Evidence map›Paper›PMID 38418689›Full record

ArticleBehavior research methods2024

Dealing with missing data in multi-informant studies: A comparison of approaches.

Po-Yi Chen, Fan Jia, Wei Wu, Min-Heng Wang, Tzi-Yang Chao

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Po-Yi ChenDepartment of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan, 106308. poyichen@ntnu.edu.tw.
Fan JiaDepartment of Psychological Sciences, University of California Merced, Merced, CA, USA.
Wei WuDepartment of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.
Min-Heng WangMount Sinai Health System, New York, NY, USA.
Tzi-Yang ChaoDepartment of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan, 106308.

Funding

INSTITUTIONAL CLINICAL AND TRANSLATIONAL SCIENCE AWARDUL1RR024134 · NCRR · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2006 to 2011
$70.4M
CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024989 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI DAVIS, PAMELA B · 2007 to 2011
$53.7M
Clinical and Translational Science Collaborative of Northern Ohio, Catalyzing Linkages to Equity in Health (CLE Health)UM1TR004528 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI GRACE A MCCOMSEY · 2023 to 2026
$32.1M
Randomized Controlled Study of Adenotonsillectomy for Childhood Sleep ApneaU01HL083075 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI REDLINE, SUSAN S. · 2006 to 2010
$7.6M
National Sleep Research Resource (NSRR)R24HL114473 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI REDLINE, SUSAN S., ZHANG, GUO-QIANG · 2013 to 2017
$7.5M
A randomized controlled study of adenotonsillectomy for childrenU01HL083129 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI ELLENBERG, SUSAN S · 2006 to 2010
$2.5M
NCRR NIH HHS UL1 RR024134NCRR NIH HHS UL1 RR024989NHLBI NIH HHS R24 HL114473NHLBI NIH HHS U01 HL083075NHLBI NIH HHS U01 HL083129
6 · The paper itself

Abstract

Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random.

Indexed as

Models, StatisticalMonte Carlo MethodComputer SimulationData Interpretation, StatisticalHumansLikelihood FunctionsResearch DesignAuxiliary variablesMissing dataMulti-informant studyPlanned missing data design models

Identifiers

PMID38418689

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.