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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Published on: March 13, 2020

On the joys of missing data.

Todd D Little1, Terrence D Jorgensen, Kyle M Lang

  • 1PhD, Center for Research Methods and Data Analysis, University of Kansas, Lawrence, KS 66045, USA. yhat@ku.edu.

Journal of Pediatric Psychology
|July 10, 2013
PubMed
Summary
This summary is machine-generated.

This study explains missing data mechanisms and advanced handling methods like full-information maximum likelihood and multiple imputation. It also discusses planned missing data designs to improve research quality.

Keywords:
full-information maximum likelihoodmissing data analysismissingness mechanismsmultiple imputationplanned missing design

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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

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Published on: November 4, 2025

Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Missing data is a common challenge in empirical research.
  • Inadequate handling of missing data can bias results.
  • Understanding missingness mechanisms and modern imputation techniques is crucial.

Purpose of the Study:

  • To introduce missingness mechanisms (MCAR, MAR, MNAR) and advanced statistical methods (FIML, MI).
  • To discuss planned missing data designs (multiform protocols, 2-method models, wave-missing designs).
  • To evaluate current practices in handling missing data within the Journal of Pediatric Psychology.

Main Methods:

  • Conceptual review of missing data mechanisms and handling techniques.
  • Systematic review of 80 empirical studies from the Journal of Pediatric Psychology (2012).
  • Illustrative data analysis using a 3-form planned missing design with FIML and MI.

Main Results:

  • Identified common missing data mechanisms and state-of-the-art handling methods.
  • Assessed the adequacy of missing data handling in published pediatric psychology research.
  • Demonstrated the advantages of FIML, MI, and planned missing designs through empirical examples.

Conclusions:

  • Researchers need to be aware of different missing data mechanisms and employ appropriate handling strategies.
  • Planned missing data designs offer efficient and effective ways to manage missingness in longitudinal and complex studies.
  • Adoption of advanced methods like FIML and MI can enhance the validity of research findings.