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Missing data: Our view of the state of the art.

Joseph L. Schafer, +1 more
- 01 Jun 2002 - 
- Vol. 7, Iss: 2, pp 147-177
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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.

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Housing Status and HIV Risk Behaviors: Implications for Prevention and Policy

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Social competence, social support, and academic achievement in minority, low-income, urban elementary school children.

TL;DR: In this article, social-emotional competence and social support were hypothesized to have strong influences on academic trajectories during the critical period of academic skill acquisition, and the findings suggest that school psychologists and others designing interventions to improve achievement of disadvantaged students should address socialemotional competencies and classroom climate, especially teacher support of students.
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New dimensions in the quantitative classification of mental illness.

TL;DR: The findings suggest that many PDs can be placed in Axis I with related clinical disorders, and that unipolar depression may be better placed with anxiety disorders than with bipolar disorders.
References
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Book

Generalized Linear Models

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.
Book

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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