Graphically Comparing the Predictive Performance of Two Selection Models

In this paper a method is demonstrated which allows for the comparison of the predictive performance of 2 preference ordering models. This method employs a 3-dimensional graph which is both informative and easy to interpret. This method is demonstrated with a Monte Carlo study designed to compare the predictive performance of a utility preference model using different weighting coefficient schemes.

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Monte Carlo study of the 3-dimensional abelian Higgs model with radially variable Higgs field

Physics Letters B ◽

10.1016/0370-2693(86)90743-4 ◽

1986 ◽

Vol 174 (2) ◽

pp. 208-212 ◽

Cited By ~ 8

Author(s):

G.N. Obodi

Keyword(s):

Monte Carlo ◽

Higgs Field ◽

Monte Carlo Study ◽

Higgs Model ◽

Abelian Higgs Model ◽

3 Dimensional

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An integral equation and Monte Carlo study of homo- and hetero nuclear square-well diatomic fluids

Molecular Physics ◽

10.1080/00268970050199770 ◽

2000 ◽

Vol 98 (24) ◽

pp. 2033-2043

Author(s):

Vít Jirásek, Stanislav Labík, Anatol Ma

Keyword(s):

Integral Equation ◽

Monte Carlo ◽

Monte Carlo Study ◽

Square Well

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A grand canonical ensemble Monte Carlo study of confined planar and homeotropically anchored Gay—Berne films

Molecular Physics ◽

10.1080/00268979809482254 ◽

1998 ◽

Vol 93 (4) ◽

pp. 681-692

Author(s):

THOMAS GRUHN ◽

MARTIN SCHOEN

Keyword(s):

Monte Carlo ◽

Canonical Ensemble ◽

Monte Carlo Study ◽

Grand Canonical Ensemble ◽

Ensemble Monte Carlo ◽

Grand Canonical

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Analyzing Observed Composite Differences Across Groups

Methodology ◽

10.1027/1614-2241/a000049 ◽

2013 ◽

Vol 9 (1) ◽

pp. 1-12 ◽

Cited By ~ 101

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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