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Journal articles on the topic 'Personality prediction'
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S V, Vijay, Pradeep S, and Sathish N. "Personality Prediction System." International Journal of Research Publication and Reviews 4, no. 10 (October 2, 2023): 1707–17. http://dx.doi.org/10.55248/gengpi.4.1023.102653.
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Hall, Andrew N., and Sandra C. Matz. "Targeting Item–level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints." European Journal of Personality 34, no. 5 (September 2020): 873–84. http://dx.doi.org/10.1002/per.2253.
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In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer–based predictions from digital footprints can complement—and in some circumstances even replace—traditional self–report measures, which suffer from well–known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction–relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so–called personality nuances—item–level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out–of–sample cross–validation. In nine out of 10 cases across the two modelling approaches, nuance–based models improved the prediction of personality over the trait–based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology
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Li, Ze. "Prediction of MBTI Personality Leveraging Machine Learning Algorithms." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 580–87. http://dx.doi.org/10.54254/2755-2721/8/20230275.
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In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leveraged to make personality predictions and compare with the training data set to measure the correctness of the predicting outcomes. The results show that logistic regression algorithm and vectorized representation of text with TfidfVectorizer can best accomplish the prediction task. This study completed a preliminary comparison of algorithms for personality prediction from text, which became the basis for subsequent personality model predictions using other media.
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Sireesha, Pendyala Sai. "Personality Prediction." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2876–81. http://dx.doi.org/10.22214/ijraset.2020.5482.
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Shenavi,, Sakshi. "PERSONALITY PREDICTION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 12, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33864.
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Personality prediction is a challenging yet crucial task in various fields such as psychology, human resources, and marketing. In this study, we propose a questionnaire-based approach using the random forest algorithm to predict personality traits. The questionnaire is designed to gather information related to the personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The dataset used for this study consists of responses from individuals who completed the questionnaire. Responses to specific questions are used as input variables for the random forest algorithm. The algorithm is trained on a portion of the dataset and then tested on the remaining portion to evaluate its performance in predicting personality traits. Our results show that the random forest algorithm achieves high accuracy in predicting personality traits, outperforming other machine learning algorithms such as logistic regression and support vector machines. This approach has the potential to be used in various applications, such as personalized marketing, recommendation systems, and mental health assessment. Key Words: Personality prediction, Random forest algorithm, personality traits, Questionnaire-based approach.
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Sunitha, Dr C., and Abirami N. "Personality Prediction Ocean Model Using Machine Learning." International Journal of Research Publication and Reviews 4, no. 10 (October 2, 2023): 1225–31. http://dx.doi.org/10.55248/gengpi.4.1023.102623.
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Artishcheva, Lira V., and Evgeniya A. Kuznetcova. "PERSONAL FEATURES OF ORPHANS IN PREDICTING." Volga Region Pedagogical Search 35, no. 1 (2021): 48–59. http://dx.doi.org/10.33065/2307-1052-2021-1-35-48-59.
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The relevance of the study is due to the existing problem of prediction. Orphans are in special life and social conditions, which determine their personal development and the formation of personal qualities. The research is aimed at revealing the relationship between the personality traits of orphans and probabilistic prediction. The aim of the study is to substantiate significant relationships between the signs of predictive abilities with such personal characteristics as resilience and self-esteem based on the analysis of the Pearson correlation statistical method. The research is aimed at solving the following issues: analysis of scientific works devoted to the problem of orphanhood; definition of the essence of the concepts of prediction, resilience, selfesteem; identification of the relationship between the signs of predictive ability and personality traits. According to the theory of probabilistic prediction, predicting the outcome of situations, the correctness of decision-making, as well as tactics of behavior depend on individual personality characteristics. As a result of the study, positive and negative significant interrelationships of indicators of predictive ability, resilience, and self-esteem were revealed. The results can be used in the field of psychology to improve the predictive ability of orphans.
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Saucier, Gerard, Kathryn Iurino, and Amber Gayle Thalmayer. "Comparing predictive validity in a community sample: High–dimensionality and traditional domain–and–facet structures of personality variation." European Journal of Personality 34, no. 6 (December 2020): 1120–37. http://dx.doi.org/10.1002/per.2235.
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Prediction of outcomes is an important way of distinguishing, among personality models, the best from the rest. Prominent previous models have tended to emphasize multiple internally consistent “facet” scales subordinate to a few broad domains. But such an organization of measurement may not be optimal for prediction. Here, we compare the predictive capacity and efficiency of assessments across two types of personality–structure model: conventional structures of facets as found in multiple platforms, and new high–dimensionality structures emphasizing those based on natural–language adjectives, in particular lexicon–based structures of 20, 23, and 28 dimensions. Predictions targeted 12 criterion variables related to health and psychopathology, in a sizeable American community sample. Results tended to favor personality–assessment platforms with (at least) a dozen or two well–selected variables having minimal intercorrelations, without sculpting of these to make them function as indicators of a few broad domains. Unsurprisingly, shorter scales, especially when derived from factor analyses of the personality lexicon, were shown to take a more efficient route to given levels of predictive capacity. Popular 20th–century personality–assessment models set out influential but suboptimal templates, including one that first identifies domains and then facets, which compromise the efficiency of measurement models, at least from a comparative–prediction standpoint. © 2020 European Association of Personality Psychology
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Muralidharan, Rohith, Neenu Kuriakose, and Sangeetha J. "Myers-Briggs Personality Prediction." Indian Journal of Data Mining 3, no. 1 (December 30, 2023): 11–19. http://dx.doi.org/10.54105/ijdm.b1630.053123.
