Several methods to investigate relative attribute impact in stated preference experiments

There is growing use of discrete choice experiments (DCEs) to investigate preferences for products and programs and for the attributes that make up such products and programs. However, a fundamental issue overlooked in the interpretation of many choice experiments is that attribute parameters estimated from DCE response data are confounded with the underlying subjective scale of the utilities, and strictly speaking cannot be interpreted as the relative “weight” or “impact” of the attributes, as is frequently done in the health economics literature. As such, relative attribute impact cannot be compared using attribute parameter size and significance. Instead, to investigate the relative impact of each attribute requires commensurable measurement units; that is, a common, comparable scale. We present and demonstrate empirically a menu of five methods that allow such comparisons: (1) partial log-likelihood analysis; (2) the marginal rate of substitution for non-linear models; (3) Hicksian welfare measures; (4) probability analysis; and (5) best–worst attribute scaling. We discuss the advantages and disadvantages of each method and suggest circumstances in which each is appropriate.