PMID- 37561492
OWN - NLM
STAT- Publisher
LR  - 20230810
IS  - 1939-1463 (Electronic)
IS  - 1082-989X (Linking)
DP  - 2023 Aug 10
TI  - Equivalence testing to judge model fit: A Monte Carlo simulation.
LID - 10.1037/met0000591 [doi]
AB  - Decades of published methodological research have shown the chi-square test of 
      model fit performs inconsistently and unreliably as a determinant of structural 
      equation model (SEM) fit. Likewise, SEM indices of model fit, such as comparative 
      fit index (CFI) and root-mean-square error of approximation (RMSEA) also perform 
      inconsistently and unreliably. Despite rather unreliable ways to statistically 
      assess model fit, researchers commonly rely on these methods for lack of a 
      suitable inferential alternative. Marcoulides and Yuan (2017) have proposed the 
      first inferential test of SEM fit in many years: an equivalence test adaptation 
      of the RMSEA and CFI indices (i.e., RMSEA(t) and CFI(t)). However, the ability of 
      this equivalence testing approach to accurately judge acceptable and unacceptable 
      model fit has not been empirically tested. This fully crossed Monte Carlo 
      simulation evaluated the accuracy of equivalence testing combining many of the 
      same independent variable (IV) conditions used in previous fit index simulation 
      studies, including sample size (N = 100-1,000), model specification (correctly 
      specified or misspecified), model type (confirmatory factor analysis [CFA], path 
      analysis, or SEM), number of variables analyzed (low or high), data distribution 
      (normal or skewed), and missing data (none, 10%, or 25%). Results show 
      equivalence testing performs rather inconsistently and unreliably across IV 
      conditions, with acceptable or unacceptable RMSEA(t) and CFIt model fit index 
      values often being contingent on complex interactions among conditions. 
      Proportional z-tests and logistic regression analyses indicated that equivalence 
      tests of model fit are problematic under multiple conditions, especially those 
      where models are mildly misspecified. Recommendations for researchers are 
      offered, but with the provision that they be used with caution until more 
      research and development is available. (PsycInfo Database Record (c) 2023 APA, 
      all rights reserved).
FAU - Peugh, James L
AU  - Peugh JL
AUID- ORCID: 0000-0002-1134-5667
AD  - Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's 
      Hospital Medical Center.
FAU - Litson, Kaylee
AU  - Litson K
AD  - Instructional Technology and Learning Sciences Department, Emma Eccles Jones 
      College of Education and Human Services, Utah State University.
FAU - Feldon, David F
AU  - Feldon DF
AD  - Instructional Technology and Learning Sciences Department, Emma Eccles Jones 
      College of Education and Human Services, Utah State University.
LA  - eng
PT  - Journal Article
DEP - 20230810
PL  - United States
TA  - Psychol Methods
JT  - Psychological methods
JID - 9606928
SB  - IM
EDAT- 2023/08/10 12:42
MHDA- 2023/08/10 12:42
CRDT- 2023/08/10 11:34
PHST- 2023/08/10 12:42 [medline]
PHST- 2023/08/10 12:42 [pubmed]
PHST- 2023/08/10 11:34 [entrez]
AID - 2023-97070-001 [pii]
AID - 10.1037/met0000591 [doi]
PST - aheadofprint
SO  - Psychol Methods. 2023 Aug 10. doi: 10.1037/met0000591.