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A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation
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Roderick J. Little; James R. Carpenter; Katherine J. Lee – Sociological Methods & Research, 2024
Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c)…
Descriptors: Foreign Countries, Probability, Robustness (Statistics), Responses
On the Robustness of Doubly Robust Estimators in Causal Inference
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Weicong Lyu; Peter M. Steiner – Society for Research on Educational Effectiveness, 2021
Doubly robust (DR) estimators that combine regression adjustments and inverse probability weighting (IPW) are widely used in causal inference with observational data because they are claimed to be consistent when either the outcome or the treatment selection model is correctly specified (Scharfstein et al., 1999). This property of "double…
Descriptors: Robustness (Statistics), Causal Models, Statistical Inference, Regression (Statistics)
Propensity Score Analysis: An Alternative Statistical Approach for HRD Researchers
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Keiffer, Greggory L.; Lane, Forrest C. – European Journal of Training and Development, 2016
Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…
Descriptors: Probability, Scores, Statistical Analysis, Statistical Inference
A Finite Mixture Method for Outlier Detection and Robustness in Meta-Analysis
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Beath, Ken J. – Research Synthesis Methods, 2014
When performing a meta-analysis unexplained variation above that predicted by within study variation is usually modeled by a random effect. However, in some cases, this is not sufficient to explain all the variation because of outlier or unusual studies. A previously described method is to define an outlier as a study requiring a higher random…
Descriptors: Mixed Methods Research, Robustness (Statistics), Meta Analysis, Prediction
Measuring Learning Progressions Using Bayesian Modeling in Complex Assessments
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Rutstein, Daisy Wise – ProQuest LLC, 2012
This research examines issues regarding model estimation and robustness in the use of Bayesian Inference Networks (BINs) for measuring Learning Progressions (LPs). It provides background information on LPs and how they might be used in practice. Two simulation studies are performed, along with real data examples. The first study examines the case…
Descriptors: Bayesian Statistics, Learning Processes, Robustness (Statistics), Statistical Inference
Case Study Applications of Statistics in Institutional Research. Resources in Institutional Research, Number Ten.
Coughlin, Mary Ann; Pagano, Marian – 1997
This monograph covers the theory, application, and interpretation of both descriptive and inferential statistical techniques in institutional research. Each chapter opens with a hypothetical case study, which is used to illustrate the application of one or more statistical procedures to typical research questions. Chapter 2 covers the comparison…
Descriptors: Analysis of Covariance, Analysis of Variance, Chi Square, Correlation