The go-to analysis engine for choice modelers is the conditional multinomial logit (MNL) model (McFadden 1974, Ben-Akiva and Lerman 1985, Train 2003). Conditional MNL predicts choices among alternatives that are conditioned on the attributes and levels of those alternatives. A less well known special case of MNL is the polytomous multinomial logit (P-MNL) model (Theil 1969, Hoffman and Duncan 1988). With P-MNL the attributes and levels describe not the products, but the chooser, the situation, or the context in which a decision occurs. Respondents choose among an invariant set of the alternatives, as the attributes and levels we have describe the situation, not the alternatives. If we also apply experimental control to an experiment featuring multi-profile choice sets analyzed via conditional logit, we have the well-known choice-based conjoint experiment (Louviere and Woodworth 1983, Louviere 1988, Louviere et al. 2000). A situational choice experiment (SCE), however, differs from a CBC in that its questions show one experimentally designed profile which describes the attributes and levels of the choice situation or context or chooser, and elicits choices among a fixed set of alternatives; SCE then uses polytomous MNL to generate utilities. Few marketers know about SCEs and there doesn’t seem to be a single source reference about them, two limitations we hope to remedy in this presentation. After introducing polytomous logit and distinguishing SCE from CBC, we’ll show how to design and program SCEs in Lighthouse Studio, how to analyze them in MBC software and how to simulate them in Excel.
