Explain why ANOVA ( Data analysis section ) is used as a statistical test. Expla

Question

Explain why ANOVA (Data analysis section) is used as a statistical test. Explain ANCOVA (Data analysis section) and how it is used for data analysis in this research. Explain Chi-square analysis (Data analysis section) and how it is used for data analysis in this research.

3. Data Analysis Analyses of variance (ANOVAs) were conducted at baseline to assess for any significant differences among the four groups (three intervention, one control). The specific hypotheses of this study were evaluated using analyses of covariance (ANCOVAs), controlling for the influence of pre-test scores on each measure. In these analyses, time of assessment was the Eat Behav. Author manuscript, available in PMC 2011 January 1. ow et al Page 7 within-subjects variable, and group (feedback only, Internet intervention only, combined intervention, or control) was the between-subjects variable. Outcomes were participants' scores at post-testing and three-month follow-up 3.1 Intent-to-Treat Analysis Analyses were conducted using an intent-to-treat (ITT) approach. This approach analyzes all the data based on participants' assigned group, regardless of whether they actually complete the intervention or not (Hulley et al., 2001). Thus, participants' most recent data were used as their post-intervention scores. ITT protects against threats to validity from attrition (Spilker, 1991). Chi-square analyses were conducted to determine whether attrition rates differed by treatment condition

Explanation / Answer

Analysis of Variance (ANOVA) is a statistical technique that assesses potential difference in a scale-level dependent variable by a nominal level variable having 2 or more categories. There are two hypotheses in ANOVA, the null hypothesis and alternative hypothesis. Null hypothesis states that there is no significant difference among the variables, while the alternative hypothesis states that there is atleast one significant difference among the groups.

In the given data analysis, ANOVA is used here as the user wants to find if there is any significant difference among the groups or not.

Analysis of Covariance (ANCOVA) examines the influence of an independent variable on a dependent variable while removing the effect of the covariate factor. Here, regression of independent variable (i.e. the covariate) is conducted on dependent variable. Then the unexplained variance in the regression model are subject to an ANOVA. Thus ANCOVA tests whether the independent variable still influences the dependent variable after the influence of covariate is removed.

Here there are two independent variables, namely time of assessment and group. There is also one dependent variable namely participants’ scores at post-testing and three-month follow-up. ANCOVA is used here as the user wants to know whether the pre-test scores has any influence on the independent variables after the influence of covariate is removed.

Chi-square tests are used to determine if there is any significant relation between two nominal variables i.e. how the observed value of a given phenomena is significantly different from the expected value.

In the given data the independent variable here is treatment conditions while the dependent variable is the attrition rate. The user here wants to determine if the attrition rates differed by treatment conditions. So he can determine if the actual attrition rate is significantly different from the derived attrition rate i.e. it will determine if the attrition rate is highly dependent on treatment conditions.

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