A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models

Subhash Sharma, Soumen Mukherjee, Ajith Kumar, William R. Dillon

Research output: Contribution to journalArticle

288 Citations (Scopus)

Abstract

In this paper, we used simulations to investigate the effect of sample size, number of indicators, factor loadings, and factor correlations on frequencies of the acceptance/rejection of models (true and misspecified) when selected goodness-of-fit indices were compared with prespecified cutoff values. We found the percent of true models accepted when a goodness-of-fit index was compared with a prespecified cutoff value was affected by the interaction of the sample size and the total number of indicators. In addition, for the Tucker-Lewis index (TLI) and the relative noncentrality index (RNI), model acceptance percentages were affected by the interaction of sample size and size of factor loadings. For misspecified models, model acceptance percentages were affected by the interaction of the number of indicators and the degree of model misspecification. This suggests that researchers should use caution in using cutoff values for evaluating model fit. However, the study suggests that researchers who prefer to use prespecified cutoff values should use TLI, RNI, NNCP, and root-mean-square-error-of-approximation (RMSEA) to assess model fit. The use of GFI should be discouraged.

Original languageEnglish (US)
Pages (from-to)935-943
Number of pages9
JournalJournal of Business Research
Volume58
Issue number7
DOIs
StatePublished - Jul 2005

Fingerprint

Sample Size
Research Personnel
Simulation study
Sample size
Acceptance
Interaction
Factor loadings
Goodness of fit

Keywords

  • Confirmatory factor analysis
  • Goodness-of-fit-indices
  • Simulation
  • Structural equation modeling

ASJC Scopus subject areas

  • Marketing
  • Applied Psychology

Cite this

A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. / Sharma, Subhash; Mukherjee, Soumen; Kumar, Ajith; Dillon, William R.

In: Journal of Business Research, Vol. 58, No. 7, 07.2005, p. 935-943.

Research output: Contribution to journalArticle

@article{548992d3275747f88a3e37991e7566b2,
title = "A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models",
abstract = "In this paper, we used simulations to investigate the effect of sample size, number of indicators, factor loadings, and factor correlations on frequencies of the acceptance/rejection of models (true and misspecified) when selected goodness-of-fit indices were compared with prespecified cutoff values. We found the percent of true models accepted when a goodness-of-fit index was compared with a prespecified cutoff value was affected by the interaction of the sample size and the total number of indicators. In addition, for the Tucker-Lewis index (TLI) and the relative noncentrality index (RNI), model acceptance percentages were affected by the interaction of sample size and size of factor loadings. For misspecified models, model acceptance percentages were affected by the interaction of the number of indicators and the degree of model misspecification. This suggests that researchers should use caution in using cutoff values for evaluating model fit. However, the study suggests that researchers who prefer to use prespecified cutoff values should use TLI, RNI, NNCP, and root-mean-square-error-of-approximation (RMSEA) to assess model fit. The use of GFI should be discouraged.",
keywords = "Confirmatory factor analysis, Goodness-of-fit-indices, Simulation, Structural equation modeling",
author = "Subhash Sharma and Soumen Mukherjee and Ajith Kumar and Dillon, {William R.}",
year = "2005",
month = "7",
doi = "10.1016/j.jbusres.2003.10.007",
language = "English (US)",
volume = "58",
pages = "935--943",
journal = "Journal of Business Research",
issn = "0148-2963",
publisher = "Elsevier Inc.",
number = "7",

}

TY - JOUR

T1 - A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models

AU - Sharma, Subhash

AU - Mukherjee, Soumen

AU - Kumar, Ajith

AU - Dillon, William R.

PY - 2005/7

Y1 - 2005/7

N2 - In this paper, we used simulations to investigate the effect of sample size, number of indicators, factor loadings, and factor correlations on frequencies of the acceptance/rejection of models (true and misspecified) when selected goodness-of-fit indices were compared with prespecified cutoff values. We found the percent of true models accepted when a goodness-of-fit index was compared with a prespecified cutoff value was affected by the interaction of the sample size and the total number of indicators. In addition, for the Tucker-Lewis index (TLI) and the relative noncentrality index (RNI), model acceptance percentages were affected by the interaction of sample size and size of factor loadings. For misspecified models, model acceptance percentages were affected by the interaction of the number of indicators and the degree of model misspecification. This suggests that researchers should use caution in using cutoff values for evaluating model fit. However, the study suggests that researchers who prefer to use prespecified cutoff values should use TLI, RNI, NNCP, and root-mean-square-error-of-approximation (RMSEA) to assess model fit. The use of GFI should be discouraged.

AB - In this paper, we used simulations to investigate the effect of sample size, number of indicators, factor loadings, and factor correlations on frequencies of the acceptance/rejection of models (true and misspecified) when selected goodness-of-fit indices were compared with prespecified cutoff values. We found the percent of true models accepted when a goodness-of-fit index was compared with a prespecified cutoff value was affected by the interaction of the sample size and the total number of indicators. In addition, for the Tucker-Lewis index (TLI) and the relative noncentrality index (RNI), model acceptance percentages were affected by the interaction of sample size and size of factor loadings. For misspecified models, model acceptance percentages were affected by the interaction of the number of indicators and the degree of model misspecification. This suggests that researchers should use caution in using cutoff values for evaluating model fit. However, the study suggests that researchers who prefer to use prespecified cutoff values should use TLI, RNI, NNCP, and root-mean-square-error-of-approximation (RMSEA) to assess model fit. The use of GFI should be discouraged.

KW - Confirmatory factor analysis

KW - Goodness-of-fit-indices

KW - Simulation

KW - Structural equation modeling

UR - http://www.scopus.com/inward/record.url?scp=12144272522&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12144272522&partnerID=8YFLogxK

U2 - 10.1016/j.jbusres.2003.10.007

DO - 10.1016/j.jbusres.2003.10.007

M3 - Article

VL - 58

SP - 935

EP - 943

JO - Journal of Business Research

JF - Journal of Business Research

SN - 0148-2963

IS - 7

ER -