Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions
A range of indicators, such as the average variance extracted (AVE), is commonly used to validate constructs. In statistics, AVE is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error. These conventional indices are forme...
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| Published in | Communications in statistics. Simulation and computation Vol. 52; no. 4; pp. 1639 - 1650 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia
Taylor & Francis
03.04.2023
Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | A range of indicators, such as the average variance extracted (AVE), is commonly used to validate constructs. In statistics, AVE is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error. These conventional indices are formed by factor loadings resulting from estimated least squares or maximum likelihood regressions. Thus, a new proposition that provides new factor loadings may result in a more informative AVE index. Consequently, this study consists of the improvement of the index by using adaptive regressions. A Monte Carlo simulation study was performed considering different numbers of outliers generated by distributions with symmetry deviations and excess kurtosis and sample sizes defined as n = 50, 100, and 200. The conclusion was that, in formative structural models, the adaptive linear regression (ALR) method showed good efficiency for correctly specified models. The results obtained from the ALR method for models with specification errors showed low efficiency, as expected. |
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| AbstractList | A range of indicators, such as the average variance extracted (AVE), is commonly used to validate constructs. In statistics, AVE is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error. These conventional indices are formed by factor loadings resulting from estimated least squares or maximum likelihood regressions. Thus, a new proposition that provides new factor loadings may result in a more informative AVE index. Consequently, this study consists of the improvement of the index by using adaptive regressions. A Monte Carlo simulation study was performed considering different numbers of outliers generated by distributions with symmetry deviations and excess kurtosis and sample sizes defined as n = 50, 100, and 200. The conclusion was that, in formative structural models, the adaptive linear regression (ALR) method showed good efficiency for correctly specified models. The results obtained from the ALR method for models with specification errors showed low efficiency, as expected. |
| Author | dos Santos, Patricia Mendes Cirillo, Marcelo Ângelo |
| Author_xml | – sequence: 1 givenname: Patricia Mendes orcidid: 0000-0002-6989-7982 surname: dos Santos fullname: dos Santos, Patricia Mendes organization: Statistics Department, Federal University of Lavras – sequence: 2 givenname: Marcelo Ângelo orcidid: 0000-0003-2026-6802 surname: Cirillo fullname: Cirillo, Marcelo Ângelo organization: Statistics Department, Federal University of Lavras |
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| SubjectTerms | Adaptive regression Error analysis Kurtosis Monte Carlo simulation Multivariate statistical analysis Outliers Outliers (statistics) Structural equation model Structural equation modeling Structural models Variance |
| Title | Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions |
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