Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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 |