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

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.

2017 ◽  
Vol 31 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Seema Mutti-Packer ◽  
David C. Hodgins ◽  
Nady el-Guebaly ◽  
David M. Casey ◽  
Shawn R. Currie ◽  
...  

2018 ◽  
Vol 24 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Justin T. McDaniel ◽  
Kate H. Thomas ◽  
David L. Albright ◽  
Kari L. Fletcher ◽  
Margaret M. Shields

2021 ◽  
pp. 135910532110216
Author(s):  
Hai-Ping Liao ◽  
Xiao-Fu Pan ◽  
Xue-Qin Yin ◽  
Ya-Fei Liu ◽  
Jie-Yang Li ◽  
...  

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.


2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm

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.


1995 ◽  
Vol 214 ◽  
pp. 103-118 ◽  
Author(s):  
Chi Song Wong ◽  
Joe Masaro ◽  
Weicai Deng

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