Random-Number Generator Validity in Simulation Studies

An Investigation of Normality

Bibliographic Data

ID20287490
AuthorsJung Woong Bang (Fort Worth Library), Randall E Schumacker (University of North Texas), Paul L Schlieve (University of North Texas)
Year1998
Volume58
Issue3
Pages430-450
Publication date1998-06-01
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164498058003005
OpenAlexW1965668721
LanguageEN
Citations received1
References cited19

Random-number generators are frequently used in Monte Carlo simulation studies to examine expected statistical outcomes. The purpose of this study was to investigate the normality of number distributions generated by various random-number generators. The research question focused on when the random-number generator reached a normal distribution (efficiency) and at what sample size. Findings suggest that when using a random-number generator in a Monte Carlo simulation study, the following steps should be observed: (a) normality of number distribution should be checked-means and standard deviations, (b) sample size should be large (n > 10,000), (c) starting seed values should be tested with regard to normality of resulting number distribution, (d) the length of periods before numbers repeat should be considered, (e) serial correlation between number sequences should be examined, and (f) uniformity of number distributions should be checked

Generator (circuit theory) · Monte Carlo method · Normal distribution · Normality · Physics · Random number generation · Sample (material) · Sample size determination · Standard deviation · Statistics · Diverse Scientific and Engineering Research · Mathematical Approximation and Integration · Mathematics · Numerical Methods and Algorithms

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