scholarly journals Beyond Traditional Measures of Personality with BISCUIT and BARE: A New Statistical Learning Technique and Behavioral Item Pool to Push Personality Psychology Forward

2020 ◽  
Author(s):  
Lorien Grey Elleman
Keyword(s):  
Statistical Learning ◽  
Big Five ◽  
Traditional Approach ◽  
Item Type ◽  
Item Pool ◽  
Pilot Studies ◽  
Personality Psychology ◽  
Learning Technique ◽  
Statistical Learning Techniques ◽  
Behavioral Frequencies

This dissertation investigates two ways in which personality psychology should move beyond the traditional approach of measuring personality with broad domains composed of trait descriptors, as exemplified by the Big Five taxonomy. The first study (Chapter 2) suggests an alternative to the traditional approach of aggregating personality items into domains. Mounting evidence indicates that, compared to domains, narrower measures of personality account for more variance in criteria and describe personality-criterion relationships more accurately. Analysis of individual personality items is the most granular approach to studying personality and is typically performed with statistical learning techniques (SLTs). The first study: (a) champions a new statistical learning technique, BISCUIT; (b) finds that BISCUIT provides a balance between prediction and parsimony; and (c) replicates previous findings that the broadness of the Big Five traits hinder their predictive power.The second study (Chapter 3) suggests an alternative to the traditional approach of measuring personality with trait descriptors, or "traditional personality items." Of the three patterns commonly associated with personality (cognitions, emotions, and behaviors), behaviors are the least studied; traditional personality items tend to measure cognitions and emotions. Historically, yearlong patterns of specific behaviors have been thought of as criteria of personality measures, but the second study posits they should be classified as personality items because they measure patterns of behavior, a component of personality. The second study reviews and extends two pilot studies that indicated behavioral frequencies predict life outcomes, sometimes better than traditional personality items. The second study: (a) estimates the extent to which behavioral frequencies strengthen personality-criterion relationships above traditional personality items; (b) determines that some criteria are differentially predicted by personality item type; and (c) publishes an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.

scholarly journals Laying personality BARE: Behavioral frequencies strengthen personality-criterion relationships

2020 ◽  
Author(s):  
Lorien Grey Elleman ◽  
David M Condon ◽  
William Revelle
Keyword(s):  
Statistical Learning ◽  
Optimal Number ◽  
Incremental Validity ◽  
Strongly Correlated ◽  
Item Pool ◽  
Unique Effect ◽  
Validation Criteria ◽  
Learning Technique ◽  
Behavioral Frequencies ◽  
Behavioral Criteria

Personality consists of stable patterns of cognitions, emotions, and behaviors, yet personality psychologists rarely study behaviors. Even when examined, behaviors typically are considered to be validation criteria for traditional personality items. In the current study (N = 332,489), we conceptualize (self-reported, yearlong) behavioral frequencies as measures of personality. We investigate whether behavioral frequencies have incremental validity over traditional personality items in correlating personality with six outcome criteria. We use BISCUIT, a statistical learning technique, to find the optimal number of items for each criterion’s model, across three pools of items: traditional personality items (k = 696), behavioral frequencies (k = 425), and a combined pool. Compared to models using only traditional personality items, models using the combined pool are more strongly correlated to four criteria. We find mixed evidence of congruence between the type of criterion and the type of personality items that are most strongly correlated with it (e.g., behavioral criteria are most strongly correlated to behavioral frequencies). Findings suggest that behavioral frequencies are measures of personality that offer a unique effect in describing personality-criterion relationships beyond traditional personality items. We provide an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.

scholarly journals That takes the BISCUIT: A comparative study of predictive accuracy and parsimony of four statistical learning techniques in personality data, with data missingness conditions

2020 ◽  
Author(s):  
Lorien Grey Elleman ◽  
Sarah K McDougald ◽  
David M Condon ◽  
William Revelle
Keyword(s):  
Random Forest ◽  
Comparative Study ◽  
Statistical Learning ◽  
Big Five ◽  
Regression Models ◽  
Predictive Accuracy ◽  
Complete Data ◽  
Elastic Net ◽  
Learning Techniques ◽  
Statistical Learning Techniques

The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative and Transparent). The predictive accuracy and parsimony of BISCUIT was compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. The elastic net and lasso dominated other techniques in terms of predictive accuracy with complete data and in conditions with up to 50% data missingness. In terms of predictive accuracy, regression using 27 narrow traits was an intermediate choice. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

scholarly journals Can Facebook likes predict the purchase probability of electricity storage systems?

