Development of Social Skills in Kindergarten: A Latent Class Growth Modeling Approach. [PDF]
Children (Basel), 2021 Social skills acquired during early childhood are often the foundation for success later in life. Using a nationwide survey dataset in Japan, this study aims to explore the multiple growth trajectories of social skills among children in kindergarten by ...
Zhu YT +10 more
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International Journal of Contemporary Educational Research, 2023 This study proposed a three-stage measurement model utilizing the Latent Growth Curve Modeling and Latent Class Growth Analysis. The measurement model was illustrated using repeated data collected through a four-week prospective study tracking the ...
Esra Sozer-Boz, Nilüfer Kahraman
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Recovery Trajectories of Executive Functioning After Pediatric TBI: A Latent Class Growth Modeling Analysis. [PDF]
J Head Trauma Rehabil, 2017 Objective: To identify latent trajectories of executive functioning (EF) recovery overtime after childhood traumatic brain injury (TBI) and examine the predictive value of known risk factors within and across recovery trajectories using latent class growth modeling (LCGM). Method: A
Narad ME +6 more
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Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes [PDF]
Frontiers in Psychology, 2017 Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance ...
Eun Sook Kim, Yan Wang
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Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study. [PDF]
BMC Geriatr, 2021 Background Depression and anxiety are common mental health conditions in the older adult population. Understanding the trajectories of these will help implement treatments and interventions.
Cheng Y, Thorpe L, Kabir R, Lim HJ.
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Temporal patterns of adolescent screen time and compulsive internet use in Moroccan high school students. [PDF]
Addict Behav RepObjective: Although compulsive internet use reflects interactions between personal vulnerabilities and situational triggers, adolescents’ day-to-day screen time patterns, especially in non-Western contexts, remain understudied. We aim to examine temporal
Imrani S, Touri B, Romo L, Zerhouni O.
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Exploring Class Enumeration in Bayesian Growth Mixture Modeling Based on Conditional Medians
Frontiers in Education, 2021 Growth mixture modeling is a popular analytic tool for longitudinal data analysis. It detects latent groups based on the shapes of growth trajectories.
Seohyun Kim, Xin Tong, Zijun Ke
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Latent Class Growth Modelling: A Tutorial [PDF]
Tutorials in Quantitative Methods for Psychology, 2009 The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi-parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases.
Andruff, Heather +4 more
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Bayesian multivariate growth curve latent class models for mixed outcomes [PDF]
Statistics in Medicine, 2012 In many clinical studies, the disease of interest is multifaceted, and multiple outcomes are needed to adequately capture information about the characteristics of the disease or its severity. In the analysis of such diseases, it is often difficult to determine what constitutes improvement because of the multivariate nature of the outcome.
Leiby, Benjamin E. +3 more
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An Introduction to Latent Variable Mixture Modeling (Part 2): Longitudinal Latent Class Growth Analysis and Growth Mixture Models [PDF]
Journal of Pediatric Psychology, 2013 Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns.
Berlin, Kristoffer S. +2 more
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