A traditional discrete choice model assumes that an individual's decision-making process is based on utility maximization and that the systematic part of the utility function depends on some observable attributes and covariates. These attributes and covariates however can only explain part of the utility and a large part remains unexplained. In recent years, researchers have recognized that psychological factors such as attitudes, lifestyle and values can affect an alternative's utility and hence the individual's choices. Therefore, extending the traditional discrete choice model by incorporating those latent or unobservable factors, can help to better understand the individual's decision making process and therefore to yield more reliable part-worth estimates and market share predictions.Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. The current research uses the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirical study that we conducted to assess student preferences on mobile phone features shows these ICLV models perform very well with respect to model t and prediction.Furthermore, we compare the different estimation procedures that exist in the literature. Results show that, as expected, the simultaneous procedure where we estimate the structural equation model (SEM) and the choice model simultaneously, gives better model fit and more accurate predictions compared to the sequential procedure that estimates the SEM first and then the choice model taking the estimated factor scores into account. But the gain of the simultaneous procedure is relatively small. Therefore one can use the easier sequential procedure without losing much efficiency if needed.