Monte Carlo study of the microphase separation of cross-linked polymer blends

2000 ◽  
Vol 113 (24) ◽  
pp. 11355-11363 ◽  
Author(s):  
Stefan Lay ◽  
Jens-Uwe Sommer ◽  
Alexander Blumen
Keyword(s):  
Monte Carlo ◽  
Polymer Blends ◽  
Microphase Separation ◽  
Monte Carlo Study

Monte Carlo study of phase separation in critical polymer blends

1993 ◽  
Vol 48 (5) ◽  
pp. 3705-3711 ◽  
Author(s):  
Gregory Brown ◽  
Amitabha Chakrabarti
Keyword(s):  
Monte Carlo ◽  
Phase Separation ◽  
Polymer Blends ◽  
Monte Carlo Study

Microphase Separation of a Diblock Copolymer Dispersed in Nanorod Arrays Grafted on a Plate: A Monte Carlo Study

2011 ◽  
Vol 20 (2) ◽  
pp. 124-132 ◽  
Author(s):  
Xingqing Xiao ◽  
Yongmin Huang ◽  
Jian Feng ◽  
Honglai Liu ◽  
Ying Hu
Keyword(s):  
Monte Carlo ◽  
Diblock Copolymer ◽  
Microphase Separation ◽  
Monte Carlo Study ◽  
Nanorod Arrays

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study
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