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefan Poier
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Control Sample ◽  
Big Five Personality Traits ◽  
Photovoltaic System ◽  
Big Five Personality ◽  
Personality Psychology ◽  
Electricity Storage ◽  
Combining Data ◽  
User Groups

AbstractThis study among owners of photovoltaic systems investigates whether users' Big Five personality traits derived from their Facebook likes contribute to whether or not they adopt an electricity storage. It is based on the finding that the digital footprint, especially the Facebook likes, can in part predict the personality of users better than friends and family. The survey was conducted among 159 Facebook users in Germany who owned a photovoltaic system. For comparison, a control sample with data from the German Socio-Economic Panel with 425 photovoltaic owners among 7286 individuals was used. The results show that, for extraversion, agreeableness, and neuroticism, the mean scores could be sufficiently predicted. However, a positive correlation could only be detected for extraversion. The comparison of the user groups could not provide satisfying results. None of the Big Five personality traits could be used to distinguish the two user groups from each other. Although the results did not support the hypotheses, this study offers insights into the possibilities of combining data mining, personality psychology, and consumer research.

scholarly journals Psychometric properties of the International Personality Item Pool Big-Five personality questionnaire for the Greek population

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Maria Ypofanti ◽  
Vasiliki Zisi ◽  
Nikolaos Zourbanos ◽  
Barbara Mouchtouri ◽  
Pothiti Tzanne ◽  
...  
Keyword(s):  
Age Differences ◽  
Big Five ◽  
Short Form ◽  
Personality Factor ◽  
Big Five Personality ◽  
Item Pool ◽  
Total N ◽  
Personality Item ◽  
Good Internal Consistency ◽  
Highly Correlated

Goldberg’s International Personality Item Pool (IPIP) big-five personality factor markers currently lack validating evidence. The structure of the 50-item IPIP was examined in two different adult samples (total N=811), in each case justifying a 5-factor solution, with only minor discrepancies. Age differences were comparable to previous findings using other inventories. One sample (N=193) also completed additionally another personality measure (the TIPI Short Form). Conscientiousness, extraversion and emotional stability/ neuroticism scales of the IPIP were highly correlated with those of the TIPI (r=0.62 to 0.65, P=0.01). Agreeableness and Intellect/Openness scales correlated less strongly (r=0.54 and 0.58 respectively, P=0.01). The IPIP scales have good internal consistency (a=0.88) and relate strongly to major dimensions of personality assessed by the two questionnaires.

scholarly journals Pengenalan Gerak Manusia Menggunakan Algoritma Relevance Vector Machine pada MSRC-12 Dataset

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Vina Ayumi ◽  
Erwin Dwika Putra
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Statistical Learning ◽  
Gesture Recognition ◽  
Learning Theory ◽  
Relevance Vector Machine ◽  
Motion Model ◽  
Communication Tools ◽  
Machine Learning Technique ◽  
Learning Technique

Relevance vector machine is a popular machine learning technique that is motivated by statistical learning theory. RVM can be used for gesture recognition which is one of the communication tools used by humans. This study proposes an experiment using the Relevance Vector Machine (RVM) algorithm on gesture data from Microsoft Research Cambridge-12 (MSRC-12) as a proposed solution to overcome unbalanced problems in data processing. The results of the study are the accuracy for 1-person motion model reaches 100% and the lowest accuracy with 5 people the motion model reaches 96%. Graphically, the more people or models, the lower the algorithm's accuracy.

scholarly journals Traits and Adaptations: A Theoretical Examination and New Empirical Evidence

2020 ◽  
Vol 34 (3) ◽  
pp. 265-284 ◽  
Author(s):  
Samuel Henry ◽  
René Mõttus
Keyword(s):  
Social Desirability ◽  
Big Five ◽  
Rank Order ◽  
Social Environments ◽  
Rater Agreement ◽  
Evolutionary Time ◽  
Personality Psychology ◽  
Environment Model ◽  
Personality Theories ◽  
Empirical Implications

We investigated the distinction between traits (also labelled basic tendencies or dispositions) and (characteristic) adaptations, two related features of the personality system postulated to influence how personality manifests throughout the lifespan. Traits are alleged to be universal, causal, and enduring entities that exist across cultures and through evolutionary time, whereas learned adaptations are acquired through sustained interaction with cultural, physical, and social environments. Although this distinction is central to several personality theories, they provide few measurable criteria to distinguish between traits and adaptations. Moreover, little research has endeavoured to operationalize it, let alone test it empirically. Drawing on insights from four frameworks—the Five–Factor Theory, Cybernetic Big Five Theory, Disposition–Adaptation–Environment Model, and New Big Five—we attempted to investigate the distinction both theoretically and empirically. Using various experimental rating conditions, we first scored 240 questionnaire items in their degrees of definitionally reflecting traits and/or adaptations. Next, we correlated these definitional ratings with the items’ estimates of rank–order stability, consensual validity, and heritability—criteria often associated with personality traits. We found some evidence that items rated as more trait–like and less adaptation–like correspond to higher cross–rater agreement and stability but not heritability. These associations survived controlling for items’ retest reliability, social desirability, and variance. The theoretical and empirical implications of these findings are discussed. © 2020 European Association of Personality Psychology

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