Research was done to compare the application of full models (or models with several covariates) and baseline models, estimated by using data meeting specific nominal conditions combined with accident-modification factors (AMFs) for predicting motor vehicle crashes. The analysis focuses on the predicted values and associated inferences for both types of models. In the past few years, researchers have questioned the approach of multiplying baseline models with AMFs. For comparison, full and baseline models are estimated by using data collected on rural four-lane highways in Texas. AMFs describing the safety effects related to lane and shoulder width as well as the number of horizontal curves per mile are extracted from previous work. Two scenarios describing AMF values and their associated uncertainty are evaluated. The results of the study show that the product of baseline models and AMFs produces a much larger variance, hence a wider 95% predicted confidence interval, than the variance calculated using the full models. This is consistent for the two scenarios evaluated and for all levels of uncertainty associated with the AMFs. The study concludes that the full model should be used instead of the product of the baseline model and AMFs when the study objective includes variance as part of the decision-making process.