Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data

Ryoo, Ji Hoon; Wang, Cixin; Swearer, Susan M.; Hull, Michael; Shi, Dingjing

doi:10.3389/fpsyg.2018.00675

Cited by 70 publications

(71 citation statements)

References 51 publications

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“…Due to sparseness of longitudinal data [16,32], only Bayesian information criteria, consistent Akaike's information criteria and entropy were used to compare absolute and relative fit of different models. The best-fitting latent transition model was tested for measurement invariance [36] and varying transition probabilities between waves [31] (Table S2).…”

Section: Continuedmentioning

confidence: 99%

“…A larger sample would empower it to differentiate more latent groups [16], so the groups we analysed are not definitive. However, we followed a recommended staged selection process [31], which led to a comprehensive six-class model. Second, transitions between latent groups are contingent on the interim between baseline and follow-up observations.…”

Section: Study Limitationsmentioning

confidence: 99%

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Transitions in smoking and nicotine use from 2016 to 2017 among a UK cohort of adult smokers and ex‐smokers

Simonavičius

McNeill

Brose

2020

Drug and Alcohol Review

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Introduction and Aims. Smokers differ in how they smoke and how they stop smoking. Use of nicotine replacement therapy (NRT) and e-cigarettes further diversifies smokers. We aimed to identify and compare latent groups of past-year smokers and to describe longitudinal transitions between the identified groups. Design and Methods. Latent transition analysis of online UK past-year smokers' data (n = 2857) collected in June 2016 and followed-up in October 2017. Latent groups were identified based on participants' smoking, e-cigarette use, NRT use, urges to smoke and last quit attempt. Sociodemographic and smoking characteristics between the groups were compared using Pearson's χ 2 test and Cramer's V. Results. Four latent groups of smokers (heavy smokers, light smokers, smokers using NRT, smokers using e-cigarettes) and two recently quit smokers (abstinent ex-smokers, ex-smokers using e-cigarettes) were identified. Nearly half the participants (48.9%) were heavy or light smokers who did not use alternative nicotine products and largely had not tried quitting smoking. Latent groups were relatively stable and transitions to quitting smoking were more probable in groups of smokers using alternative nicotine products. Smokers using NRT transitioned mostly to abstinent ex-smokers, and smokers using e-cigarettes to exsmokers using e-cigarettes' groups (11.2% and 11.4%, respectively). The abstinent ex-smokers group grew the most at follow-up (+6.3%). Discussion and Conclusions. Nearly half of the participants continued smoking without having tried to reduce harm or quit. Of smokers who transitioned, the majority quit smoking and abstained from nicotine altogether. Use of alternative nicotine products alongside smoking was associated with higher probabilities of becoming an ex-smoker.

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Section: Continuedmentioning

confidence: 99%

Section: Study Limitationsmentioning

confidence: 99%

Transitions in smoking and nicotine use from 2016 to 2017 among a UK cohort of adult smokers and ex‐smokers

Simonavičius

McNeill

Brose

2020

Drug and Alcohol Review

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“…A fully dimensional, quasi-dimensional, or latent construct, would provide signi cant implications regarding modeling change and scaling speci c differences. For example, if this construct is really of a latent type, we need to consider the change models of latent transition [33], not latent growth curve analysis, in order to investigate this hypothesis.…”

Section: Discussionmentioning

confidence: 99%

Item Response Theory Analysis of the Hospitalized Patients Spiritual Needs Questionnaire

Hosseini

Guàrdia

Momennasab³

et al. 2020

Preprint

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Background: Hospitalized patients Spiritual Needs Questionnaire (HPSNQ) has been developed in Iran. This paper uses the classical test theory (CTT) to test the validity of the HPSNQ. Using Item response theory, a scale can be constructed with items that can be interpreted regarding the spiritual needs of patients revealing evidence that calls for more effective health interventions regarding meeting the spiritual needs of patients. Analysis of the hospitalized patients Spiritual Needs Questionnaire using Item Response Theory with the aim of providing insight into the functioning of the questionnaire to improve the psychometric robustness of the instrument. Methods: This analytical study was carried out in 2018 on 301 subjects hospitalized in non-acute wards of hospitals in Shiraz, Iran. The analysis was carried out using Item Response Theory: The dimensionality, assessing the items parameters evaluation, and the scale construction were assessed using Cronbach’s alpha, item-to-total correlations and confirmatory factor analysis was done for investigation of dimensionality. The Gradual Response Model of Samejima was used to estimate the item parameters, and the MPLUS software was utilized to analyses the data.Results: Findings revealed that HPSNQ scales are highly unidimensional. In this study, regarding the dimension of “relationship with God”, very high discriminations were observed among all items, whereas the items in “peaceful environment” had relatively low discriminations, hence was not as informative as the items of “relationship to god”.Conclusions: Whereas the psychometric properties of the HPSNQ was generally supported from an IRT perspective, there was also evidence of further improvement for this measure. It seems that the nature of the construct under study, has a significant impact on the items’ discrimination and information value; however, the wording correction or deletion of certain items or even the response options used in this tool may significantly increase the reliability of the tool.

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“…However, LTA requires that the number of classes be chosen before adding covariates principally to avoid a potential change in class number with and without covariates. 63 …”

Section: Latent Transition Analysis (Lta)mentioning

confidence: 99%

<p>Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches</p>

Nguefack

Pagé

Katz

et al. 2020

CLEP

161

145

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Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers’ efficiency when it comes to choosing the most appropriate technique that best suits their research questions.

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Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data

Cited by 70 publications

References 51 publications

Transitions in smoking and nicotine use from 2016 to 2017 among a UK cohort of adult smokers and ex‐smokers

Transitions in smoking and nicotine use from 2016 to 2017 among a UK cohort of adult smokers and ex‐smokers

Item Response Theory Analysis of the Hospitalized Patients Spiritual Needs Questionnaire

<p>Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches</p>

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