Bayesian inference for an item response model for modeling test anxiety

We develop a Bayesian binary Item Response Model (IRM), which we denote as Test Anxiety Model (TAM), for estimating the proficiency scores when individuals might experience test anxiety. We consider order restricted item parameters conditionally to the examinees’ reported emotional state at the testing session. We consider three test anxiety levels: calm, anxious and very anxious. Using simulated data we show that taking into account test anxiety levels in an IRM help us to obtain fair proficiency estimates as opposed to the ones obtained with three two-parameter logistic IRM (3PM) by Birnbaum (1957, 1968). For the 3PM, the proficiency estimates tend to be positively biased for both, calm and anxious examinees.