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Related Experiment Video

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Study of Siphon Breaker Experiment and Simulation for a Research Reactor

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A Problem with Discretizing Vale-Maurelli in Simulation Studies.

Steffen Grønneberg¹, Njål Foldnes²

¹Department of Economics, BI Norwegian Business School, 0484, Oslo, Norway.

|March 7, 2019

Summary

Simulation studies on ordinal data non-normality using the Vale-Maurelli (VM) method often analyze normally distributed data. This misinterpretation impacts the understanding of previous research findings on ordinal data.

Keywords:

Vale–Maurelli non-normal data ordinal data polychoric correlation structural equation modeling

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Area of Science:

Statistics
Psychometrics
Data Analysis

Background:

Previous simulation studies explored non-normality in ordinal data using the Vale-Maurelli (VM) method.
The VM method is influential in understanding ordinal data characteristics.

Purpose of the Study:

To re-evaluate the underlying data distributions in simulation studies using the VM method.
To clarify the impact of data generation methods on simulation results for ordinal data.

Main Methods:

Numerical analysis of data generated via the Vale-Maurelli (VM) method.
Comparison of covariance structures between target and actual generated data.

Main Results:

Discretized data from the VM method often originate from a multivariate normal distribution.
The covariance matrix of the underlying normal distribution differs from the target covariance matrix.
Simulation studies may inadvertently analyze normally distributed data with misspecified covariance.

Conclusions:

The interpretation of previous simulation studies on ordinal data non-normality needs revision.
The VM method's application requires careful consideration of the underlying data distribution.
Accurate simulation of non-normal ordinal data necessitates alternative approaches.