In any given survey, individuals are likely to differ in attitudes toward the subject matter. They also may differ in terms of the duration and persistence of attitudes, with some persons' beliefs being much more stable than others. For the purpose of jointly assessing attitude and temporal attitudinal stability, we propose a latent bivariate item response model. Attitudinal stability is operationalized as a construct called response consistency, which is indicated by the concordance of observed responses between two-time points. A simulation experiment assesses the parameter recovery of the proposed model. A real data analysis example uses data collected from a study on folklore beliefs about diabetes (563 individuals from multiple rural communities in North Carolina). On two different occasions, the individuals in the sample completed a 31-item common-sense model of diabetes inventory, which measures the congruence of their beliefs with a biomedical model. Results from the simulation study showed that the model parameters and factor correlation in the latent bivariate IRT model overall recovered well. Results from the real data analysis demonstrated the saliency of the construct. A weak association between having beliefs congruent with the biomedical model and response consistency across the two administrations was found.