Dummy and effects coding variables in discrete choice analysis
比较了离散选择模型中分类变量的两种编码方法（虚拟编码与效应编码），证明两者等价且不存在混淆问题，但效应编码易导致结果误读，建议使用虚拟编码。
Abstract Discrete choice models typically incorporate product/service attributes, many of which are categorical. Researchers code these attributes in one of two ways: dummy coding and effects coding. Whereas previous studies favor effects coding citing that it resolves confounding between attributes, our analysis demonstrates that such confounding does not exist in either method, even when a choice model contains alternative specific constants. Furthermore, we show that because of the lack of understanding of the equivalence between the two coding methods, a sizeable number of previously published articles have misinterpreted effects coded results. The misinterpretation generates conflicting preference ordering and renders t‐statistics, marginal willingness to pay, as well as consumer surplus/compensating variation estimates invalid. We show that severe misinterpretation occurs for any categorical attribute that contains more than two discrete levels. The frequency of two‐level attributes used in discrete choice analyses may have led some past studies to overlook this error. Given its equivalence and lower likelihood of misinterpretation, we recommend dummy coding.