Validación de un modelo de rasgos positivos y negativos de personalidad como predictores del bienestar psicológico aplicando algoritmos de machine learning
DOI:
https://doi.org/10.22235/cp.v18i1.3286Palabras clave:
rasgos positivos, rasgos negativos, personalidad, bienestar psicológico, algoritmosResumen
El objetivo de este estudio fue verificar un modelo predictivo de rasgos de personalidad positivos y negativos tomando como criterio el bienestar psicológico mediante la implementación de algoritmos de machine learning. Participaron 2038 sujetos adultos (51.9 % mujeres). Para la recolección de datos se utilizó: Big Five Inventory y Mental Health Continuum-Short Form. Además, para evaluar los rasgos positivos y negativos de personalidad se utilizaron los ítems ya validados de los modelos de rasgos positivos (HFM) y negativos (BAM) de forma conjunta. A partir de los hallazgos encontrados se pudo verificar que la eficacia predictiva del modelo testeado de rasgos positivos y negativos derivados de un enfoque léxico resultó superior a la capacidad predictiva de los rasgos normales de personalidad para la predicción del bienestar.
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Allport, G. W. & Odbert, H. S. (1936). Trait-names: A psycholexical study. Psychological Monographs, 47(1), i-171. https://doi.org/10.1037/h0093360
Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting psychological and subjective well-being from personality: A meta-analysis. Psychological Bulletin, 146(4), 279-323. https://doi.org/10.1037/bul0000226
Blasco-Belled, A., Tejada-Gallardo, C., Alsinet, C., & Rogoza, R. (2024). The links of subjective and psychological well-being with the Dark Triad traits: A meta-analysis. Journal of Personality, 92, 584-600. https://doi.org/10.1111/jopy.12853
Bleidorn, W., & Hopwood, C. J. (2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23(2), 190-203. https://doi.org/10.1177/1088868318772990
Castro Solano, A., & Casullo, M. M. (2001). Rasgos de personalidad, bienestar psicológico y rendimiento académico en adolescentes argentinos. Interdisciplinaria 18, 65–85.
Castro Solano, A., & Cosentino, A. (2017). High Five Model: Los factores altos están asociados con bajo riesgo de enfermedades médicas, mentales y de personalidad. Psicodebate. Psicología, Cultura y Sociedad, 17(2), 69-82. https://doi.org/10.18682/pd.v17i2.712
Castro Solano, A., & Cosentino, A. (2019). The High Five Model: Associations of the high factors with complete mental well-being and academic adjustment in university students. Europe’s Journal of Psychology, 15(4), 656–670. https://doi.org/10.5964/ejop.v15i4.1759
Cawley, M. J., Martin, J. E., & Johnson, J. A. (2000). A virtues approach to personality. Personality and Individual Differences, 28(5), 997-1013. https://doi.org/10.1016/s0191-8869(99)00207-x
Chow, S. L. (2002). Methods in psychological research. Encyclopedia of Life Support Systems.
Christopher, J. C., & Hickinbottom, S. (2008). Positive psychology, ethnocentrism, and the disguised ideology of individualism. Theory & Psychology, 18(5), 563-589. https://doi.org/10.1177/0959354308093396
Cosentino, A., & Castro Solano, A. (2017). The High Five: Associations of the five positive factors with the Big Five and well-being. Frontiers in Psychology, 8, e1250. https://doi.org/10.3389/fpsyg.2017.01250
Cosentino, A., & Castro Solano, A. (2023, en prensa). An Inductively derived Model of Negative Personality Traits. Testing, Psychometrics, Methodology in Applied Psychology.
Costa, P. T., & McCrae, R. R. (1984). The NEO Personality Inventory Manual. Psychological Assessment Resources. https://doi.org/10.1007/springerreference_184625
De la Iglesia, G. & Castro Solano A. (2020). Inventario de los Cinco Continuos de la Personalidad –ICCP-. Evaluación de Rasgos Positivos y Patológicos de la Personalidad. Paidós Informatizados.
De Raad, B., & Van Oudenhoven, J. P. (2011). A psycholexical Study of Virtues in the Dutch Language, and Relations between Virtues and Personality. European Journal of Personality, 25(1), 43-52. https://doi.org/10.1002/per.777
Delgadillo, J., & Gonzalez Salas Duhne, P. (2020). Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach. Journal of Consulting and Clinical Psychology, 88(1), 14-24. https://doi.org/10.1037/ccp0000476
Dey, A. (2016). Machine Learning Algorithms: A review. International Journal of Computer Science and Information Technologies, 7(3), 1174-1179.