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The Myers-Briggs Type Indicator (MBTI) is one of the most commonly used tool for assessing an individual's personality. This tool allows us to identify the psychological proclivity in the way they take decisions and perceive the world. MBTI has it’s applications spread across several fields which include career development and personal growth. This test consists of a set of questions which are specifically designed to evaluate and measure an individual's choices based on four dichotomies - Extraversion (E) vs. Introversion (I), Sensing (S) vs. Intuition (N), Thinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P). Myers-Briggs Personality Prediction project aims to develop and deploy a system using machine learning which is capable of predicting one's MBTI personality type based on their online written interactions such as social media posts, comments, blogs etc. This project has significant implications for various applications, including improving customer experience, optimizing team dynamics, and developing personalized coaching programs. Through this project, we hope to gain a deeper understanding of how language use and personality type are related and to develop a robust tool for personality prediction.
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Eysenck, H. J. "Personality and prediction: Principles of personality assessment." Personality and Individual Differences 11, no. 1 (January 1990): 97. http://dx.doi.org/10.1016/0191-8869(90)90177-s.
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Zupancic, Maja, and Tina Kavcic. "Predicting early academic achievement: The role of higher-versus lower-order personality traits." Psihologija 44, no. 4 (2011): 295–306. http://dx.doi.org/10.2298/psi1104295z.
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The study explored the role of children?s (N = 193) individual differences and parental characteristics at the beginning of the first year of schooling in predicting students? attainment of academic standards at the end of the year. Special attention was paid to children?s personality as perceived by the teachers? assistants. Along with parents? education, parenting practices and first-graders? cognitive ability, the incremental predictive power of children?s higher-order (robust) personality traits was compared to the contribution of lower-order (specific) traits in explaining academic achievement. The specific traits provided a somewhat more accurate prediction than the robust traits. Unique contributions of maternal authoritative parenting, children?s cognitive ability, and personality to academic achievement were established. The ratings of first-graders? conscientiousness (a higher-order trait) improved the prediction of academic achievement based on parenting and cognitive ability by 12%, whereas assistant teacher?s perceived children?s intelligence and low antagonism (lower-order traits) improved the prediction by 17%.
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Salsabila, Ghina Dwi, and Erwin Budi Setiawan. "Semantic Approach for Big Five Personality Prediction on Twitter." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 4 (August 20, 2021): 680–87. http://dx.doi.org/10.29207/resti.v5i4.3197.
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Personality provides a deep insight of someone and has an important part in someone’s job performance. Predicting personality through social media has been studied on several research. The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personality on Twitter users and increase the performance of the personality prediction system. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the personality prediction system.
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Somaye Robatmili and Mahla Yazdani. "Predicting cheating among married individuals based on sexual satisfaction and personality traits: A review." Open Access Research Journal of Biology and Pharmacy 10, no. 1 (February 28, 2024): 023–25. http://dx.doi.org/10.53022/oarjbp.2024.10.1.0035.
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This article discusses the prediction of cheating among married individuals based on two significant factors: sexual satisfaction and personality traits. Sexual satisfaction is an essential component of a healthy and fulfilling relationship, and individuals who experience a lack of fulfillment in their sexual lives may seek satisfaction outside their marriage. Personality traits play a crucial role in determining an individual's propensity for cheating, with several traits commonly associated with a higher likelihood of engaging in infidelity, including low levels of conscientiousness, high levels of impulsivity, narcissism, and a desire for novelty and excitement. While sexual satisfaction and personality traits each contribute to the prediction of cheating, their interaction can further enhance our understanding. Developing reliable predictive models for cheating based on sexual satisfaction and personality traits is a complex task, with researchers using various methods to discern patterns and make accurate predictions. Data analysis techniques, such as logistic regression and machine learning algorithms, have been applied to collected data on married individuals and their tendencies to cheat, achieving moderate success in predictive accuracy. However, it is essential to acknowledge the limitations of these predictions, such as individual circumstances, relationship dynamics, and other external variables that are not accounted for in these models. Privacy concerns and ethical considerations surrounding the monitoring of individuals' personal lives must also be taken into account. These predictive models should be used with caution and in conjunction with comprehensive counseling and support systems to maintain the integrity of relationships.
