Replication Data for: No Evidence that Measuring Moderators Alters Treatment Effectsdoi:10.7910/DVN/0YHJRGHarvard Dataverse2023-06-161Sheagley, Geoffrey; Clifford, Scott, 2023, "Replication Data for: No Evidence that Measuring Moderators Alters Treatment Effects", https://doi.org/10.7910/DVN/0YHJRG, Harvard Dataverse, V1, UNF:6:n5fj0wdAFt8LERYw7Ew+bg== [fileUNF]Replication Data for: No Evidence that Measuring Moderators Alters Treatment Effectsdoi:10.7910/DVN/0YHJRGSheagley, GeoffreyClifford, ScottSheagley, GeoffreyHarvard DataverseSheagley, GeoffreySheagley, Geoffrey2022-11-09Social SciencesSurvey experimentPrimingModeratorAs survey experiments have become more common in political science, so too have efforts to identify who is most responsive to a treatment. By necessity, moderation experiments frequently rely on observed, rather than manipulated moderators, such as partisan identity or racial attitudes. These designs have led to an ongoing debate about where to measure moderators – immediately prior to the treatment, after the treatment, or in a prior wave of a panel survey. Each design choice has its downsides and detractors. Measuring a moderator after the treatment opens the possibility of posttreatment bias. Measuring it prior to the experiment may create priming effects. And panel studies are costly and sometimes infeasible. We contribute to this debate by systematically studying whether measuring moderators prior to an experiment affects the results. Across six different experiments, each involving one of the most commonly used moderators, we find no evidence of priming effects. Additionally, we find no evidence that the distance between a moderator and an experiment within a survey affects the results. Our findings thus help resolve the debate, suggesting that the pretreatment measurement of a moderator often poses little threat to the inferences drawn from an experiment. We conclude with advice on designing well-powered moderation experiments.This dataset underwent an independent verification process that replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of code. The verification process was carried out by the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. <br></br> The associated article has been awarded Open Materials and Open Data Badges. Learn more about the Open Practice Badges from the <a href="https://osf.io/tvyxz/wiki/home/" target="_blank">Center for Open Science</a>.<br></br> <img src="https://odum.unc.edu/files/2020/03/OpenData_PR-1.png" alt="Open Data Badge" height="77" width="80"> <img src="https://odum.unc.edu/files/2020/03/OpenMaterials_PR-1.png" alt="Open Materials Badge" height="77" width="80">The <i>American Journal of Political Science</i> and the Odum Institute for Research in Social Science are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.This dataset is made available with limited information on how it can be used. You may wish to communicate with the Contact(s) specified before use.Sheagley, Geoffrey, and Scott Clifford. [date]. “No Evidence that Measuring Moderators Alters Treatment Effects.” <i>American Journal of Political Science</i> Forthcoming. <a href="http://ajps.org/" target="_blank">http://ajps.org/</a>Sheagley, Geoffrey, and Scott Clifford. 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file generates Figures 1 and 2 in the main manuscript. type/x-r-syntaxanalyses_open-ended.Rtype/x-r-syntaxanalyses_pid.Rtype/x-r-syntaxanalyses_race.Rtype/x-r-syntaxanalyses_sdo.Rtype/x-r-syntaxCodebook_2020CES.pdfCodebook for 2020 CES module. application/pdfCodebook_FormattedDatasets.pdfCodebook for formatted tataapplication/pdfdp_models.htmltext/htmldp_models(moderator variation).htmltext/htmlfilelist.txtList of figures that should populate this folder when code is run. text/plainformatting_bovitz_2019.doCode to format 2019 bovitz survey data application/x-stata-syntaxformatting_bovitz_2022.doCode to format clean 2022 bovitz data application/x-stata-syntaxformatting_CCES2020.doCode to format clean 2020 CES data application/x-stata-syntaxformatting_mturk_2020.doCode to format clean 2020 MTurk data application/x-stata-syntaxformatting_mturk_2021.doCode to format clean 2021 MTurk data application/x-stata-syntaxformatting_mturk_2022.doCode to format clean 2022 MTurk data application/x-stata-syntaxformatting_uga.doCode to format clean 2020 undergraduate/UGA data application/x-stata-syntaxframing_models.htmltext/htmlknowledge_models(moderator variation).htmltext/htmlknow_models.htmltext/htmlparty_models.htmltext/htmlpid_models(moderator variation).htmltext/htmlReadMe.txtReadme document for project. text/plainReplication_NoEvidence.RprojProject that can be used to create starting directory for R analyses. Scripts assume they are opened through this project. application/octet-streamrr_models.htmltext/htmlrr_models(moderator variation).htmltext/htmlrr_models_withinsurvey.htmltext/htmlRun all Main Paper R Scripts.RCode to run all R scripts. type/x-r-syntaxsdo_models.htmltext/htmlsdo_models(moderator variation).htmltext/htmlthree_dp.htmltext/htmlthree_framing.htmltext/htmlthree_know.htmltext/htmlthree_Party.htmltext/htmlthree_rr.htmltext/htmlthree_SDO.htmltext/htmlvalues_models(moderator variation).htmltext/html