Cited by 2,263Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceStatistical Methods and Applications

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Structural equations modeling: Fit Indices, sample size, and advanced topics

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Iacobucci, Dawn·2010·Journal of Consumer Psychology

DOI 10.1016/j.jcps.2009.09.003

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Abstract

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.

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“09 [44,45].”
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“However, given that even trivial deviations from a perfect model are likely to produce a significant χ2 statistic with large sample sizes (Iacobucci, 2010), the “normed” χ2 statistic (χ2/df) was also examined.”
Background
Understanding motivational structures that differentially predict engagement and achievement in middle school science
Lee, C. S., Hayes, K. N., Seitz, J. C., DiStefano, R., & O’Connor, D. (2016) · International Journal of Science Education · doi:10.1080/09500693.2015.1136452 · Cited by 117
“However, because χ2 is sensitive to sample size and model complexity (Iacobucci, 2010; Kline, 2011), the absolute, relative, and comparative GOF indices were used to determine model fit.”
Method
Team participation and online gamer loyalty
Teng, C., & Chen, W. (2013) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2013.08.001 · Cited by 80
“The IFI and CFI indices exceeded .95, fulfilling the criteria provided by Bagozzi and Yi (1988) and Iacobucci (2010).”
Method
Customer experience and brand loyalty in the full-service hotel sector: the role of brand affect
Guan, J., Wang, W., Guo, Z., Chan, J. H., & Qi, X. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2020-1177 · Cited by 126
A Study on Millennial Purchase Intention of Green Products in India: Applying Extended Theory of Planned Behavior Model
Shukla, S. (2019) · Journal of Asia-Pacific Business · doi:10.1080/10599231.2019.1684171 · Cited by 100
“A maximum likelihood algorithm (Iacobucci, 2010) was employed to examine both the measurement and structural model as has been confirmed as a substantial method (Mohd Suki, 2016) of evaluating parameter complications.”
Method
Using structural equation modeling to examine pathways between perceived residential green space and mental health among internal migrants in China
Yang, M., Dijst, M., Faber, J., & Helbich, M. (2020) · Environmental Research · doi:10.1016/j.envres.2020.109121 · Cited by 104
“Following the suggestions of others (Iacobucci, 2010), the model goodness-of-fit was assessed by the following multiple indicators: goodness of fit index (GFI> 0.95), adjusted goodness of fit index (AGFI> 0.90), comparative fit index (CFI> 0.95), root mean square error of approximation…”
MethodInfluential
Customer loyalty in the fast food restaurants of Bangladesh
Uddin, M. B. (2019) · British Food Journal · doi:10.1108/bfj-02-2019-0140 · Cited by 85
Antecedents and consequences of passenger satisfaction with the airport
Bezerra, G. C. L., & Gomes, C. F. (2020) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2020.101766 · Cited by 99
“For model estimation, the covariance matrix and the maximum likelihood estimation (MLE) method were used (Byrne, 2010; Hair et al., 2018; Iacobucci, 2010; Kline, 2011).”
Method
Procedural and Distributive Fairness: Determinants of Overall Price Fairness
Ferguson, J. L., Ellen, P. S., & Bearden, W. O. (2013) · Journal of Business Ethics · doi:10.1007/s10551-013-1694-2 · Cited by 102
Pathways between parental and individual determinants of dental caries and dental visit behaviours among children: Validation of a new conceptual model.
Buldur, B. (2020) · Community Dentistry and Oral Epidemiology · doi:10.1111/cdoe.12530 · Cited by 83
Do Investors Value Higher Financial Reporting Quality, and Can Expanded Audit Reports Unlock This Value?
Elliott, W. B., Fanning, K., & Peecher, M. E. (2019) · The Accounting Review · doi:10.2308/accr-52508 · Cited by 107
Impact of noise on self-rated job satisfaction and health in open-plan offices: a structural equation modelling approach