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Gita Safitri and Erwin Budi Setiawan. "Optimization Prediction of Big Five Personality in Twitter Users." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 1 (February 27, 2022): 85–91. http://dx.doi.org/10.29207/resti.v6i1.3529.
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Various kinds of information can be acquired from social media platforms; one of them is on Twitter. User biographical information and tweets are the essential assets for research that can describe the Big Five Personality, including openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several previous studies have tried the prediction of Big Five Personality. However, the authors found problems in how to optimize the work of the personality prediction system. So, in this study, Big Five Personality predictions were carried out on users of Twitter and improved the performance of the personality prediction system. We implement optimization techniques such as sampling, feature selection, and hyperparameter tuning to enhance the performance. This study also applies linguistic feature extraction, such as LIWC and TF-IDF. By using 287 Twitter users that have permitted their data to be crawled acquired from an online survey using Big Five Inventory (BFI), and applying all optimization techniques, the average accuracy result is 84.22% which is a 74.44% gain over the specified baseline.
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Hu, Manjiang, Yuan Liao, Wenjun Wang, Guofa Li, Bo Cheng, and Fang Chen. "Decision Tree-Based Maneuver Prediction for Driver Rear-End Risk-Avoidance Behaviors in Cut-In Scenarios." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7170358.
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Predicting driver rear-end risk-avoidance maneuvers in cut-in scenarios, especially dangerous precrash scenarios, benefits the customization of automatic driving, particularly automatic steering. This paper studies driver rear-end risk-avoidance behaviors in cut-in scenarios on a straight three-lane highway. Data from 24 participants in 1326 valid trials were collected using a motion-based driving simulator. An Eysenck Personality Questionnaire (revised for Chinese participants) was used to obtain the personality traits of the participants. Based on a statistical analysis, the candidate features used in the driver maneuver prediction were determined as a combination of objective risk indicators and driver characteristics. A decision tree-based model was constructed for maneuver prediction in cut-in scenarios. The prediction accuracy of the extracted classification rules was 79.2% for the training data set and 80.3% for the test data set. The most powerful predictive variables were extracted, and their effects on maneuver decisions were analyzed. The results show that driver characteristics strongly influence the prediction of maneuver decisions.
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Cai, Huanhuan, Jiajia Zhu, and Yongqiang Yu. "Robust prediction of individual personality from brain functional connectome." Social Cognitive and Affective Neuroscience 15, no. 3 (March 2020): 359–69. http://dx.doi.org/10.1093/scan/nsaa044.
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Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.
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McCrae, Robert R., Jian Yang, Paul T. Costa, Jr., Xiaoyang Dai, Shuqiao Yao, Taisheng Cai, and Beiling Gao. "Personality Profiles and the Prediction of Categorical Personality Disorders." Journal of Personality 69, no. 2 (April 2001): 155–74. http://dx.doi.org/10.1111/1467-6494.00140.
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Narwade, Rutuja, Srujami Palkar, Isha Zade, and Nidhi Sanghavi. "Personality Prediction with CV Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 970–74. http://dx.doi.org/10.22214/ijraset.2022.41359.
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Abstract: When it comes to demography, personality plays a crucial role in deciphering a person’s caliber and work ethic. An individual’s personality becomes a vital resource for the organization that he/she works for. One way to adjudicate an individual’s personality is to frame a questionnaire or by analysis of their resume (CV). In the traditional sense, when it comes to hiring an individual, employers manually filter through the applicants’ CVs as per the job description. In this paper, we render a system that motorizes the eligibility check and aptitude evaluation of a candidate in the shortlisting strategy. To overcome the predicaments encountered in the traditional procedure, a web application has been curated for aptitude analysis (personality evaluation) and CV analysis. The system’s primary aim is to analyze the professional ability of the candidate based on the uploaded CV and the prepared questionnaire. The system employs Natural Language Processing (NLP) for the CV analysis and Machine Learning (ML) for the personality evaluation. The output of the curated system aids in applicant filtering. Further, the resulting scores help in evaluating the qualities of the applicant such as the kind of mindset he/she has and the skills he/she has accumulated over time. This approach has been proposed keeping in mind the hurdles encountered while analyzing an applicant during the hiring process and aims in providing a seamless system that will be able to aid in making a fair decision in the selection process. Keywords: Personality Prediction, CV Analysis, Machine Learning, Natural Language Processing, Big Five Personality Model, Psychometric Analysis, Hiring, and Selection.
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Borkenau, Peter, and Nadine Mauer. "Personality, Emotionality, and Risk Prediction." Journal of Individual Differences 27, no. 3 (January 2006): 127–35. http://dx.doi.org/10.1027/1614-0001.27.3.127.
