Journal ArticleDOI
Inference and missing data
TLDR
In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.Abstract:
Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then such inferences are generally conditional on the observed pattern of missing data. Second, ignoring the process that causes missing data when making Bayesian inferences about θ is generally appropriate if and only if the missing data are missing at random and the parameter of the missing data is “independent” of θ. Examples and discussion indicating the implications of these results are included.read more
Citations
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Journal ArticleDOI
Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis.
Brett K. Beaulieu-Jones,Daniel R. Lavage,John W Snyder,Jason H. Moore,Sarah A. Pendergrass,Christopher R. Bauer +5 more
TL;DR: This study demonstrates how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describes some of the most frequent problems that can be encountered in dealing with missing EHR data.
Journal ArticleDOI
Test use and assessment practices of school psychologists in the United States: Findings from the 2017 National Survey.
Nicholas Benson,Randy G. Floyd,John H. Kranzler,Tanya L. Eckert,Sarah A. Fefer,Grant B. Morgan +5 more
TL;DR: Results of this study indicate that school psychologists regularly conduct multi-method assessments to prevent, identify, monitor, and remediate child and adolescent learning difficulties and other presenting problems in the schools.
Journal ArticleDOI
Ignorable and informative designs in survey sampling inference
R. A. Sugden,T. M. F. Smith +1 more
TL;DR: In this article, the role of the sample selection mechanism in a model-based approach to finite population inference is examined and conditions under which partially known designs can be ignored are established.
Journal ArticleDOI
Development of Life Satisfaction in Old Age: Another View on the "Paradox''
TL;DR: In this article, the development of life satisfaction in middle and late adulthood was analysed longitudinally by using data from the German Socio-economic Panel. But, as this evidence was mainly derived from cross-sectional age group comparisons, it does neither clearly indicate the absence of age-related mean level change, nor intra-individual stability of LS.
Journal ArticleDOI
Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation
TL;DR: This work proposes a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item- level data without altering the substantive analysis.
References
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Book
Bayesian inference in statistical analysis
George E. P. Box,George C. Tiao +1 more
TL;DR: In this article, the effect of non-normality on inference about a population mean with generalizations was investigated. But the authors focused on the effect on the mean with information from more than one source.
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Bayesian Inference in Statistical Analysis
Journal ArticleDOI
Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are Missing
TL;DR: In this paper, the authors give an approach to derive maximum likelihood estimates of parameters of multivariate normal distributions in cases where some observations are missing (Edgett [2] and Lord [3], [4]).
Journal ArticleDOI
Missing Observations in Multivariate Statistics I. Review of the Literature
TL;DR: In this paper, a review of the literature on the problem of handling multivariate data with observations missing on some or all of the variables under study is presented, where the authors examine the ways that statisticians have devised to estimate means, variances, correlations and linear regression functions.