number of resamples of the data. In contrast with the traditional model selection method in survival analysis, the RCSIC has an additional penalization for the within-cluster subsampling variability. Our simulations show the satisfactory results where the RCSIC provides a more robust power for variable selection in terms of clustered survival analysis, regardless of whether informative cluster size exists or not. Applying the RCSIC method to a periodontal disease studies, we identify the tooth loss in patients associated with the risk factors, Age, Filled Tooth, Molar, Crown, Decayed Tooth, and Smoking Status, respectively.