71 to 79 of 79 Results
Oct 21, 2012 - Teppei Yamamoto Dataverse
Imai, Kosuke; Yamamoto, Teppei, 2012, "Replication data for: Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments", https://doi.org/10.7910/DVN/OU6D17, Harvard Dataverse, V1
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are caus... |
Nov 30, 2011 - Dustin Tingley Dataverse
Kosuke Imai; Dustin Tingley, 2011, "Replication data for: A Statistical Method for Empirical Testing of Competing Theories", https://doi.org/10.7910/DVN/9BCWKN, Harvard Dataverse, V3
Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the comp... |
Sep 28, 2011 - Teppei Yamamoto Dataverse
Kosuke Imai; Dustin Tingley; Teppei Yamamoto, 2011, "Replication data for: Experimental Designs for Identifying Causal Mechanisms", https://doi.org/10.7910/DVN/LMC3FM, Harvard Dataverse, V1, UNF:5:knfQrHu8s/s7cnu4nIlUXg== [fileUNF]
Experimentation is a powerful methodology that enables scientists to empirically establish causal claims. However, one important criticism is that experiments merely provide a black-box view of causality and fail to identify causal mechanisms. Specifically, critics argue that although experiments can identify average causal effects, they cannot exp... |
Sep 4, 2011 - Will Bullock Dataverse
Will Bullock; Kosuke Imai; Jacob Shapiro, 2010, "Replication data for: Statistical analysis of endorsement experiments: Measuring support for militant groups in Pakistan", https://doi.org/10.7910/DVN/DQ23DN, Harvard Dataverse, V5
Political scientists have long been interested in citizens' support level for socially sensitive actors such as ethnic minorities, militant groups, and authoritarian regimes. Attempts to use direct questioning in surveys, however, have largely yielded unreliable measures of these attitudes as they are contaminated by social desirability bias and hi... |
Aug 19, 2011 - Dustin Tingley Dataverse
Kosuke Imai; Luke Keele; Dustin Tingley; Teppei Yamamoto, 2011, "Replication data for: Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies", https://doi.org/10.7910/DVN/X73I3J, Harvard Dataverse, V1
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet, commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumpti... |
Yusaku Horiuchi; Kosuke Imai; Naoko Taniguchi, 2007, "Replication data for: Designing and Analyzing Randomized Experiments: Application to a Japanese Election Survey Experiment", https://doi.org/10.7910/DVN/B6OJKG, Harvard Dataverse, V4
Randomized experiments are becoming increasingly common in political science. Despite their well-known advantages over observational studies, randomized experiments are not free from complications. In particular, researchers often cannot force subjects to comply with treatment assignment and to provide the requested information. Furthermore, simple... |
Jun 24, 2010 - Dustin Tingley Dataverse
Kosuke Imai, Luke Keele, Dustin Tingley, 2010, "Replication data for: A General Approach to Causal Mediation Analysis", https://doi.org/10.7910/DVN/UMEYXD, Harvard Dataverse, V2, UNF:5:tgVHueK9oO3WFjAFEHl0NA== [fileUNF]
Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for three reasons; the lack of a general definition of causal mediation effects independent of a particular statistical model, t... |
Jan 25, 2010 - Teppei Yamamoto Dataverse
Kosuke Imai; Teppei Yamamoto, 2010, "Replication data for: Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis", https://doi.org/10.7910/DVN/TZOGL9, Harvard Dataverse, V1
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measuremen... |
Kosuke Imai; Samir Soneji, 2007, "Replication data for: On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension", https://doi.org/10.7910/DVN/I5O6OS, Harvard Dataverse, V1
A rapidly aging population, such as the United States today, is characterized by the increased prevalence of chronic impairment. Robust estimation of disability-free life expectancy (DFLE) is essential for examining whether additional years of life are spent in good health and whether life expectancy is increasing faster than the decline of disabil... |
