OBJECTIVEThe application of digital phenotyping in personality research leverages smartphone-generated data to quantify individual differences in personality constructs. It can be conceptualized as an extension of Experience Sampling Methods (ESMs), as it allows for the continuous, in situ collection of behavioral and contextual data. This study expands beyond the FFM/Big5 model to include 59 traits/types from 16 personality constructs, including temperament and personal value theories.METHODDigital footprints were collected from 104 participants' smartphones over 7-10 days. Both hypothesis-testing (deductive) and machine learning (inductive) methods were applied to analyze the data.RESULTSFour personality constructs of 16 (25%) were successfully predicted (r 0.034-0.53): Adult Attachment, FFM/Big5, Distress Tolerance, and Creativity, given an adopted r ≥ 0.34 threshold for successful predictions. Overall, a total of 22 out of 59 individual traits and types of the 16 constructs were successfully predicted (37.29%). Gradient Boosted Trees emerged as the most effective machine learning predictive model (compared with Decision Tree, Random Forest, and Support Vector Machine), particularly when analyzing communication-related information features.CONCLUSIONSThis study demonstrates the capacity of Digital Phenotyping of smartphone data to broaden the possibilities of remote personality psychology research and highlights its potential applicability in People Analytics research and additional cross-disciplinaryscholarly fields.
{"title":"Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping-Based Exploration.","authors":"Maya Hocherman,Yonathan Mizrachi,Hila Chalutz-BenGal","doi":"10.1111/jopy.70006","DOIUrl":"https://doi.org/10.1111/jopy.70006","url":null,"abstract":"OBJECTIVEThe application of digital phenotyping in personality research leverages smartphone-generated data to quantify individual differences in personality constructs. It can be conceptualized as an extension of Experience Sampling Methods (ESMs), as it allows for the continuous, in situ collection of behavioral and contextual data. This study expands beyond the FFM/Big5 model to include 59 traits/types from 16 personality constructs, including temperament and personal value theories.METHODDigital footprints were collected from 104 participants' smartphones over 7-10 days. Both hypothesis-testing (deductive) and machine learning (inductive) methods were applied to analyze the data.RESULTSFour personality constructs of 16 (25%) were successfully predicted (r 0.034-0.53): Adult Attachment, FFM/Big5, Distress Tolerance, and Creativity, given an adopted r ≥ 0.34 threshold for successful predictions. Overall, a total of 22 out of 59 individual traits and types of the 16 constructs were successfully predicted (37.29%). Gradient Boosted Trees emerged as the most effective machine learning predictive model (compared with Decision Tree, Random Forest, and Support Vector Machine), particularly when analyzing communication-related information features.CONCLUSIONSThis study demonstrates the capacity of Digital Phenotyping of smartphone data to broaden the possibilities of remote personality psychology research and highlights its potential applicability in People Analytics research and additional cross-disciplinaryscholarly fields.","PeriodicalId":48421,"journal":{"name":"Journal of Personality","volume":"29 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael D Robinson, Roberta L Irvin, Muhammad R Asad, Hamidreza Fereidouni, Lauren L Rahier, Grace A Lawrence
Introduction: Situations figure prominently in people's lives, but extant approaches to assessment rarely model situational specificity.
Methods: Using a situational judgment base, the present studies (N = 356) created a behavioral tendency of life test that sought to simulate person-specific responses to a wide variety of life situations. Behavioral tendencies thought to be conducive to happiness were quantified by linking each participant's 160 self-likelihood ratings to a prototype of the happy person, with the idea that participants who matched the prototype to a greater extent would act in ways that promote happiness in their daily lives.
Results: This person-in-context approach to assessment worked in that higher behavioral tendencies of happiness (BT-H) scores were strongly predictive of happiness and well-being, with additional results providing insights into how the relevant tendencies operate.
Conclusions: The research demonstrates the value of understanding broad constructs, such as happiness, on the basis of more particular person-situation-behavior units.
{"title":"Behavioral Tendencies of Happiness: A Person-In-Context Approach to Construct Measurement.","authors":"Michael D Robinson, Roberta L Irvin, Muhammad R Asad, Hamidreza Fereidouni, Lauren L Rahier, Grace A Lawrence","doi":"10.1111/jopy.70011","DOIUrl":"https://doi.org/10.1111/jopy.70011","url":null,"abstract":"<p><strong>Introduction: </strong>Situations figure prominently in people's lives, but extant approaches to assessment rarely model situational specificity.</p><p><strong>Methods: </strong>Using a situational judgment base, the present studies (N = 356) created a behavioral tendency of life test that sought to simulate person-specific responses to a wide variety of life situations. Behavioral tendencies thought to be conducive to happiness were quantified by linking each participant's 160 self-likelihood ratings to a prototype of the happy person, with the idea that participants who matched the prototype to a greater extent would act in ways that promote happiness in their daily lives.</p><p><strong>Results: </strong>This person-in-context approach to assessment worked in that higher behavioral tendencies of happiness (BT-H) scores were strongly predictive of happiness and well-being, with additional results providing insights into how the relevant tendencies operate.</p><p><strong>Conclusions: </strong>The research demonstrates the value of understanding broad constructs, such as happiness, on the basis of more particular person-situation-behavior units.</p>","PeriodicalId":48421,"journal":{"name":"Journal of Personality","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}