Replication Data for: "Estimators for Topic-Sampling Designs" (doi:10.7910/DVN/YBV9Z8)

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Document Description

Citation

Title:

Replication Data for: "Estimators for Topic-Sampling Designs"

Identification Number:

doi:10.7910/DVN/YBV9Z8

Distributor:

Harvard Dataverse

Date of Distribution:

2024-02-27

Version:

1

Bibliographic Citation:

Rainey, Carlisle, 2024, "Replication Data for: "Estimators for Topic-Sampling Designs"", https://doi.org/10.7910/DVN/YBV9Z8, Harvard Dataverse, V1

Study Description

Citation

Title:

Replication Data for: "Estimators for Topic-Sampling Designs"

Identification Number:

doi:10.7910/DVN/YBV9Z8

Authoring Entity:

Rainey, Carlisle (Florida State University)

Producer:

<i>Political Analysis</i>

Distributor:

Harvard Dataverse

Access Authority:

Rainey, Carlisle

Depositor:

Rainey, Carlisle

Date of Deposit:

2024-01-31

Holdings Information:

https://doi.org/10.7910/DVN/YBV9Z8

Study Scope

Keywords:

Social Sciences

Abstract:

When researchers design an experiment, they usually hold potentially relevant features of the experiment constant. We call these details the “topic” of the experiment. For example, researchers studying the impact of party cues on attitudes must inform respondents of the parties’ positions on a particular policy. In doing so, researchers implement just one of many possible designs. Clifford, Leeper, and Rainey (2023) argue that researchers should implement many of the possible designs in parallel—what they call “topic sampling”—to generalize to a larger population of topics. We describe two estimators for topic-sampling designs. First, we describe a nonparametric estimator of the typical effect that is unbiased under the assumptions of the design. Second, we describe a hierarchical model that researchers can use to describe the heterogeneity. We suggest describing the heterogeneity across topics in three ways: (1) the standard deviation in treatment effects across topics, (2) the treatment effects for particular topics, and (3) how the treatment effects for particular topics vary with topic-level predictors. We evaluate the performance of the hierarchical model using the Strengthening Democracy Challenge megastudy and show that the hierarchical model works well.

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Sources Statement

Data Access

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Related Publications

Citation

Title:

Forthcoming, Political Analysis

Bibliographic Citation:

Forthcoming, Political Analysis

Other Study-Related Materials

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estimators.zip

Text:

zipped reproduction archive

Notes:

application/zip

Other Study-Related Materials

Label:

README.pdf

Text:

README

Notes:

application/pdf