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We suggest a new approach for measuring individual differences in optimistic bias that adjusts risk estimates for oneself for: (1) risk estimates for other persons to control for response tendencies, and (2) risk estimates by knowledgeable informants to control for differences in actual risk. In two studies, we measured Positive and Negative Emotionality by self-reports and reports by knowledgeable informants. Moreover, likelihood estimates that pleasant and unpleasant events will occur to oneself and to an average other person were collected, and the knowledgeable informants provided risk estimates for the research participants. Risk estimates by knowledgeable informants were even more optimistic than self-estimates, and optimistic bias was related directly to Positive Emotionality and inversely to Negative Emotionality. These effects of personality on optimistic bias were not mediated by current mood.
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Eyman, James R., and Susanne Kohn Eyman. "Personality Assessment in Suicide Prediction." Suicide and Life-Threatening Behavior 21, no. 1 (March 1991): 37–55. http://dx.doi.org/10.1111/j.1943-278x.1991.tb00793.x.
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R, Valanarasu. "Comparative Analysis for Personality Prediction by Digital Footprints in Social Media." June 2021 3, no. 2 (May 31, 2021): 77–91. http://dx.doi.org/10.36548/jitdw.2021.2.002.
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The use of social media and leaving a digital footprint has recently increased all around the world. It is being used as a platform for people to communicate their sentiments, emotions, and expectations with their data. The data available in social media are publicly viewable and accessible. Any social media network user's personality is predicted based on their posts and status in order to deliver a better accuracy. In this perspective, the proposed research article proposes novel machine learning methods for predicting the personality of humans based on their social media digital footprints. The proposed model may be reviewed for any job applicant during the times of COVID'19 through online enrolment for any organisation. Previously, the personality prediction methods are failed due to the differing perspectives of recruiters on job applicants. Also, this estimation is modernized and the prediction time is also reduced due to the implementation of the proposed hybrid approach on machine learning prediction. The artificial intelligence based calculation is used for predicting the personality of job applicants or any person. The proposed algorithm is organized with dynamic multi-context information and it also contains the account information of multiple platforms such as Facebook, Twitter, and YouTube. The collection of the various dataset from different social media sites constitute to the increase in the prediction rate of any machine learning algorithm. Therefore, the accuracy of personality prediction is higher than any other existing methods. Despite the fact that a person's logic varies from season to season, the proposed algorithm consistently outperforms other existing and traditional approaches in predicting a person's mentality.
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Maulidah, Mawadatul, and Hilman Ferdinandus Pardede. "Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory." Jurnal Elektronika dan Telekomunikasi 21, no. 2 (December 31, 2021): 104. http://dx.doi.org/10.14203/jet.v21.104-111.
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Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.
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Lee, Seung-hyeong, and Eun-Ju Baek. "Development of a predictive model for university students’ core competency index using machine learning: Focusing on D University." Korean Association For Learner-Centered Curriculum And Instruction 22, no. 11 (June 15, 2022): 831–49. http://dx.doi.org/10.22251/jlcci.2022.22.11.831.
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Objectives The purpose of this study was to present implications for the development and operation of the core competency-based curriculum by predicting the core competency of college students.
Methods The panel data of the D-CODA result panel data for the past 3 years (2019-2021) of D-University students located in the Busan area were analyzed. Machine learning prediction models such as multiple linear regression analysis (LR), random forest (RF), and support vector machine (SVM), were used to predict core competencies.
Results The following research results were derived from the study. First, the optimal prediction model for each core competency is as follows. Professional competency was shown in the RF (random forest) model, personality competency in SVM (support vector machine), creative competency in the RF (random forest) model, challenge competency and glocal (global and local) competency in the SVM (support vector machine) model, and communication competency in the LR (multi-linear regression analysis) model. Second, in the analysis of competencies, it was found that professional competency contributes to the prediction of professional competency, and both personality competency and communication competency to that of personality competency. Third, in the model analysis to predict the overall core competency index, the optimal predictive model was found to be the RF (random forest) model which showed the least error. Fourth, in the prediction of key competency indicators in 2022, it is predicted that expertise, personality, creativity, and challenging competency will improve.
Conclusions This study revealed that the analysis using accumulated core competency data and machine learning is useful in predicting and discriminating the core competency of college students. This study is meaningful in that it suggests the importance of periodic core competency index management at the university level and provides the basis for designing a core competency-based curriculum.
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Xu, Jia, Weijian Tian, Guoyun Lv, Shiya Liu, and Yangyu Fan. "2.5D Facial Personality Prediction Based on Deep Learning." Journal of Advanced Transportation 2021 (June 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/5581984.
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The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.
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Ramon, Yanou, R. A. Farrokhnia, Sandra C. Matz, and David Martens. "Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records." Information 12, no. 12 (December 13, 2021): 518. http://dx.doi.org/10.3390/info12120518.
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Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.
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Mereu, A. "Study retention prediction with AI." European Psychiatry 64, S1 (April 2021): S140. http://dx.doi.org/10.1192/j.eurpsy.2021.385.
