Pore size effect on the separation of polymers by interaction chromatography. A Monte Carlo study

2019 ◽  
Vol 1064 ◽  
pp. 126-137 ◽  
Author(s):  
Xiu Wang ◽  
Karel Procházka ◽  
Zuzana Limpouchová
Keyword(s):  
Monte Carlo ◽  
Size Effect ◽  
Pore Size ◽  
Monte Carlo Study ◽  
Separation Of Polymers

Size effect on the magnetic properties of CoFe2O4 nanoparticles: A Monte Carlo study

2020 ◽  
Vol 46 (6) ◽  
pp. 8092-8096 ◽  
Author(s):  
R. Lamouri ◽  
O. Mounkachi ◽  
E. Salmani ◽  
M. Hamedoun ◽  
A. Benyoussef ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Magnetic Properties ◽  
Size Effect ◽  
Monte Carlo Study ◽  
Cofe2o4 Nanoparticles

The Size Effect on Magnetic Properties of HoRh Compounds: Monte Carlo Study

2014 ◽  
Vol 27 (10) ◽  
pp. 2299-2303 ◽  
Author(s):  
R. Masrour ◽  
L. Bahmad ◽  
E. K. Hlil ◽  
M. Hamedoun ◽  
A. Benyoussef
Keyword(s):  
Monte Carlo ◽  
Magnetic Properties ◽  
Size Effect ◽  
Monte Carlo Study

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.

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