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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An effectiveness trial of a dissonance-based eating disorder prevention program for high-risk adolescent girls.
TL;DR: Adolescent girls with body image concerns randomized to the dissonance intervention showed significantly greater decreases in thin-ideal internalization, body dissatisfaction, dieting attempts, and eating disorder symptoms from pretest to posttest than did those assigned to a psychoeducational brochure control condition.
Journal ArticleDOI
Exposure to Repetitive Versus Varied Stress during Prenatal Development Generates Two Distinct Anxiogenic and Neuroendocrine Profiles in Adulthood
TL;DR: Repeated exposure to the same stressor (restraint) generated the most robust changes, including increased anxiety-related behaviors and a delayed and prolonged hypothalamic-pituitary-adrenal (HPA) axis response to stress in female offspring.
Journal ArticleDOI
Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example
Mirjam J. Knol,Kristel J.M. Janssen,A. Rogier T. Donders,Antoine C. G. Egberts,E. Rob Heerdink,Diederick E. Grobbee,Karel G.M. Moons,Mirjam I. Geerlings +7 more
TL;DR: MIM should not be used in handling missing confounder data because it gives unpredictable bias of the odds ratio even with small percentages of missing values, and CC can be used when missing values are completely random, but it gives loss of statistical power.
Journal ArticleDOI
Corporal punishment, maternal warmth, and child adjustment: a longitudinal study in eight countries
Jennifer E. Lansford,Chinmayi Sharma,Patrick S. Malone,Darren T. Woodlief,Kenneth A. Dodge,Paul Oburu,Concetta Pastorelli,Ann T. Skinner,Emma Sorbring,Sombat Tapanya,Liliana Maria Uribe Tirado,Arnaldo Zelli,Suha M. Al-Hassan,Liane Peña Alampay,Dario Bacchini,Anna Silvia Bombi,Marc H. Bornstein,Lei Chang,Kirby Deater-Deckard,Laura Di Giunta +19 more
TL;DR: Clinicians across countries should advise parents against using corporal punishment, even in the context of parent–child relationships that are otherwise warm, and should assist parents in finding other ways to manage children's behaviors.
Journal ArticleDOI
A Comparison of Item-Level and Scale-Level Multiple Imputation for Questionnaire Batteries
TL;DR: A Monte Carlo simulation was used to assess the impact of imputation method on the bias and efficiency of scale-level parameter estimates, including scale score means, between-scale correlations, and regression coefficients, and suggested that researchers should be cautious when implementing planned missing data designs that necessitate scale- level imputation.
References
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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).
Book
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.
Book
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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