Researchers often measure stress using questionnaire data on the occurrence of potentially stress-inducing life events and the strength of reaction to these events, characterized as negative or positive and assigned an ordinal ranking. In studying the health effects of stress, one needs to obtain measures of an individual's negative and positive stress levels to be used as predictors. Motivated by data of this type, we propose a latent variable model, which is characterized by event-specific negative and positive reaction scores. If the positive reaction score dominates the negative reaction score for an event, then the individual's reported response to that event will be positive, with an ordinal ranking determined by the value of the score. Measures of overall positive and negative stress can be obtained by summing the reactivity scores across the events that occur for an individual. By incorporating these measures as predictors in a regression model and fitting the stress and outcome models jointly using Bayesian methods, inferences can be conducted without the need to assume known weights for the different events. We propose an MCMC algorithm for posterior computation and apply the approach to study the effects of stress on preterm delivery.