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IntroductionOpenness, conscientiousness, extroversion, agreeableness and neuroticism are dimensional personality traits known as the Big Five. Study attrition is a common but often hard to anticipate problem. Artificial intelligence (AI) could examine both fronts to mitigate the unpredictability of the latter.ObjectivesTo investigate whether AI could predict study attrition employing personality traits scores.MethodsData from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the Big Five personality traits on the first of three planned waves. The personality traits scores were employed to predict the missing of at least one wave. Overall attrition was 17.6%. The AI was conservatively tuned to minimize the negative likelihood ratio when confronting predicted and real attrition. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1ResultsPredictions obtained a negative likelihood ratio of 0.333 and a negative predictive value of 0.933. The results were indicative of fair performance.ConclusionsAI might be useful to predict study retention. Furthermore, the results of this study might indicate a moderate effect of the Big Five on the probability of study retention. Finally, the AI used in this study is freely available, allowing anyone to experiment.DisclosureNo significant relationships.
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Seeboth, Anne, and René Mõttus. "Successful Explanations Start with Accurate Descriptions: Questionnaire Items as Personality Markers for More Accurate Predictions." European Journal of Personality 32, no. 3 (May 2018): 186–201. http://dx.doi.org/10.1002/per.2147.
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Personality–outcome associations, typically represented using the Big Five personality domains, are ubiquitous, but often weak and possibly driven by the constituents of these domains. We hypothesized that representing the associations using personality questionnaire items (as markers for personality nuances) could increase prediction strength. Using the National Child Development Study ( N = 8719), we predicted 40 diverse outcomes from both the Big Five domains and their 50 items. Models were trained (using penalized regression) and applied for prediction in independent sample partitions (with 100 permutations). Item models tended to out–predict Big Five models (explaining on average 30% more variance), regardless of outcomes’ independently rated breadth versus behavioural specificity. Moreover, the predictive power of Big Five domains per se was at least partly inflated by the unique variance of their constituent items, especially for generally more predictable outcomes. Removing the Big Five variance from items marginally reduced their predictive power. These findings are consistent with the possibility that the associations of personality with outcomes often pertain to (potentially large numbers of) specific behavioural, cognitive, affective, and motivational characteristics represented by single questionnaire items rather than to the broader (underlying) traits that these items are ostensibly indicators of. This may also have implications for personality–based interventions. Copyright © 2018 European Association of Personality Psychology
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Ghafari, Seyed M., Amin Beheshti, Aditya Joshi, Cecile Paris, Shahpar Yakhchi, Mehmet A. Orgun, Alireza Jolfaei, and Quan Z. Sheng. "Modeling Personality Effect in Trust Prediction." journal of Data Intelligence 2, no. 4 (November 2021): 401–17. http://dx.doi.org/10.26421/jdi2.4-1.
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Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.
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Chen, Tsung-Yi, Meng-Che Tsai, and Yuh-Min Chen. "A user’s personality prediction approach by mining network interaction behaviors on Facebook." Online Information Review 40, no. 7 (November 14, 2016): 913–37. http://dx.doi.org/10.1108/oir-08-2015-0267.
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Purpose For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues. Design/methodology/approach The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs. Findings Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy. Research limitations/implications In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited. Practical implications This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features. Originality/value This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.
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Arya, Resham, Ashok Kumar, Megha Bhushan, and Piyush Samant. "Big Five Personality Traits Prediction Using Brain Signals." International Journal of Fuzzy System Applications 11, no. 2 (April 2022): 1–10. http://dx.doi.org/10.4018/ijfsa.296596.
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Brain activity ensures the identification of emotions that are generally influenced by the personality of an individual. Similar to emotions, there exists a relationship between personality and brain signals. These brain signals could be of a mentally healthy person or someone having psychological illness as well. In this paper, first, the survey related to work done on the personality prediction of healthy subjects is explored. Thereafter, the relationship between personality and psychologically ill subjects is also briefly presented based on the existing literature. Following this, an analysis of physiological signals (EEG) is also done for more understanding of personality prediction. ASCERTAIN – a multimodal database for implicit personality and recognition, is considered. It contains EEG recordings and self-annotated big five personality values of 58 students. Some time and frequency domain features are extracted and then put into various classifiers to predict the personality in five dimensions.
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Marengo, Davide, and Christian Montag. "Digital Phenotyping of Big Five Personality via Facebook Data Mining: A Meta-Analysis." Digital Psychology 1, no. 1 (June 8, 2020): 52–64. http://dx.doi.org/10.24989/dp.v1i1.1823.
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Background: 2.7 billion people around the world currently use a product from Facebook such as Instagram, WhatsApp or Facebook itself. These online platforms belong to the most important social media/messenger applications in the world, in particular with a Western view on this topic. Objectives: A growing movement in the scientific community aims to predict psychological traits and states via the study of digital footprints left on these platforms. In particular several researchers demonstrated already that it is feasible to predict personality from posted text on Facebook, but also from a person’s “Like” behavior and so forth. Methods: In the present work we carried out a meta-analysis on the available literature predicting personality from Facebook. Results: Results showed that on average, the accuracy of prediction of user personality scores by mining Facebook data is moderate (r = .33). Discussions: Currently, personality-predictions from social media and smartphone data are feasible, but far away from perfect. Therefore, current predictions from this data cannot be made on individual level. In the near future though, with both more data sets available and more elaborate analysis strategies from artificial intelligence to be applied, this might change.
