Factorial validation and measurement invariance of the French version of the multidimensional competitive orientation inventory (FVMCOI) in the sport field

IntroductionLittle is known about the importance of the adaptive, maladaptive and neutral facets of competitive orientations among French athletes.ObjectiveThe aim of this study is to test the factorial validation and measurement invariance of the French version of the multidimensional competitive orientation inventory (FVMCOI) from the original version (MCOI, Orosz et al., 2018), which included 12 items and four factors (i.e., hypercompetitive orientation, self-developmental competitive orientation, anxiety-driven competition avoidance, and lack of interest toward competition).MethodA series of three complementary studies was carried out with a total sample of 891 competitors.ResultsFollowing cross-cultural validation methods a preliminary version of the FVMCOI was constructed and the clarity of its items was established (study 1). Then, exploratory structural equation modeling (ESEM) confirmed that the four-factor model for the FVMCOI was factorially valid, and consistent with the model of the original version. As such, the multiple-group invariance tests showed that the FVMCOI was partially invariant across both age and gender and fully invariant across type of sport, level of competition and over time (study 2). Finally, the concurrent validity of the FVMCOI was demonstrated through its expected relationships with achievement goals, competitive aggressiveness and anger in sport (study 3).ConclusionThe establishment of the FVMCOI extends the research in sport psychology by allowing an extensive psychometric study of the MCOI in the sport domain and providing deeper insights into the healthy, unhealthy, and neutral nature of competitive orientation in this context. The FVMCOI's potential practical applications in sport context, with some limitations and perspectives of this study, are also discussed.     

Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China

Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders’ psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of “Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors”. The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs.     

Autonomous vehicle safety: Understanding perceptions of pedestrians and bicyclists

Autonomous vehicle (AV) technologies have been rapidly advancing. One benefit of AVs is that the technology could eliminate many driver errors and also mitigate many pedestrian and bicyclist collisions. Real-world AVs have been tested in many cities. Five companies are running around 50 AVs in Pittsburgh, following the autonomous testing guidelines. BikePGH, a non-profit organization located in Pittsburgh, Pennsylvania conducted a follow-up survey in 2019 (the first survey was conducted in 2017) to understand non-motorists’ opinions of AVs. This study examined how pedestrians and bicyclists perceived AV safety based on their understanding and experiences. At first, this study performed a comparison group test to determine which questions vary by participants’ AV safety rating. The responses were later analyzed with a data mining method known as ‘association rules mining.’ A new performance measure, known as the rule power factor, was then used to identify the significant patterns in the form of rules. The participants also provided their thoughts in responses to the open-ended questions. Using Latent Dirichlet Allocation (LDA), a topic modeling algorithm, 40 topic models were developed based on five open-ended questions. The findings show that the non-motorists showed comparatively fewer negative opinions towards AVs than positive assessments. The results also show that perception patterns vary by the participant’s rating on AV safety. Findings of this study would be beneficial for the AV stakeholders in making AVs and roadways safer for non-motorists.     

Correlations of multiple rider behaviors with self-reported attitudes,perspectives on traffic rule strictness and social desirability

Powered Two-Wheeler (PTW) riders constitute a very vulnerable group of road users, while riding a PTW is considerably more dangerous than using any other motor vehicle. Behavioral issues have been identified major moderating factors to PTW crashes, as riders display great variability in their attitudes towards road safety. Τhe aim of this paper is to present a thorough, overarching structure of relationships correlating various unsafe stated PTW rider behaviors (riding after alcohol consumption, speeding, helmet use and texting) with several self-reported attitude parameters and factors regarding rider perspectives on traffic rule strictness and social desirability. A structural equation model (SEM) was developed using data from the ESRA2 survey, which provided a broad sample encompassing 5,958 respondent riders from 32 countries. Numerous statistical relationships were discovered and quantified correlating the four examined unsafe rider behaviors with eight latent unobserved variables. All covariances between unsafe behaviors were found to be positive and statistically significant, indicating that a rider who will engage more frequently in every single one of the four examined unsafe riding behaviors is more likely to also engage in all the others as well.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.     

Belief in Hell and Parenting Priorities Concerning Child Independence and Obedience: Does Economic Context Matter?

This study examines the ways that belief in hell is associated with parental goals concerning child independence and obedience. Further, it assesses how this association is contingent upon economic circumstances of a nation, reflected by a country's Gross Domestic Product (GDP) per capita. Multilevel analyses with data from the sixth wave of the World Values Survey (2010-2014) show that belief in hell is associated with a greater likelihood of opposing independence and endorsing obedience. Yet, these observed associations differ across national economic context. Specifically, the negative association between belief in hell and parental endorsement of independence is greater in countries that are more economically developed. Similarly, the positive association between belief in hell and parental endorsement of obedience is greater in countries that are more economically developed. The theoretical implications of these findings are discussed regarding the interplay of religion, parental values about child socialization, and social context.     

A brief review of technology-based antecedent training procedures

This brief review summarizes recent research on technology-based antecedent staff training procedures, highlights key methodological components, and concludes with potential directions for future research.     

Neuroticism as the intensity,reactivity, and variability in day-to-day affect

Neuroticism has been linked to typical levels of affect, affect reactivity to negative events, and variability in affect over time. However, the intercorrelations among these characteristics make it unclear whether neuroticism reflects unique variance in each of these aspects of emotional life. Data from two daily-diary samples revealed that neuroticism was associated with average levels and variability of positive and negative affect and reactivity of negative affect to stressors, but was only uniquely related to mean levels of positive and negative affect. Findings highlight the substantial overlap in affect indices, suggesting that mean levels of affect, at the very least, are at the core of neuroticism, and reveal the need for further research using more nuanced approaches.     

Inherited and Environmental Moderators of Mother-Child Behavioral Contingency and Contingent Negativity at 27 Months

Previous research has shown that as depressive symptomology increases, mothers tend to show withdrawn or harsh interaction patterns with their children, and the quality of these interaction patterns have subsequently been linked to child behavior problems. However, little research has examined bidirectional influences between mothers and their children, and how these moment-to-moment contingencies differ based on heritable and environmental characteristics. We used data from the Early Growth and Development Study a prospective adoption study to examine how adoptive mothers’ depressive symptoms and children’s heritable tendencies for negative affectivity interact to predict the quality of mother-child interactions at child age 27-months. Results detected two distinct dyadic interaction patterns. The first was a withdrawn interaction style and was observed in children with a high heritable tendency for negative affect. In that style, mother and child interactions were not contingent upon each other, suggesting a lack of joint engagement. The second was a volatile interaction style, observed in children with a low heritable tendency for negative affect. In these cases, mother and child interactions were highly contingent but negative. Our findings demonstrate essential differences in how dyadic interaction patterns vary according to level of depressive symptomology and heritable tendency for negative affect.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.