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.     

多元智能理论的神经心理学基础

香港和澳门地区近年积极提倡素质教育 ,美国学者加德纳 (HowardGardner)的多元智能理论 (TheoryofMultipleIntellgences)逐渐受到教育工作者的重视 ,期望这理论可以作为适性教育的指导思想。本文旨在阐释人类心灵的生物构造 ,以及从神经心理学 (neuropsychology)角度分析心灵 (mind)发展的三个层面 (感知的、创意的、统整的 ) ;继而介绍加德纳如何通过介定智能的八项基准 ,来建立至目前为止关于人类认知发展最完整的理论 ;最后尝试根据心灵发展的三个层面 ,辨别“智能”和“智慧”的涵意     

Psychological distress: A hierarchical factor model of the Multiple Affect Adjective Check List (MAACL)

Recent investigations of the structure of psychological distress have indicated that hierarchical models can accommodate both unitary and multifaceted conceptions of distress. The present study tested the hierarchical framework suggested by Zuckerman, Lubin, and Rinck (1983) for the Multiple Affect Adjective Check List (MAACL), a commonly used measure of psychological distress. One- and two-factor models were estimated using maximum-likelihood methods. Results indicated that the two-factor solution, with correlated positive and negative affect factors, provided a significantly better fit to the data than did the omnibus one-factor solution. These results provide further support for hierarchical models of distress.     

Neuropsychological Aspects of Multiple Sclerosis

Since S. Rao's [Neuropsychology of Multiple Sclerosis: A Critical Review, A Journal of Clinical and Experimental Neuropsychology, Vol. 85, pp. 503–542] (1986) seminal review, considerable research has been undertaken on the neuropsychological consequences of multiple sclerosis. This review incorporates the research literature of the last decade in presenting an overview of the current state of our knowledge concerning the etiology, course, symptoms, assessment, consequences, and treatment of multiple sclerosis (MS). The concept of subcortical dementia is revisited in light of the most recent literature documenting the neuropsychological deficits in patients with MS. The view that cognitively heterogeneous patient groups may disguise more specific patterns of focal neuropsychological impairment is considered. A critical review of the recent literature is also presented, detailing the degree to which recent research has addressed the areas of research need identified by Rao in 1986. Given recent advances in our knowledge, the need for more attention to be directed toward the evaluation of rehabilitation and psychological intervention is highlighted.     

Should “multiple imputations” be treated as “multiple indicators”?

Rubin's multiple imputation approach to missing data creates synthetic data sets, in which each missing variable is replaced by a draw from its predictive distribution, conditional on the observed data. By construction, analyses of such filled-in data sets as if the imputations were true values have the correct expectations for population parameters. In a recent paper, Mislevy showed how this approach can be applied to estimate the distributions of latent variables from complex samples. Multiple imputations for a latent variable bear a surface similarity to classical multiple indicators of a latent variable, as might be addressed in structural equation modelling or hierarchical modelling of successive stages of random sampling. This note demonstrates with a simple example why analyzing multiple imputations as if they were multiple indicators does not generally yield correct results; they must instead be analyzed by means concordant with their construction.I am grateful to Frank Jenkins, John Mazzeo, Kentaro Yamamoto, and Rebecca Zwick for comments on earlier versions of this paper.     

目标与非目标身份特征的范畴间差异对多身份追踪的促进作用

以数字和字母为实验材料, 考察了多身份追踪任务中目标与非目标的身份特征范畴性差异对目标追踪与身份识别的影响, 并探讨了其可能的认知加工机制。实验1A让被试追踪5个目标, 并操作目标与非目标的身份特征从无差异(目标与非目标为相同的数字)、范畴内差异(目标与非目标为各不相同的数字)到范畴间差异(目标为各不相同的数字, 非目标为各不相同的字母)变化, 发现随着目标与非目标差异水平的提高, 追踪正确率也显著提高。实验1B通过变化追踪目标数量为4和5操作追踪负荷, 发现中等负荷条件下(目标数量为5)实验结果与实验1A相同, 即目标与非目标身份特征的范畴内差异和范畴间差异均可促进追踪表现。实验2考察了目标与非目标身份特征的范畴间差异水平对追踪表现与身份识别的影响。结果发现随着范畴间差异水平的提高, 被试的追踪表现与身份识别表现都得到了显著提高, 但身份识别表现低于追踪表现。对可能解释以上现象的工作记忆理论、分组理论以及多身份追踪模型进行了分析讨论。     

Affective commitment to the organization, supervisor, and work group: Antecedents and outcomes

Three longitudinal studies investigated the usefulness of distinguishing among employees’ affective commitments to the organization, the supervisor, and the work group. Study 1, with 199 employees from various organizations, found that affective commitments to these entities were factorially distinct and related differentially to their theorized antecedents. Study 2, with a diversified sample of 316 employees, showed that organizational commitment (a) had an indirect effect on turnover through intent to quit, (b) partially mediated the effect of commitment to the supervisor on intent to quit, and (c) completely mediated the effect of commitment to the work group on intent to quit. Study 3, with matched data collected from 194 nurses and their immediate supervisors, determined that (a) commitment to the supervisor had a direct effect on job performance and (b) organizational commitment had an indirect effect on job performance through commitment to the supervisor. However, Study 3 failed to show any effect of commitment to the work group on performance. These findings are interpreted in light of the relative salience of commitment foci with regard to the outcome under study.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.