scholarly journals Personality computing: New frontiers in personality assessment

2021 ◽  
Author(s):  
Le Vy Phan ◽  
John F. Rauthmann
Keyword(s):  
Personality Assessment ◽  
Personality Computing

scholarly journals Assessing the quality of studies using machine learning for personality assessment: A systematic review

2021 ◽  
Author(s):  
Joanne Hinds ◽  
Thomas Parkhouse ◽  
Victoria Hotchin
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Personality Assessment ◽  
Ground Truth ◽  
Open Science ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
Personality Computing ◽  
Future Work

In recent years, the use of machine learning to predict personality from digital data has gained increasing interest from organisations, academics and the public. In turn, a new field of personality computing has developed, which involves combining machine learning techniques with psychological measures of personality. However, effectively integrating these approaches is challenging - the fields of machine learning and psychology are highly disparate, with different objectives, methodologies, and perspectives on performing and reporting research. In this article, we report findings from a systematic review that analysed 178 personality computing studies published before November 2020. We developed a novel set of criteria that was used to evaluate the quality of study design and reporting of each study according to 10 criteria: hypotheses, study rationale, selection of features, algorithm training, ground truth, sampling, the evaluation of algorithms’ performance (i.e., classification, regression), the performance measures reported, and detail concerning ethics and open science practices. Our findings highlight that a large proportion of studies lack detail on the above criteria, which leads to questions over the validity, reliability, and replicability of the findings. We discuss the implications of this research for practice and recommend directions for future work.

scholarly journals Automatic Personality Identification using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3528-3534
Author(s):  
Aditi Das
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Large Scale ◽  
Short Term Memory ◽  
Average Score ◽  
Learning Approaches ◽  
Neural Network Learning ◽  
Personality Computing ◽  
Applications Of Machine Learning ◽  
Personality Recognition

Machine Learning has made significant changes in the world making our life more easier and comfortable .One of the most exciting applications is the prediction of Personality automatically using different algorithms. Personality computing and emotive computing, where the popularity of temperament traits is important, have gained increasing interest and a spotlight in several analysis areas recently. These applications can powerfully predict the personality of a Person. The aim of this paper is to use a more rigorous construct Validation system to extend the potential of machine learning approaches to personality assessment. We have reviewed multiple recent applications of Machine Learning to recognize personality, thus providing a broader context of fundamental principles of constructing, validating, and then providing recommendations on how to use Machine Learning to advance the level of our understanding and applying our learnings to develop advanced personality recognition applications. araphrased Text Output text rewrite / rewrite We use deep neural network learning to recognize characteristics independently and, through feature-level fusion of these networks, we obtain final predictions of obvious personalities. We use a previously trained long-term and short-term memory network to integrate time information. We train large-scale models comprised of specific subnetworks- modalities through a two-stage training process. We first train the subnets separately for and then use these trained networks to fit the overall model. We used the ChaLearn First Impressions V2 challenge dataset to evaluate the proposed method. Our method achieves the most effective overall "medium precision" score, with an average score of for 5 personality characteristics, which is compared to the state-of-the-art method.

Handwriting Analysis and Personality Assessment

2000 ◽  
Vol 5 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Peter Greasley
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Empirical Studies ◽  
Handwriting Analysis ◽  
Recruitment Process ◽  
Academic Literature ◽  
Personnel Recruitment ◽  
The Subject ◽  
Better Than

It has been estimated that graphology is used by over 80% of European companies as part of their personnel recruitment process. And yet, after over three decades of research into the validity of graphology as a means of assessing personality, we are left with a legacy of equivocal results. For every experiment that has provided evidence to show that graphologists are able to identify personality traits from features of handwriting, there are just as many to show that, under rigorously controlled conditions, graphologists perform no better than chance expectations. In light of this confusion, this paper takes a different approach to the subject by focusing on the rationale and modus operandi of graphology. When we take a closer look at the academic literature, we note that there is no discussion of the actual rules by which graphologists make their assessments of personality from handwriting samples. Examination of these rules reveals a practice founded upon analogy, symbolism, and metaphor in the absence of empirical studies that have established the associations between particular features of handwriting and personality traits proposed by graphologists. These rules guide both popular graphology and that practiced by professional graphologists in personnel selection.

The Persnickety Pervasiveness of Rating Enhancement in Personality Assessment

2020 ◽  
pp. 1-13
Author(s):  
Alicia A. Stachowski ◽  
John T. Kulas
Keyword(s):  
Response Bias ◽  
Personality Assessment ◽  
Current Paper ◽  
Psychological Measurement ◽  
Shared Sets ◽  
Self Reports ◽  
Original Target ◽  
Target Profile ◽  
Do So ◽  
Normative Influences

Abstract. The current paper explores whether self and observer reports of personality are properly viewed through a contrasting lens (as opposed to a more consonant framework). Specifically, we challenge the assumption that self-reports are more susceptible to certain forms of response bias than are informant reports. We do so by examining whether selves and observers are similarly or differently drawn to socially desirable and/or normative influences in personality assessment. Targets rated their own personalities and recommended another person to also do so along shared sets of items diversely contaminated with socially desirable content. The recommended informant then invited a third individual to additionally make ratings of the original target. Profile correlations, analysis of variances (ANOVAs), and simple patterns of agreement/disagreement consistently converged on a strong normative effect paralleling item desirability, with all three rater types exhibiting a tendency to reject socially undesirable descriptors while also endorsing desirable indicators. These tendencies were, in fact, more prominent for informants than they were for self-raters. In their entirety, our results provide a note of caution regarding the strategy of using non-self informants as a comforting comparative benchmark within psychological measurement applications.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

