Author pages are created from data sourced from our academic publisher partnerships and public sources.
Share This Author
Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments
A key justification for using nonrandomized experiments is that, with proper adjustment, their results can well approximate results from randomized experiments. This hypothesis has not been…
Experimental Vignette Studies in Survey Research
Vignette studies use short descriptions of situations or persons (vignettes) that are usually shown to respondents within surveys in order to elicit their judgments about these scenarios. By…
The importance of covariate selection in controlling for selection bias in observational studies.
An extensive reanalysis of a within-study comparison that contrasts a randomized experiment and a quasi-experiment is reported on to provide strong clues about preferred strategies for identifying the covariates most likely to reduce bias when planning a study and when the true selection process is not known.
A Primer on Propensity Score Analysis
This article discusses the role that propensity score analysis can play in assessing the effects of interventions. It mostly focuses on identifying the range of solutions to practical problems that…
Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables
In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a…
Experimentelle vignettendesigns in faktoriellen surveys
ZusammenfassungDer faktorielle Survey (Vignettenanalyse) als empirisches Messmodell zur differenzierten Untersuchung komplexer Fragestellungen wurde vor allem in der jüngeren Vergangenheit wieder…
Quasi-Experimental Designs for Causal Inference
For each design the basic rationale for causal inference is introduced, the assumptions required for identifying a causal effect are discussed, methods for estimating the effect are outlined, and potential validity threats and strategies for dealing with them are highlighted.
Matching and Propensity Scores
This work discusses multivariate matching techniques and several propensity score methods, like propensity score matching, subclassification, inverse-propensity weighting, and regression estimation, and gives practical guidelines for implementing these techniques and discusses the conditions under which these techniques warrant a causal interpretation of the estimated treatment effect.
Assessing Correspondence Between Experimental and Nonexperimental Estimates in Within-Study Comparisons
Different distance-based correspondence measures for assessing correspondence in experimental and nonexperimental estimates are examined and a new and straightforward approach that combines traditional significance testing and equivalence testing in the same framework is recommended.
On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores
The effect of unreliability of measurement on propensity score (PS) adjusted treatment effects has not been previously studied. The authors report on a study simulating different degrees of…