Evidence map›Paper›PMID 31246110›Full record

ArticleMultivariate behavioral research

Testing Variance Components in Linear Mixed Modeling Using Permutation.

Han Du, Lijuan Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Multivariate behavioral research. 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

2 authors.

Han DuDepartment of Psychology, University of California, Los Angeles, Los Angeles, California, USA.
Lijuan WangUniversity of Notre Dame, Notre Dame, Indiana, USA.

Funding

Efficacy of Family Programs for Improving Child and Family Health and Development.R01HD087319 · NICHD · UNIVERSITY OF NOTRE DAME · PI BRAUNGART-RIEKER, JULIA, CUMMINGS, EDWARD MARK · 2017 to 2021
$3.0M
Fostering healthy development among maltreated preschool-aged childrenR01HD071933 · NICHD · UNIVERSITY OF NOTRE DAME · PI VALENTINO, KRISTIN · 2013 to 2017
$2.9M
Pathways Linking Early Adversity and Support to Behavioral and Physical HealthR01HD091235 · NICHD · UNIVERSITY OF NOTRE DAME · PI VALENTINO, KRISTIN · 2018 to 2022
$2.8M
Families of Youth with Developmental Disabilities: A Theory-Based InterventionR01HD088482 · NICHD · UNIVERSITY OF NOTRE DAME · PI CUMMINGS, EDWARD MARK, DIEHL, JOSHUA · 2017 to 2021
$2.7M
NICHD NIH HHS R01 HD071933NICHD NIH HHS R01 HD087319NICHD NIH HHS R01 HD088482NICHD NIH HHS R01 HD091235
6 · The paper itself

Abstract

Inference of variance components in linear mixed modeling (LMM) provides evidence of heterogeneity between individuals or clusters. When only nonnegative variances are allowed, there is a boundary (i.e., 0) in the variances' parameter space, and regular inference statistical procedures for such a parameter could be problematic. The goal of this article is to introduce a practically feasible permutation method to make inferences about variance components while considering the boundary issue in LMM. The permutation tests with different settings (i.e., constrained vs. unconstrained estimation, specific vs. generalized test, different ways of calculating

Indexed as

Models, StatisticalBehavioral ResearchBiostatisticsHumansLinear ModelsStatistics, NonparametricHierarchical linear modelingnonparametric statisticsvariance testing

Identifiers

PMID31246110
PMCPMC6933104

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Registered trials

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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.