2011
DOI: 10.1080/09720510.2011.10701607
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Effect of the number of categories, number of time-points, and sample size on the recovery of random-effect ordinal regression model parameters

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“…Our use of ordinal regression is justified because DLLP progressions may be thought of as coarse representations of continuous underlying variables (Bauer & Sterba, 2011). In addition, treating ordinal outcomes as continuous may be problematic, and as the descriptives in Figure 1 show, our outcomes are not normally distributed (Ali et al, 2016;Bauer & Sterba, 2011;Hung & Huang, 2011). Finally, treating ordinal outcomes as continuous in scenarios when data are nested may exacerbate issues related to inflated or spurious estimates of random slope and random quadratic variances (see Bauer & Cai, 2009;and Bauer & Sterba, 2011, for more information), both of which are relevant to RQ1 concerning DLLP growth patterns.…”
Section: Bayesian Framework Overviewmentioning
confidence: 99%
“…Our use of ordinal regression is justified because DLLP progressions may be thought of as coarse representations of continuous underlying variables (Bauer & Sterba, 2011). In addition, treating ordinal outcomes as continuous may be problematic, and as the descriptives in Figure 1 show, our outcomes are not normally distributed (Ali et al, 2016;Bauer & Sterba, 2011;Hung & Huang, 2011). Finally, treating ordinal outcomes as continuous in scenarios when data are nested may exacerbate issues related to inflated or spurious estimates of random slope and random quadratic variances (see Bauer & Cai, 2009;and Bauer & Sterba, 2011, for more information), both of which are relevant to RQ1 concerning DLLP growth patterns.…”
Section: Bayesian Framework Overviewmentioning
confidence: 99%