Evidence map›Paper›PMID 40087859›Full record

ArticleInternational journal of psychology : Journal international de psychologie2025

Introduction to the Best Practice Recommendations for Longitudinal Latent Transition Analysis.

E Whitney G Moore, Alessandro Quartiroli, Todd D Little

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

3 authors.

E Whitney G MooreEast Carolina University, Greenville, North Carolina, USA.ORCID https://orcid.org/0000-0003-2482-1957
Alessandro QuartiroliUniversity of Wisconsin-La Crosse, La Crosse, Wisconsin, USA.
Todd D LittleTexas Tech University, Lubbock, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of finite mixture models to identify a limited number of mutually exclusive latent (i.e., unknown) subgroups (i.e., classes or profiles) based on individuals' survey responses, observational and/or physiological values. Finite mixture modelling is also an active area of quantitative methodological research and advancement, which means best practices continue to evolve. This tutorial on latent transition analysis (LTA) is written to facilitate researchers using this modelling approach to answer research questions about transitions from membership in one latent class or profile construct to another latent class or profile construct rigorously and following best practices. This tutorial will cover the purpose, analysis steps, interpretation and recommended reporting practices for LTA. To increase the applicability and approachability of this LTA tutorial article example, three timepoint sport psychology data on the health behaviours of collegiate student-athletes, the Mplus syntax for the analysis and the decision process along with the results, tables and figures are included in the supplemental online materials.

Indexed as

Latent Class AnalysisAthletesHealth BehaviorHumansLongitudinal StudiesSportsdrinking behaviourlongitudinal dataLTAmixture modelsMplus

Identifiers

PMID40087859
PMCPMC11909493

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.