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Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood

In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increa...

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Autores principales: Chen, Lihan, Savalei, Victoria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426626/
https://www.ncbi.nlm.nih.gov/pubmed/34512436
http://dx.doi.org/10.3389/fpsyg.2021.667802
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author Chen, Lihan
Savalei, Victoria
author_facet Chen, Lihan
Savalei, Victoria
author_sort Chen, Lihan
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description In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increase from the information loss due to ignorable missing data. FMI is well-known in the multiple imputation literature (Rubin, 1987), but it has only been more recently developed for full information maximum likelihood (Savalei and Rhemtulla, 2012). Sample FMI estimates using this approach have since then been made accessible as part of the lavaan package (Rosseel, 2012) in the R statistical programming language. However, the properties of FMI estimates at finite sample sizes have not been the subject of comprehensive investigation. In this paper, we present a simulation study on the properties of three sample FMI estimates from FIML in two common models in psychology, regression and two-factor analysis. We summarize the performance of these FMI estimates and make recommendations on their application.
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spelling pubmed-84266262021-09-10 Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood Chen, Lihan Savalei, Victoria Front Psychol Psychology In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increase from the information loss due to ignorable missing data. FMI is well-known in the multiple imputation literature (Rubin, 1987), but it has only been more recently developed for full information maximum likelihood (Savalei and Rhemtulla, 2012). Sample FMI estimates using this approach have since then been made accessible as part of the lavaan package (Rosseel, 2012) in the R statistical programming language. However, the properties of FMI estimates at finite sample sizes have not been the subject of comprehensive investigation. In this paper, we present a simulation study on the properties of three sample FMI estimates from FIML in two common models in psychology, regression and two-factor analysis. We summarize the performance of these FMI estimates and make recommendations on their application. Frontiers Media S.A. 2021-08-26 /pmc/articles/PMC8426626/ /pubmed/34512436 http://dx.doi.org/10.3389/fpsyg.2021.667802 Text en Copyright © 2021 Chen and Savalei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Chen, Lihan
Savalei, Victoria
Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title_full Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title_fullStr Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title_full_unstemmed Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title_short Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood
title_sort three sample estimates of fraction of missing information from full information maximum likelihood
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426626/
https://www.ncbi.nlm.nih.gov/pubmed/34512436
http://dx.doi.org/10.3389/fpsyg.2021.667802
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