Examples: Monte Carlo Simulation Studies
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CHAPTER 11
EXAMPLES: MONTE CARLO
SIMULATION STUDIES
Monte Carlo simulation studies are often used for methodological
investigations of the performance of statistical estimators under various
conditions. They can also be used to decide on the sample size needed
for a study and to determine power (Muthén & Muthén, 2002). Monte
Carlo studies are sometimes referred to as simulation studies.
Mplus has extensive Monte Carlo simulation facilities for both data
generation and data analysis. Several types of data can be generated:
simple random samples, clustered (multilevel) data, missing data, and
data from populations that are observed (multiple groups) or unobserved
(latent classes). Data generation models can include random effects,
interactions between continuous latent variables, interactions between
continuous latent variables and observed variables, and between
categorical latent variables. Dependent variables can be continuous,
censored, binary, ordered categorical (ordinal), unordered categorical
(nominal), counts, or combinations of these variable types. In addition,
two-part (semicontinuous) variables and time-to-event variables can be
generated. Independent variables can be binary or continuous. All or
some of the Monte Carlo generated data sets can be saved.
The analysis model can be different from the data generation model. For
example, variables can be generated as categorical and analyzed as
continuous or data can be generated as a three-class model and analyzed
as a two-class model. In some situations, a special external Monte Carlo
feature is needed to generate data by one model and analyze it by a
different model. For example, variables can be generated using a
clustered design and analyzed ignoring the clustering. Data generated
outside of Mplus can also be analyzed using this special Monte Carlo
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