Oscar L Olvera Astivia
Biographic Data
| ID | 6629965 |
|---|---|
| NAME | Oscar L Olvera Astivia |
| GIVEN NAMES | Oscar L Olvera |
| FAMILY NAME | Astivia |
| SIGNATURE | ASTIVIA O L O |
| AFFILIATIONS | University of South Florida |
| ORCID | 0000-0002-5744-2403 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Polychoric correlations under the assumption of elliptical latent traits
Categorical structural equation models (cat‐SEM) typically rely on tetrachoric/polychoric correlations under a latent multivariate normality assumption. This can be generalized to an elliptical latent trait, whose radial symmetry justifies treating a single correlation parameter as the target of estimation. This article makes two contributions. First, it introduces an elliptical sieve estimator that profiles the latent correlation over a non‐para…
Advances in the Study of Smoothing Methods for Correlation Matrices in Confirmatory Factor Analysis
The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores
Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of “passing” such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target group…
The Role of Item Distributions on Reliability Estimation
Simulations concerning the distributional assumptions of coefficient alpha are contradictory. To provide a more principled theoretical framework, this article relies on the Fréchet–Hoeffding bounds, in order to showcase that the distribution of the items play a role on the estimation of correlations and covariances. More specifically, these bounds restrict the theoretical correlation range [−1, 1] such that certain correlation structures may be u…
Centering in Multiple Regression Does Not Always Reduce Multicollinearity
Within the context of moderated multiple regression, mean centering is recommended both to simplify the interpretation of the coefficients and to reduce the problem of multicollinearity. For almost 30 years, theoreticians and applied researchers have advocated for centering as an effective way to reduce the correlation between variables and thus produce more stable estimates of regression coefficients. By reviewing the theory on which this recomm…
A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes
To further understand the properties of data-generation algorithms for multivariate, nonnormal data, two Monte Carlo simulation studies comparing the Vale and Maurelli method and the Headrick fifth-order polynomial method were implemented. Combinations of skewness and kurtosis found in four published articles were run and attention was specifically paid to the quality of the sample estimates of univariate skewness and kurtosis. In the first study…
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A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes
To further understand the properties of data-generation algorithms for multivariate, nonnormal data, two Monte Carlo simulation studies comparing the Vale and Maurelli method and the Headrick fifth-order polynomial method were implemented. Combinations of skewness and kurtosis found in four published articles were run and attention was specifically paid to the quality of the sample estimates of univariate skewness and kurtosis. In the first study…
Centering in Multiple Regression Does Not Always Reduce Multicollinearity
Within the context of moderated multiple regression, mean centering is recommended both to simplify the interpretation of the coefficients and to reduce the problem of multicollinearity. For almost 30 years, theoreticians and applied researchers have advocated for centering as an effective way to reduce the correlation between variables and thus produce more stable estimates of regression coefficients. By reviewing the theory on which this recomm…
The Role of Item Distributions on Reliability Estimation
Simulations concerning the distributional assumptions of coefficient alpha are contradictory. To provide a more principled theoretical framework, this article relies on the Fréchet–Hoeffding bounds, in order to showcase that the distribution of the items play a role on the estimation of correlations and covariances. More specifically, these bounds restrict the theoretical correlation range [−1, 1] such that certain correlation structures may be u…
The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores
Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of “passing” such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target group…
Advances in the Study of Smoothing Methods for Correlation Matrices in Confirmatory Factor Analysis
Polychoric correlations under the assumption of elliptical latent traits
Categorical structural equation models (cat‐SEM) typically rely on tetrachoric/polychoric correlations under a latent multivariate normality assumption. This can be generalized to an elliptical latent trait, whose radial symmetry justifies treating a single correlation parameter as the target of estimation. This article makes two contributions. First, it introduces an elliptical sieve estimator that profiles the latent correlation over a non‐para…
Mathematics (5 works) · Statistics (5 works) · Advanced Statistical Methods and Models (4 works) · Econometrics (4 works) · Computer Science (3 works) · Psychology (3 works) · Statistical Methods and Inference (3 works) · Correlation (2 works) · Psychometrics (2 works) · Random variable (2 works)