Probit Regression and Response Models (Statistical Associates Blue Book Series 38)

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About Probit Regression And Response Models

Probit regression is method of working with categorical dependent variables whose underlying distribution is assumed to be normal. That is, the assumptions of probit regression are consistent with having a dichotomous dependent variable whose distribution is assumed to be a proxy for a true underlying continuous normal distribution. Probit regression has been extended to cover multinomial dependent variables (more than two nominal categories) and to cover ordinal categorical dependent variables. Probit regression is an umbrella term meaning different things in different contexts, though the common denominator is treating categorical dependent variables assumed to have an underlying normal distribution. This volume discusses ordinal probit regression, probit signal-response models, probit response models, and multilevel probit regression.Table of ContentsIntroduction7Overview7Ordinal probit regression7Probit signal-response models7Probit response models8Multilevel probit regression8Key concepts and terms9Probit transformations9The cumulative normal distribution9Probit coefficients10Elasticity10Significance testing11Frequently asked questions11What about probit in Stata?11Binary and ordinal probit regression13Binary and ordinal probit regression models13Binary probit regression in generalized linear models13Example13Overview13Binary probit regression output in SPSS GZLM22Ordinal probit regression in generalized linear models28Overview28Example28SPSS set-up28SPSS ordinal probit output30Ordinal regression with a probit link33Overview33SPSS set-up33Output for ordinal regression with a probit link36Model fitting information, goodness-of-fit, and pseudo R-square tables36Test of parallel lines37Parameter estimates table38Probit signal-response models39