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Just my type : understanding personality profiles
Personality tests have become increasingly popular in the digital age. Examine a wide variety of online personality assessments, and learn how to distinguish useful applications from biased typecasting.
Book
Two genetic analyses to elucidate causality between body mass index and personality
Background/objectivesMany personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.Subjects/methodsWe employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.ResultsIn Mendelian randomization, the allele score for BMI predicted domain-general (β = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (β = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (β = −0.05; 95% CI: −0.08, −0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (β = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (β = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25).ConclusionsAlthough both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.
Journal Article
Interpreting the MMPI-3
An essential guide to detailed and accurate interpretation of the MMPI-3 The MMPI-3 builds on the history and strengths of the MMPI instruments to provide an empirically validated, psychometrically up-to-date standard for psychological assessment. Updating and expanding the information found in MMPI-3 test manuals, Interpreting the MMPI-3 is an indispensable resource for practicing clinicians and a vital textbook for graduate psychological assessment courses that use and study this singular psychological instrument. Yossef S. Ben-Porath, coauthor of the MMPI-3, and Martin Sellbom, a leading expert on the MMPI instruments, provide detailed descriptions and interpretive recommendations for test scales, along with illustrative cases from a wide variety of settings, including forensic (criminal and civil), medical, and personnel screening. This core interpretive content places the MMPI-3 at the forefront of contemporary psychological assessment, while also providing important background on older versions of the test. This volume includes an in-depth look at the test's history, development, administration, and interpretation, and it also addresses diversity-sensitive assessment with the test. A comprehensive guide for clinicians, researchers, and students, this book sets the standard for interpretation of and instruction on the MMPI-3. A book-based exam offering Continuing Education (CE) credit is available for this publication. Visit upress.umn.edu/test-division for more information.
eBook
The SAPAS, Personality Traits, and Personality Disorder
Many argue that current categorical personality disorder (PD) classification systems should be more dimensional and consider personality traits. The present study examined whether a brief PD screening tool, the Standardized Assessment of Personality: Abbreviated Scale (SAPAS) primarily screened for traits of low emotional stability, low extraversion, and low agreeableness, rather than PD per se. A general community sample (n = 237) completed the SAPAS, a personality trait measure, and the International Personality Disorder Examination (IPDE) screening questionnaire. Regressions showed that the SAPAS provided substantial incremental validity over personality trait scores in predicting total IPDE scores, indicating that the SAPAS captures variance unique to PD, rather than just extremes of general disposition. The SAPAS is an empirically valid rapid PD screen for nonclinical populations, correctly identifying 78% of individuals who screen positively for PD on the IPDE. However, the SAPAS was not effective for screening antisocial PD, limiting its utility in forensic settings.
Journal Article
Fit : when talent and intelligence just won't cut it
This book answers the fundamental performance questions that people have asked for generations. Why is that some individuals are consistently high performers, how do they keep performing in varying situations, organisations and contexts, why can some people just not seem to be able to crack that code, and why do some individuals perform exceptionally well in certain organisations but not in others? This fresh new book challenges current thinking about the war for talent and the role intelligence plays in high performance sport and business. Over 3,000 profiles of elite corporate managers and professional elites have been studied to find the answers as to why certain individuals consistently get exceptional results and why great talent doesn't transfer across teams and businesses. This book considers real live cases and well-known examples of spectacular successes and failures through the lens of the Hogan Personality Tools. This shows how elite performance is dependent on three things; understanding what role your behaviours are best suited to, what culture you perform your best in and how you're likely to derail your career. Armed with this knowledge, this innovative text allows you to connect the dots on your past performances and prepares you to find roles, organisations and teams which best fit you, opening the door for elite performance. Instead of talent management and changing behaviour, look to Fit as a key to your performance improvement. You'll find that performance does not have a one-size-fits-all formula - it is bespoke, personal and different for each individual.
Book
Assessing Police and Other Public Safety Personnel with the MMPI-3
A hands-on guide for using the MMPI-3 when assessing suitability and fitness for duty of public safety personnel Factors unique to police and public safety candidate selection require adjustments to standard guidelines when interpreting MMPI-3 scores.David M.Corey and Yossef S.
eBook
Application of Artificial Intelligence for Better Investment in Human Capital
Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals.
Journal Article