Meta-Analysis

Many political scientists measure dispositions—like populism, conspiracism, racism, and sexism—using agree/disagree statements, but that format can produce acquiescence bias that inflates endorsement rates. This article asks how acquiescence affects criterion validity and evaluates common remedies. Drawing on 160 tests and an internal meta-analysis, the authors examine how bias changes estimated relationships between constructs and assess balanced scales and measurement models as potential solutions. They end with design advice and recommendations for redesigning several popular measures of political and psychological dispositions.

A meta-analytical review synthesizes over 1,000 empirical estimates from 71 studies conducted across three decades to assess how the distribution of executive power relates to corruption in democracies.

This paper reviews and critiques prior embedding analyses of the democratic peace, proposes an improved methodology that leverages foundational embedding models, and reports replication results. The author evaluates Rathbun, Parker, and Pomeroy’s microstructural work—including surveys, computational studies, and meta-analysis—offers a systematic critique of embedding analysis practices, and introduces a new approach intended to address those issues while taking advantage of foundational embedding models. The replication presented in the abstract accords with the earlier finding that the democratic peace effect is racialized, and the paper frames its contribution as a guide for conducting embedding analysis in political science.

Scholars often assume informal workers in Latin America vote less and tilt rightward, but prior empirical results vary. This article synthesizes the quantitative evidence by conducting meta-analyses on the universe of previous studies that link labor informality to voting outcomes, estimating average effects for turnout and vote choice and exploring conditions that modify those effects.

Publication bias is common in social sciences because journals favor statistically significant results. However, publication bias depends on a variable's role—key or control—in regression models. This study shows that when democracy is a key variable (rather than a statistical control) it faces stronger publication bias.