The Temporal Consistency of Personality Effects: Evidence from the British Household Panel Survey

Personality traits have been posited to function as stable influences on political attitudes and behavior. Although personality traits themselves exhibit high levels of temporal stability, it is not yet known whether the effects of these traits are marked by comparable temporal consistency. To address this question, this research note examines data from Wave 13 (2003–2004), Wave 15 (2005–2006) and Wave 17 (2007–2008) of the British Household Panel Survey (BHPS). Twenty‐seven behavioral and 14 attitudinal dependent variables are studied. Consistency of effects is gauged via a series of multilevel models in which personality effects are permitted to vary by year. High levels of temporal consistency are observed for personality traits as represented by the Big Five framework.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.     

Consequences of voluntary job changes in Germany: A multilevel analysis for 1985–2013

Analyzing the development of the consequences of voluntary job changes in Germany between 1985 and 2013, the study focuses on income gains and job satisfaction increases. Drawing on arguments of the job-search literature on the one hand and the proliferation of choices on the other we investigate whether the returns of job changes have increased or decreased. Results show that income gains have decreased over time while the job satisfaction surplus has remained stable. We further conclude that in determining the outcomes of job changes over time, structural factors seem to be more important than individual ones.     

Impact of time pressure on acceleration behavior and crossing decision at the onset of yellow signal

This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants’ data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions.     

Does personality moderate reaction and adaptation to major life events? Evidence from the British Household Panel Survey

A nationally representative panel study of British households was used to examine the extent to which Big Five personality traits interact with the experience of major life events (marriage, childbirth, unemployment, and widowhood) to predict increases and decreases in life satisfaction following the event. Results show that major life events are associated with changes in life satisfaction, and some of these changes are very long lasting. Personality traits did not have consistent moderating effects on the association between stressful life events and life satisfaction over time.     

When does random responding distort self-report personality assessment? An example with the NEO PI-R

Recent meta-analyses suggest that, notwithstanding almost 100 years of study, concerns about the impact of response biases on the validity of self-report personality scales remain to be justified. This study addresses the topic by demonstrating that random responding is indeed an issue for self-report personality scales, and that limitations associated with base rates exist that affect the ability of moderated multiple regression to evaluate fully the effects of a response bias. Overall, it is suggested that dismissing response biases as a concern for self-report personality assessment is premature when based only on a lack of significant regression moderator effects.     

Probabilistic choice (models) as a result of balancing multiple goals

We conceptualize probabilistic choice as the result of the simultaneous pursuit of multiple goals in a vector optimization representation, which is reduced to a scalar optimization that implies goal balancing. The majority of prior theoretical and empirical work on such probabilistic choice is based on random utility models, the most basic of which assume that each choice option has a valuation that has a deterministic (systematic) component plus a random component determined by some specified distribution. An alternate approach to probabilistic choice has considered maximization of one quantity (e.g., utility), subject to constraints on one or more other quantities (e.g., cost). The multiple goal perspective integrates the results regarding the well-studied multinomial logit model of probabilistic choice that has been derived from each of the above approaches; extends the results to other models in the generalized extreme value (GEV) class; and relates them to recent axiomatic work on the utility of gambling.     

Trans employees,transitioning, and job satisfaction

While trans employees have become more widely recognized in society, our understanding of their work experiences remains underdeveloped. In the current study, we investigate whether transitioning is associated with job satisfaction in England, Wales and Scotland. Using longitudinal data collected before and after sex reassignment surgery associations between job satisfaction and mental health/life satisfaction are examined for trans men and women. The estimations suggest that employees experience higher job satisfaction, mental health and life satisfaction after sex reassignment surgery than before. In addition, the estimations suggest that after sex reassignment surgery, the associations between job satisfaction and mental health/life satisfaction are stronger than before. Moreover, the estimations suggest that employees not only experience higher job satisfaction after sex reassignment surgery, but also during transitioning. The results suggest that, firms should not treat transitioning employees in a biased way, since their transition might entail positive personal and workplace advancements.     

Processing time predictions of current models of perception in the classic additive factors paradigm

This article explores the consequences for factorial additivity in a Sternberg [(1969). The discovery of processing stages: Extensions of donders method In: W.G. Koster (Ed.), Attention and performance II, Acta Psychologica, 30, 276-315] additive-factors paradigm of the assumptions adopted by models of perception that relate the representation of a stimulus to decision time. Three example models, signal detection theory with the latency-distance hypothesis, stochastic general recognition theory, and a random walk model of exemplar classification, are interrogated to determine what type of interaction they predict factors will yield in a hypothetical factorial (choice) reaction time experiment in which the ‘empirical’ factors’ effects are manifest as parameter changes. All frameworks make the critical assumption that decision time depends on the perceptual representation of the stimulus as well as the architecture. As a consequence, nonadditivity of factors thought to affect different “stages” in the classical approach emerges within the current modeling approach. The nature of this influence is revealed through analytic investigations and simulation. Earlier empirical findings of failures of selective influence that have defied adequate explanation are reinterpreted in light of the present findings.