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Suman, Chanchal, Sriparna Saha, Aditya Gupta, Saurabh Kumar Pandey, and Pushpak Bhattacharyya. "A multi-modal personality prediction system." Knowledge-Based Systems 236 (January 2022): 107715. http://dx.doi.org/10.1016/j.knosys.2021.107715.
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selvi M, Muthu, Angeline Ranjitha Mani, and Abinaya V. "Personality Prediction using through CV Analysis." International Journal on Cybernetics & Informatics 10, no. 2 (May 31, 2021): 181–92. http://dx.doi.org/10.5121/ijci.2021.100220.
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This Human Resource Management is obviously bolstered by and gave more open doors by the improvement of Job Characteristics Model (JCM) which thusly depends on the idea of present day occupation plan. Luckily, the advancement in present day data framework, computerized innovations, the general access of electronic innovation and web prompted the tendency of the worldwide Human Resource. The board improvement and make the framework more pertinent. Following the pattern, the proposed framework attempts to structure an arrangement to coordinate Job Characteristics Model into E-HR framework to scan for another model of proficient activity on Human Resource Management in the Internet Age. In this venture, we present a lot of strategies that makes the entirety enlistment process increasingly viable and productive. We have executed a framework that positions the competitors dependent on weight-age arrangement just as a bent test. Today there is a developing enthusiasm for the character attributes of an up-and-comer by the association to more readily look at and comprehend the competitor's reaction to comparable conditions. Along these lines, the framework directs a character expectation test to decide the character attributes of the applicant. At long last, it shows the consequences of the contender to the selection representative who assesses the top competitors and waitlists the applicant. This system can used in many business sectors that may require expert candidate. This system will reduce workload of the human resources. This system will help the human resource to select right candidate for desired job profile, which in turn provide expert workforce for the organization. Admin can easily shortlist a candidate based on their online test marks and select the appropriate candidate for particular job profile. This will enable a more effective way to short list submitted candidate CVs from a large number of applicants providing aconsistent and fair CV ranking policy, which can be legally justified
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Helmes, Edward, and Douglas N. Jackson. "Prediction Models of Personality Item Responding." Multivariate Behavioral Research 24, no. 1 (January 1989): 71–91. http://dx.doi.org/10.1207/s15327906mbr2401_5.
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Tandera, Tommy, Hendro, Derwin Suhartono, Rini Wongso, and Yen Lina Prasetio. "Personality Prediction System from Facebook Users." Procedia Computer Science 116 (2017): 604–11. http://dx.doi.org/10.1016/j.procs.2017.10.016.
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Zhang, Ting, Ri-Zhen Qin, Qiu-Lei Dong, Wei Gao, Hua-Rong Xu, and Zhan-Yi Hu. "Physiognomy: Personality traits prediction by learning." International Journal of Automation and Computing 14, no. 4 (June 24, 2017): 386–95. http://dx.doi.org/10.1007/s11633-017-1085-8.
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Ingham, Jack. "Personality and the prediction of behavior." Journal of Psychosomatic Research 30, no. 2 (January 1986): 253. http://dx.doi.org/10.1016/0022-3999(86)90057-7.
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Dhanke, Sham, Sakshi Dhepe, Manthan Dave, and Shantanu Inamdar. "Personality Prediction using CV, Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3136–40. http://dx.doi.org/10.22214/ijraset.2023.50718.
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Abstract: In this study, Deep Learning technologies are used to recognize personalities from CVs or resumes. The system for automating the evaluation of candidates' overall qualifications throughout the recruiting process is presented in this work. Based on the submitted CV or resume, which is assessed by an artificial intelligence algorithm, the system verifies the professional skills. The list comprises of terms that are pertinent to the position and best align with the applicant's qualifications. Recruiters may swiftly scan big candidate pools using the tool, which was created based on this study, to look for prospective job matches. This system's key benefit is that it automates the appropriateness evaluation procedure, freeing recruiters to concentrate on other crucial activities. Another benefit is that it gives applicant profiles to help employers and employees match up for open positions. When reviewing a CV, for instance, if a candidate hasn't completed a personality test but has been hired in the past successfully, the CV will automatically receive a score based on that evaluation.
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Ramezani, Majid, Mohammad-Reza Feizi-Derakhshi, and Mohammad-Ali Balafar. "Knowledge Graph-Enabled Text-Based Automatic Personality Prediction." Computational Intelligence and Neuroscience 2022 (June 20, 2022): 1–18. http://dx.doi.org/10.1155/2022/3732351.
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How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text’s concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.
