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Articles 1 - 2 of 2
Full-Text Articles in Statistical Methodology
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Blair T. Johnson
In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. “meta-regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at …
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Russell T Warne
Exploratory factor analysis (EFA) has become a common procedure in educational and psychological research. In the course of performing an EFA, researchers often base the decision of how many factors to retain on the eigenvalues for the factors. However, many researchers do not realize that eigenvalues, like all sample statistics, are subject to sampling error, which means that confidence intervals (CIs) can be estimated for each eigenvalue. In the present article, we demonstrate two methods of estimating CIs for eigenvalues: one based on the mathematical properties of the central limit theorem, and the other based on bootstrapping. References to appropriate …