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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Using Qualitative Information to Improve Causal Inference |
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Identification Number: |
doi:10.7910/DVN/26642 |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2014-07-08 |
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Version: |
2 |
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Bibliographic Citation: |
Glynn, Adam N.; Ichino, Nahomi, 2014, "Replication data for: Using Qualitative Information to Improve Causal Inference", https://doi.org/10.7910/DVN/26642, Harvard Dataverse, V2 |
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Citation |
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Title: |
Replication data for: Using Qualitative Information to Improve Causal Inference |
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Identification Number: |
doi:10.7910/DVN/26642 |
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Authoring Entity: |
Glynn, Adam N. (Emory University) |
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Ichino, Nahomi (University of Michigan) |
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Producer: |
Adam N. Glynn |
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Nahomi Ichino |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Nahomi Ichino |
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Depositor: |
Nahomi Ichino |
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Date of Deposit: |
2014-07-04 |
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Date of Distribution: |
2014-07 |
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Holdings Information: |
https://doi.org/10.7910/DVN/26642 |
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Study Scope |
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Keywords: |
Social Sciences, Causal inference, Electoral rules, Randomization inference, Africa, Mixed methods |
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Abstract: |
Using the Rosenbaum (2002; 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p-values. Second, additional information across matched sets enables the construction of qualitative confidence intervals on effect size. Third, qualitative information on unmeasured confounders within matched sets reduces the conservativeness of Rosenbaum-style sensitivity analysis. This approach accommodates small to medium sample sizes in a non- parametric framework, and therefore may be particularly useful for analyses of the effects of policies or institutions in a given set of units. We illustrate these methods by examining the effect of using plurality rules in transitional presidential elections on opposition harassment in 1990s sub-Saharan Africa. |
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Time Period: |
1991-1996 |
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Geographic Coverage: |
sub-Saharan Africa |
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Geographic Unit(s): |
country |
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Kind of Data: |
Country-level political and economic data |
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Notes: |
Version Date: 2014Version Text: 1 |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Glynn, Adam N., and Nahomi Ichino. 2015. “Using Qualitative Information to Improve Causal Inference.” <i>American Journal of Political Science</i> 59 (4): 1055-1071. |
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Identification Number: |
10.1111/ajps.12154 |
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Bibliographic Citation: |
Glynn, Adam N., and Nahomi Ichino. 2015. “Using Qualitative Information to Improve Causal Inference.” <i>American Journal of Political Science</i> 59 (4): 1055-1071. |
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Label: |
Africa_qualCI.csv |
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Text: |
Replication data for Africa example in Glynn and Ichino (2014) |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
GlynnIchino_qualCI_replicationcode.R |
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Text: |
Replication code for Glynn and Ichino (2014) |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
Glynn_Ichino_SI_qualinfo_20140703.pdf |
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Text: |
Supplementary Information for Glynn and Ichino (2014), contains variable descriptions |
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Notes: |
application/pdf |