Evidence map›Paper›PMID 31579365›Full record

ArticleStructural equation modeling : a multidisciplinary journal2019

Time-Varying Effect Sizes for Quadratic Growth Models in Multilevel and Latent Growth Modeling.

Alan Feingold

Abstract read
In one paragraph

Article in Structural equation modeling : a multidisciplinary journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  9. Longitudinal analysis of the ABCD® study.Developmental cognitive neuroscience · 2025
    Review
  10. Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial.Clinical psychological science : a journal of the Association for Psychological Science · 2023
    Article
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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

1 author.

Alan FeingoldOregon Social Learning Center, Eugene, Oregon.

Funding

Practical Significance of Effects from Growth Modeling of Alcohol Use DataR01AA025069 · NIAAA · OREGON SOCIAL LEARNING CENTER, INC. · PI FEINGOLD, ALAN J. · 2017 to 2019
$946k
NIAAA NIH HHS R01 AA025069
6 · The paper itself

Abstract

Multilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coefficient for the effect of group on the slope from a linear GMA can be transformed into a model-estimated effect size for the group difference at the end of a study. Because effect sizes vary non-linearly in quadratic GMA, the effect size at the end of a study using quadratic GMA cannot be derived from a single coefficient, and cannot be used to estimate effect sizes at intermediate time points with backwards extrapolation. This article formulates equations and associated input commands in Mplus for time-varying effect sizes for quadratic GMA. Illustrative analyses that produced these time-varying effect sizes were presented, and Monte Carlo study found that bias in the effect sizes and their confidence intervals was ignorable.

Indexed as

confidence intervalseffect sizeslatent growth modelsmultilevel analysis

Identifiers

PMID31579365
PMCPMC6774388

What OpenQuestion holds

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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.