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In one of the most influential works in the public opinion literature, Philip Converse proposed a "black-and-white" model that divided respondents into two groups: opinion holders and unstable opinion changers. We extend the model by allowing for a group that makes rational opinion changes over time. This enables us to (1) explore hypotheses about the adequacy of Converse's original model, (2) estimate the percentage of the population that belongs to each of the groups, and (3) examine the evidence for Converse's basic claim that unstable changers truly exhibit nonattitudes. Contrary to Converse's suggestion that the unstable group is essentially giving random responses, the results imply that the response behavior of this group may be best interpreted in terms of Zaller's notion of ambivalence. The results also undermine the measurement-error model, which maintains that unstable responses are mainly attributable to deficient survey instruments, not individual opinion change. We use data collected at four time points over nearly two years, which track Swiss citizens' support for pollution reduction.

In one of the most influential works in the public opinion literature, Philip Converse proposed a "black-and-white" model that divided respondents into two groups: opinion holders & unstable opinion changers. We extend the model by allowing for a group that makes rational opinion changes over time. This enables us to (1) explore hypotheses about the adequacy of Converse's original model, (2) estimate the % of the population that belongs to each of the groups, & (3) examine the evidence for Converse's basic claim that unstable changers truly exhibit nonattitudes. Contrary to Converse's suggestion that the unstable group is essentially giving random responses, the results imply that the response behavior of this group may be best interpreted in terms of Zaller's notion of ambivalence. The results also undermined the measurement-error model, which maintains that unstable responses are mainly attributable to deficient survey instruments, not individual opinion change. We use data collected at four time points over nearly two years, which track Swiss citizens' support for pollution reduction. 7 Tables, 4 Figures, 2 Appendixes, 40 References. Adapted from the source document.

We illustrate the use of a class of statistical models, finite mixture models, that can be used to allow for differences in model parameterizations across groups, even in the absence of group labels. We also introduce a methodology for fitting these models, data augmentation. Neither finite mixture models nor data augmentation is routine in the world of political science methodology, but both are quite standard in the statistical literature. The techniques are applied to an investigation of the empirical support for a theory (developed fully by Hill and Kriesi 2001) that extends Converse's (1964) "black-and-white" model of response stability. Our model formulation enables us (1) to provide reliable estimates of the size of the two groups of individuals originally distinguished in this model, opinion holders and unstable opinion changers; (2) to examine the evidence for Converse's basic claim that these unstable changers truly exhibit nonattitudes; and (3) to estimate the size of a newly defined group, durable changers, whose members exhibit more stable opinion change. Our application uses survey data collected at four time points over nearly 2 years which track Swiss citizens' readiness to support pollution-reduction policies. The results, combined with flexible model checks, provide support for portions of Converse and Zaller's (1992) theories on response instability and appear to weaken the measurement-error arguments of Achen (1975) and others. This paper concentrates on modeling issues and serves as a companion paper to Hill and Kriesi (2001), which uses the same data set and model but focuses more on the details of the opinion-changing behavior debate.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.