A taxonomy of path-related goodness-of-fit indices and recommended criterion values.

Lance, Charles E.; Beck, Stefanie S.; Fan, Yi; Carter, Nathan T.

doi:10.1037/met0000068

Cited by 27 publications

(77 citation statements)

References 49 publications

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“…For example, in the most extreme cases models with six and five paths incorrectly omitted had one or more mean global fit values that would lead to favorable evaluation. This pattern of results was replicated by Lance, Beck, Fan, and Carter (2016). In terms of real data, McDonald and Ho (2002) were the first to investigate empirically, and they found that in 14 studies from top psychology journals reporting adequate information for analysis, nearly all demonstrated acceptable global fit, but only a few yielded adequate path model fit.…”

Section: Comments Recommendations and Future Directions For Conditiomentioning

confidence: 79%

“…Its empirical performance was supported in a simulation study by Williams and O’Boyle (2011), who found that the RMSEA-P successfully distinguished between correctly and incorrectly specified models in all but one example that was atypical in that it was based on a small number of indicators per latent variable. More recently, Lance et al (2016) reported less positive findings for the RMSEA-P, describing that with their simulation study the RMSEA-P values for moderately misspecified models (three true paths omitted) were less than .08, which would lead to these models being retained. More positive results for the RMSEA-P have been found by Williams and Williams (2017), who first reanalyzed data from Lance et al and reached a more favorable assessment.…”

Section: Steps For Improved Theory Testing and Model Evaluationmentioning

confidence: 94%

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Condition 9 and 10 Tests of Model Confirmation: A Review of James, Mulaik, and Brett (1982) and Contemporary Alternatives

Williams

O’Boyle

2017

Organizational Research Methods

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Structural equation modeling (SEM) serves as one of the most important advances in the social sciences in the past 40 years. Through a combination of factor analysis and path analysis, SEM allows organizational researchers to test causal models while accounting for random and nonrandom (bias) measurement error. SEM is now one of the most commonly used analytic techniques and its modern day ubiquity can be traced in large part to a series of intellectual contributions by Larry James. The current article focuses on the seminal work, James, Mulaik, and Brett (1982), and the unique contribution of the “conditions” required for appropriate confirmatory inference with the path and latent variable models. We discuss the importance of James et al.’s Condition 9 and 10 tests, systematically review 14 years of studies using SEM in leading management journals and reanalyze results based on new techniques that extend James et al. (1982), and conclude with suggestions for improved Condition 9 and 10 assessments.

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Section: Comments Recommendations and Future Directions For Conditiomentioning

confidence: 79%

Section: Steps For Improved Theory Testing and Model Evaluationmentioning

confidence: 94%

Condition 9 and 10 Tests of Model Confirmation: A Review of James, Mulaik, and Brett (1982) and Contemporary Alternatives

Williams

O’Boyle

2017

Organizational Research Methods

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“…To summarize, we evaluated whether 2016-2017 Behavior Genetic papers met the standard for best practice (5%), better practice (50%), and acceptable practice (72.5%). Very few papers met the best standard (Appelbaum et al, 2018;Lance et al, 2016;, which recommended that multiple indices across classes (parsimonious, relative, and absolute) be used in conjunction with the χ² statistic. We believe that these recommendations are both generous and reasonable across all SEM models.…”

Section: Discussionmentioning

confidence: 99%

Fitting Problems: Evaluating Model Fit in Behavior Genetic Models

Garrison

2021

Preprint

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In behavior genetics, like many fields, researchers must decide whether their models adequately explain their data – whether their models “fit” at some satisfactory level. Well-fitting models are compelling, whereas poorly-fitting models are not (Rodgers & Rowe, 2002). Oftentimes, researchers evaluate model fit by employing “universal” rules of thumb (e.g., Hu and Bentler, 1999). However, these rules are not universal, and should be treated as model specific (Kang et al., 2016). Accordingly, we focused on developing fit criteria emulating Hu and Bentler (1999) for classic univariate models (ACE; CE; AE) by fitting simulated twin data with correctly- and incorrectly-specified models. Ideal criteria should consistently accept correct models and reject incorrect models. Classic ACE models were indistinguishable and virtually all fit indices were non-informative because (or especially when) they were obtained in saturated models. For non-ACE models, criteria were informative. Nevertheless, every fit metric employed, except TLI, differed markedly across models and/or conditions. Universal solutions remain elusive, but promising and valid approaches include nested model comparisons, increasing degrees of freedom, and ruthless skepticism.

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“…Within the context of structural equation modeling, the guidelines for best practice are broad (Appelbaum et al, 2018;Lance et al, 2016;. For evaluating model fit, best practice recommends that multiple indices across classes be used in conjunction with the χ² statistic.…”

Section: Best Practices For Evaluating Model Fitmentioning

confidence: 99%