Measuring Creativity with Planned Missing Data

This paper introduces a method for the assessment of creativity that relies on creativity tasks, a subjective evaluation procedure, and a planned missing data design that offers a drastic reduction in the overall implementation costs (administration time and scoring procedure). This method was tested on a sample of 149 people, using three creativity tasks as a basis. Participants were instructed to produce several ideas in each task and then to select what they considered to be their best two ideas (i.e., “Top 2” procedure; Silvia, Winterstein, Willse, Barona, et al., Psychology of Aesthetics, Creativity, and the Arts, 2 , 2008 and 68). These ideas were then evaluated by a panel of peers and experts. Creativity ratings were analyzed with structural equations; measurement models were estimated for each task and correlations between factor-scores across the three tasks were investigated. Further insights regarding validity are provided through systematic investigation of the relationship between fluency scores, creativity ratings, intelligence tasks, self-reported idea generation abilities, and creative activities and achievements. Overall, the results support the viability of this new approach, providing evidence of convergent and discriminant validity. They are discussed in relation to past research and avenues for further extension are proposed.     

A comparison of sources of baseline data for treatments of problem behavior following a functional analysis

It is common practice in research on the treatment of problem behavior to compare levels of targeted behaviors during treatment to levels when treatment is not in place. Some researchers use data collected as part of a multielement functional analysis as the initial baseline, whereas others collect new baseline data following completion of the functional analysis. We evaluated whether the source of baseline data influences the reliability and efficiency of decision-making. Results suggest that similar decisions are made in regard to treatment efficacy using the different sources of baseline data, but using data from a multielement functional analysis as baseline may save time. Interrater agreement was adequate, but lower for some graphs than has been observed in past studies. Several potential explanations for this discrepancy are discussed.     

Personality in Action: Assessing Personality to Identify an ‘Ideal’ Conscientious Response Type with Two Different Behavioural Tasks

Performance on achievement tests is characterized by an interplay of different individual attributes such as personality traits, motivation or cognitive styles. However, the prediction of individuals' performance from classical self-report personality measures obtained during large and comprehensive aptitude assessments is biased by, for example, subjective response tendencies. This study goes beyond by using behavioural data based on two different types of tasks, requiring different conscientious-related response behaviours. Moreover, a typological approach is proposed, which includes different behavioural indicators to obtain information on complex personality characteristics. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Modelling heterogeneity in behavioral response to peak-avoidance policy utilizing naturalistic data of Beijing subway travelers

Studies of travelers’ response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers’ responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers’ heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current ‘one size fits all’ approach.     

医学生服务社区农村意向的调查与思考

医学生服务社区农村，是加强社区农村卫生服务体系队伍建设的必要条件。通过对不同专业（方向）高年级医学生服务社区农村就业意向的调查和相关资料分析，对我国社区农村医疗卫生服务人才培养模式进行了探讨，为建立健全中国式社区农村医疗卫生服务人才培养体系提供参考。     

Sharing the road with autonomous vehicles: A qualitative analysis of the perceptions of pedestrians and bicyclists

Public perception assessment is important for gaining a better understanding of the acceptance of autonomous vehicles (AVs) and identifying potential ways to resolve public concerns. This study investigated how pedestrians and bicyclists perceived AVs based on their knowledge and road sharing experiences, applying a combined inductive and deductive data analysis approach. Survey responses of pedestrians and bicyclists in Pittsburgh, Pennsylvania, USA collected by Bike Pittsburgh (BikePGH) in 2019, were analyzed in this research. AVs following traffic rules appropriately and AVs driving safer than the human drivers were the most notable positive perceptions towards AVs. Pedestrians and bicyclists showed comparatively fewer negative perceptions towards AVs than positive perceptions. Negative perceptions mostly included a lack of perceived safety and comfort around AVs and trust in the AV technology. Respondents also concerned about AV technology issues (e.g., slow and defensive driving, disruptive maneuver), while sharing the road with AVs. Perceptions of the respondents were significantly influenced by their views on AV safety, familiarity with the technology, the extent respondents followed AV on the news, and household automobile ownership. Regulating AV movement on roadways, developing safety assessment guidelines, and controlling oversights of improper practices by AV companies were the major suggestions from the survey participants. Findings of this study might help AV companies to identify potential improvement needed in AV technology to increase pedestrians and bicyclists acceptance, and policymakers to develop policy guidelines to ensure safe road sharing among pedestrians, bicyclists, and AVs.     

纵向数据的中介效应分析

目前中介效应检验主要是基于截面数据,但许多时候截面数据的中介分析不适合进行因果推断,因而需要收集历时性的纵向数据,进行纵向数据的中介分析。评介了基于交叉滞后面板模型、多层线性模型和潜变量增长模型的纵向数据的中介分析方法及其四个发展。第一,中介效应随时间变化,如连续时间模型、多层时变系数模型。第二,中介效应随个体变化,如随机效应的交叉滞后面板模型和多层自回归模型。第三,中介模型的整合,如交叉滞后面板模型与多层线性模型整合为多层自回归模型。第四,中介检验方法的发展,建议使用Monte Carlo、Bootstrap和贝叶斯法进行纵向数据的中介分析。总结出一个纵向数据的中介分析流程并给出相应的Mplus程序。随后展望了纵向数据的中介分析的拓展方向。     

Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

The polychoric instrumental variable (PIV) approach is a recently proposed method to fit a confirmatory factor analysis model with ordinal data. In this paper, we first examine the small-sample properties of the specification tests for testing the validity of instrumental variables (IVs). Second, we investigate the effects of using different numbers of IVs. Our results show that specification tests derived for continuous data are extremely oversized at all sample sizes when applied to ordinal variables. Possible modifications for ordinal data are proposed in the present study. Simulation results show that the modified specification tests with all available IVs are able to detect model misspecification. In terms of estimation accuracy, the PIV approach where the IVs outnumber the endogenous variables by one produces a lower bias but a higher variation than the PIV approach with more IVs for correctly specified factor loadings at small samples.     

How to handle missing data: A comparison of different approaches

Many researchers face the problem of missing data in longitudinal research. Especially, high risk samples are characterized by missing data which can complicate analyses and the interpretation of results. In the current study, our aim was to find the most optimal and best method to deal with the missing data in a specific study with many missing data on the outcome variable. Therefore, different techniques to handle missing data were evaluated, and a solution to efficiently handle substantial amounts of missing data was provided. A simulation study was conducted to determine the most optimal method to deal with the missing data. Results revealed that multiple imputation (MI) using predictive mean matching was the most optimal method with respect to lowest bias and the smallest confidence interval (CI) while maintaining power. Listwise deletion and last observation carried backward also scored acceptable with respect to bias; however, CIs were much larger and sample size almost halved using these methods. Longitudinal research in high risk samples could benefit from using MI in future research to handle missing data. The paper ends with a checklist for handling missing data.