Bridging Traditional-Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Nonindependent Observations, and Missing Data

dc.contributor.author	Lavelle-Hill, Rosa
dc.contributor.author	Murayama, Kou
dc.date.accessioned	2026-10-08T06:08:31Z
dc.date.available	2026-10-08T06:08:31Z
dc.date.issued	2025
dc.identifier.issn	2515-2459
dc.identifier.uri	http://hdl.handle.net/10900/184148
dc.language.iso	en	en
dc.publisher	Thousand Oaks : Sage Publications Inc	de_DE
dc.relation.uri	http://dx.doi.org/10.1177/25152459251345696
dc.subject.ddc	150	de_DE
dc.title	Bridging Traditional-Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Nonindependent Observations, and Missing Data	de_DE
dc.type	Article	de_DE
utue.quellen.id	20251118000000_01689
utue.personen.roh	Lavelle-Hill, Rosa
utue.personen.roh	Smith, Gavin
utue.personen.roh	Murayama, Kou
dcterms.isPartOf.ZSTitelID	Advances in Methods and Practices in Psychological Science	de_DE
dcterms.isPartOf.ZS-Issue	3	de_DE
dcterms.isPartOf.ZS-Volume	8	de_DE
utue.fakultaet	06 Wirtschafts- und sozialwissenschaftliche Fakultät