In the absence of clear incentives, achievement tests may be subject to the effect of slipping where item response functions have upper asymptotes below one. Slipping reduces score precision for higher latent scores and distorts test developers' understandings of item and test information. A multidimensional four-parameter normal ogive model was developed for large-scale assessments and applied to dichotomous items of the 2011 National Assessment of Educational Progress eighth-grade mathematics and reading tests. The results suggest that the probability of slipping exceeded 5% for 47.2% and 51.1% of the dichotomous mathematics and reading items, respectively. Furthermore, allowing for slipping resulted in larger item discrimination parameters, increased information in the lower-to-middle range of the latent trait, and decreased precision for scores one standard deviation above the mean. The results provide evidence that slipping is a factor that should be considered during test development and construction to ensure adequate measurement across the latent continuum.