Integración de Inputs Positivos y Negativos en Juicios Psicofísicos de Equidad / Integration of Positive and Negative Inputs in Equity Psychophysical Judgements

RESUMEN

Resumen Los modelos de equidad han predicho adecuadamente las ganancias monetarias entre dos empleados hipotéticos que difieren en méritos, sin embargo, han sido incapaces de predecir pérdidas monetarias y condiciones de n>2; se propone la Ecuación General de Distribución de Recursos En Equidad (Función de Equidad) para superar dichas limitantes por lo que el objetivo de la presente investigación consistió en evaluar la generalidad de la Función de Equidad en contextos de pérdidas y ganancias. Participaron voluntariamente 30 estudiantes universitarios de los cuales el 65% fueron mujeres tenían 19.87 años (DE=1.23). En 18 escenarios hipotéticos de acuerdo con un diseño de medidas repetidas (3 niveles de mérito de A) X 3 (niveles de mérito de B) x 2 (Ganancias y Pérdidas) los participantes asignaron montos en ganancias y pérdidas monetarias. Se encontraron reglas de integración aditiva en el campo de las ganancias e indicios de reglas de integración multiplicativa en el campo de las pérdidas. La Ecuación General de Distribución de Recursos en Equidad predice adecuadamente los montos en ganancias y pérdidas. Los datos se discuten a la luz de la Teoría de Integración de Información y las Ciencias Cognitivas y del Comportamiento.

ABSTRACT

Abstract Equity models properly predict monetary outcomes between two hypothetical employees who differ in inputs; however, they have been unable to predict monetary losses and conditions of n> 2; General Equation of Equity Resource Allocation (Equity Function) is proposed to overcome these limitations, so the aim of this work was to evaluate the generality of the Equity Function in gains and losses contexts. A non-probabilistic factorial design with convenience sampling was used. Sample size was calculated from the desired effect size, the final sample was made up of 30 university students of which 65% were women who were 19.87 years old (SD = 1.23). A hypothetical task of resource allocation was proposed to employees who differ in their levels of merits, in which throughout 18 scenarios according to a repeated measures design (3 levels of merits of A) X 3 (levels of merits of B) x 2 (Gains and Losses) the participants assigned amounts in monetary gains and losses. Data was analyzed using Repeated Measures ANOVA, the effect size calculation using the Partial Square Eta parameter and the simple linear regression analysis of each curve were performed to obtain the slope of each line. In the context of gains, main effects of employee A and B were found, no interaction effects were found. In the context of losses, main effects of employee A and B were contrasted, as well as interaction effects. Robust effect sizes were found for all factors. Analysis of regression equations slopes shows that the loss amounts were larger than the gains amounts. Additive integration rules were found in the field of gains and indications of multiplicative integration rules in the field of losses. The General Equation of Equity Resource Allocation adequately predicts the amounts of gains and losses, being more precise in the field of gains compared to losses. According from these results, it is proposed that cognitive process of assigning a gain is different from those of assigning a loss. Limitations and alternative courses of action were raised.