A pilot Monte Carlo study of two sequential estimation procedures based on generalized U-statistics

Conjoint analysis has been applied in a large number of commercial projects as well as in many noncommercial studies. Often MONANOVA, a nonmetric technique, is applied to a preference rank order obtained for a set of hypothetical objects. The authors report simulation results obtained for four alternative estimation procedures, ANOVA, LINMAP, LOGIT, and MONANOVA. The results suggest, within the limitations of the simulation study, that ANOVA may be the preferred procedure for compensatory models, whereas LINMAP is most likely to provide the best predictive validity for models with a dominant attribute.

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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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