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In this article, I revisit a widely used measure of legislative professionalism that I developed over a decade ago (Squire 1992a). I argue that professionalism has different implications for legislators than for legislatures and that the concept is distinct from careerism. I then discuss the mechanics of compiling the measure, its reliability and validity, and potential criticism of the measure. Finally, I provide scores on the measure for 1979, 1986, 1996, and 2003, as well as scores for 1979 and 2003 for a revised measure that is theoretically appropriate for use in dynamic analyses.

Researchers are often interested in the effects of state policies and institutions on individual behavior or other outcomes in sub-state-level observational units, such as election results in state legislative districts. In this article, we examine the issue of clustered data in state and local politics research and the analytical problems it can cause. Standard estimation methods applied in most regression models do not properly account for the clustering of observations within states, leading analysts to overstate the statistical significance of coefficient estimates, especially of state-level factors. We discuss the theory behind two approaches for dealing with clustering—clustered standard errors and multilevel modeling—and argue that calculating clustered standard errors is a more straightforward and practical approach, especially when working with large datasets or many cross-level interactions. We demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005).

U.S. state politics researchers often analyze data with observations grouped into clusters. This structure commonly produces unmodeled correlation within clusters, leading to downward bias in the standard errors of regression coefficients. Estimating robust cluster standard errors (RCSE) is a common approach to correcting this bias. However, despite their frequent use, recent work indicates that RCSE can also be biased downward. Here the author provides evidence of that bias and offers a potential solution. Through Monte Carlo simulation of an ordinary least squares (OLS) regression model, the author compares conventional standard error (OLS-SE) and RCSE performance to that of a bootstrap method that resamples clusters of observations (BCSE). The author shows that both OLS-SE and RCSE are biased downward, with OLS-SE being the most biased. In contrast, BCSE are not biased and consistently outperform the other two methods. The author concludes with three replications from recent work and offers recommendations to researchers.

Scholars have begun to move beyond the dichotomous dependent variable—indicating whether a state adopts a policy or not in a given year—usually employed in event history analysis. In particular, they have devoted increasing attention to the components of policies that states adopt. I discuss a variety of estimators that have been employed to analyze the adoption and modification of policies with multiple components, including various forms of event history analysis, OLS, and event count models. With various modifications, the researcher can estimate models that treat each component as distinct, pool these models to leverage commonalities across components, or treat the components as identical parts of the same process. Each of these has its strengths and may be appropriate in certain circumstances. Nonetheless, in the majority of cases, some version of event history analysis for multiple or repeat failures is likely to be preferred. The different approaches are illustrated by studying state adoption of various obesity-related policies.

This note examines problems associated with measuring the partisan balance of state government. A description of a new publicly available dataset is given, as well as of the methods used to collect these data. The results of three data analyses using different measures of state government partisan balance demonstrate that sometimes measurement error on this variable can influence substantive findings.

Since 1990, the standard statistical approach for studying state policy adoption has been an event history analysis using binary link models, such as logit or probit. In this article, we evaluate this logit-probit approach and consider some alternative strategies for state policy adoption research. In particular, we discuss the Cox model, which avoids the need to parameterize the baseline hazard function and, therefore, is often preferable to the logit-probit approach. Furthermore, we demonstrate how the Cox model can be modified to deal effectively with repeatable and competing events, events that the logit-probit approach cannot be used to model.

We use a survey of State House reporters to measure corruption in state government and assess the priority federal prosecutors place on corruption investigations. The reliability and validity of the corruption measures are assessed, as are the relationships among corruption level, federal prosecutorial effort, and the number of federal prosecutions. Federal corruption prosecutions are positively correlated with both corruption and prosecutorial effort. Hence, we argue that federal prosecution data provide a potentially biased and unreliable measure of state public corruption.

Numerous studies attempt to assess direct democracy's impact on state policy using measures of direct democracy based on dummy variables or the frequency with which initiatives appear on a state's ballots. We offer an alternative to these measures that accounts for how rules governing the initiative process vary among the states. We replicate several studies using different measures of direct democracy and demonstrate that the results of hypothesis tests can be contingent on how these institutions are measured. We contend that commonly used dummy variable measures of state direct democracy have validity problems and that hypothesis tests using such measures produce imprecise estimates of the initiative's effect on policy.

By measuring U.S. term limits dichotomously, investigators ignore the vast differences among laws limiting state legislative service. Furthermore, this measurement problem increases the risk of false negatives and confounds the effects of term limits with those of the citizen initiative. To address this, I propose two sets of continuous measures of term-limitedness. The first set compares mandated turnover after term limits to turnover in the 1980s, the decade before term limits began sweeping elected officials from office. A second set adjusts the first set to reflect the potential for legislators to cycle repeatedly between legislative chambers when only their consecutive years of service are limited. These continuous measures outperformed a dichotomous designation of term limits in two tests, suggesting that the proposed measures can reduce the risk of false negatives about term limits in U.S. multi-state research and that they are more robust in the face of confounding effects from the citizen initiative.

While there is a growing literature on the factors linked to the power held by leaders in state legislatures, the complexity of leadership power as a concept makes assessing it difficult The author demonstrates that measures of formal leadership power derived from the written rules are uncorrelated with survey measures capturing legislators' own assessments of their leader's strength. These differences have practical importance, with each type of measure yielding different substantive findings in models predicting leadership power.