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Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models.

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Title: Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models.
Authors: Falk, Carl F.1, Biesanz, Jeremy C.2
Source: Structural Equation Modeling. Jan-Mar2015, Vol. 22 Issue 1, p24-38. 15p.
Subjects: Latent structure analysis, Common method variance, Variance inflation factors (Statistics), Statistical hypothesis testing, Confidence regions (Mathematics)
Abstract: Although much is known about the performance of recent methods for inference and interval estimation for indirect or mediated effects with observed variables, little is known about their performance in latent variable models. This article presents an extensive Monte Carlo study of 11 different leading or popular methods adapted to structural equation models with latent variables. Manipulated variables included sample size, number of indicators per latent variable, internal consistency per set of indicators, and 16 different path combinations between latent variables. Results indicate that some popular or previously recommended methods, such as the bias-corrected bootstrap and asymptotic standard errors had poorly calibrated Type I error and coverage rates in some conditions. Likelihood-based confidence intervals, the distribution of the product method, and the percentile bootstrap emerged as leading methods for both interval estimation and inference, whereas joint significance tests and the partial posterior method performed well for inference. [ABSTRACT FROM AUTHOR]
Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models.
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  Data: <searchLink fieldCode="JN" term="%22Structural+Equation+Modeling%22">Structural Equation Modeling</searchLink>. Jan-Mar2015, Vol. 22 Issue 1, p24-38. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Latent+structure+analysis%22">Latent structure analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Common+method+variance%22">Common method variance</searchLink><br /><searchLink fieldCode="DE" term="%22Variance+inflation+factors+%28Statistics%29%22">Variance inflation factors (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+regions+%28Mathematics%29%22">Confidence regions (Mathematics)</searchLink>
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  Data: Although much is known about the performance of recent methods for inference and interval estimation for indirect or mediated effects with observed variables, little is known about their performance in latent variable models. This article presents an extensive Monte Carlo study of 11 different leading or popular methods adapted to structural equation models with latent variables. Manipulated variables included sample size, number of indicators per latent variable, internal consistency per set of indicators, and 16 different path combinations between latent variables. Results indicate that some popular or previously recommended methods, such as the bias-corrected bootstrap and asymptotic standard errors had poorly calibrated Type I error and coverage rates in some conditions. Likelihood-based confidence intervals, the distribution of the product method, and the percentile bootstrap emerged as leading methods for both interval estimation and inference, whereas joint significance tests and the partial posterior method performed well for inference. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10705511.2014.935266
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        Text: English
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      – SubjectFull: Latent structure analysis
        Type: general
      – SubjectFull: Common method variance
        Type: general
      – SubjectFull: Variance inflation factors (Statistics)
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      – SubjectFull: Statistical hypothesis testing
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      – SubjectFull: Confidence regions (Mathematics)
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      – TitleFull: Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models.
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              Text: Jan-Mar2015
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