ArticleStructural equation modeling : a multidisciplinary journal2023
Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial.
Article in Structural equation modeling : a multidisciplinary journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Introduction to the Special Issue on Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- Evaluating differences in latent means across studies: Extending meta-analytic confirmatory factor analysis with the analysis of means.Research synthesis methods · 2026Article
- Who plays a more crucial role in adolescent well-being: Interactions with parents or peers? An investigation of adolescents aged 10 to 18 years.Applied psychology. Health and well-being · 2026Article
- Being Mindful About Overuse of Total Scores: a Comparison of Total Scores and Moderated Nonlinear Factor Analysis Scores in Assessing Mindfulness Across Race/Ethnicity, Age, and PTSD Diagnosis.Prevention science : the official journal of the Society for Prevention Research · 2025Article
- Testing measurement and structural invariance in latent mediation models - A comparison of IPCR and Bayesian MNLFA.Behavior research methods · 2025Article
- Using moderated nonlinear factor models to adjust for differential item functioning in the Student-Teacher Relationship Scale from kindergarten to Grade 6.Journal of school psychology · 2024Article
- Systematic Integration of Multi-Informant Externalizing Ratings in Clinical Settings.Research on child and adolescent psychopathology · 2024Article
- Examining the validity of the addictions neuroclinical assessment domains in a crowdsourced sample of adults with current alcohol use.Experimental and clinical psychopharmacology · 2024Article
- Psychosis Symptom Trajectories Across Childhood and Adolescence in Three Longitudinal Studies: An Integrative Data Analysis with Mixture Modeling.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Racial and Ethnic Representation in Preventive Intervention Research: a Methodological Study.Prevention science : the official journal of the Society for Prevention Research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.
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Registered trials
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.