Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
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TLDR
2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.Abstract:
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.read more
Citations
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Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study
Kristin Gustavson,Tilmann von Soest,Tilmann von Soest,Evalill Karevold,Espen Røysamb,Espen Røysamb +5 more
TL;DR: Examination of predictors of attrition as well as baseline associations between variables among those who stayed and those who dropped out of a population-based study found long-term longitudinal studies are valuable for studying associations between risk/protective factors and health outcomes.
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Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
Jelte M. Wicherts,Coosje Lisabet Sterre Veldkamp,Hilde E. M. Augusteijn,Marjan Bakker,Robbie C. M. van Aert,Marcel A.L.M. van Assen +5 more
TL;DR: An extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research is presented.
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A comprehensive action determination model: Toward a broader understanding of ecological behaviour using the example of travel mode choice
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Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data
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References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
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Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
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