Lee, P. J., Lee, B. K., Jeon, J. Y., Zhang, M., & Kang, J. (2015) · Ergonomics · doi:10.1080/00140139.2015.1066877 · Cited by 91
“It was assumed that the sample sizes for China and Korea were adequate, because previous studies had reported that a sample size of 100 is usually sufficient for convergence (Anderson and Gerbing 1984; Iacobucci 2010).”
Background
How technical features of virtual live shopping platforms affect purchase intention: Based on the theory of interactive media effects
Sun, Y., Zhong, Y., Zhang, Z., Wang, Y., & Zhu, M. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114189 · Cited by 83
Ten basic questions about structural equations modeling you should know the answers to – But perhaps you don't
Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2020.07.016 · Cited by 82
Work Characteristics or Workers’ Characteristics? An Input-Process-Output Perspective on Informal Workplace Learning of Blue-Collar Workers
Decius, J., Schaper, N., & Seifert, A. (2021) · Vocations and Learning · doi:10.1007/s12186-021-09265-5 · Cited by 122
“…present study, we additionally considered two alternative, separate submodels as a robustness check, following methodological recommendations (e.g., Iacobucci 2010; Weston and Gore 2006): on the one hand only with the antecedents and IWL, on the other hand only with IWL and the learning outcomes.”
BackgroundMethod
Path analysis in Mplus: A tutorial using a conceptual model of psychological and behavioral antecedents of bulimic symptoms in young adults
Barbeau, K., Boileau, K., Sarr, F., & Smith, K. (2019) · The Quantitative Methods for Psychology · doi:10.20982/tqmp.15.1.p038 · Cited by 102
“Type of data: Endogenous variables must be continuous or categorical data, which includes scale, interval, ordinal, or nominal data (Iacobucci, 2010).”
Background
Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology—Mediating Effects of Attitude
Wang, Y., Li-fu, J., & Mao, H. (2019) · Information Systems Frontiers · doi:10.1007/s10796-019-09909-x · Cited by 87
Supply chain risk mitigation competency: an individual-level knowledge-based perspective
Ambulkar, S., Blackhurst, J. V., & Cantor, D. E. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1070972 · Cited by 95
“The CFA analysis revealed an overall excellent fit for the measurement model: χ2 = 257.77 (df = 146, χ2/df = 1.77, p < 0.001), CFI = 0.94, TLI = 0.93, and RMSEA = 0.062 (0.049, 0.074) (Hu and Bentler 1999; Iacobucci 2010) indicating acceptable psychometric properties for all constructs.”
MethodInfluential
Factor Structure of the National Alzheimer's Coordinating Centers Uniform Dataset Neuropsychological Battery
Hayden, K. M., Jones, R. N., Zimmer, C., Plassman, B. L., Browndyke, J. N., Pieper, C. F., Warren, L. H., & Welsh-Bohmer, K. A. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181ffa76d · Cited by 80
The development of students' motivation in the transition from secondary to higher education: A longitudinal study
Kyndt, E., Coertjens, L., van Daal, T., Donche, V., Gijbels, D., & van Petegem, P. (2015) · Learning and Individual Differences · doi:10.1016/j.lindif.2015.03.001 · Cited by 77
The Impact of User-Generated Content on Tourist Visit Intentions: The Mediating Role of Destination Imagery
Aboalganam, K. M., AlFraihat, S.-F.-A., & Tarabieh, S. (2025) · Administrative Sciences · doi:10.3390/admsci15040117 · Cited by 76
Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals’ Health Technology Readiness: Initial Testing in a Cancer Rehabilitation Setting
Kayser, L., Rossen, S., Karnoe, A., Elsworth, G. R., Vibe-Petersen, J., Christensen, J. F., Ried‐Larsen, M., & Osborne, R. H. (2018) · Journal of Medical Internet Research · doi:10.2196/10377 · Cited by 85
“For example, Iacobucci [19], in reviewing the effect of sample size on convergence, numerical quality, and the typical fit indices used in CFA (and which are also available for EFA and exploratory structural equation modeling [ESEM] in Mplus), concluded that “It is of some comfort that SEM models…”
Do ongoing relationships buffer the effects of service recovery on customers' trust and loyalty?
dos Santos, C. P., & Basso, K. (2012) · International Journal of Bank Marketing · doi:10.1108/02652321211222540 · Cited by 78
