Meta-Analysis Procedures Studies were included in the meta-analysis if they utilized a treatment-comparison or quasi-experimental design and reported sufficient information to allow effect sizes to be computed. Some research reports contained studies with multiple treatment/comparison groups. In total, 10 research reports containing 22 distinct treatment/comparison group contrasts were included in the meta-analysis. Because all studies utilized multiple outcome measures, these measures were categorized by type (reading comprehension, fluency, word, word fluency, and spelling), and a separate meta-analysis was conducted for each measure type. In cases where a study included more than one outcome measure within each category, effect sizes were averaged and the average and its standard error were included in the meta-analysis. A random-effects model was used to analyze effect sizes. Recent methodological innovations in meta-analysis such as multi-level modeling ([MLM] Hox, 2002) and structural equation modeling ([SEM] Cheung, 2008) were considered as approaches to the random-effects analyses of the effect sizes. However, the small number of effect sizes in each of the analyses (n = 5 to 22 effect sizes depending on outcome type) presented a significant limitation to the implementation of MLM or SEM. Therefore, a traditional approach was taken to the Mean effect size statistics and their standard errors were computed and heterogeneity of variance was evaluated using the Q-statistic. When statistically significant variance was found, moderator variables were introduced into the random-effects models, resulting in models. Categorical moderator variables were used in all cases due to the way that the variables of interest were reported in the studies included in the meta-analysis. Moderators included (a) total hours of intervention (less than 115 vs. 115 or more), (b) size of instructional
Meta-Analysis Procedures Studies were included in the meta-analysis if they utilized a treatment-comparison or quasi-experimental design and reported sufficient information to allow effect sizes to be computed. Some research reports contained studies with multiple treatment/comparison groups. In total, 10 research reports containing 22 distinct treatment/comparison group contrasts were included in the meta-analysis. Because all studies utilized multiple outcome measures, these measures were categorized by type (reading comprehension, fluency, word, word fluency, and spelling), and a separate meta-analysis was conducted for each measure type. In cases where a study included more than one outcome measure within each category, effect sizes were averaged and the average and its standard error were included in the meta-analysis. A random-effects model was used to analyze effect sizes. Recent methodological innovations in meta-analysis such as multi-level modeling ([MLM] Hox, 2002) and structural equation modeling ([SEM] Cheung, 2008) were considered as approaches to the random-effects analyses of the effect sizes. However, the small number of effect sizes in each of the analyses (n = 5 to 22 effect sizes depending on outcome type) presented a significant limitation to the implementation of MLM or SEM. Therefore, a traditional approach was taken to the Mean effect size statistics and their standard errors were computed and heterogeneity of variance was evaluated using the Q-statistic. When statistically significant variance was found, moderator variables were introduced into the random-effects models, resulting in models. Categorical moderator variables were used in all cases due to the way that the variables of interest were reported in the studies included in the meta-analysis. Moderators included (a) total hours of intervention (less than 115 vs. 115 or more), (b) size of instructional
Wanzek%2C_et_al._%282013%29.docx