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Structural equations modeling: Fit Indices, sample size, and advanced topics
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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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“The results in Figure 1 suggested excellent model fit (Iacobucci 2010: p. 91): χ 2 (32) = 97.994(p=0.000); and χ 2 /df = 3.06.Further, GFI = .968;CFI = .984;RMSEA = .061;and SRMR = .0278;thus corroborating the excellent model fit to the data”
From Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation (2014) · cites this paper as Iacobucci 2010
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“However, according to the Chi-square test, the model fit was not acceptable (Miles and Shevlin, 2007; Iacobucci, 2010).”
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“95, the model can be considered to fit even better [132], which is the case for both of the countries: 0.”
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“Although the name is similar to the RMSEA, the two indices are quite different (Iacobucci, 2009).”
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“The X2 should be non-significant, but due to sensitivity to sample size this is often difficult (Iacobucci, 2010, Millsap, 2007).”
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“SEM models with small sample sizes can perform well with reliable measures (Iacobucci 2010); therefore, we used reliable, validated measures and limited the models to variables with strong empirical and theoretical support.”
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“121 statistics, hence, in general, rules-of-thumb are applied to assess goodness-of-fit (Iacobucci, 2010).”
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“…approach when the moderator is categorical, and especially when it is dichotomous, as is the case in the current model where the moderator is child gender (Brody et al., 2006; Fabrigar & Wegener, 2014; Iacobucci, 2010), and this is a strategy we have successfully used in other studies (*BLINDED*).”
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“…our references section: Hair et al. (2019), 10 Bollen (1990), 11 Hu and Bentler (1998), 12 Kenny and McCoach (2003), 13 Peugh and Feldon (2020), 14 Iacobucci (2010), 15 These differing approaches cause absolute and incremental fit indices to pose differing considerations when interpreting model…”
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“0) (Iacobucci, 2010), all the goodness-of-fit indices for the two-factor model were within the recommended cutoff ranges (SRMR 6 .”
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Galhardo, A., Cunha, M., & Pinto-Gouveia, J. (2013) · Research in Nursing and Health · doi:10.1002/nur.21516 · Cited by 23
“…values of 0.90 or greater indicate a good fit; SRMR values of 0.09 or lower suggest a good fit; and RMSEA values of 0.05 or lower suggest excellent fit, values of 0.08 or lower suggest good fit, and values of 0.10 or lower reflect marginal fit (Brown & Cudeck, 1993; Iacobucci, 2010; Kline, 2005).”
BackgroundInfluentialOnline customer service and retail type-product congruence
Suryandari, R., & Paswan, A. (2014) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2013.08.004 · Cited by 23
Köhler, L., & Han, S. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104745 · Cited by 22
Carchano, M., Carrasco, I., González, Á., & Sáez-Martínez, F. (2024) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2894 · Cited by 22
Drawing goals nearer: Using the goal-gradient perspective to increase online game usage
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Huijts, N., Contzen, N., & Roeser, S. (2022) · Energy Policy · doi:10.1016/j.enpol.2022.112963 · Cited by 22
“…have a good fit (χ2 = 956.61, df = 65, χ2/df = 14.72, p = .000, CFI = 0.664, RMSEA = 0.212 (90% CI: 0.201, 0.223), SRMR = 0.123; Hu and Bentler, 1999; Iacobucci, 2010; Kline, 2016)(χ2 = 956.61, df = 65, χ2/df = 14.72, p = .000, CFI = 0.664, RMSEA = 0.212 (90% CI: 0.201, 0.223), SRMR = 0.123; Hu…”
MethodInfluentialDeng, C., Yang, J., Su, Z., & Zhang, S. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.06.047 · Cited by 22
Gârdan, D. A., Epuran, G., Paștiu, C. A., Gârdan, I. P., Jiroveanu, D. C., Tecău, A. S., & Prihoanca, D. (2021) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer16070157 · Cited by 37
Letkiewicz, A. M., Funkhouser, C. J., & Shankman, S. A. (2021) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2021.105140 · Cited by 28
Xue, J. F., Li, G., & Li, N. (2021) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2021.1914564 · Cited by 24
“According to Iacobucci (2010) and Doll, Xia, and Torkzadeh (1994), the model fitness index was within the acceptable thresholds (Chi-square/df = 1.939, RMSEA = 0.056, GFI = 0.866, CFI = 0.955, NFI = 0.912). Although chi-square value was significant at the level of 0.00, it was largely influenced by the sample size (Ne…”
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“According to prior research, CFI < 0.85 and RMSEA > 0.08 indicate poor fit (Iacobucci 2010; Rigdon 1996).”
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Yoder, J. R., Grady, M. D., Brown, A., & Dillard, R. (2019) · Sexual Abuse · doi:10.1177/1079063218821108 · Cited by 26
“95) are additional indicators of a good fitting model (Brown, 2006; Hu & Bentler, 1999; Iacobucci, 2010).”
BackgroundMethodLee, D. (2019) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-09-2018-0373 · Cited by 22
Yoder, J. R., Grady, M. D., & Precht, M. (2019) · Journal of Child Sexual Abuse · doi:10.1080/10538712.2019.1588819 · Cited by 25
“The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) have good fit cutoffs of > .95 (Brown, 2006; Hu & Bentler, 1999; Iacobucci, 2010).”
BackgroundAssessing timebanking use and coordination: implications for service exchange tools
Yuan, C. W., Hanrahan, B. V., & Carroll, J. M. (2018) · Information Technology and People · doi:10.1108/itp-09-2017-0311 · Cited by 26
Han, J., Kang, H., & Kwon, G. H. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.08.023 · Cited by 23
Sustainable Adaptation of New Technology — The Case of Humanoids Used for the Care of Older AdultsPreprint
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“There is a consensus among researchers regarding the basic measures of model fit [81].”
BackgroundInvestigating virtual community participation and promotion from a social influence perspective
Hsu, L.-C., Wang, K.-Y., & Chih, W.-H. (2018) · Industrial management & data systems · doi:10.1108/imds-10-2017-0477 · Cited by 22
Grano, C., Fernandes, M., Bucci, S., Aminoff, D., Lucidi, F., & Violani, C. (2018) · Colorectal Disease · doi:10.1111/codi.14259 · Cited by 22
Aesthetic Experience as an Essential Factor to Trigger Positive Environmental Consciousness
Wang, P.-C., & Yu, C.-Y. (2018) · doi:10.3390/su10041098 · Cited by 22
Metadata
- 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
- 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
- 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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