The Impact of Two MMPI-2-Based Models of Personality in Predicting Driving Behavior. Can Demographic Variables Be Disregarded?
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ABSTRACT:
SUBMITTER: Tinella L
PROVIDER: S-EPMC8000114 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Road traffic injuries are the ninth cause of death across all age groups, globally (WHO, 2015). Many road traffic crashes are caused by Driving Under the Influence (DUI) of alcohol by persons who have previously had their license suspended for the same reason. The aim of this study was to identify specific risk factors and personality characteristics in repeat offenders. The sample was comprised of 260 subjects who were not repeat DUI offenders (DUI-NR), but had a single license suspension between 2010 and 2011; and 97 repeat offenders who received at least two DUI convictions within a period of 5 years. At the time of their first driving license suspension, participants provided their blood alcohol content (BAC) and completed a valid MMPI-2 test. ANOVA and MANOVAs were performed to determine whether there were significant differences in BAC and MMPI-2 profiles between DUI-NR and DUI-R participants and a logistic regression was run to identify whether BAC at the time of the first suspension and specific personality features could predict recidivism. A two-step cluster analysis was run to identify recidivist typologies. Results showed that, relative to DUI-NR participants, DUI-R participants had higher BAC at the time of their first conviction and more problematic MMPI-2 profiles, despite the presence of social desirability responding. The best predictors of recidivism were BAC and the scales of Lie (L), Correction (K), Psychopathic Deviate (4-Pd), Hypomania (9-Ma), and Low Self-Esteem (LSE). Two-step cluster analyses identified two recidivist profiles, according to 32 selected MMPI-2 validity, clinical, content, supplementary, and PSY-5 scales. Comparisons with previous research are discussed and ideas for further study are generated.
| S-EPMC6314619 | biostudies-literature
Project description:ObjectiveThere is a substantial body of evidence that the recidivism of impaired-driving offenders is reduced while an ignition interlock device (IID) is on their vehicles. This study examines changes in driving behaviors and drinking behaviors used by DWI offenders to manage driving with the IID.MethodsA total of 166 IID participants who completed two surveys covering the period from arrest to IID installation (T1) and during IID use (T2) were examined. Four domains were covered: demographics, driving environments and transportation needs, reported driving activity, and reported drinking activities. Participants were on average 38 years old, 43% were female, 35% completed college, 34% had an income of more than $50,000, and 83% were employed. For those who provided it, the mean blood alcohol content (BAC) at arrest was .184 g/dL, with only 8 (5%) individuals below .08 g/dL, and 93 (56%) at over .18 g/dL. About 45% were repeat DWI offenders.ResultsBetween T1 and T2 there was a slight increase in acknowledging public transportation was available (p=.001), an increase in other individuals driving the interlock-equipped vehicle (p=.002), an increase in the number of vehicles in the household not registered to the DWI offender (p< .001), and an increase in the number of participants who reported that driving was important to their lifestyle (p=.008). Initial (T1) expectations about whether the interlock would be a problem were significantly different from actual experiences reported in T2 (p<.001). With respect to alcohol consumption, 14% reported abstinence at T2 compared to 2% at T1 (p=.001) and the number of drinks per drinking occasion decreased from a mean of 4.90 at T1 to 3.14 at T2 (p=.001), but the number of drinking occasions increased by a third (p=.003). The number of drinking locations (p=.001), the frequency of stopping after work for a drink (p=.001), and drinking at a bar all decreased (p<.001).ConclusionsInterlock users make some adjustments in how they drink, the amount they drink, and where they drink. This finding suggests that there may be methods that can be used to extend the benefits of the IID beyond the sanction period.
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Project description:Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.
| S-EPMC6626991 | biostudies-literature
Project description:Assessments and predictions of driving behavior are very important to improve traffic safety. We hypothesized that there were some patterns of driving behaviors, and these patterns had some correlation with cognitive states and personalities. To test this hypothesis, an evaluation of driving status, based on electroencephalography (EEG) and steering behavior in a simulated driving experiment, was designed and performed. Unity 3D was utilized to design the simulated driving scene. A photoelectric encoder fixed on the steering wheel and the corresponding data collection, transmission, and storage device was developed by Arduino, to acquire the rotation direction, angle, angular velocity, and angular acceleration of the steering wheel. Biopac MP 150 was utilized to collect the EEG data simultaneously during driving. A total of 23 subjects (mean age 23.6 ± 1.3 years, driving years: 2.4 ± 1.6 years, 21 males and two females) participated in this study. The Fuzzy C-means algorithm (FCMA) was utilized to extract patterns of driving behavior and the cognitive state within the window width of 20 s. The behaviors were divided into five kinds, i.e., negative, normal, alert, stress, and violent behavior, respectively, based on the standard deviation of steering wheel data. The cognitive states were divided into four kinds, i.e., negative, calm, alert, and tension, respectively, based on the EEG data. The correlation of these data, together with the personality traits evaluated using Cattell 16 Personality Factor Questionnaire (16PF) were analyzed using multiclass logistic regression. Results indicated the significance of the cognitive state and seven personality traits [apprehension (O), rule consciousness (G), reasoning (B), emotional stability (C), liveliness (F), vigilance (L), and perfectionism (Q3)] in predicting driving behaviors, and the prediction accuracy was 80.2%. The negative and alert cognitive states were highly correlated with dangerous driving, including negative and violent behaviors. Personality traits complicate the relationship with driving behaviors, which may vary across different types of subjects and traffic accidents.
