1,720,989 research outputs found
Replication Data for "News from the Other Side: How Topic Relevance Limits the Prevalence of Partisan Selective Exposure"
No full textIncluded are survey data sets and .R script files necessary to replicate all tables and figures. Tables will display in the R console. Figures will save as .pdf files ot your working directory.
Instructions for Replication:
These materials will allow for replication in R. You can download data files in .R or .tab format. Save all files in a common folder (directory). Open the .R script file named “jop_replication_dataverse2.R” and change the working directory at the top of the script to the directory where you saved the replication materials.
Execute the code in this script file to generate all tables and figures displayed in the manuscript. The script is annotated. Take care to execute the appropriate lines when loading data sets depending on whether you downloaded the data in .R or .tab format (the script is written to accommodate both formats).
Note: the files "results.diff_rep.Rdata" and "results.diff2.Rdata" are R list objects and can only be opened in R.
Should you encounter any problems or have any questions, please contact the author at [email protected]
Replication Data for "News from the Other Side: How Topic Relevance Limits the Prevalence of Partisan Selective Exposure"
No full textIncluded are survey data sets and .R script files necessary to replicate all tables and figures. Tables will display in the R console. Figures will save as .pdf files ot your working directory.
Instructions for Replication:
These materials will allow for replication in R. You can download data files in .R or .tab format. Save all files in a common folder (directory). Open the .R script file named “jop_replication_dataverse2.R” and change the working directory at the top of the script to the directory where you saved the replication materials.
Execute the code in this script file to generate all tables and figures displayed in the manuscript. The script is annotated. Take care to execute the appropriate lines when loading data sets depending on whether you downloaded the data in .R or .tab format (the script is written to accommodate both formats).
Note: the files "results.diff_rep.Rdata" and "results.diff2.Rdata" are R list objects and can only be opened in R.
Should you encounter any problems or have any questions, please contact the author at [email protected]
Militarization Fails to Enhance Police Safety or Reduce Crime, But May Harm Police Reputation
No full textThe increasingly visible presence of heavily armed police units in American communities has stoked widespread concern over the militarization of local law enforcement. Advocates claim militarized policing protects officers and deters violent crime, while critics allege these tactics are targeted at racial minorities and erode trust in law enforcement. Using a rare geocoded census of SWAT team deployments from Maryland, I show that militarized police units are more often deployed in communities with large shares of African American residents, even after controlling for local crime rates. Further, using nationwide panel data on local police militarization, I demonstrate that militarized policing fails to enhance officer safety or reduce local crime. Finally, using survey experiments---one of which includes a large oversample of African American respondents---I show that seeing militarized police in news reports may diminish police reputation in the mass public. In the case of militarized policing, the results suggest that the often-cited trade-off between public safety and civil liberties is a false choice
Militarization Fails to Enhance Police Safety or Reduce Crime, But May Harm Police Reputation
No full textThe increasingly visible presence of heavily armed police units in American communities has stoked widespread concern over the militarization of local law enforcement. Advocates claim militarized policing protects officers and deters violent crime, while critics allege these tactics are targeted at racial minorities and erode trust in law enforcement. Using a rare geocoded census of SWAT team deployments from Maryland, I show that militarized police units are more often deployed in communities with large shares of African American residents, even after controlling for local crime rates. Further, using nationwide panel data on local police militarization, I demonstrate that militarized policing fails to enhance officer safety or reduce local crime. Finally, using survey experiments---one of which includes a large oversample of African American respondents---I show that seeing militarized police in news reports may diminish police reputation in the mass public. In the case of militarized policing, the results suggest that the often-cited trade-off between public safety and civil liberties is a false choice
Replication Data for: Improving the Interpretation of Fixed Effects Regression Results
No full textFixed effects estimators are frequently used to limit selection bias. For example, it is well-known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable's effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable's effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls
Replication Data for: Improving the Interpretation of Fixed Effects Regression Results
No full textFixed effects estimators are frequently used to limit selection bias. For example, it is well-known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable's effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable's effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls
"Modern Police Tactics, Police-Citizen Interactions and the Prospects for Reform
No full textReplication materials for main text and online appendix
"Modern Police Tactics, Police-Citizen Interactions and the Prospects for Reform
No full textReplication materials for main text and online appendix
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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