Now showing items 1-20 of 50

    • A Bayesian Perspective on Factorial Experiments Using Potential Outcomes 

      Espinosa, Valeria (2014-02-25)
      Factorial designs have been widely used in many scientific and industrial settings, where it is important to distinguish "active'' or real factorial effects from "inactive" or noise factorial effects used to estimate ...
    • Causal Effects of Perceived Immutable Characteristics 

      Greiner, Daniel James; Rubin, Donald B. (Massachusetts Institute of Technology Press (MIT Press), 2011)
      Despite their ubiquity, observational studies to infer the causal effect of a so-called immutable characteristic, such as race or sex, have struggled for coherence, given the unavailability of a manipulation analogous to ...
    • The Central Role of the Propensity Score in Observational Studies for Causal Effects 

      Rosenbaum, Paul R.; Rubin, Donald B. (Oxford University Press, 1983)
      The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is ...
    • Comment: Assumptions and Procedures in the File Drawer Problem 

      Rosenthal, Robert; Rubin, Donald B. (Institute of Mathematical Statistics, 1988)
    • Comments on the Neyman–Fisher Controversy and Its Consequences 

      Sabbaghi, Arman; Rubin, Donald B. (Institute of Mathematical Statistics, 2014)
      The Neyman–Fisher controversy considered here originated with the 1935 presentation of Jerzy Neyman’s Statistical Problems in Agricultural Experimentation to the Royal Statistical Society. Neyman asserted that the standard ...
    • Comparing Correlated but Nonoverlapping Correlations 

      Rosenthal, Robert; Raghunathan, T. E.; Rubin, Donald B. (American Psychological Association, 1996)
      A common situation in psychological research involves the comparison of two correlations on the same sample of subjects, in which the correlations are nonoverlapping in the sense of having a variable in common (e.g., ru ...
    • Comparing Correlated Correlation Coefficients 

      Rosenthal, Robert; Rubin, Donald B.; Meng, Xiao-Li (American Psychological Association, 1992)
      The purpose of this article is to provide simple but accurate methods for comparing correlation coefficients between a dependent variable and a set of independent variables. The methods are simple extensions of Dunn & ...
    • Comparing Effect Sizes of Independent Studies 

      Rosenthal, Robert; Rubin, Donald B. (American Psychological Association, 1982)
      This article presents a general set of procedures for comparing the effect sizes of two or more independent studies. The procedures include a method for calculating the approximate significance level for the heterogeneity ...
    • Comparing Significance Levels of Independent Studies 

      Rosenthal, Robert; Rubin, Donald B. (American Psychological Association, 1979)
      Methods for comparing two or more statistical significance (p) levels are described; these methods are more rigorous, systematic, and informative than the comparisons that are commonly made by using a significant/not ...
    • Comparing Within- and Between-Subjects Studies 

      Rosenthal, Robert; Rubin, Donald B. (Sage, 1980)
      Studies employing within-subjects designs may be compared with those employing between-subjects designs in a variety of ways. We discuss and illustrate the comparisons of variabilities, including within-condition variances ...
    • Complications in Causal Inference: Incorporating Information Observed After Treatment is Assigned 

      Watson, David Allan (2014-06-06)
      Randomized experiments are the gold standard for inferring causal effects of treatments. However, complications often arise in randomized experiments when trying to incorporate additional information that is observed after ...
    • Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score 

      Rosenbaum, Paul R.; Rubin, Donald B. (American Statistical Association, 1985)
      Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the distribution of observed ...
    • Contrasts and Correlations in Effect-size Estimation 

      Rosnow, Ralph L.; Rosenthal, Robert; Rubin, Donald (Blackwell Publishers, 2000)
      This article describes procedures for presenting standardized measures of effect size when contrasts are used to ask focused questions of data. The simplest contrasts consist of comparisons of two samples (e.g., based on ...
    • THE COUNTERNULL VALUE OF AN EFFECT SIZE: A New Statistic 

      Rosenthal, Robert; Rubin, Donald B. (SAGE Publications, 1994)
      We introduce a new, readily computed statistic, the counternull value of an obtained effect size, which is the nonnull magnitude of effect size that is supported by exactly the same amount of evidence as supports the null ...
    • Credible causal inference for empirical legal studies 

      Ho, Daniel E.; Rubin, Donald B. (Annual Reviews, 2011)
      We review advances toward credible causal inference that have wide application for empirical legal studies. Our chief point is simple: Research design trumps methods of analysis. We explain matching and regression discontinuity ...
    • Dealing with noncompliance and missing outcomes in a randomized trial using Bayesian technology: Prevention of perinatal sepsis clinical trial, Soweto, South Africa 

      Rubin, Donald B.; Zell, Elizabeth R. (Elsevier BV, 2010)
      The success of interventions designed to address important issues in social and medical science is best addressed by randomized experiments. With human beings there are often complications, however, such as noncompliance ...
    • Dilemmas in Design: From Neyman and Fisher to 3D Printing 

      Sabbaghi, Arman (2014-06-06)
      This manuscript addresses three dilemmas in experimental design.
    • Effect Size Estimation for One-Sample Multiple-Choice-Type Data: Design, Analysis, and Meta-Analysis 

      Rosenthal, Robert; Rubin, Donald B. (American Psychological Association, 1989)
      This article proposes a standard, easy-to-interpret effect size estimate for one-sample research. The proportion index (*•) shows the hit rate on a scale on which .50 is always the null value regardless of the number of ...
    • Ensemble-Adjusted p Values 

      Rosenthal, Robert; Rubin, Donald B. (American Psychological Association, 1983)
      When contrasts or other tests of significance can be ordered according to their importance, adjusted p values can be computed that permit greater power to be brought to bear on contrasts of greater interest or importance. ...
    • Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies 

      Rubin, Donald B. (American Psychological Association, 1974)
      Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments. ...