The Moving-IAT

2017 ◽  
Vol 33 (6) ◽  
pp. 445-452
Author(s):  
Monika Fleischhauer
Keyword(s):  
Internal Consistency ◽  
Personality Assessment ◽  
Block Structure ◽  
Mental Representations ◽  
Indirect Measures ◽  
Attitude Research ◽  
Self Reports ◽  
Implicit Personality ◽  
Single Block ◽  
Psychometric Quality

Abstract. Accumulated evidence suggests that indirect measures such as the Implicit Association Test (IAT) provide an increment in personality assessment explaining behavioral variance over and above self-reports. Likewise, it has been shown that there are several unwanted sources of variance in personality IATs potentially reducing their psychometric quality. For example, there is evidence that individuals use imagery-based facilitation strategies while performing the IAT. That is, individuals actively create mental representations of their person that fit to the category combination in the respective block, but do not necessarily fit to their implicit personality self-concept. A single-block IAT variant proposed by attitude research, where compatible and incompatible trials are presented in one and the same block, may prevent individuals from using such facilitation strategies. Consequently, for the trait need for cognition (NFC), a new single-block IAT version was developed (called Moving-IAT) and tested against the standard IAT for differences in internal consistency and predictive validity in a sample of 126 participants. Although the Moving-IAT showed lower internal consistency, its predictive value for NFC-typical behavior was higher than that of the standard IAT. Given individual’s strategy reports, the single-block structure of the Moving-IAT indeed reduces the likelihood of imagery-based strategies.

Diagnostic Concordance Between Categorical and Dimensional Instruments to Assess Personality Disorder in Chronic Fatigue Syndrome

2017 ◽  
Vol 33 (3) ◽  
pp. 158-165
Author(s):  
Natalia Calvo ◽  
Naia Sáez-Francàs ◽  
Sergi Valero ◽  
Jesús Castro-Marrero ◽  
José Alegre Martín ◽  
...  
Keyword(s):  
Personality Disorder ◽  
Chronic Fatigue Syndrome ◽  
Personality Assessment ◽  
Chronic Fatigue ◽  
Specific Pattern ◽  
Harm Avoidance ◽  
Assessment Instrument ◽  
Compulsive Disorder ◽  
Fatigue Syndrome ◽  
Self Transcendence

Abstract. The study examines the relationship between a categorical and a dimensional personality assessment instrument in patients with Chronic Fatigue Syndrome (CFS). A total of 162 CFS patients were included in the study (91.4% women; mean age 47.5 years). All subjects completed the Spanish versions of the Personality Diagnostic Questionnaire-4+ (PDQ-4+) and the Temperament and Character Inventory-Revised (TCI-R). Results: 78 (48.1%) of the patients presented a Personality Disorder (PD), the most frequent being Cluster C, specifically Obsessive-compulsive disorder, followed by Avoidant disorder. PDs showed a specific pattern of correlation with temperament scales. All PD clusters correlated positively with Harm Avoidance and Self-Transcendence, and negatively with Reward Dependence, Self-Directedness, and Cooperativeness. In a logistic regression analysis, Self-Directedness and Cooperativeness predicted PD presence. The findings are consistent with previous studies in non-CFS samples and suggest that the combination of the Temperament and Character dimensions (low Self-Directedness and Cooperativeness and high Harm Avoidance and Self-Transcendence) correlates with PD severity, and that Self-Directedness and Cooperativeness are associated with PD presence in CFS patients. The integration of these two perspectives expands the current comprehension of personality pathology in CFS patients.

Effects of Targeted Self-Coaching on Applicant Distortion of Personality Measures

2012 ◽  
Vol 11 (4) ◽  
pp. 169-175 ◽  
Author(s):  
Katherine A. Sliter ◽  
Neil D. Christiansen
Keyword(s):  
Impression Management ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Lie Detection ◽  
Personality Test ◽  
Personality Measures ◽  
Personality Tests ◽  
The Impact

The present study evaluated the impact of reading self-coaching book excerpts on success at faking a personality test. Participants (N = 207) completed an initial honest personality assessment and a subsequent assessment with faking instructions under one of the following self-coaching conditions: no coaching, chapters from a commercial book on how to fake preemployment personality scales, and personality coaching plus a chapter on avoiding lie-detection scales. Results showed that those receiving coaching materials had greater success in raising their personality scores, primarily on the traits that had been targeted in the chapters. In addition, those who read the chapter on avoiding lie-detection scales scored significantly lower on a popular impression management scale while simultaneously increasing their personality scores. Implications for the use of personality tests in personnel selection are discussed.

Test-Taking Motivation and Personality Test Validity

2010 ◽  
Vol 9 (3) ◽  
pp. 117-125 ◽  
Author(s):  
Thomas A. O’Neill ◽  
Richard D. Goffin ◽  
Ian R. Gellatly
Keyword(s):  
Personality Assessment ◽  
Personnel Selection ◽  
Test Validity ◽  
Personality Testing ◽  
Personality Test ◽  
Field Sample ◽  
Moderation Effect ◽  
Test Taking ◽  
Low Levels ◽  
Valid Criterion

In this study we assessed whether the predictive validity of personality scores is stronger when respondent test-taking motivation (TTM) is higher rather than lower. Results from a field sample comprising 269 employees provided evidence for this moderation effect for one trait, Steadfastness. However, for Conscientiousness, valid criterion prediction was only obtained at low levels of TTM. Thus, it appears that TTM relates to the criterion validity of personality testing differently depending on the personality trait assessed. Overall, these and additional findings regarding the nomological net of TTM suggest that it is a unique construct that may have significant implications when personality assessment is used in personnel selection.

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