scholarly journals Fifteen-Year Prevalence, Trajectories, and Predictors of Body Dissatisfaction From Adolescence to Middle Adulthood

2019 ◽  
Vol 7 (6) ◽  
pp. 1403-1415 ◽  
Author(s):  
Shirley B. Wang ◽  
Ann F. Haynos ◽  
Melanie M. Wall ◽  
Chen Chen ◽  
Marla E. Eisenberg ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Body Dissatisfaction ◽  
Critical Period ◽  
Growth Mixture Modeling ◽  
Self Esteem ◽  
Population Based ◽  
Mixture Modeling ◽  
Parental Communication ◽  
Growth Mixture ◽  
Weight Growth

Body dissatisfaction is common in adolescence and associated with negative outcomes (e.g., eating disorders). We identified common individual trajectories of body dissatisfaction from midadolescence to adulthood and predictors of divergent patterns. Participants were 1,455 individuals from four waves of Project EAT (Eating and Activity in Teens and Young Adults), a population-based, 15-year longitudinal study. Aggregate body dissatisfaction increased over 15 years, which was largely attributable to increases in weight. Growth mixture modeling identified four common patterns of body dissatisfaction, revealing nearly 95% of individuals experienced relatively stable body dissatisfaction from adolescence through adulthood. Baseline depression, self-esteem, parental communication/caring, peer dieting, and weight-based teasing predicted differing trajectories. Body dissatisfaction appears largely stable from midadolescence onward. There may be a critical period for body image development during childhood/early adolescence. Clinicians should intervene with clients experiencing body dissatisfaction before it becomes chronic and target depression, self-esteem, parent/child connectedness, and responses to teasing and peer dieting.

scholarly journals Trajectories of Calling in the Transition From University to Work: A Growth Mixture Analysis

2020 ◽  
Vol 29 (1) ◽  
pp. 98-114
Author(s):  
Chunyu Zhang ◽  
Andreas Hirschi ◽  
Xuqun You
Keyword(s):  
Longitudinal Study ◽  
University Students ◽  
Developmental Trajectories ◽  
Growth Mixture Modeling ◽  
Career Transition ◽  
Mixture Modeling ◽  
Proactive Personality ◽  
Mixture Analysis ◽  
Growth Mixture ◽  
Growth Mixture Analysis

Research on the development of calling is still in its infancy and rarely focused on how calling changes during a major career transition. The current study examined the developmental trajectories of calling and their relation with personality (i.e., conscientiousness, proactive personality) in the transition from university to work with a three-wave longitudinal study with 340 Chinese graduating university students. Results based on growth mixture modeling indicated three developmental trajectories of calling: high and stable calling (23% of sample), high but decreasing calling (74%), and low and increasing calling (3%). Moreover, higher conscientiousness related to a higher chance of being classified into the high and stable calling trajectory. These findings add notable insights to the literature by exploring the previously neglected developmental trajectories of calling and their association with personality in the transition from university to work.

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

2007 ◽  
Vol 36 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Wolfgang Lutz ◽  
Niklaus Stulz ◽  
David W. Smart ◽  
Michael J. Lambert
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Mixture

Zusammenfassung. Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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