Näytä suppeat kuvailutiedot

dc.contributor.authorAguirre-Urreta, Miguel I.
dc.contributor.authorRönkkö, Mikko
dc.contributor.authorMcIntosh, Cameron N.
dc.date.accessioned2024-08-23T11:39:33Z
dc.date.available2024-08-23T11:39:33Z
dc.date.issued2024
dc.identifier.citationAguirre-Urreta, M. I., Rönkkö, M., & McIntosh, C. N. (2024). Too Small to Succeed : Small Samples and the p-Value Problem. <i>Data Base for Advances in Information Systems</i>, <i>55</i>(3), 12-49. <a href="https://doi.org/10.1145/3685235.3685238" target="_blank">https://doi.org/10.1145/3685235.3685238</a>
dc.identifier.otherCONVID_216083820
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/96747
dc.description.abstractDetermining an appropriate sample size is a critical planning decision in quantitative empirical research. In recent years, there has been a growing concern that researchers have excessively focused on statistical significance in large sample studies to the detriment of effect sizes. This research focuses on a related concern at the other end of the spectrum. We argue that a combination of bias in significant estimates obtained from small samples (compared to their population values) and an editorial preference for the publication of significant results compound to produce marked bias in published small sample studies. We then present a simulation study covering a variety of statistical techniques commonly used to examine structural equation models with latent variables. Our results support our contention that significant results obtained from small samples are likely biased and should be considered with skepticism. We also argue for the need to provide a priori power analyses to understand the behavior of parameter estimates under the small sample conditions we examine.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofseriesData Base for Advances in Information Systems
dc.rightsIn Copyright
dc.subject.otherstatistical significance
dc.subject.othersmall samples
dc.subject.otherpartial least squares (PLS)
dc.subject.otherregression
dc.subject.otherstructural equation modeling
dc.subject.othersimulation
dc.subject.otherestimation bias
dc.subject.otherpublication bias
dc.titleToo Small to Succeed : Small Samples and the p-Value Problem
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202408235637
dc.contributor.laitosKauppakorkeakoulufi
dc.contributor.laitosSchool of Business and Economicsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange12-49
dc.relation.issn1532-0936
dc.relation.numberinseries3
dc.relation.volume55
dc.type.versionacceptedVersion
dc.rights.copyright© 2024 ACM
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311309
dc.subject.ysorakenneyhtälömallit
dc.subject.ysotilastomenetelmät
dc.subject.ysoregressioanalyysi
dc.subject.ysotietojärjestelmätiede
dc.subject.ysokvantitatiivinen tutkimus
dc.subject.ysootanta
dc.subject.ysosimulointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28201
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p2130
jyx.subject.urihttp://www.yso.fi/onto/yso/p38880
jyx.subject.urihttp://www.yso.fi/onto/yso/p18834
jyx.subject.urihttp://www.yso.fi/onto/yso/p12939
jyx.subject.urihttp://www.yso.fi/onto/yso/p4787
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3685235.3685238
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundinginformationMikko Rönkkö acknowledges the Academy of Finland grant number 311309.
dc.type.okmA1


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

In Copyright
Ellei muuten mainita, aineiston lisenssi on In Copyright