Dhall, D., Kaur, R., & Juneja, M. (2020). Machine Learning: A review of the algorithms and its applications. En P. Singh, A. Kar, Y. Singh, M. Kolekar, & S. Tanwar (Eds.), Proceedings of ICRIC 2019. Lecture Notes in Electrical Engineering (pp. 47-63). Springer. https://doi.org/10.1007/9 78-3-030-29407-6_5
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine Learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91-118. https://doi.org/10.1146/annurev-clinpsy-032816-045037
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. https://doi.org/10.18637/jss.v033.i01
Gareth, J., Daniela, W., Trevor, H., & Robert, T. (2013). An introduction to statistical learning: with applications in R. Spinger. https://doi.org/10.1111/j.1467-9868.2005.00503.x
Garge, N. R., Bobashev, G., & Eggleston, B. (2013). Random forest methodology for model-based recursive partitioning: the mobForest package for R. BMC bioinformatics, 14(1), 1-8. https://doi.org/10.1186/1471-2105-14-125
Gómez Penedo, J. M., Schwartz, B., Giesemann, J., Rubel, J. A., Deisenhofer, A. K., & Lutz, W. (2022). For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization. Psychotherapy Research, 32(2), 151-164. https://doi.org/10.1080/10503307.2021.1930242
Jacobucci, R., & Grimm, K. J. (2020). Machine Learning and psychological research: the unexplored effect of measurement. Perspectives on Psychological Science: a Journal of the Association for Psychological Science, 15(3), 809-816. https://doi.org/10.1177/1745691620902467
John, O. P., Donahue, E. M., & Kentle, R. L. (1991). Big Five Inventory (BFI) [Database record]. APA PsycTests. https://doi.org/10.1037/t07550-000
Jones, D. N., & Paulhus, D. L. (2014). Introducing the short dark triad (SD3) a brief measure of dark personality traits. Assessment, 21(1), 28-41. https://doi.org/10.1177/1073191113514105
Kaufman S. B., Yaden D. B., Hyde, E., & Tsukayama, E. (2019). The light vs. dark triad of personality: contrasting two very different profiles of human nature. Frontiers in Psychology, 10, e467. https://doi.org/10.3389/fpsyg.2019.00467
Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43, 207-222. https://doi.org/10.2307/3090197
Keyes, C. L. M. (2005). The subjective well-being of America’s youth: toward a comprehensive assessment. Adolescent and Family Health, 4, 3-11.
Koul, A., Becchio, C., & Cavallo, A. (2018). PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research. Behavior research methods, 50(4), 1657-1672. https://doi.org/10.3758/s13428-017-0987-2
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Lin, E., Lin, C. H., & Lane, H. Y. (2020). Precision psychiatry applications with pharmacogenomics: artificial intelligence and Machine Learning approaches. International Journal of Molecular Sciences, 21(3), 969. https://doi.org/10.3390/ijms21030969
Liu, Y., Zhao, N., & Ma, M. (2021). The Dark Triad Traits and the Prediction of Eudaimonic Wellbeing. Frontiers in Psychology, 12, 693778. https://doi.org/10.3389/fpsyg.2021.693778
Lopez, S. J., Prosser, E. C., Edwards, L. M., Magyar-Moe, J. L., Neufeld, J. E., & Rasmussen, H. N. (2002). Putting positive psychology in a multicultural context. En C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 700–714). Oxford University Press.
Lupano Perugini M. L., de la Iglesia, G., Castro Solano A., & Keyes, C. L. M. (2017). The Mental Health Continuum-Short Form (MHC-SF) in the Argentinean context: confirmatory factor analysis and measurement invariance. Europe Journal of Psychology, 13, 93-108. https://doi.org/10.5964/ejop.v13i1.1163
McCullough, M. E., & Snyder, C. R. (2000). Classical source of human strength: revisiting an old home and building a new one. Journal of Social and Clinical Psychology, 19, 1-10. https://doi.org/10.1521/jscp.2000.19.1.1
Morales-Vives, F., De Raad, B., & Vigil-Colet, A. (2014). Psycho-lexically based virtue factors in Spain and their relation with personality traits. The Journal of General Psychology, 141(4), 297-325. https://doi.org/10.1080/00221309.2014.938719
Nave, G., Minxha, J., Greenberg, D. M., Kosinski, M., Stillwell, D., & Rentfrow, J. (2018). Musical preferences predict personality: evidence from active listening and Facebook likes. Psychological Science, 29(7), 1145-1158. https://doi.org/10.1177/0956797618761659
Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine Learning in psychometrics and psychological research. Frontiers in Psychology, 10, 2970. https://doi.org/10.3389/fpsyg.2019.02970
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., & Seligman, M. E. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934. https://doi.org/10.1037/pspp0000020
Park, N., Peterson, C. & Seligman, M. E. P. (2004). Strengths of character and well-being. Journal of Social and Clinical Psychology, 23(5), 603-619. https://doi.org/10.1521/jscp.23.5.603.50748
Paulhus, D. L., & Williams, K. M. (2002). The dark triad of personality: narcissism, machiavellianism, and psychopathy. Journal of Research in Personality, 36, 556-563. https://doi.org/10.1016/S0092-6566(02)00505-6
Peterson, C. & Seligman, M. E. P. (2004). Character strengths and virtues: A handbook and classification. Oxford University Press.
Ryff, C. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57, 1069-1081. https://doi.org/10.1037/0022-3514.57.6.1069
Shatte, A., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426-1448. https://doi.org/10.1017/S0033291719000151
Snyder, C. R., Lopez, S. J., & Pedrotti, J. T. (2011). Positive Psychology: The Scientific and Practical Explorations of Human Strengths (2a ed.). Sage.
Stavraki, M., Artacho-Mata, E., Bajo, M., Diaz, D. (2023). The dark and light of human nature: Spanish adaptation of the light triad scale and its relationship with psychological well-being. Current Psychology, 42, 26979-26988 https://doi.org/10.1007/s12144-022-03732-5
Tkach, C., & Lyubomirsky, S. (2006). How do people pursue happiness? Relating personality, happiness-increasing strategies, and well-being. Journal of Happiness Studies, 7(2), 183-225. https://doi.org/10.1007/s10902-005-4754-1
Walker, L. J., & Pitts, R. C. (1998). Naturalistic conceptions of moral maturity. Developmental Psychology, 34(3), 403-419. https://doi.org/10.1037/0012-1649.34.3.403
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122. https://doi.org/10.1177/1745691617693393
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (statistical methodology), 67(2), 301-320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
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Derechos de autor 2024 Universidad Católica del Uruguay
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