Improving the Financial Security of National Health Insurance using Cloud-Based Blockchain Technology Application
Amponsah, A. A., Adekoya, A. F., & Weyori, B. A. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100081 · Cited by 91
Investigating the causes of delay in grain bin construction projects: the case of China
Chen, G.-X., Shan, M., Chan, A., Liu, X., & Zhao, Y.-Q. (2019) · International Journal of Construction Management · doi:10.1080/15623599.2017.1354514 · Cited by 74
“Also, according to Iacobucci (2010), SEM can still perform well even with small sample sizes (e.g. 50–100).”
Background
Evaluating the impact of security management practices on resilience capability in maritime firms—a relational perspective
Yang, C.-C., & Hsu, W.-L. (2017) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2017.06.005 · Cited by 74
Drinking motives and drinking behavior over time: A full cross-lagged panel study among adults.
Crutzen, R., Kuntsche, E. N., & Schelleman‐Offermans, K. (2012) · Psychology of Addictive Behaviors · doi:10.1037/a0029824 · Cited by 70
A Structural Equation Modeling Approach to Fatigue-related Risk Factors for Occupational Injury
Arlinghaus, A., Lombardi, D. A., Willetts, J. L., Folkard, S., & Christiani, D. C. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws219 · Cited by 75
“While the χ(2) test is likely to yield a model rejection in large samples, these model fit indicators provide information independent of the sample size (49, 50).”
Background
Children’s fine motor skills in kindergarten predict reading in grade 1
Suggate, S. P., Pufke, E., & Stoeger, H. (2019) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2018.12.015 · Cited by 105
Examining the relationship between place attachment and behavioral loyalty in an urban park setting
Plunkett, D., Fulthorp, K., & Paris, C. M. (2019) · Journal of Outdoor Recreation and Tourism · doi:10.1016/j.jort.2018.11.006 · Cited by 73
Peer-to-peer selling in online platforms: A salient business model for virtual entrepreneurship
Chandna, V., & Salimath, M. S. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.11.019 · Cited by 82
Revisiting the Development and Validation of the Authentic Leadership Questionnaire: Analytical Clarifications
Avolio, B. J., Wernsing, T. S., & Gardner, W. L. (2017) · Journal of Management · doi:10.1177/0149206317739960 · Cited by 89
“Further, during our review of the SEM literature, we learned of an ongoing debate concerning the appropriate use and weight given to the chi-square statistic due to its sensitivity to sample size, data nonnormality, and estimation method (for extensive information on the context and viewpoints, see Bentler, 2000; Boll…”
Method
Investigating the drivers of mobile learning acceptance among young adults in the World Heritage town of Luang Prabang, Laos
Poong, Y. S., Yamaguchi, S., & Takada, J. (2016) · Information Development · doi:10.1177/0266666916638136 · Cited by 77
“However, chisquare is sensitive to sample size (Hooper, Coughlan and Mullen, 2008; Iacobucci, 2010).”
Background

Authors: Iacobucci, D.
Affiliations: Vanderbilt University (US)
Corresponding author: Iacobucci, Dawn
ORCID: Iacobucci, Dawn
Venue: Journal of Consumer Psychology
Publisher: Wiley
Published: 2009-10-03
Volume: 20
Issue: 1
Pages: 90-98
ISSN: 1057-7408 (ISSN-L)
1532-7663
DOI: 10.1016/j.jcps.2009.09.003
OpenAlex: W2038318317
Cited by: 2,263
References: 0
Citations by year: 2026: 111 · 2025: 193 · 2024: 193 · 2023: 186 · 2022: 210 · 2021: 208 · 2020: 158 · 2019: 193 · 2018: 169 · 2017: 129 · 2016: 128 · 2015: 123 · 2014: 94 · 2013: 86 · 2012: 53
FWCI: 34.78
Type: journal article
Language: en
Open access: green
papers.ssrn.com
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Journal of Consumer Psychology (publishedVersion)
SSRN Electronic Journal (submittedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: structural equation modeling, model fit indices, sample size, moderators, longitudinal data, mediation, measurement model, structural model

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Structural equations modeling: Fit Indices, sample size, and advanced topics

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