The issue of statistical power for overall model fit in evaluating structural equation models
Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM) is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA) index of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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"Nicolae Titulescu" University of Bucharest
2015-06-01
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| Series: | Computational Methods in Social Sciences |
| Subjects: | |
| Online Access: | http://cmss.univnt.ro/wp-content/uploads/vol/split/vol_III_issue_1/CMSS_vol_III_issue_1_art.003.pdf |
| _version_ | 1797764920543019008 |
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| author | Richard HERMIDA Joseph N. LUCHMAN Vias NICOLAIDES Cristina WILCOX |
| author_facet | Richard HERMIDA Joseph N. LUCHMAN Vias NICOLAIDES Cristina WILCOX |
| author_sort | Richard HERMIDA |
| collection | DOAJ |
| description | Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM) is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA) index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O) Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues. |
| first_indexed | 2024-03-12T20:02:40Z |
| format | Article |
| id | doaj.art-4cd8dd2db59a4925a5a52256b3a3c0e9 |
| institution | Directory Open Access Journal |
| issn | 2344-1232 2344-1232 |
| language | English |
| last_indexed | 2024-03-12T20:02:40Z |
| publishDate | 2015-06-01 |
| publisher | "Nicolae Titulescu" University of Bucharest |
| record_format | Article |
| series | Computational Methods in Social Sciences |
| spelling | doaj.art-4cd8dd2db59a4925a5a52256b3a3c0e92023-08-02T02:16:39Zeng"Nicolae Titulescu" University of BucharestComputational Methods in Social Sciences2344-12322344-12322015-06-01312542The issue of statistical power for overall model fit in evaluating structural equation modelsRichard HERMIDA0Joseph N. LUCHMAN1Vias NICOLAIDES2Cristina WILCOX3George Mason University, rhermida3@gmail.com, 3575 Owasso Street, Shoreview, MN, USA, 55126George Mason University, jluchman@gmu.edu, 4400 University Drive, Fairfax, VA, USA, 22030George Mason University, vnicolai@gmu.edu, 4400 University Drive, Fairfax, VA, USA, 22030George Mason University, cfwilcox9@gmu.edu, 4400 University Drive, Fairfax, VA, USA, 22030Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM) is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA) index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O) Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.http://cmss.univnt.ro/wp-content/uploads/vol/split/vol_III_issue_1/CMSS_vol_III_issue_1_art.003.pdfStatistical PowerStructural Equation ModelingMeasurementStatisticsResearch Methods. |
| spellingShingle | Richard HERMIDA Joseph N. LUCHMAN Vias NICOLAIDES Cristina WILCOX The issue of statistical power for overall model fit in evaluating structural equation models Computational Methods in Social Sciences Statistical Power Structural Equation Modeling Measurement Statistics Research Methods. |
| title | The issue of statistical power for overall model fit in evaluating structural equation models |
| title_full | The issue of statistical power for overall model fit in evaluating structural equation models |
| title_fullStr | The issue of statistical power for overall model fit in evaluating structural equation models |
| title_full_unstemmed | The issue of statistical power for overall model fit in evaluating structural equation models |
| title_short | The issue of statistical power for overall model fit in evaluating structural equation models |
| title_sort | issue of statistical power for overall model fit in evaluating structural equation models |
| topic | Statistical Power Structural Equation Modeling Measurement Statistics Research Methods. |
| url | http://cmss.univnt.ro/wp-content/uploads/vol/split/vol_III_issue_1/CMSS_vol_III_issue_1_art.003.pdf |
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