scholarly journals A Monte Carlo study of CO oxidation with oscillations induced by site blocking

1997 ◽  
Vol 106 (5) ◽  
pp. 2038-2044 ◽  
Author(s):  
A. P. J. Jansen ◽  
R. M. Nieminen
Keyword(s):  
Monte Carlo ◽  
Co Oxidation ◽  
Monte Carlo Study ◽  
Site Blocking

Two-Component Catalysts for Low-Temperature CO Oxidation: A Monte Carlo Study

1993 ◽  
Vol 141 (1) ◽  
pp. 219-238 ◽  
Author(s):  
R.K. Herz ◽  
A. Badlani ◽  
D.R. Schryer ◽  
B.T. Upchurch
Keyword(s):  
Monte Carlo ◽  
Low Temperature ◽  
Co Oxidation ◽  
Monte Carlo Study ◽  
Two Component ◽  
Low Temperature Co Oxidation

Monte Carlo study of CO oxidation on an anisotropic surface

1991 ◽  
Vol 43 (4) ◽  
pp. 1770-1776 ◽  
Author(s):  
B. Yu ◽  
D. A. Browne ◽  
P. Kleban
Keyword(s):  
Monte Carlo ◽  
Co Oxidation ◽  
Monte Carlo Study ◽  
Anisotropic Surface

scholarly journals CO Oxidation on Pd(111): A First-Principles-Based Kinetic Monte Carlo Study

ACS Catalysis ◽  
2014 ◽  
Vol 4 (7) ◽  
pp. 2143-2152 ◽  
Author(s):  
Simone Piccinin ◽  
Michail Stamatakis
Keyword(s):  
Monte Carlo ◽  
Co Oxidation ◽  
First Principles ◽  
Kinetic Monte Carlo ◽  
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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