1. Explain why mixed-model ANCOVAs were used to assess the efficacy and efficiency of TF-CBT with and without Trauma Narrative.
The analysis of covariance (ANCOVA) is known as a statistical approach designed and propagated by Sir Ronald Fisher (1948). Fisher claims that the method "combines the advantages and reconciles the requirements of the two very widely applicable procedures known as regression and analysis of variance" (Fisher, 1948, p. 281). It can be said that Fisher (1948) initially advanced ANCOVA as a technique for decreasing error variance in tests with random data, in that way growing both the statistical efficacy of hypothesis tests as well as the accuracy in assessed outcomes.
As opposed to the analysis of variance (ANOVA), mixed-model ANCOVA was employed by Deblinger et el. (2011) to inspect the differential effects of Trauma-Focused Cognitive Behavioral Therapy ( TF-CBT) including or excluding the Trauma Narrative component in eight versus sixteen sessions. The results revealed that substantial post-treatment positive developments transpired in regard to fourteen outcome data across all conditions.
ANOVA was expanded here to take account more continuous variables that envisage the result (or dependent variable). It should be mentioned that continuous variables that do not belong to the central experimental operation, however, exert an impact on the dependent variable, are referred to as covariates and they tend to be incorporated in an ANOVA analysis, rendering it into ANCOVA (Miller & Chapman, 2001).
In the case of the experiment under discussion , prior to the proceeding, Deblinger et el. (2011) studied a number of features of the participants. Provided that the age variety of the sample crossed a number of developmental stages, the researchers examined age as both a covariate and likewise grouped the participants into those who were less or equal to 7 years old as well as those who were less or equal to 7 years old for all of their resulting data in their mixed-model analyses of covariance. The fact was that age appeared to be neither an important covariate nor produced noteworthy connections with the other core effects for whether the participants had been arbitrarily allocated to be given 8 or 16 treatment sessions (interval), or if there was a definite TN component in the way they were treated. For the reason that the participants were arbitrarily allocated to the four sets representing amount of sessions as well as Trauma Narrative, age seemed not to be meaningfully connected with either amount of sessions (eight as opposed to sixteen) or presence of the Trauma Narrative constituent (No versus Yes). Sarno and Wurtele (1997) maintained that suggested covariates with correlations less than 0.30 do not possess any important or telling clinical influence upon results. Consequently, Deblinger et el. (2011) did not incorporate age as a covariate. Deblinger et el. (2011) likewise studied the influence of sibling pairs allocated to ANCOVAs tests as either a key effect or a possible covariate. ANCOVAs were eventually defined wherein the most influential factors appeared to be the interval of treatment (eight weeks as opposed to sixteen weeks) and whether the Trauma Narrative constituent was employed (yes vs. no).
The covariate was the corresponding pretreatment rating for the resulting score within the analysis. ANCOVA turned out to be dependable regarding the incidence of arbitrarily missing information. As a result, all the obtainable records at each assessment were used in the mixed model analyses, irrespective of whether a participant or parent did not take part in one of the eight- or sixteen-week assessments.
2.Explain the meaning of the following sentence..."Significant main and interaction effects differences were found across conditions with respect to specific outcomes." Using specific examples of main and interaction effects, explain the implications for clinical practice.
A “main effect” is known as the effect of one of the employed independent variables on the the corresponding dependent variable, disregarding the impact of all other independent variables. Generally speaking, there is a single main effect for every independent variable in a research. On the other hand, a statistical interaction takes place in the event that the effect of one independent variable on the corresponding dependent variable in the given study displays fluctuations in accordance with the measures of another independent variable. The simplest method to demonstrate an interaction is to consider it in relation to the simple main effects.