American Ambivalence Towards Abortion Policy: Development of a Heteroskedastic Probit Model of Competing Values

Abstract

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Arbeitsmarktflexibilisierung und Ungleichheit: Der Wandel der Risikomuster befristeter Beschäftigung in Westdeutschland

“…Whereas in the context of ordinary least squares regression heteroscedasticity does not bias the parameter estimates, this causes more problems in logit regressions: In case of varying variances of the error term, not only are the standard errors incorrect, but the parameters are also biased and inconsistent. In order to deal with these problems, heterogeneous choice models for the logit model have been developed (Alvarez and Brehm 1995;Keele and Park 2006). Deriving the binary choice model in a latent variable framework (e.g.…”

Take-up and variation of advice for new firm founders in different local contexts

“…The argument for market failure focuses on the demand for business advice because it is the impact of uncertainty on market demand, which causes the problem; therefore, solving the market failure requires action on the demand side to reduce uncertainty. The heteroskedastic probit model has been used to assess ambivalence and uncertainty and therefore can enable us to assess the demand side of the business (Keele and Park 2006, Alvarez and Brehm 1997, Alvarez and Brehm 1995.…”

“…Alvarez and Brehm (1995) suggest exp(zi°) captures uncertainty and ambivalence: "Moreover, the underlying variance in a respondent's answers yields direct information about the degree of certainty that a respondent has in his or her opinions" Brehm 1995: 1060). Other authors have constructed measures of industry variation to assess an uncertainty in industry (see Hmieleski andBaron 2008, Downey, Hellriegel andSlocum Jr 1975).…”

Suicidal Thoughts in Adopted Versus Non-Adopted Youth: A Longitudinal Analysis in Adolescence, Early Young Adulthood, and Young Adulthood

“…For the dichotomous variable of suicidal thought, data were modeled using a modified linear probability framework, which has advantages in SEM relative to more traditional logit and probit analyses (Cheung, 2007;Daniloski, Jaccard, & Brinberg, 2011;Williams, 2009). Statisticians have questioned logistic regression for consequential assumptions of variance homogeneity and the standardized nature of logistic coefficients (Alvarez & Brehm, 1995Williams, 2009;Yatchew & Griliches, 1985). We therefore used the modified linear probability model with a HuberWhite robust estimator as implemented in Mplus.…”