Random Variables and Correlational Overkill

Bibliographic Data

ID20283136
AuthorsJoseph T Kunce (University of Missouri), Daniel W Cook (0000-0003-0621-3624, University of Missouri), Douglas E Miller (0000-0002-4913-4232, Michigan State University)
Year1975
Volume35
Issue3
Pages529-534
Publication date1975-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/001316447503500301
OpenAlexW1971523651
LanguageEN
Citations received6
References cited9

Research findings may be more publishable if significant results are reported. This type of publication bias would increase the likelihood of "chance" relationships being disseminated. The implications of these assumptions are empirically investigated in a correlational analogue study. A large number of significant relationships were found in several groups of subjects between their actual scores on 45 SVIB scales and scores on 10 "experimental" scales which were determined by a set of random numbers. Furthermore, "logical" factors were shown to underly relationships which existed among scores on a given random scale with its significant correlations to SVIB scales. Considerations in such overkill in simple correlational studies are the subject-to-variable ratio, variable independence, and more stringent probability levels

Correlation · Econometrics · Independence (probability theory) · Random variable · Scale (ratio) · Set (abstract data type) · Statistics · Variable (mathematics) · Variables · Computer Science · Crime Patterns and Interventions · Mathematics · Psychology · Psychopathy, Forensic Psychiatry, Sexual Offending

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