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Although there is a growing body of research focused on the personality characteristics of victims and offenders, only a few studies have investigated both groups through robust and comprehensive measures of personality. The present study aimed to compare the PAI and MMPI-2-RF profiles between victims and offenders and investigate the influence of adverse childhood experiences (ACEs) on their results. Samples of 107 female victims (age: M = 42.71; SD = 11.25) and 154 male inmates (age: M = 36.51; SD = 12.72) were compared, and statistically significant differences were found on several PAI and MMPI-2-RF scales. While the victims tended to score higher on scales such as Anxiety, Stress, Somatic Complaints and Thought Dysfunction, the inmates scored higher on scales related to Antisocial Traits, Drug Problems, and Aggressiveness-Revised, among others. Both groups reported a large number of ACEs, and linear regression analyses revealed that ACEs predicted PAI and MMPI-2-RF scores. A discriminant analysis also found that specific ACEs accurately discriminate psychological characteristics between victim and offender groups. In conclusion, the PAI and the MMPI-2-RF provided valuable information on the characteristics of victims and inmates, contributing to a better understanding of the nature of victimization and crime perpetration. Full article

The purpose of this study was to verify the relationship between the risk of smartphone dependence, mental health, and personality traits in university students using the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF), and to identify the MMPI-2-RF scales that can predict the risk of smartphone dependence. Of the 772 university students who participated in the study, 163 were in the smartphone overdependence group, accounting for 21.1% of the total survey respondents, which was one in five of those surveyed. High T-scores on the measure indicate greater psychopathology. The smartphone overdependence group had significantly higher T-scores than the general user group on all but three of the MMPI-2-RF scales, and the degree of smartphone overdependence was positively correlated with scores on these scales. There was no difference between the dependent and non-dependent groups on the interpersonal passivity, aesthetic-literary interest, and aggression scales, and scores on these three were not correlated with smartphone dependence. Among the MMPI-2-RF scales, those found to predict the risk of smartphone overdependence were the emotional/internalizing problems, behavioral/externalizing problems, antisocial behavior, cognitive complaints, helplessness/hopelessness, inefficacy, juvenile conduct problems, aggression, interpersonal problems, disconstraint, negative emotionality/neuroticism, and introversion/low positive introversion/low positive emotionality scales. Based on these findings, we propose that effective prevention and intervention for smartphone overdependence must be comprehensive and holistic rather than focusing on specific aspects of mental health or personality. The implications of the findings are discussed. Full article

(1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD. Full article