A two-step method for understanding and fitting growth curve models

1990 ◽  
Vol 9 (7) ◽  
pp. 841-851 ◽  
Author(s):  
Edward J. Stanek
Keyword(s):  
Growth Curve ◽  
Step Method ◽  
Growth Curve Models

scholarly journals [Prediction of Future Observations in Growth Curve Models]: Comment

1987 ◽  
Vol 2 (4) ◽  
pp. 451-454 ◽  
Author(s):  
Nan Laird ◽  
Nick Lange
Keyword(s):  
Growth Curve ◽  
Growth Curve Models

Analysis of cross-over designs via growth curve models

2006 ◽  
Vol 136 (2) ◽  
pp. 475-497
Author(s):  
Nils Åsenblad ◽  
Dietrich von Rosen
Keyword(s):  
Growth Curve ◽  
Growth Curve Models

Analysis of the Development Trend of Chinese Textile Industry Based on Growth Curve Model

2011 ◽  
Vol 332-334 ◽  
pp. 1386-1389 ◽  
Author(s):  
Tao Ma ◽  
Hong Zhao
Keyword(s):  
Growth Curve ◽  
Textile Industry ◽  
Development Trend ◽  
Annual Growth Rate ◽  
Growth Curve Model ◽  
Growth Curve Models ◽  
Formative Stage ◽  
Curve Model ◽  
Important Position ◽  
The Development Trend

Growth-curve models are generalized multivariate analysis-of-variance models. This kind of method was widely used in the prediction of industry development cycle. The textile industry occupies an important position in the national economy of China. The paper analyzed the development trend of Chinese textile industry based on growth curve model and found out that Chinese textile industry is in the formative stage and is about to begin entered into matured period. The next five years the average annual growth rate of Chinese textile industry can reach more than 9 percent, and the textile industry output will reach 6 trillions in 2015.

scholarly journals The Mindful Adolescent: Developmental Changes in Nonreactivity to Inner Experiences during the Transition to High School, and Relations to Stress and Positive Affect

2018 ◽  
Author(s):  
Brian Galla ◽  
Eli Tsukayama ◽  
Daeun Park ◽  
Alisa Yu ◽  
Angela Duckworth
Keyword(s):  
High School ◽  
Middle School ◽  
Positive Affect ◽  
Perceived Stress ◽  
Growth Curve ◽  
Developmental Change ◽  
Latent Growth Curve ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Latent Growth Curve Models

Little is known about the naturalistic development of mindfulness in adolescence, and whether changes in this mental faculty are associated with perceived stress and emotional well-being. The current longitudinal study examined the development of one dimension of mindfulness, nonreactivity to inner experience, in a racially and socioeconomically diverse sample (N = 1,657) during the transition from middle school to high school. Students participated in up to four assessment waves, from fall of 8th grade through spring of 9th grade, during which they completed self-report measures assessing nonreactivity, perceived stress, and positive affect. Latent growth curve models indicated that levels of nonreactivity increased linearly during the two-year study period. Developmental change in nonreactivity varied minimally by gender, socioeconomic status, and race/ethnicity. Parallel process latent growth curve models showed that changes in nonreactivity were associated with concomitant reductions in perceived stress and increases in positive affect. Random intercept cross-lagged panel models showed that within-person nonreactivity prospectively predicted changes in perceived stress and positive affect. This is the first study to track naturalistic developmental change in mindfulness during adolescence. Results suggest that the nonreactivity dimension of mindfulness may boost resilience during the transition from middle school to high school.

scholarly journals Bayesian Power Equivalence in Latent Growth Curve Models

2019 ◽  
Author(s):  
Angelika Stefan ◽  
Timo von Oertzen
Keyword(s):  
Social Sciences ◽  
Growth Curve ◽  
Gold Standard ◽  
Latent Structure ◽  
Latent Growth Curve ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Latent Growth Curve Models ◽  
The Social ◽  
Design Factors

Longitudinal studies are the gold standard for research on time-dependentphenomena in the social sciences. However, they often entail high costs dueto multiple measurement occasions and a long overall study duration. It istherefore useful to optimize these design factors while maintaining a highinformativeness of the design. Von Oertzen and Brandmaier (2013) appliedpower equivalence to show that Latent Growth Curve Models (LGCMs)with different design factors can have the same power for likelihood-ratiotests on the latent structure. In this paper, we show that the notion ofpower equivalence can be extended to Bayesian hypothesis tests of the latentstructure constants. Specifically, we show that the results of a Bayes FactorDesign Analysis (BFDA; Schönbrodt & Wagenmakers, 2018) of two powerequivalent LGCMs are equivalent. This will be useful for researchers whoaim to plan for compelling evidence instead of frequentist power and providesa contribution towards more efficient procedures for BFDA.

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