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Khademi, Ali, Shekofe Rostaminejad, and Heman Mahmoudfakhe. "Follow-Up Prediction of Personality Types A and B Based on Neo Personality Traits." International Letters of Social and Humanistic Sciences 56 (July 2015): 8–14. http://dx.doi.org/10.18052/www.scipress.com/ilshs.56.8.
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The current paper was aimed at prediction of follow-up of personality types A and B based on Neo personality traits. Participants in the present article included 100 people referring to diet centers in Tankabon who were selected by simple random sampling method. Neo personality characteristics questionnaire and McCrae and Costa questionnaire (1985) and the Personality types A and B questionnaire by Burn Reuter were used. Findings revealed that there was a significant relationship between personality types A and B Scores and Neo personality characteristics components at the 0/05 level and the latter, i.e. Neo personality characteristics components would predict 65/8% of the personality types A and B variations. Also, results of variance analysis indicate significance of this prediction and from among the Neo personality characteristics components; the component of conscientiousness contributed most to prediction of personality types A and B.
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O'Neill, Thomas A., and Natalie J. Allen. "Personality and the Prediction of Team Performance." European Journal of Personality 25, no. 1 (January 2011): 31–42. http://dx.doi.org/10.1002/per.769.
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Although much is known about personality and individuals’ job performance, only a few studies have considered the effects of team–level personality on team performance. Existing research examining the effects of personality on team performance has found that, of the Big Five factors of personality, Conscientiousness is often the most important predictor. Accordingly, we investigated the criterion validity of lower–level Conscientiousness traits to determine whether any one trait is particularly predictive of team performance. In addition to Conscientiousness, we examined the criterion validity of the other Big Five personality factors. We found that Conscientiousness and its facets predicted team performance. Agreeableness, Extraversion and Neuroticism were not predictive of team performance, whereas Openness had a modest negative relation with team performance. Copyright © 2010 John Wiley & Sons, Ltd.
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Forns-Santacana, Maria, Bernardí Martorell-Balanzó, Juan Antonio Amador-Campos, and Judit Abad-Gil. "Relationships of Personality Factors with Clinical Dimensions and School Achievement." Perceptual and Motor Skills 82, no. 1 (February 1996): 243–53. http://dx.doi.org/10.2466/pms.1996.82.1.243.
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This study analyzes the association of personality traits, psychopathological factors, and school achievement. High School Personality Questionnaire, Clinical Analysis Questionnaire, and academic marks of 224 high school students (90 boys and 134 girls) are used. It can be stated that the predictive ability of measures of personality traits and clinical dimensions is very weak for both boys and girls. The Clinical Analysis Questionnaire does not seem to be useful in the prediction of school achievement.
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Strange, Carolyn. "The Personality of Environmental Prediction: Griffith Taylor as 'Latter-day Prophet'." Historical Records of Australian Science 21, no. 2 (2010): 133. http://dx.doi.org/10.1071/hr09026.
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Environmental prediction is a practice that may establish and enhance the status of predictors but it also carries risks that vary in relation to the professional and political contexts of its communication. Exploring the lives of scientists involved in the difficult task of environmental prediction highlights the significance of personal identities in the cultural history of science. Geographer Griffith Taylor (1880?1963), whose raison d'�tre was environmental prediction, is an ideal subject to examine from this perspective. Facing opposition to his early predictions of Australia's limited settlement prospects, owing to the continent's aridity, he used intemperate language to deliver sober warnings and sparred with naysayers and doubters in the popular media. By the 1920s he saw himself as a ?latter-day prophet', and he carried that sense of self forward when he moved to North America in 1928. Yet in Canada his environmental predictions, although favourable, were considered overly optimistic and often disregarded altogether. This prophet realized that he was happier being attacked than ignored. Taylor's career suggests that positive prognostication, when dismissed, offers less personal compensation than cautionary prophesies that face opposition in hostile political or intellectual contexts.
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Wu, Yudi. "A review-based approach to user profiling." Applied and Computational Engineering 33, no. 1 (January 22, 2024): 65–72. http://dx.doi.org/10.54254/2755-2721/33/20230235.
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With the popularity of social media, sentiment analysis and text categorisation by analysing the information people post online has become an effective method to study personality prediction. This paper focuses on how to use a personality prediction model based on Bidirectional LSTM for personality prediction. Accurate personality prediction results can provide personalised recommendation services for individuals, which has certain commercial value. In this paper, the dataset of Kaggle is first preprocessed, and then the Bidirectional LSTM model is constructed and the hyperparameters are set.The processed data are then put into the model for training and testing. Finally, the above steps are repeated using other different machine learning models. After comparison experiments with other common machine learning models, it was found that the Bidirectional LSTM model showed significant advantages in the personality prediction task, and its accuracy reached 93.5%, which was significantly higher than the traditional machine learning model.
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Fu, Xiaomeng, Suyan Cheng, Li Zhao, and Jiaguo Lv. "Retweet Prediction Based on Multidimensional Features." Wireless Communications and Mobile Computing 2022 (February 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/1863568.
