Exploring multi-homing behavior of ride-sourcing drivers via real-world multiple platforms data

Multi-homing behavior refers to the behavior that ride-sourcing drivers simultaneously register and sequentially provide services on multiple ride-sourcing platforms. The multi-homing behavior of ride-sourcing drivers significantly impacts the competition among multiple ride-sourcing platforms in a competitive market. To better understand the multi-homing behavior, we present exploratory evidence on the factors that influence drivers' platform switching behavior. The RF-MNL (random forest multinomial logistic regression) framework is applied to analyze multi-homing driver behavior in a competitive ride-sourcing market. Multinomial logistic regression (MLR) is adopted to model the platform switching behavior of multi-homing drivers. The random forest is employed to seek the best combination of variables for the MLR model, which is calibrated by using the one-month multi-platform ride-sourcing data in Hangzhou, China. A variety of explanatory variables that influence ride-sourcing drivers' multi-homing behavior are estimated. The results show that the driver's socio-demographic characteristics, income level, bonus income (e.g., long-distance price rise), and work time related factors (e.g., the time gap of order dispatching, and wait time) play an essential role in determining the platform switching decision. This study corroborates the evidence of significant factors that impact drivers' switching from one ride-sourcing platform to another, which can support decision-making for ride-sourcing platforms to attract drivers serving the platform exclusively. We also examine how heterogeneity in drivers' multi-homing tendencies affects the platform's policy. To our best knowledge, this paper is one of the first quantitative studies that empirically reveal the commonly observed multi-homing behavior of ride-sourcing drivers by exploring real-world city-wide data collected on multiple platforms.     

IRT_Δb法和修正LR法对矩阵取样DIF检验的有效性

矩阵取样测验包含多个题册,单个题册的总分不能直接作为匹配变量用于 DIF 检测。本研究首先基于模拟数据,同时采用 I RT_Δb法,以及用 I RT模型估计的考生能力作为匹配变量修订后的 L R法对矩阵取样测验进行DIF检测,分析二者进行DIF检测的有效性及其相关影响因素;并根据已有的LR法DIF判断标准划定出I RT_Δb法分类标准;最后使用实证数据加以验证。结果显示：矩阵取样测验中, I RT_Δb法和修正LR法均能较好地区分DIF量不同的题目;样本量、题册中DIF题目的比例和考生群体间真实能力的差异对两种方法的检验力、犯I类错误的概率和分类结果都有较大影响。     

Direction of Effects in Multiple Linear Regression Models

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D’Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.     

Multiple regression analyses in artificial-grammar learning: The importance of control groups

In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis can be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training when making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions—judgements of control participants were treated as if they were based on knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.     

The Longevity Effects of Religious and Nonreligious Participation: A Meta‐Analysis and Meta‐Regression

Scholars have long argued that the reduced mortality risk associated with frequent participation in religious services derives from two sources: social participation and religious belief efficacy. In contrast, the reduced mortality risk associated with participation in nonreligious groups is thought to derive solely from the social participation component. This study tests the religious efficacy hypothesis by comparing the effects of religious participation with nonreligious participation using meta‐analyses of 312 mortality risk estimates from 74 publications (providing data on more than 300,000 persons). We found no significant difference between the mean hazard ratio (HR) for low religious participation (HR, 1.32; 95% CI, 1.24–1.41) and the mean HR for low nonreligious participation (HR, 1.25; 95% CI, 1.17–1.33). These findings suggest that the positive health effects of religious participation may largely be attributed to the social participation component, rather than to the religious component of the act.     

Meta-Analysis of Pragmatic and Theoretical Research: A Critique

Meta-analysis refers to a set of statistical procedures used to summarize and integrate many empirical studies that focus on one issue. This numerical method of integrating research findings is said to be superior to the narrative type of reviews because it is more objective, reliable, and rigorous. Moreover, the meta-analytic approach is supposedly capable of resolving research controversies, strengthening empirical hypotheses, and discovering new relationships among variables. In this study, these claims are examined and found to be wanting. Some objections to the use of meta-analysis as a means of substantiating theoretical assertions are raised with reference to the rationale of experimentation and to how knowledge evolves. It is concluded that it is inappropriate to apply meta-analysis to integrate theoretical research.     

A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change. While their proposed method can be applied to many kinds of outcome variables, they focused mainly on linear models for normally distributed outcomes. Practical guidelines and extensions are required to implement this methodology with other types of repeated outcome measures common in behavioral research. In this article, we discuss implementation of this method with repeated binary outcomes. We explain how to compare adaptive interventions in terms of various summaries of repeated binary outcome measures, including average outcome (area under the curve) and delayed effects. The method is illustrated using an empirical example from a SMART study to develop an adaptive intervention for engaging alcohol- and cocaine-dependent patients in treatment. Monte Carlo simulations are provided to demonstrate the good performance of the proposed technique.     

Religious belief and intelligence: Worldwide evidence

Is there a positive impact of intelligence on religious disbelief after we account for the fact that both average intelligence and religious disbelief tend to be higher in more developed countries? We carry out four beta regression analyses and conclude that the answer is yes. We also compute impact curves that show how the effect of intelligence on atheism changes with average intelligence quotients. The impact is stronger at lower intelligence levels, peaks somewhere between 100 and 110, and then weakens. Bootstrap standard errors for our point estimates and bootstrap confidence intervals are also computed.     

Towards a Relational Affective Theory of personality disorder

This paper introduces a Relational Affective Formulation of severe narcissistic and borderline disorders. The formulation is grounded in psychoanalytic and neuroscientific theory, clinical observation and therapeutic work with hospitalised patients. It provides a conceptual framework for psychodynamic psychiatrists, therapists and psychosocial practitioners working in teams. Paying particular attention to the central place of affect, projective and introjective processes, claustro-agoraphobic phenomena, regression and the place of both deficits and dynamic defences in the failure of symbolisation, the case is made that this framework is suited to therapeutic work with patients who are highly suicidal and complex. The application of this formulation to service design and treatment pathways is described along with a summary of the body of psychoanalytic thinking which contributed to its development. The formulation is the cornerstone of a practice guide currently under development.     

The impact of the health professional on the individual's experience of cancer: A psychodynamic perspective

Abstract

This paper examines how interactions with health professionals may come to influence the patient's experience of cancer. It considers the idea that the experience of cancer returns an individual to a state of dependence and lack of integration similar to that of the young infant. The subsequent interactions with health professionals can then be considered in light of its ability to hold the patient's un-integrated self and facilitate its re-integration.