<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Replication Data for: Reducing Political Bias in Political Science Estimates</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/PZLCJM</dcterms:identifier><dcterms:creator>Zigerell, Lawrence</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2016-11-13</dcterms:issued><dcterms:modified>2016-11-13T08:56:48Z</dcterms:modified><dcterms:description>Political science researchers have flexibility in how to analyze data, how to report data, and whether to report on data. Review of examples of reporting flexibility from the race and sex discrimination literature illustrates how research design choices can influence estimates and inferences. This reporting flexibility—coupled with the political imbalance among political scientists—creates the potential for political bias in reported political science estimates, but this potential for political bias can be reduced or eliminated through preregistration and preacceptance, in which researchers commit to a research design before completing data collection. Removing the potential for reporting flexibility can raise the credibility of political science research.</dcterms:description><dcterms:subject>Social Sciences</dcterms:subject><dcterms:date>2016-11-13</dcterms:date><dcterms:contributor>Zigerell, Lawrence</dcterms:contributor><dcterms:dateSubmitted>2016-11-13</dcterms:dateSubmitted><dcterms:license>CC0 1.0</dcterms:license></metadata>