Monte Carlo study of Si(111) dimer-stacking-fault surface reconstruction

1998 ◽  
Vol 58 (7) ◽  
pp. 3537-3540 ◽  
Author(s):  
Makoto Itoh
Keyword(s):  
Monte Carlo ◽  
Surface Reconstruction ◽  
Monte Carlo Study ◽  
Stacking Fault ◽  
Fault Surface

Monte Carlo study of surface reconstruction in Ge(001)

1991 ◽  
Vol 243 (1-3) ◽  
pp. 295-302 ◽  
Author(s):  
V.E Zubkus ◽  
P.J Kundrotas ◽  
S.N Molotkov ◽  
V.V Tatarskij ◽  
E.E Tornau
Keyword(s):  
Monte Carlo ◽  
Surface Reconstruction ◽  
Monte Carlo Study

Monte Carlo study of the W(001) surface reconstruction transition based on total-energy calculations

1989 ◽  
Vol 40 (13) ◽  
pp. 9147-9154 ◽  
Author(s):  
L. D. Roelofs ◽  
T. Ramseyer ◽  
L. L. Taylor ◽  
D. Singh ◽  
H. Krakauer
Keyword(s):  
Monte Carlo ◽  
Total Energy ◽  
Surface Reconstruction ◽  
Monte Carlo Study ◽  
Energy Calculations

DFT and Monte Carlo study of the W(001) surface reconstruction

2017 ◽  
Vol 90 (7) ◽  
Author(s):  
Ivan N. Yakovkin ◽  
Ivan I. Yakovkin ◽  
Nataliia V. Petrova
Keyword(s):  
Monte Carlo ◽  
Surface Reconstruction ◽  
Monte Carlo Study

A Monte Carlo Study of Surface Reconstruction in (100) and (111) Diamond Surfaces and Nanodiamond

2004 ◽  
Vol 30 (1) ◽  
pp. 1-8 ◽  
Author(s):  
A.S. Barnard* ◽  
P. Bath† ◽  
S.P. Russo ◽  
I.K. Snook¶
Keyword(s):  
Monte Carlo ◽  
Surface Reconstruction ◽  
Monte Carlo Study ◽  
Diamond Surfaces

Monte Carlo study of surface reconstruction in Ge(001)

1991 ◽  
Vol 243 (1-3) ◽  
pp. A92
Author(s):  
V.E. Zubkus ◽  
P.J. Kundrotas ◽  
S.N. Molotkov ◽  
V.V. Tatarskij ◽  
E.E. Tornau
Keyword(s):  
Monte Carlo ◽  
Surface Reconstruction ◽  
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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