Evidence map›Paper›PMID 34708309›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2022

A Primer on Meta-Analytic Structural Equation Modeling: the Case of Depression.

Jeffrey C Valentine, Mike W-L Cheung, Eric J Smith, Olivia Alexander, Jessica M Hatton, Ryan Y Hong, Lucas T Huckaby, Samantha C Patton, Patrick Pössel, Hayley D Seely

Abstract read
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In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Leveraging Research Synthesis Methods to Support Evidence-Based Policy- and Decision-Making.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
  4. Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
  5. 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

10 authors.

Jeffrey C ValentineCollege of Education and Human Development, University of Louisville, Louisville, USA. jeff.valentine@louisville.edu.ORCID 0000-0002-2673-1915
Mike W-L CheungDepartment of Psychology, National University Singapore, Singapore, Singapore.
Eric J SmithCollege of Education and Human Development, University of Louisville, Louisville, USA.
Olivia AlexanderCollege of Education and Human Development, University of Louisville, Louisville, USA.
Jessica M HattonCollege of Education and Human Development, University of Louisville, Louisville, USA.
Ryan Y HongDepartment of Psychology, National University Singapore, Singapore, Singapore.
Lucas T HuckabyCollege of Education and Human Development, University of Louisville, Louisville, USA.
Samantha C PattonSchool of Medicine, Emory University, Atlanta, USA.
Patrick PösselCollege of Education and Human Development, University of Louisville, Louisville, USA.
Hayley D SeelyCollege of Education and Human Development, University of Louisville, Louisville, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we show how the methods of systematic reviewing and meta-analysis can be used in conjunction with structural equation modeling to summarize the results of studies in a way that will facilitate the theory development and testing needed to advance prevention science. We begin with a high-level overview of the considerations that researchers need to address when using meta-analytic structural equation modeling (MASEM) and then discuss a research project that brings together theoretically important cognitive constructs related to depression to (a) show how these constructs are related, (b) test the direct and indirect effects of dysfunctional attitudes on depression, and (c) test the effects of study-level moderating variables. Our results suggest that the indirect effect of dysfunctional attitudes (via negative automatic thinking) on depression is two and a half times larger than the direct effect of dysfunctional attitudes on depression. Of the three study-level moderators tested, only sample recruitment method (clinical vs general vs mixed) yielded different patterns of results. The primary difference observed was that the dysfunctional attitudes → automatic thoughts path was less strong for clinical samples than it was for general and mixed samples. These results illustrate how MASEM can be used to compare theoretically derived models and predictions resulting in a richer understanding of both the empirical results and the theories underlying them.

Indexed as

DepressionModels, StatisticalAttitudeHumansLatent Class AnalysisMeta-Analysis as TopicResearch DesignMASEMMeta-analysisMeta-analytic structural equation modelingSystematic review

Identifiers

What OpenQuestion holds

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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.