Academic Journal
Tobit modeling for dependent-sample t-tests and moderated regression with ceiling or floor data.
| Τίτλος: | Tobit modeling for dependent-sample t-tests and moderated regression with ceiling or floor data. |
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| Συγγραφείς: | Wang, Lijuan1 (AUTHOR) lwang4@nd.edu, Li, Ruoxuan1 (AUTHOR) |
| Πηγή: | Behavior Research Methods. Jan2026, Vol. 58 Issue 1, p1-15. 15p. |
| Περίληψη: | Ceiling or floor effects pose analytic challenges in behavioral and psychological research. In this study, we developed novel Tobit modeling approaches, estimated using maximum likelihood (ML) or Bayesian methods, to address these effects for widely used statistical analyses, including the dependent-sample t-test and moderated regression. Simulation studies were conducted to compare the performance of the proposed modeling approaches to the conventional approach where ceiling or floor data are treated as if true values. The conventional approach was found to yield biased estimates, inflated Type I error rates, and poor confidence interval coverage, even with as little as 10% ceiling data. In contrast, the proposed approaches with either ML or Bayesian estimation provided accurate estimates and inference results across most studied conditions (e.g., with 30% ceiling data). Real data examples further illustrated the impact of modeling choices. To facilitate implementations of the proposed Tobit modeling approaches, we provide simulated datasets along with R and Mplus scripts online. Implications of the findings and future research directions were discussed. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.3758/s13428-025-02904-y Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:asx&genre=article&issn=1554351X&ISBN=&volume=58&issue=1&date=20260101&spage=1&pages=1-15&title=Behavior Research Methods&atitle=Tobit%20modeling%20for%20dependent-sample%20t-tests%20and%20moderated%20regression%20with%20ceiling%20or%20floor%20data.&aulast=Wang%2C%20Lijuan&id=DOI:10.3758/s13428-025-02904-y Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: asx DbLabel: Academic Search Index An: 191672792 RelevancyScore: 1393 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1393.34948730469 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tobit modeling for dependent-sample t-tests and moderated regression with ceiling or floor data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Lijuan%22">Wang, Lijuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lwang4@nd.edu</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ruoxuan%22">Li, Ruoxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Behavior+Research+Methods%22">Behavior Research Methods</searchLink>. Jan2026, Vol. 58 Issue 1, p1-15. 15p. – Name: Abstract Label: Abstract Group: Ab Data: Ceiling or floor effects pose analytic challenges in behavioral and psychological research. In this study, we developed novel Tobit modeling approaches, estimated using maximum likelihood (ML) or Bayesian methods, to address these effects for widely used statistical analyses, including the dependent-sample t-test and moderated regression. Simulation studies were conducted to compare the performance of the proposed modeling approaches to the conventional approach where ceiling or floor data are treated as if true values. The conventional approach was found to yield biased estimates, inflated Type I error rates, and poor confidence interval coverage, even with as little as 10% ceiling data. In contrast, the proposed approaches with either ML or Bayesian estimation provided accurate estimates and inference results across most studied conditions (e.g., with 30% ceiling data). Real data examples further illustrated the impact of modeling choices. To facilitate implementations of the proposed Tobit modeling approaches, we provide simulated datasets along with R and Mplus scripts online. Implications of the findings and future research directions were discussed. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=191672792 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3758/s13428-025-02904-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Titles: – TitleFull: Tobit modeling for dependent-sample t-tests and moderated regression with ceiling or floor data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Lijuan – PersonEntity: Name: NameFull: Li, Ruoxuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1554351X Numbering: – Type: volume Value: 58 – Type: issue Value: 1 Titles: – TitleFull: Behavior Research Methods Type: main |
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