TY - JOUR
T1 - dynamic
T2 - An R Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis
AU - Wolf, Melissa G.
AU - McNeish, Daniel
N1 - Funding Information:
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through R305D220003 to Arizona State University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. We thank GJ Peters for his thoughtful input related to this article and the software package upon which it is based. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institutions, the Institute of Educational Sciences, or the US Department of Education is not intended and should not be inferred.
Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.
PY - 2023
Y1 - 2023
N2 - To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of.08,.06, and.96, respectively, established by Hu and Bentler (Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55). However, these indices are affected by model characteristics and their sensitivity to misfit can change across models. Decisions about model fit can therefore be improved by tailoring cutoffs to each model. The methodological literature has proposed methods for deriving customized cutoffs, although it can require knowledge of linear algebra and Monte Carlo simulation. Given that many empirical researchers do not have training in these technical areas, empirical studies largely continue to rely on fixed benchmarks even though they are known to generalize poorly and can be poor arbiters of fit. To address this, this paper introduces the R package, dynamic, to make computation of dynamic fit index cutoffs (which are tailored to the user’s model) more accessible to empirical researchers. dynamic heavily automatizes this process and only requires a lavaan object to automatically conduct several custom Monte Carlo simulations and output fit index cutoffs designed to be sensitive to misfit with the user’s model characteristics.
AB - To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of.08,.06, and.96, respectively, established by Hu and Bentler (Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55). However, these indices are affected by model characteristics and their sensitivity to misfit can change across models. Decisions about model fit can therefore be improved by tailoring cutoffs to each model. The methodological literature has proposed methods for deriving customized cutoffs, although it can require knowledge of linear algebra and Monte Carlo simulation. Given that many empirical researchers do not have training in these technical areas, empirical studies largely continue to rely on fixed benchmarks even though they are known to generalize poorly and can be poor arbiters of fit. To address this, this paper introduces the R package, dynamic, to make computation of dynamic fit index cutoffs (which are tailored to the user’s model) more accessible to empirical researchers. dynamic heavily automatizes this process and only requires a lavaan object to automatically conduct several custom Monte Carlo simulations and output fit index cutoffs designed to be sensitive to misfit with the user’s model characteristics.
KW - Confirmatory factor analysis
KW - R package
KW - fit indices
UR - http://www.scopus.com/inward/record.url?scp=85148436561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85148436561&partnerID=8YFLogxK
U2 - 10.1080/00273171.2022.2163476
DO - 10.1080/00273171.2022.2163476
M3 - Article
C2 - 36787513
AN - SCOPUS:85148436561
SN - 0027-3171
VL - 58
SP - 189
EP - 194
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
IS - 1
ER -