Evidence map›Paper›PMID 39425575›Full record

ArticleInternational journal of psychology : Journal international de psychologie2025

Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.

Tuo Liu, Ruyi Ding, Zhonghuang Su, Zixuan Peng, Andrea Hildebrandt

Abstract read
In one paragraph

Article in International journal of psychology : Journal international de psychologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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.

Tuo LiuInstitute of Psychology, Goethe-Universität Frankfurt am Main, Frankfurt, Germany.ORCID https://orcid.org/0000-0001-8037-7809
Ruyi DingDepartment of Psychology, Sun Yat-Sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-0151-2548
Zhonghuang SuDepartment of Psychology, Sun Yat-Sen University, Guangzhou, China.ORCID https://orcid.org/0009-0006-4322-804X
Zixuan PengDepartment of Psychology, Sun Yat-Sen University, Guangzhou, China.ORCID https://orcid.org/0009-0002-5751-1509
Andrea HildebrandtDepartment of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.ORCID https://orcid.org/0000-0001-5564-0126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the differential strength of effects in the presence of a third variable, known as a moderation effect, is a common research goal in many psychological and behavioural science fields. If structural equation modelling is applied to test effects of interest, the investigation of differential strength of effects will typically ask how parameters of a latent variable model are influenced by categorical or continuous moderators, such as age, socio-economic status, personality traits, etc. Traditional approaches to continuous moderators in SEMs predominantly address linear moderation effects, risking the oversight of nonlinear effects. Moreover, some approaches have methodological limitations, for example, the need to categorise moderators or to pre-specify parametric forms of moderation. This tutorial introduces local structural equation modelling (LSEM) in a non-technical way. LSEM is a nonparametric approach that allows the analysis of nonlinear moderation effects without the above-mentioned limitations. Using an empirical dataset, we demonstrate the implementation of LSEM through the R-sirt package, emphasising its versatility in both exploratory analysis of nonlinear moderation without prior knowledge and confirmatory testing of hypothesised moderation functions. The tutorial also addresses common modelling issues and extends the discussion to different application scenarios, demonstrating its flexibility.

Indexed as

Latent Class AnalysisHumansModels, StatisticalNonlinear DynamicsLocal structural equation modellingModerationNonlinearity

Identifiers

PMID39425575
PMCPMC11626091

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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.