scholarly journals Explicit estimators of parameters in the Growth Curve model with linearly structured covariance matrices

2010 ◽  
Vol 101 (5) ◽  
pp. 1284-1295 ◽  
Author(s):  
Martin Ohlson ◽  
Dietrich von Rosen
Keyword(s):  
Growth Curve ◽  
Covariance Matrices ◽  
Growth Curve Model ◽  
Curve Model ◽  
Explicit Estimators

Problem gambling symptomatology and alcohol misuse among adolescents: A parallel-process latent growth curve model.

2017 ◽  
Vol 31 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Seema Mutti-Packer ◽  
David C. Hodgins ◽  
Nady el-Guebaly ◽  
David M. Casey ◽  
Shawn R. Currie ◽  
...  
Keyword(s):  
Problem Gambling ◽  
Growth Curve ◽  
Alcohol Misuse ◽  
Parallel Process ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Mental health outcomes in military veterans: A latent growth curve model.

2018 ◽  
Vol 24 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Justin T. McDaniel ◽  
Kate H. Thomas ◽  
David L. Albright ◽  
Kari L. Fletcher ◽  
Margaret M. Shields
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Growth Curve ◽  
Military Veterans ◽  
Latent Growth Curve ◽  
Mental Health Outcomes ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model

2021 ◽  
pp. 135910532110216
Author(s):  
Hai-Ping Liao ◽  
Xiao-Fu Pan ◽  
Xue-Qin Yin ◽  
Ya-Fei Liu ◽  
Jie-Yang Li ◽  
...  
Keyword(s):  
Negative Affect ◽  
Adaptive Behavior ◽  
Growth Curve ◽  
Initial Level ◽  
Behavioral Changes ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

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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