ABSTRACT
This chapter presents tools for illuminating structure in data in the presence of measurement difficulties, endogeneity, and unobservable or latent variables. Structure in data refers to relationships between variables in the data, including direct or causal relationships, indirect or mediated relationships, associations, and the role of errors of measurement in the models. Measurement difficulties include challenges encountered when an unobservable or latent construct is indirectly measured through exogenous observable variables. Examples of latent variables in transportation include attitudes toward transportation policies or programs such as gas taxes, van pooling, high-occupancy vehicle lanes, and mass transit. Interest might also be centered on the role of education, socioeconomic status, or attitudes on driver decision making — which are also difficult to measure directly. As discussed in previous chapters, endogeneity refers to variables whose values are largely determined by factors or variables included in the statistical model or system.
