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Background: There is an increased focus on randomized trials for proximal behavioral outcomes in early childhood research. However, planning sample sizes for such designs requires extant information on the size of effect, variance decomposition, and effectiveness of covariates. Objectives: The purpose of this article is to employ a recent large representative sample of early childhood longitudinal study kindergartners to estimate design parameters for use in planning cluster randomized trials. A secondary objective is to compare the results of math and reading with the previous kindergartner cohort of 1999. Research Design: For each measure, fall–spring gains in effect size units are calculated. In addition, multilevel models are fit to estimate variance components that are used to calculate intraclass correlations (ICCs) and R2 statistics. The implications of the reported parameters are summarized in tables of required school sample sizes to detect small effects. Measures: The outcomes include information about student scores regarding learning behaviors, general behaviors, and academic abilities. Results: Aside from math and reading, there were small gains in these measures from fall to spring, leading to effect sizes between about .1 and .2. In addition, the nonacademic ICCs are smaller than the academic ICCs but are still nontrivial. Use of a pretest covariate is generally effective in reducing the required sample size in power analyses. The ICCs for math and reading are smaller for the current sample compared with the 1999 sample.

AbstractWhile scholars have long recognized that social networks impact political engagement for partisans, comparatively little work has examined the role of networks for independent voters. In this article, we contribute to existing research on social networks and politics by surveying Arizona registered voters about their political persuasion, personal networks, and media consumption habits. Our findings show that independents have networks that are structurally different from partisans. Specifically, we found that both Democrat and Republican respondents were more likely to frequently talk about politics with independents than with members of the opposing party. Independents were also less likely than partisans to end a friendship over a political dispute. Taken together these findings show that independents may be frequent and reliable discussion partners for partisans and may be able to moderate political views. We find evidence for the moderating force of independents is especially apparent in the media consumption habits of Republican respondents.Related ArticlesCormack, Lindsey. 2019. "Leveraging Peer‐to‐Peer Connections to Increase Voter Participation in Local Elections."Politics & Policy47(2): 248–66.https://doi.org/10.1111/polp.12297.Malmberg, Fredrik G., and Henrik Serup Christensen. 2021. "Voting Women, Protesting Men: A Multilevel Analysis of Corruption, Gender, and Political Participation."Politics & Policy49(1): 126–61.https://doi.org/10.1111/polp.12393.Rowe, Andrew D., and David E. Pitfield. 2019. "The Challenge of Social Media Incorporation: A Case Study of HACAN Clearskies."Politics & Policy47(4): 775–806.https://doi.org/10.1111/polp.12319.

Background: Randomized experiments are often considered the strongest designs to study the impact of educational interventions. Perhaps the most prevalent class of designs used in large-scale education experiments is the cluster randomized design in which entire schools are assigned to treatments. In cluster randomized trials that assign schools to treatments within a set of school districts, the statistical power of the test for treatment effects depends on the within-district school-level intraclass correlation (ICC). Hedges and Hedberg (2014) recently computed within-district ICC values in 11 states using three-level models (students in schools in districts) that pooled results across all the districts within each state. Although values from these analyses are useful when working with a representative sample of districts, they may be misleading for other samples of districts because the magnitude of ICCs appears to be related to district size. To plan studies with small or nonrepresentative samples of districts, better information are needed about the relation of within-district school-level ICCs to district size. Objective: Our objective is to explore the relation between district size and within-district ICCs to provide reference values for math and reading achievement for Grades 3–8 by district size, poverty level, and urbanicity level. These values are not derived from pooling across all districts within a state as in previous work but are based on the direct calculation of within-district school-level ICCs for each school district. Research Design: We use mixed models to estimate over 7,000 district-specific ICCs for math and reading achievement in 11 states and for Grades 3–8. We then perform a random effects meta-analysis on the estimated within-district ICCs. Our analysis is performed by grade and subject for different strata designated by district size (number of schools), urbanicity, and poverty rates.

Background: Cluster-randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels. This variance decomposition is usually summarized by the intraclass correlation (ICC) structure and, if covariates are used, the effectiveness of the covariates in explaining variation at each level of the design. Objectives: This article provides a compilation of school- and district-level ICC values of academic achievement and related covariate effectiveness based on state longitudinal data systems. These values are designed to be used for planning group-randomized experiments in education. The use of these values to compute statistical power and plan two- and three-level group-randomized experiments is illustrated. Research Design: We fit several hierarchical linear models to state data by grade and subject to estimate ICCs and covariate effectiveness. The total sample size is over 4.8 million students. We then compare our average of state estimates with the national work by Hedges and Hedberg.

Investigations of how criminal justice actors contribute to variation in sentencing typically focus on the role played by the judge. We argue that sentencing should be viewed as a collaborative process involving actors other than the judge and that the role of the prosecutor is particularly salient. We also contend that the courtroom workgroup literature has suggested that sentences may vary depending on the particular judge and prosecutor to whom the case is assigned. By using a unique data set from three U.S. district courts (N = 2,686) that identifies both the judge and the prosecutor handling the case, we examine how the judge, the prosecutor, and the judge–prosecutor dyad contribute to variance in offender sentences. We do this by employing cross‐classified random‐effects models to estimate the variance components associated with judges, prosecutors, and judge–prosecutor interactions. The results indicate that disparity attributable to the prosecutor is larger than disparity from the judge. Moreover, the role that the judge plays is moderated by the prosecutor to whom the case is assigned, as the judge–prosecutor effect is consistently larger than other random effects across the models. We also find that results vary by judicial district.

ObjectivesThe objectives of this study were to examine whether an increasing number of foreclosures in a neighborhood subsequently increase disorder and whether the temporal relationship between foreclosures and disorder is different before and during the housing crisis.MethodsWe employ longitudinal data to examine the impact of foreclosure on crime in Glendale, Arizona, a city at the epicenter of the nation's foreclosure problem. We rely on three data sources: (1) foreclosure data, (2) Computer‐Aided Dispatch/Police Records Management System data, and (3) U.S. Census and census estimate data.ResultsOur findings suggest that foreclosures do have a short‐term, four‐month effect on overall disorder and social disorder; however, that relationship only holds during the months preceding the housing crisis. During the housing crisis, there is no effect of foreclosures on disorder.ConclusionsOur results suggest that instead of the long‐standing negative impact that foreclosures have on disorder in communities, their negative effect is short lived and limited. Thus, foreclosures during the housing crisis do not signal disorder and decay as expected. A number of communities across the country have enacted prevention, enforcement, and reuse policies and programs aimed at foreclosure for the purpose of reducing disorder and subsequent crime; our results suggest that some of these policies and programs require substantial resources and might not have their desired impact.