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Ordinal and Multinomial models

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Ordinal and Multinomial models

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    1. Ordinal and Multinomial models

    2. Ordinal Outcomes 3 or more categorical outcomes, which can be treated as ordered Bond ratings (AAA, AA, … B, C, …) Likert scales (e.g. responses on a 1-7 scale, from strongly disagree to strongly agree) Often analyzed as continuous Can use logit or probit That was a not-so-quick review of binary models. Now we’re ready for ordinal models. [Explain what’s on the slide.] Statistical packages vary as to how these ordered outcomes are coded – typically using consecutive integers. For this talk, I’ll assume we’re analyzing a Likert scale starting with 1, so outcome 1 corresponds to the lowest ordered category, 2 the next, and so on. Analyzing as continuous assumes it’s interval data, I.e. that the distance from 1 to 2 is the same as the distance from 2 to 3, etc. But this may not be true if the categories are e.g. strongly disagree, disagree, neutral. That was a not-so-quick review of binary models. Now we’re ready for ordinal models. [Explain what’s on the slide.] Statistical packages vary as to how these ordered outcomes are coded – typically using consecutive integers. For this talk, I’ll assume we’re analyzing a Likert scale starting with 1, so outcome 1 corresponds to the lowest ordered category, 2 the next, and so on. Analyzing as continuous assumes it’s interval data, I.e. that the distance from 1 to 2 is the same as the distance from 2 to 3, etc. But this may not be true if the categories are e.g. strongly disagree, disagree, neutral.

    3. Ordinal logit

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