This article provides a new maximum-likelihood estimator for selection models with dichotomous dependent variables when identical factors affect the selection equation and the equation of interest. Such situations arise naturally in game-theoretic models where selection is typically nonrandom and identical explanatory variables influence all decisions under investigation. When identical explanatory variables influence selection and a subsequent outcome of interest, the commonly used Heckman-type estimators identify from distributional assumptions about the residuals alone. When its own identifying assumption is reasonable, the new estimator allows the researcher to avoid the painful choice between identifying from distributional assumptions alone and adding a theoretically unjustified variable to the selection equation in a mistaken attempt to “boost” identification. The article uses Monte Carlo methods to compare the small-sample properties of the estimator with those of the Heckman-type estimator and ordinary probit.

This article introduces a model of ordinal unidimensional measurement known as Mokken scale analysis. Mokken scaling is based on principles of Item Response Theory (IRT) that originated in the Guttman scale. I compare the Mokken model with both Classical Test Theory (reliability or factor analysis) and parametric IRT models (especially with the one-parameter logistic model known as the Rasch model). Two nonparametric probabilistic versions of the Mokken model are described: the model of Monotone Homogeneity and the model of Double Monotonicity. I give procedures for dealing with both dichotomous and polytomous data, along with two scale analyses of data from the World Values Study that demonstrate the usefulness of the Mokken model.

Survey participation, electoral participation, and political interest have been given wide attention in the research literature, but no one so far has combined these three variables in one model. Taking the social isolation-hypothesis as our starting point, we developed a model with one factor, social involvement, as the common factor underlying these three types of participation. We reviewed the literature and concluded that we had to include a second underlying factor: attachment to society. Using a new data set, gathered on the occasion of the 1998 Dutch national elections and including validated voter turnout measures, we were able to test the model. After making some adaptions, we found a model with a satisfactory fit. The results show that, by including social involvement and attachment to society as mediating variables, we can reach much higher levels of explained variances of survey and electoral participation than we can with traditional models. The results also add to our understanding of the relationship between survey and electoral participation and political interest.

Consumers of the National Election Study (NES) should be concerned if the survey has a bias that is increasing with time. A recent article by Barry Burden claims that for presidential elections, there is an increasing overreport bias, or turnout gap, between the NES turnout rate and the observed turnout rate caused by declining NES response rates. I show that the increasing turnout gap is an artifact of the universes these two turnout rates are based on. Reconciling the two universes shows no systematic increase of the reconciled turnout gap in presidential elections from 1948 to 2000, and furthermore demonstrates that the post-1976 rise in NES response rates (until 2000) is rewarded in a lower turnout gap. In addition, I offer another theory to explain the turnout gap. If respondents have an equal propensity to misreport that they voted when they did not, as turnout declines, the number of nonvoter respondents increases and so does the turnout gap. I show that in multivariate analysis this theory outperforms Burden's response rate driven theory, though neither theory reaches statistical significance.

A recent article by Barry Burden in Political Analysis alerts us to a steadily increasing gap during presidential election years between self-reported turnout in the NES (National Election Studies) and “official turnout” figures based on the voting-age population (VAP), and points to declining response rates as a culprit. Changing the baseline from the VAP to the VEP (voting-eligible population) significantly changes these conclusions, and point to panel effects as a culprit. The rise in the gap was not linear, but it does emerge rather suddenly in 1996. Gaps between NES self-reported turnout and VEP estimates are higher in presidential election years than in off-years, and self-reported turnout is higher among long-term panel participants than among cross-section respondents in multielection panels.

In an earlier issue of this journal I brought attention to the fact that estimates of voter turnout in U.S. presidential elections from the National Election Study (NES) series have been increasingly biased. Although researchers had already noted that the NES overestimated turnout, I was concerned with the growing severity of the problem. While admitting that other factors were at work, my explanation centered on the representativeness of surveys, in particular that selection bias in the sample is correlated with the likelihood of voting (Burden 2000). Martinez (2003) and McDonald (2003) offer three possible additions to my argument. First, panel effects are responsible for particularly egregious discrepancies in a few presidential elections, particularly in the 1996 survey. Second, official turnout statistics that rely on the Voting Age Population (VAP) are themselves biased and lack perfect comparability with the NES. Third, the degree of misreporting might also depend on actual voter turnout.

This article shows that highly correlated measures can produce different results. We identify a democratization model from the literature and test it in more than 120 countries from 1951 to 1992. Then, we check whether the results are robust regarding measures of democracy, time periods, and levels of development. The findings show that measures do matter: Whereas some of the findings are robust, most of them are not. This explains, in part, why the debates on democracy have continued rather than been resolved. More important, it underscores the need for more careful use of measures and further testing to increase confidence in the findings. Scholars in comparative politics are drawn increasingly to large-N statistical analyses, often using data sets collected by others. As in any field, we show how they must be careful in choosing the most appropriate measures for their studies, without assuming that any correlated measure will do.

This note replicates and extends Paolino's discussion (2001, Political Analysis 9:325–346) on the estimation of models with beta-distributed dependent variables in two ways. First, it introduces an easy-to-use program for estimating the model using Stata. Second, it presents a Bayesian estimator for beta variables based on Paolino's model. Results are compared to those in the original article.