Monte Carlo Experiments: Design and Implementation

By Pamela Paxton, P. Curran, Kenneth A. Bollen, James B. Kirby, Feinian Chen. 2001

330 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ad64b294a712681610e2d1d3160bb5ef0b16788b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models (2008, background): “See Paxton et al. (2001) for detailed power estimates on each model specification.”
Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods (2018, methodology): “The ﬁ rst method presented here relies on Monte Carlo simulations (Paxton et al., 2001; Robert & Casella, 2013).”
A Statistically Justified Pairwise ML Method for Incomplete Nonnormal Data: A Comparison With Direct ML and Pairwise ADF (2005, background): “However, some authors have suggested that listwise deletion might be more appropriate ( Paxton, Curran, Bollen, Kirby, & Chen, 2001 ).”
Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing (2004, methodology): “Following Anderson and Gerbing (1984) and Paxton et al. (2001), we generated 200 proper solutions for each cell of the design.”
Bias in the Correlated Uniqueness Model for MTMM Data (2004, methodology): “We undertook the following steps to design the simulation study (see Paxton et al., 2001).”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, background): “Any simulation study begins by specifying a population—a true state from which data are sampled (Paxton et al. 2001).”
Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review (2020, methodology): “Monte Carlo simulation also has been employed as a method of determining the minimum sample size of the PLS-SME (Kock, 2016; Paxton et al., 2001; Robert & Casella, 1999; Wolf et al., 2013).”
9. Model Fit in Structural Equation Models with Censored, Ordinal, and Dichotomous Variables: Testing Vanishing Tetrads (2003, methodology): “model can be found in Paxton et al. (2001). A single data set was simulated from these population parameters, and we categorized the indicators into four categories of approximately equal frequencies.”
Goodness-of-fit indices for partial least squares path modeling (2013, methodology): “The population model, which includes a mediating effect, was selected based on the recommendation of Paxton et al. (2001).”
Structural equation modeling: strengths, limitations, and misconceptions. (2005, neutral): “For interested readers, we recommend several excellent pedagogical papers that have appeared in recent years on these topics (MacCallum et al. 1996, Muthén & Curran 1997, Muthén & Muthén 2002, Paxton et al. 2001).”