Parametric Variance Analysis Assumptions

Parametric analysis of variance (ANOVA) is a statistical technique used to compare the means of two or more groups. To ensure the results are reliable and valid, certain assumptions must be met before performing the analysis.

Key Assumptions: Independence, Assignment, Variance

The primary assumptions required for a parametric ANOVA are:

• Independence of scores (A): Each observation (score) should be independent of all other observations. This means one subject's score should not influence another's. This assumption is critical for the mathematical basis of ANOVA.
• Random assignment of subjects (C): Participants must be randomly assigned to different treatment groups or conditions. Random assignment helps ensure that groups are equivalent at the start of the study, contributing to the independence of observations and reducing bias.
• Homogeneity of variance (D): The variance (spread of scores) of the dependent variable should be approximately equal across all groups being compared. This is also known as homoscedasticity. Violating this can affect the accuracy of the F-test results.

Dependent Variable Type Assumption

Option B is incorrect because parametric ANOVA requires the dependent variable to be measured on a continuous scale (interval or ratio), not a categorical one. Categorical dependent variables necessitate different types of analysis, such as chi-square tests.

Conclusion

Assuring independence of scores (A), random assignment (C), and homogeneity of variance (D) are necessary prerequisites for conducting a valid parametric ANOVA. Therefore, the correct combination is A, C, and D.