| S-EPMC6558165 | biostudies-literature
Project description:Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
| S-EPMC7395458 | biostudies-literature
Project description:Although it is well-known that teaching is one of the most stressful jobs, teacher burnout during the COVID-19 pandemic has not been thoroughly investigated. The main aims of this study were to identify distinct teacher burnout profiles and examine their association with HEXACO personality factors and sociodemographic variables. Data were collected from 522 teachers (77% women; Mage = 37.45 years, SD = 9.28) in November 2021. Latent profile analysis (LPA) identified five latent profiles: "No burnout risk" (41.3%), "Low burnout risk" (21.9%), "Cynics" (7.7%), "Exhausted and cynics" (16.1%), and "High burnout risk" (13%). Our results showed a significant correlation between all six HEXACO personality traits and teacher burnout based on the variable-centered method, but the LPA highlighted that only the emotionality trait was antecedent of profile membership. In terms of sociodemographic variables, gender and rural/urban teaching environment did not have significant impact on teacher burnout profiles, but professional experience did. This study is the first to explore teacher burnout during the COVID-19 pandemic in relation to dispositional traits based on the HEXACO model using a person-centered approach. Our results can inform specialists about the role of emotionality in the occurrence of teacher burnout and the greater vulnerability of experienced teachers in the context of remote learning. Tailored programs of intervention are necessary.
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Project description:Risk-taking is subject to considerable individual differences. In the current study, we tested whether resting-state activity in the prefrontal cortex and trait sensitivity to reward and punishment can help predict risk-taking behavior. Prefrontal activity at rest was assessed in seventy healthy volunteers using electroencephalography, and compared to their choice behavior on an economic risk-taking task. The Behavioral Inhibition System/Behavioral Activation System scale was used to measure participants' trait sensitivity to reward and punishment. Our results confirmed both prefrontal resting-state activity and personality traits as sources of individual differences in risk-taking behavior. Right-left asymmetry in prefrontal activity and scores on the Behavioral Inhibition System scale, reflecting trait sensitivity to punishment, were correlated with the level of risk-taking on the task. We further discovered that scores on the Behavioral Inhibition System scale modulated the relationship between asymmetry in prefrontal resting-state activity and risk-taking. The results of this study demonstrate that heterogeneity in risk-taking behavior can be traced back to differences in the basic physiology of decision-makers' brains, and suggest that baseline prefrontal activity and personality traits might interplay in guiding risk-taking behavior.
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Project description:ObjectivesExamine the reliability of sociodemographic variables in predicting initiation of breastfeeding within an hour of birth (EarlyBF), using data from 1998, 2003 and 2008-2009.Study designA replication analysis using the Kenya Demographic and Health Survey (KDHS) data collected in 1998, 2003 and 2008-2009. The candidate predictor variables were child's gender, home or health facility place of birth, vaginal or caesarean mode of birth, urban or rural setting, province of residence, Wealth Index and maternal education, occupation, literacy and media exposure.SettingKenya.Participants6375 dyads of mothers aged 15-49 and their children aged 0-23 months (2125 dyads in each of the survey years).ResultsMode of birth and province were statistically significant predictors of EarlyBF in 1998, 2003 and 2008-2009. Children delivered through caesarean section were non-EarlyBF in 1998 (OR 2.63, 95% CI 1.72 to 4.04), 2003 (OR 3.36, 95% CI 1.83 to 6.16) and 2008 (OR 3.51, 95% CI 2.17 to 5.69). The same was true of those living in the Western province in 1998 (OR 2.67, 95% CI 1.61 to 4.43), 2003 (OR 4.92, 95% CI 3.01 to 8.04) and 2008 (OR 6.07, 95% CI 3.54 to 10.39).ConclusionsThe 1998 KDHS data do not provide the basis for reliable prediction of EarlyBF, with reliability conceptualised as replicability of findings using highly similar data sets from 2003 and 2008-2009. Most of the demographic and socioeconomic variables were unreliable predictors of EarlyBF. We speculate that activities in parts or all of Kenya changed the analysis context in the period between 1998 and 2008-2009, and these changes were of a sufficient magnitude to affect the relationships under investigation. The degree to which this is a general problem in child health research is not known, calling for further research to investigate this methodological issue with other health end points and other data.
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Project description:Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.
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Project description:1. Understanding the role of predation in shaping the dynamics of animal communities is a fundamental issue in ecological research. Nevertheless, the complex nature of predator-prey interactions often prevents researchers from modelling them explicitly. 2. By using periodic Leslie-Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator-prey demographic interactions and compared the dynamics of the roe deer-red fox-Eurasian lynx-human harvest system with those of the moose-brown bear-gray wolf-human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula. 3. The functional relationship of both roe deer and moose ? to changes in predation rates from the four predators was remarkably different. Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population ?. Elasticity values of lynx, wolf, fox and bear predation rates were -0·157, -0·056, -0·031 and -0·006, respectively, but varied with both predator and prey densities. 4. Differences in predation impact were only partially related to differences in kill or predation rates, but were rather a result of different distribution of predation events among prey age classes. Therefore, the age composition of killed individuals emerged as the main underlying factor determining the overall per capita impact of predation. 5. Our results confirm the complex nature of predator-prey interactions in large terrestrial mammals, by showing that different carnivores preying on the same prey species can exert a dramatically different demographic impact, even in the same ecological context, as a direct consequence of their predation patterns. Similar applications of this analytical framework in other geographical and ecological contexts are needed, but a more general evaluation of the subject is also required, aimed to assess, on a broader systematic and ecological range, what specific traits of a carnivore are most related to its potential impact on prey species.
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