The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.

Although dissatisfaction with the limitations associated with tests for statistical significance has been growing for several decades, applied researchers have continued to rely almost exclusively on these indicators of effect when reporting their findings. To encourage an increased use of alternative measures of effect, the present paper discusses several measures of effect size that might be used in group comparison studies involving univariate and/or multivariate models. For the methods discussed, formulas are presented and data from an experimental study are used to demonstrate the application and interpretation of these indices. The paper concludes with some cautionary notes on the limitations associated with these measures of effect size.

Describes interim results of a study examining the effectiveness of parent-child interaction therapy (PCIT) with families of preschool-age children with oppositional defiant disorder. Following an initial assessment, 64 clinic-referred families were randomly assigned to an immediate treatment (i.t.) or a wait-list control (WL) condition. Results indicated that parents in the IT condition interacted more positively with their child and were more successful in gaining their child's compliance than parents in the WL condition. In addition, parents who received treatment reported decreased parenting stress and a more internal locus of control. Parents in the IT group reported statistically and clinically significant improvements in their child's behavior following PCIT. All families who received treatment reported high levels of satisfaction with both the content and process of PCIT. Preliminary 4-month follow-up data showed that parents maintained gains on all self-report measures.

The results provide a profile of the strengths and limitations of these computer programs. The programs should be used by physicians who can identify and use the relevant information and ignore the irrelevant information that can be produced.

The authors argue that a robust version of Cohen's effect size constructed by replacing population means with 20% trimmed means and the population standard deviation with the square root of a 20% Winsorized variance is a better measure of population separation than is Cohen's effect size. The authors investigated coverage probability for confidence intervals for the new effect size measure. The confidence intervals were constructed by using the noncentral t distribution and the percentile bootstrap. Over the range of distributions and effect sizes investigated in the study, coverage probability was better for the percentile bootstrap confidence interval.

Repeated measures ANOVA can refer to many different types of analysis. Speci®cally, this vague term can refer to conventional tests of signi®cance, one of three univariate solutions with adjusted degrees of freedom, two different types of multivariate statistic, or approaches that combine univariate and multivariate tests. Accordingly, it is argued that, by only reporting probability values and referring to statistical analyses as repeated measures ANOVA, authors convey neither the type of analysis that was used nor the validity of the reported probability value, since each of these approaches has its own strengths and weaknesses. The various approaches are presented with a discussion of their strengths and weaknesses, and recommendations are made regarding the`best' choice of analysis. Additional topics discussed include analyses for missing data and tests of linear contrasts.

No abstract

This study explored predictors of treatment response and attrition in Parent-Child Interaction Therapy (PCIT). Participants were 99 families of 3- to 6-year-old children with disruptive behavior disorders. Multiple logistic regression was used to identify those pretreatment child, family, and accessibility factors that were predictive of success or attrition. For all study participants, waitlist group assignment and maternal age were the significant predictors of outcome. For treatment participants (study participants excluding those who dropped out after the initial evaluation but before treatment began), only maternal ratings of parenting stress and maternal inappropriate behavior during parent-child interactions were significant predictors of treatment outcome. These results suggest that for treatment studies of disruptive preschoolers, the benefits of using a waitlist control group may be outweighed by the disproportionate number of dropouts from this group. Once families begin PCIT, however, parent-related variables become salient in predicting treatment outcome.