scholarly journals Monte Carlo study of a geometrically frustrated rare-earth magnetic compound: SrGd2O4

2017 ◽  
Vol 96 (9) ◽  
Author(s):  
Emrul Hasan ◽  
B. W. Southern
Keyword(s):  
Monte Carlo ◽  
Rare Earth ◽  
Monte Carlo Study ◽  
Geometrically Frustrated

Monte Carlo study of two-dimensional Ising dipolar antiferromagnets as a model for rare-earth ordering in theR-Ba-Cu-O compounds (R=rare earth)

1992 ◽  
Vol 46 (10) ◽  
pp. 6387-6394 ◽  
Author(s):  
A. B. MacIsaac ◽  
J. P. Whitehead ◽  
K. De’Bell ◽  
K. Sowmya Narayanan
Keyword(s):  
Monte Carlo ◽  
Rare Earth ◽  
Monte Carlo Study ◽  
Two Dimensional

Magnetocaloric effect in rare-earth-based compounds: A Monte Carlo study

2006 ◽  
Vol 378-380 ◽  
pp. 716-717 ◽  
Author(s):  
E.P. Nóbrega ◽  
N.A. de Oliveira ◽  
P.J. von Ranke ◽  
A. Troper
Keyword(s):  
Monte Carlo ◽  
Rare Earth ◽  
Magnetocaloric Effect ◽  
Monte Carlo Study

A combined DFT + U and Monte Carlo study on rare earth doped ceria

2014 ◽  
Vol 16 (21) ◽  
pp. 9974 ◽  
Author(s):  
Steffen Grieshammer ◽  
Benjamin O. H. Grope ◽  
Julius Koettgen ◽  
Manfred Martin
Keyword(s):  
Monte Carlo ◽  
Rare Earth ◽  
Monte Carlo Study ◽  
Doped Ceria ◽  
Rare Earth Doped

Monte Carlo study of the magnetic properties of the rare earth-based perovskite LaSr2Cr3O9

2019 ◽  
Vol 125 (8) ◽  
Author(s):  
S. Mtougui ◽  
N. El Mekkaoui ◽  
S. Ziti ◽  
S. Idrissi ◽  
H. Labrim ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Magnetic Properties ◽  
Rare Earth ◽  
Monte Carlo Study ◽  
The Rare Earth

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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