This research aimed to analyzewhether positive traits could explain optimal functioning, the combination of high well-being and performance, of active workers. The sample comprised 400 active Argentinean workers between the ages of 18 and 66 years old (MAGE=36.50, SD=11.81, 49.5% male, 50.5% female). Multiple lineal regressions revealed that positive traits increase the explained variance of the work variables (performance, satisfaction and well-being) in comparison to a prediction model that only included normal traits. Also, discriminant analysis revealed that of all traits, positive and normal, the only one that better discriminated the high/low optimal functioning groups was the positive trait sprightliness.

El objetivo de este trabajo fue identificar si el funcionamiento óptimo de trabajadores activos —entendido como una combinación de alto bienestar y performance— podía expli-carse por la presencia de rasgos positivos de la personalidad. Se trabajó con una muestra de 400 trabajadores activos argentinos de entre 18 y 66 años (MEDAD=36.50, DE=11.81, 49.5% varones, 50.5% mujeres). Los resultados de regresiones lineales múltiples indicaron que los rasgos positivos incrementan la varianza explicada de las variables laborales estudiadas —per-formance, satisfacción y bienestar— al comparárselo con un modelo de predicción que solo incluía rasgos normales. Además, en un análisis discriminante, se concluyó que de todos los rasgos —positivos y normales— el que mejor discriminaba los grupos de alto/bajo funcio-namiento óptimo era el rasgo positivo sprightliness.

The field of psychological assessment is undergoing a marked transition from traditional methodologies toward approaches that incorporate technological innovations, including digital platforms, serious games, chatbots, virtual reality, and artificial intelligence (AI). This article presents a review of current and emerging technological tools in psychological assessment, with particular emphasis on technology-based assessments (TBAs)—instruments that rely on digital technologies to evaluate a wide range of psychological constructs. The article describes the main types of TBAs, outlines their applications in assessing diverse psychological variables, and analyzes their growing use in psychological assessments. It highlights both the advantages and limitations of these tools and considers international guidelines for developing and implementing TBAs thatare valid, reliable, and fair. Ove...

This research aimed to analyzewhether positive traits could explain optimal functioning, the combination of high well-being and performance, of active workers. The sample comprised 400 active Argentinean workers between the ages of 18 and 66 years old (MAGE=36.50, SD=11.81, 49.5% male, 50.5% female). Multiple lineal regressions revealed that positive traits increase the explained variance of the work variables (performance, satisfaction and well-being) in comparison to a prediction model that only included normal traits. Also, discriminant analysis revealed that of all traits, positive and normal, the only one that better discriminated the high/low optimal functioning groups was the positive trait sprightliness.

The field of psychological assessment is undergoing a marked transition from traditional methodologies toward approaches that incorporate technological innovations, including digital platforms, serious games, chatbots, virtual reality, and artificial intelligence (AI). This article presents a review of current and emerging technological tools in psychological assessment, with particular emphasis on technology-based assessments (TBAs)—instruments that rely on digital technologies to evaluate a wide range of psychological constructs. The article describes the main types of TBAs, outlines their applications in assessing diverse psychological variables, and analyzes their growing use in psychological assessments. It highlights both the advantages and limitations of these tools and considers international guidelines for developing and implementing TBAs thatare valid, reliable, and fair. Ove...