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Below results based on the criteria 'inverse probability'
Total number of records returned: 3
The Insignificance of Null Hypothesis Significance Testing
The current method of hypothesis testing in the social sciences is under intense criticism yet most political scientists are unaware of the important issues being raised. Criticisms focus on the construction and interpretation of a procedure that has dominated the reporting of empirical results for over fifty years. There is evidence that null hypothesis significance testing as practiced in political science is deeply flawed and widely misunderstood. This is important since most empirical work in political science argues the value of findings through the use of the null hypothesis significance test. In this article I review the history of the null hypothesis significance testing paradigm in the social sciences and discuss major problems, some of which are logical inconsistencies while others are more interpretive in nature. I suggest alternative techniques to convey effectively the importance of data-analytic findings. These recommendations are illustrated with examples using empirical political science publications.
Weighted Estimation for Analyses with Missing Data
inverse probability weighting
Missing data plague data analyses in political science. The recent applied statistics literature reflects renewed interest in weighting methods for missing data problems. Three properties are stressed in this literature: (i) robustness, (ii) the ability to use post-treatment information in causal analysis, and (iii) methods to gain efficiency. I present these results, hoping to show the potential in using refashioned weighting methods for political science research.
The Covariate Balancing Propensity Score for Generalized Treatment Regimes
generalized propensity score
Under the assumption of unconfoundedness, propensity score matching and inverse-probability weighting enable researchers to estimate causal effects by balancing observed covariates across different treatment values. While their extensions to general treatment regimes exist, applied researchers often inappropriately dichotomize a non-binary treatment to utilize binary propensity score methods. In this paper, we extend the covariate balancing propensity score methodology of Imai and Ratkovic (2014) to general treatment regimes. We conduct a simulation study to assess the performance of our methodology. Two social science applications are used to demonstrate that the proposed methodology significantly improves covariate balance and offer substantive insights the original analyses fail to identify.