2015
DOI: 10.1037/a0037721
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Confidence interval estimation for standardized effect sizes in multilevel and latent growth modeling.

Abstract: Objective Multilevel and latent growth models are frequently used interchangeably to examine differences between groups in trajectories of outcomes from controlled clinical trials. The unstandardized coefficient for the effect from group to slope (the treatment effect) from such models can be converted to a standardized mean difference (Cohen's d) between the treatment and control groups at end of study. This article addresses the confidence interval (CI) for this effect size. Method Two sets of equations fo… Show more

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Cited by 113 publications
(109 citation statements)
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References 74 publications
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“…Effect sizes were referred to the standard benchmarks of “small,” “medium,” and “large” proposed by Cohen (1988). Confidence intervals around each d were computed using methods developed by Feingold (2015). The significance of interaction effects was tested using likelihood ratio chi square tests to compare nested models.…”
Section: Methodsmentioning
confidence: 99%
“…Effect sizes were referred to the standard benchmarks of “small,” “medium,” and “large” proposed by Cohen (1988). Confidence intervals around each d were computed using methods developed by Feingold (2015). The significance of interaction effects was tested using likelihood ratio chi square tests to compare nested models.…”
Section: Methodsmentioning
confidence: 99%
“…We calculated effect size estimates for the pre-post treatment design using recommendations put forth by Feingold [19]. Effect sizes (reported as Cohen’s d) were calculated for the group × wave interactions.…”
Section: Methodsmentioning
confidence: 99%
“…The latent intercept factor was also regressed on intervention status because (despite randomization) there were sometimes non-negligible intervention/control differences in baseline child behavior within the smaller classes (standardized differences ranged from 0.02 [class 2] to 0.52 [class 4]). The model-estimated intervention effect size in each latent class was computed by multiplying the coefficient relating intervention status to the slope factor by three (the number of time intervals) and dividing the result by the full-sample standard deviation in aggressive and oppositional behavior at baseline (SD = 0.34; Equation 7 in Feingold, 2015). Finally, to examine whether the effect of the FCU was moderated by latent class, the MODEL CONSTRAINT command was used to test the significance of differences in intervention status coefficient across each of the latent classes.…”
Section: Methodsmentioning
confidence: 99%