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With the wide use of artificial intelligence-driven mobile devices, more and more Chinese people take part in the Twitter-like social sites, which makes Weibo an excellent communication platform. In view of the wide application of information diffusion in various fields, Weibo has become one of the most important research issues in mobile social computing. In Weibo, the retweet statuses of tweets of other users are considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention in recent years. Research shows that the users’ retweet behavior is driven by their interest and personality. However, most previous works ignore the roles of users’ personality in their retweet behavior. To this end, a prediction model MDF-RP (multidimensional feature-based retweeting prediction) including personality feature is proposed. The prediction model integrates the features from three dimensions, such as author, tweet, and user. And the personality score is obtained based on the well-known Big Five personality trait model. The experimental results under different classifiers show that the performances of MDF-RP features outperform the basic features. And the experiments of cross-validation also demonstrate the stability of MDF-RP features.
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Maharani, Warih, and Veronikha Effendy. "Big five personality prediction based in Indonesian tweets using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1973. http://dx.doi.org/10.11591/ijece.v12i2.pp1973-1981.
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<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>
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Beck, Emorie, and Joshua Jackson. "A Mega-Analysis of Personality Prediction: Robustness and Boundary Conditions." Innovation in Aging 5, Supplement_1 (December 1, 2021): 562. http://dx.doi.org/10.1093/geroni/igab046.2157.
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Abstract Decades of studies identify prospective associations between personality characteristics and life outcomes. However, previous investigations of personality characteristic-outcome associations have not taken a principled approach to sampling strategies to ensure the robustness of personality-outcome associations. In a preregistered study, we test whether and for whom personality-outcome associations are robust against selection bias using prospective associations between 14 personality characteristics and 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework with propensity score matching. Two findings emerged: First, personality characteristics remain robustly associated with later life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. In sum, personality characteristics are robustly associated with later life outcomes with few moderated associations. We discuss how these findings can inform studies of personality-outcome associations.
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McAbee, Samuel T., Frederick L. Oswald, and Brian S. Connelly. "Bifactor Models of Personality and College Student Performance: A Broad versus Narrow View." European Journal of Personality 28, no. 6 (November 2014): 604–19. http://dx.doi.org/10.1002/per.1975.
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Research in the area of personality traits and academic performance has been supported by consistent meta–analytic evidence demonstrating positive relationships between Conscientiousness and grade point average (GPA). However, academic performance is not solely a function of GPA but also a number of other important intellectual, interpersonal and intrapersonal behaviours. This wider criterion space opens up the possibility for many personality factors and their underlying facets to relate to academic performance. Using bifactor latent variable modelling, the current study investigates the six–factor HEXACO model of personality, along with their 24 underlying facets, for predicting students’ academic performance. Model results reveal interpretable and meaningful relationships between both broad factors and narrow personality facets in predicting college student outcomes. Implications for measurement, modelling and prediction are discussed. Copyright © 2014 European Association of Personality Psychology
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Zivanovic, Marko, Sofija Cerovic, and Jovana Bjekic. "A six-factor model of brand personality and its predictive validity." Psihologija 50, no. 2 (2017): 141–55. http://dx.doi.org/10.2298/psi161031002z.
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The study examines applicability and usefulness of HEXACO-based model in the description of brand personality. Following contemporary theoretical developments in human personality research, Study 1 explored the latent personality structure of 120 brands using descriptors of six personality traits as defined in HEXACO model: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness. The results of exploratory factor analyses have supported HEXACO personality six-factor structure to a large extent. In Study 2 we addressed the question of predictive validity of HEXACO-based brand personality. Brand personality traits, but predominantly Honesty-Humility, accounted for substantial amount of variance in prediction of important aspects of consumer-brand relationship: attitude toward brand, perceived quality of a brand, and brand loyalty. The implications of applying HEXACO-based brand personality in marketing research are discussed.
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Reeve, P. E., M. D. Vickers, and J. N. Horton. "Selecting Anaesthetists: The Use of Psychological Tests and Structured Interviews." Journal of the Royal Society of Medicine 86, no. 7 (July 1993): 400–403. http://dx.doi.org/10.1177/014107689308600710.
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To test the predictive validity of a selection system for Senior House Officers (SHOs) and registrars in anaesthetics, 140 doctors short-listed from 635 applications between 1980 and 1987 were assessed by a semi-structured interview assessed and a personality questionnaire (Cattell 16PFQ-form C). The 62 doctors selected were followed up for between 3 and 8 years. Future performance was predicted from the psychological tests and by the interviewers. Academic, clinical, behavioural, and overall performance were used as criteria of outcome. Correlation coefficients between prediction and outcome measures were statistically highly significant ( P<0.01). Using multiple regression, equations could be derived from five of the Cattell personality factors to predict overall performance. Personality measures discriminated significantly between the best and poorest performers. Interview predictions were also statistically significant ( P<0.01). The method provides a blueprint for the effective selection of junior anaesthetists. Wastage in terms of those leaving the specialty was 16%.