Study of logistic growth curve model for mobile user growth

Author(s):  
Jin Tao ◽  
Gao Deyong
2011 ◽  
Vol 3 (2) ◽  
pp. 119
Author(s):  
Douglas G. Bonett

A logistic growth curve model for new product sales forecasting is proposed as an alternative to the traditional subjective forecasting methods. The parameters of the logistic growth curve model are redefined to represent meaningful growth characteristics that may more easily be specified by expert panels.


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.


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