A comparison of statistical selection strategies for univariate and bivariate log-linear models.
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| Title: | A comparison of statistical selection strategies for univariate and bivariate log-linear models. |
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| Authors: | Moses, Tim (AUTHOR), Holland, Paul W. (AUTHOR) |
| Source: | British Journal of Mathematical & Statistical Psychology. Nov2010, Vol. 63 Issue 3, p557-574. 18p. 13 Charts, 4 Graphs. |
| Subjects: | Variate difference method, Psychometrics, Chi-squared test, Quantitative research, Log-linear models, Parameter estimation, Test scoring |
| Abstract: | In this study, eight statistical selection strategies were evaluated for selecting the parameterizations of log‐linear models used to model the distributions of psychometric tests. The selection strategies included significance tests based on four chi‐squared statistics (likelihood ratio, Pearson, Freeman–Tukey, and Cressie–Read) and four additional strategies (Akaike information criterion (AIC), Bayesian information criterion (BIC), consistent Akaike information criterion (CAIC), and a measure attributed to Goodman). The strategies were evaluated in simulations for different log‐linear models of univariate and bivariate test‐score distributions and two sample sizes. Results showed that all eight selection strategies were most accurate for the largest sample size considered. For univariate distributions, the AIC selection strategy was especially accurate for selecting the correct parameterization of a complex log‐linear model and the likelihood ratio chi‐squared selection strategy was the most accurate strategy for selecting the correct parameterization of a relatively simple log‐linear model. For bivariate distributions, the likelihood ratio chi‐squared, Freeman–Tukey chi‐squared, BIC, and CAIC selection strategies had similarly high selection accuracies. [ABSTRACT FROM AUTHOR] |
| Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell 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: | Psychology and Behavioral Sciences Collection |
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| Abstract: | In this study, eight statistical selection strategies were evaluated for selecting the parameterizations of log‐linear models used to model the distributions of psychometric tests. The selection strategies included significance tests based on four chi‐squared statistics (likelihood ratio, Pearson, Freeman–Tukey, and Cressie–Read) and four additional strategies (Akaike information criterion (AIC), Bayesian information criterion (BIC), consistent Akaike information criterion (CAIC), and a measure attributed to Goodman). The strategies were evaluated in simulations for different log‐linear models of univariate and bivariate test‐score distributions and two sample sizes. Results showed that all eight selection strategies were most accurate for the largest sample size considered. For univariate distributions, the AIC selection strategy was especially accurate for selecting the correct parameterization of a complex log‐linear model and the likelihood ratio chi‐squared selection strategy was the most accurate strategy for selecting the correct parameterization of a relatively simple log‐linear model. For bivariate distributions, the likelihood ratio chi‐squared, Freeman–Tukey chi‐squared, BIC, and CAIC selection strategies had similarly high selection accuracies. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00071102 |
| DOI: | 10.1348/000711009X478580 |
