A new method called MISC was developed to estimate sparse covariance matrices in logit mixture models. Instead of assuming all coefficients are related or none are, MISC finds the best groups of related coefficients to estimate covariances. By using a mix of optimization and simulation techniques, MISC can accurately identify the true covariance structure from data. In a transportation survey, MISC successfully showed how preferences for different attributes are connected.