La medida de la comprensión emocional con el modelo de Rasch

  1. Delgado, Ana R. 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revue:
Actualidades en Psicología

ISSN: 2215-3535,  0258-6444

Année de publication: 2016

Titre de la publication: Actualidades en Psicología

Volumen: 30

Número: 120

Pages: 47-56

Type: Article

DOI: 10.15517/AP.V29I119.21516 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: Actualidades en Psicología

Résumé

The aim of this study was to construct emotion understanding items from the Rasch Model approach by experimenting through comparison between verbal /image response format. The participants were 204 subjects from a Spanish community sample. A randomized experiment was carried out to test the effect of response format (verbal/image) and participants’ gender on the emotion understanding Rasch measurements. The effect on item difficulty of the social distance (close/far) was also contrasted. No interaction effect was found. Response format had a significant effect on measurement regardless of gender: the verbal response format was easier than the image one. There were significant gender differences on emotion understanding favoring women. Items describing situations with close receivers were significantly easier than the items showing far recipients.

Références bibliographiques

  • Delgado, A. R. (2012). Using the Rasch Model to test the psychometric quality of emotion recognition items. Journal of the International Neuropsychological Society, 18 (S1), 69.
  • Delgado, A. R. (2009a). Spanish basic emotion words are consistently ordered. Quality & Quantity, 43, 509-517.
  • Delgado, A. R., (2009b). Social robots, moral emotions. Proceedings of the 11th International Conference on Enterprise Information Systems, vol. AIDSS (pp. 263-270). ICEIS.
  • Delgado, A. R., & Máarquez, M. G. (2013a). Intracategorial varieties of contempt in a heterogeneous sample. Universitas Psychologica, 12, 357-362.
  • Delgado, A. R., & Máarquez, M. G. (2013b). Social robots, cross-cultural differences. Proceedings of the 15th International Conference on Enterprise Information Systems, vol. 3 (pp. 109-112). INSTICC.
  • Engelhard, G. (2013). Invariant measurement: Using Rasch Models in the social, behavioral and health sciences. New York, NY: Routledge.
  • Henrich, J. Heine, S.J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 61-135.
  • Inchausti, F., Prieto, G., & Delgado, A. R. (2014). Análisis Rasch de la versión española de la escala Mindful Attention Awareness Scale en una muestra clínica. Revista de Psiquiatría y Salud Mental, 7, 32-41.
  • Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: an integrative meta-analysis and cascading model. Journal of Applied Psychology, 95, 54-78.
  • Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud faces database. Cognition and Emotion, 24, 1377-1388. DOI:10.1080/02699930903485076
  • Linacre, J. M. (2013). Winsteps Rasch measurement computer program, version 3.80.1. Chicago: Winsteps.com.
  • LiveCode 4.6 [computer software]. (2011). Retrieved from http://runrev.com/
  • MacCann, C., Joseph, D. L., Newman, D. A., & Roberts, R. D. (2014). Emotional intelligence is a second stratum factor of intelligence: evidence from hierarchical and bifactor models. Emotion, 14, 358-74.
  • Máarquez, M. G., & Delgado, A. R. (2012). Revisión de las medidas de reconocimiento y expresión de emociones. Anales de Psicología, 28, 978-985.
  • Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human abilities: emotional intelligence. Annual Review of Psychology, 59, 507-536.
  • Newton, P. E., & Shaw, S. D. (2013). Standards for talking and thinking about validity. Psychological Methods, 18, 301-19.
  • Prieto, G., & Delgado, A. R. (2003). Análisis de un test mediante el modelo de Rasch. [Rasch-modelling a test]. Psicothema, 15, 94-100.
  • Prieto, G., & Díaz Velasco, A. (2003). Uso del modelo de Rasch para poner en la misma escala las puntuaciones de distintos tests. Actualidades en Psicología, 19, 5-23.
  • Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danish Institute for Educational Research.
  • Reckase, M. D. (1979). Unifactor latent trait models applied to multifactor tests: results and implications. Journal of Educational Statistics, 4, 207-230.
  • Roznowski, M., & y Reith, J. (1999). Examining the measurement quality of tests containing differentially functioning items: do biased items result in poor measurement? Educational and Psychological Measurement, 59, 248–69.
  • Smith, R. M. (2004). Detecting item bias with the Rasch model. Journal of Applied Measurement, 5, 430-449.
  • Soderberg, C. K., Callahan, S. P., Kochersberger, A., Amit, E., & Ledgerwood, A. (2015). The effect of psychological distance on abstraction: A meta- analysis of construal level theory. Psychological Bulletin, 141, 525-548.
  • Tracy, J. L., & Randles, D. (2011). Four models of basic emotions: a review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt. Emotion Review, 3, 397-405.
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