Effect of surface active sites on adsorption of associating chain molecules in pores: A Monte Carlo study

Adsorption ◽  
1996 ◽  
Vol 2 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Lourdes F. Vega ◽  
Erich A. M�ller ◽  
Luis F. Rull ◽  
Keith E. Gubbins
Keyword(s):  
Monte Carlo ◽  
Active Sites ◽  
Monte Carlo Study ◽  
Chain Molecules ◽  
Surface Active ◽  
Effect Of Surface

Self-assembling of chain molecules in low dimensions: A Monte Carlo study

1995 ◽  
Vol 4 (6) ◽  
pp. 1015-1037 ◽  
Author(s):  
Heiko Stettin ◽  
Mirco Wahab ◽  
Peter Schiller ◽  
Hans-Jörg Mögel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Chain Molecules ◽  
Low Dimensions ◽  
Self Assembling

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.

Sign in / Sign up

Export Citation Format

Share Document