Academic Journal
Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data.
| Τίτλος: | Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data. |
|---|---|
| Συγγραφείς: | Lee YR; Department of Educational Psychology, University of Texas at Austin., Pustejovsky JE; Department of Educational Psychology, University of Wisconsin-Madison. |
| Πηγή: | Psychological methods [Psychol Methods] 2024 Dec; Vol. 29 (6), pp. 1084-1099. Date of Electronic Publication: 2023 Mar 09. |
| Τύπος έκδοσης: | Journal Article; Comparative Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Psychological Association Country of Publication: United States NLM ID: 9606928 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1463 (Electronic) Linking ISSN: 1082989X NLM ISO Abbreviation: Psychol Methods Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Washington, DC : American Psychological Association, c1996- |
| Ιατρικοί όροι (MeSH): | Psychology*/methods , Psychology*/standards , Models, Statistical*, Humans ; Least-Squares Analysis ; Data Interpretation, Statistical ; Monte Carlo Method |
| Περίληψη: | Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2024 APA, all rights reserved). |
| Entry Date(s): | Date Created: 20230309 Date Completed: 20241223 Latest Revision: 20250924 |
| Update Code: | 20260130 |
| DOI: | 10.1037/met0000538 |
| PMID: | 36892913 |
| Βάση Δεδομένων: | MEDLINE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=19391463&ISBN=&volume=29&issue=6&date=20241201&spage=1084&pages=1084-1099&title=Psychological methods&atitle=Comparing%20random%20effects%20models%2C%20ordinary%20least%20squares%2C%20or%20fixed%20effects%20with%20cluster%20robust%20standard%20errors%20for%20cross-classified%20data.&aulast=Lee%20YR&id=DOI:10.1037/met0000538 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: cmedm DbLabel: MEDLINE An: 36892913 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Lee+YR%22">Lee YR</searchLink>; Department of Educational Psychology, University of Texas at Austin.<br /><searchLink fieldCode="AU" term="%22Pustejovsky+JE%22">Pustejovsky JE</searchLink>; Department of Educational Psychology, University of Wisconsin-Madison. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229606928%22">Psychological methods</searchLink> [Psychol Methods] 2024 Dec; Vol. 29 (6), pp. 1084-1099. <i>Date of Electronic Publication: </i>2023 Mar 09. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Comparative Study – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22American+Psychological+Association%22">American Psychological Association </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9606928 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1939-1463 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221082989X%22">1082989X </searchLink><i>NLM ISO Abbreviation: </i>Psychol Methods <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: Washington, DC : American Psychological Association, c1996- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Psychology%22">Psychology*</searchLink>/<searchLink fieldCode="MM" term="%22Psychology+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Psychology%22">Psychology*</searchLink>/<searchLink fieldCode="MM" term="%22Psychology+standards%22">standards</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Statistical%22">Models, Statistical*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Least-Squares+Analysis%22">Least-Squares Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Data+Interpretation%2C+Statistical%22">Data Interpretation, Statistical</searchLink> ; <searchLink fieldCode="MH" term="%22Monte+Carlo+Method%22">Monte Carlo Method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2024 APA, all rights reserved). – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20230309 <i>Date Completed: </i>20241223 <i>Latest Revision: </i>20250924 – Name: DateUpdate Label: Update Code Group: Date Data: 20260130 – Name: DOI Label: DOI Group: ID Data: 10.1037/met0000538 – Name: AN Label: PMID Group: ID Data: 36892913 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=36892913 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1037/met0000538 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 1084 Subjects: – SubjectFull: Humans Type: general – SubjectFull: Least-Squares Analysis Type: general – SubjectFull: Data Interpretation, Statistical Type: general – SubjectFull: Monte Carlo Method Type: general – SubjectFull: Psychology methods Type: general – SubjectFull: Psychology standards Type: general – SubjectFull: Models, Statistical Type: general Titles: – TitleFull: Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee YR – PersonEntity: Name: NameFull: Pustejovsky JE IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2024 Dec Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 1939-1463 Numbering: – Type: volume Value: 29 – Type: issue Value: 6 Titles: – TitleFull: Psychological methods Type: main |
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