Introduction to Latent Class Analysis With Applications

Porcu, Mariano; Giambona, Francesca

doi:10.1177/0272431616648452

Cited by 109 publications

(91 citation statements)

References 44 publications

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“…The BLRT compares each model with the model with one less number of profiles, and significant p values indicate that the model with more profiles should be retained. Although it was not used for selecting the number of profiles, entropy indicates the accuracy of classifications of individuals into profiles and was also considered (i.e., entropy should be greater than .80; Magidson & Vermunt, 2004;Porcu & Giambona, 2017;Tein, Coxe, & Cham, 2013).…”

Section: Hypotheses Testingmentioning

confidence: 99%

Reducing meat consumption: Identifying group-specific inhibitors using latent profile analysis

Lacroix

Gifford

2019

Appetite

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Climate change mitigation requires changes in greenhouse gas emitting behaviours. This dissertation aims to provide insights into the influences of behaviour change for two high-impact pro-environmental behaviours: climate policy support and consumption of animal products. It does so by using quasi-and randomized experiments and by monitoring changes in behaviour over time. Study 1 examined changes in climate policy support and climate change risk perception over the course of a naturally occurring event: seasonal forest fires. It employed growth curve modeling techniques in a structural equation modeling framework to analyze longitudinal relations between these two constructs over time, and to examine growth in climate change risk perception while controlling for the effect of exposure to forest fires and other extreme weather. Indirect exposure to forest fires (e.g., media) had a modest effect on climate change risk perception. Climate change risk perception for individuals with above-mean perceptions of scientific agreement tended to increase faster than for those with below-mean perceptions. Individuals whose climate change risk perception grew at a faster-than-average rate tended to also grow at a faster-than-average rate for climate policy support. Study 2 provided insight into the psychological influences on consumption of animal products and on willingness to reduce. Following a comprehensive literature review, known influences were examined using Latent Profile Analysis to identify groups of individuals with similar perceptions of facilitators of meat consumption and obstacles to reducing it. Three groups were identified: stronghindrance meat eaters, moderate-hindrance meat eaters, and reducers. Validation variables confirmed the practicality of the three profiles: groups differed in their current consumption of animal products and in their willingness to reduce. Using these findings, three group-matched interventions were designed in Study 3. Intervention design was informed by four behaviour-iv change frameworks. Participants were randomly assigned to one of four conditions: control condition, implementation-intention condition, information-and-healthy-recipe condition, and information-and-substitution condition. Then, they completed up to 28 days of food diaries.Multilevel model analyses were employed to examine changes in the consumption of animal products over time. Participants reduced their consumption by 20 grams of CO2 per day on average. Individuals that were randomly assigned to an intervention condition that matched their meat-eater profile reduced their consumption of animal products by 40 grams CO2 per day on average. Taken together, these studies highlight the importance of considering individual differences (i.e., tailoring) when designing pro-environmental behaviour interventions. v

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Section: Hypotheses Testingmentioning

confidence: 99%

Reducing meat consumption: Identifying group-specific inhibitors using latent profile analysis

Lacroix

Gifford

2019

Appetite

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“…In contrast with the variable-centered approach (i.e., the focus is on relationships among variables, such as exploratory factor analysis), LCA is a person-centered approach (i.e., the interest is finding heterogeneous groups of individuals) [12]. Using LCA to group patients with T2DM may provide a practical alternative to previous classification criteria.…”

Section: Procedures For Data Collectionmentioning

confidence: 99%

“…The third part was model selection, and the last part was latent class cluster analysis, which means that, for each individual, the posterior probability of belonging to each class was calculated using Bayes' theorem and assigned to an exclusive latent class based upon the maximum probability. Individuals within the same latent class were homogeneous for certain criteria, whereas those in different latent classes were dissimilar from each other [12].…”

Section: Latent Class Analysismentioning

confidence: 99%

Latent class analysis suggests four classes of persons with type 2 diabetes mellitus based on complications and comorbidities in Tianjin, China: a cross-sectional analysis

et al. 2017

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“…Determination of the number of classes depends on a combination of factors including fit indices, class size and interpretability [30]. Model selection was performed using the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC), consistent AIC (cAIC) and entropy [31] and G 2 .…”

Section: Discussionmentioning

confidence: 99%

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Nosetti

Paglietti

Brunetti³

et al. 2020

PLoS ONE

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AimTo identify subgroups regarding paediatricians' awareness, attitude, practice and satisfaction about management of Sleep-Disordered Breathing (SDB) in Italy using Latent Class Analysis (LCA). MethodsA cross-sectional study was conducted on a large sample of Italian paediatricians. Using a self-administered questionnaire, the study collected information on 420 Paediatric Hospital Paediatricians (PHPs) and 594 Family Care Paediatricians (FCPs). LCA was used to discover underlying response patterns, thus allowing identification of respondent groups with similar awareness, attitude, practice and satisfaction. A logistic regression model was used to investigate which independent variables influenced latent class membership. Analyses were performed using R 3.5.2 software. A p-value<0.05 was considered statistically significant. ResultsTwo classes were identified: Class 1 (n = 368, 36.29%) "Untrained and poorly satisfied" and Class 2 (n = 646, 63.71%) "Trained and satisfied." Involving paediatric pneumologists or otorhinolaryngologists in clinical practice was associated with an increased probability of Class 2 membership (OR = 5.88,]; OR = 15.95, 95%

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Introduction to Latent Class Analysis With Applications

Cited by 109 publications

References 44 publications

Reducing meat consumption: Identifying group-specific inhibitors using latent profile analysis

Reducing meat consumption: Identifying group-specific inhibitors using latent profile analysis

Latent class analysis suggests four classes of persons with type 2 diabetes mellitus based on complications and comorbidities in Tianjin, China: a cross-sectional analysis

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

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