Study retention prediction with AI
Introduction Openness, 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...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-04-01
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| Series: | European Psychiatry |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933821003850/type/journal_article |
| _version_ | 1797617072720576512 |
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| author | A. Mereu |
| author_facet | A. Mereu |
| author_sort | A. Mereu |
| collection | DOAJ |
| description |
Introduction
Openness, 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.
Objectives
To investigate whether AI could predict study attrition employing personality traits scores.
Methods
Data 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/g3jx8zt2t9.1
Results
Predictions obtained a negative likelihood ratio of 0.333 and a negative predictive value of 0.933. The results were indicative of fair performance.
Conclusions
AI 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.
Disclosure
No significant relationships.
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| first_indexed | 2024-03-11T07:49:24Z |
| format | Article |
| id | doaj.art-830a088f132f431e9e637249b96d68d9 |
| institution | Directory Open Access Journal |
| issn | 0924-9338 1778-3585 |
| language | English |
| last_indexed | 2024-03-11T07:49:24Z |
| publishDate | 2021-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj.art-830a088f132f431e9e637249b96d68d92023-11-17T05:06:57ZengCambridge University PressEuropean Psychiatry0924-93381778-35852021-04-0164S140S14010.1192/j.eurpsy.2021.385Study retention prediction with AIA. Mereu0Research performed independently, Cagliari, Italy Introduction Openness, 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. Objectives To investigate whether AI could predict study attrition employing personality traits scores. Methods Data 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/g3jx8zt2t9.1 Results Predictions obtained a negative likelihood ratio of 0.333 and a negative predictive value of 0.933. The results were indicative of fair performance. Conclusions AI 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. Disclosure No significant relationships. https://www.cambridge.org/core/product/identifier/S0924933821003850/type/journal_articletraitsretentionArtificial IntelligencePersonality |
| spellingShingle | A. Mereu Study retention prediction with AI European Psychiatry traits retention Artificial Intelligence Personality |
| title | Study retention prediction with AI |
| title_full | Study retention prediction with AI |
| title_fullStr | Study retention prediction with AI |
| title_full_unstemmed | Study retention prediction with AI |
| title_short | Study retention prediction with AI |
| title_sort | study retention prediction with ai |
| topic | traits retention Artificial Intelligence Personality |
| url | https://www.cambridge.org/core/product/identifier/S0924933821003850/type/journal_article |
| work_keys_str_mv | AT amereu studyretentionpredictionwithai |