How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

2002 ◽  
Vol 9 (4) ◽  
pp. 599-620 ◽  
Author(s):  
Linda K. Muthén ◽  
Bengt O. Muthén
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Monte Carlo Study

A Solution to the Effect of Sample Size on Outlier Elimination

1994 ◽  
Vol 47 (3) ◽  
pp. 631-650 ◽  
Author(s):  
Mark Van Selst ◽  
Pierre Jolicoeur
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Sample Size ◽  
Monte Carlo Study ◽  
The Other ◽  
Recursive Procedure ◽  
Elimination Procedure ◽  
New Procedures

Results from a Monte Carlo study demonstrate how a non-recursive, a simple recursive, a modified recursive, and a hybrid outlier elimination procedure are influenced by population skew and sample size. All the procedures are based on computing a mean and a standard deviation from a sample in order to determine whether an observation is an outlier. Miller (1991) showed that the estimated mean produced by the simple non-recursive procedure can be affected by sample size and that this effect can produce a bias in certain kinds of experiments. We extended this result to the other three procedures. We also create two new procedures in which the criterion used to identify outliers is adjusted as a function of sample size so as to produce results that are unaffected by sample size.

scholarly journals Skewness of Maximum Likelihood Estimators in the Weibull Censored Data

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1351 ◽  
Author(s):  
Tiago M. Magalhães ◽  
Diego I. Gallardo ◽  
Héctor W. Gómez
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Sample Size ◽  
Censored Data ◽  
Maximum Likelihood Estimators ◽  
Monte Carlo Study ◽  
Parameter Distribution ◽  
Regression Parameter ◽  
Skewness Coefficient ◽  
Matrix Formula

In this paper, we obtain a matrix formula of order n − 1 / 2 , where n is the sample size, for the skewness coefficient of the distribution of the maximum likelihood estimators in the Weibull censored data. The present result is a nice approach to verify if the assumption of the normality of the regression parameter distribution is satisfied. Also, the expression derived is simple, as one only has to define a few matrices. We conduct an extensive Monte Carlo study to illustrate the behavior of the skewness coefficient and we apply it in two real datasets.

Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study

1976 ◽  
Vol 1 (2) ◽  
pp. 113-125 ◽  
Author(s):  
Paul A. Games ◽  
John F. Howell
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Error Rate ◽  
Type I Error ◽  
Monte Carlo Study ◽  
Multiple Comparison ◽  
Type I ◽  
Type I Errors ◽  
Type I Error Rate ◽  
Multiple Comparison Procedures

Three different methods for testing all pairs of yȳk, - yȳk’ were contrasted under varying sample size (n) and variance conditions. With unequal n’s of six and up, only the Behrens-Fisher statistic provided satisfactory control of both the familywise rate of Type I errors and Type I error rate on each contrast. Satisfactory control with unequal n’s of three and up is dubious even with this statistic.

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.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study

Monte Carlo study of 50 nm-long single and dual-gate MODFETs: suppression of short-channel effects

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Short Channel Effects ◽  
Short Channel ◽  
Channel Effects ◽  
Dual Gate
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