What statistical test is used when there are two or more dependent variables?

The selected answer identifies Multivariate Analysis of Variance (MANOVA) as the appropriate statistical test when there are two or more dependent variables. MANOVA is specifically designed to assess whether the means of multiple dependent variables differ across the levels of one or more independent variables. This is crucial in research scenarios where examining the effect of independent variables on more than one dependent variable simultaneously can provide a more comprehensive understanding of data patterns and relationships.

In situations where researchers are interested in the effects of independent variables on various dependent variables at the same time, using a multivariate approach allows for the consideration of the potential interactions and correlations among those dependent variables, which would not be captured by conducting separate univariate tests for each dependent measure. This enhances statistical power and control over Type I error rates, making MANOVA a more robust choice for analyses with multiple dependent outcomes.

Other statistical tests listed, such as multiple regression analysis, focus on predicting a single dependent variable based on one or more independent variables. Univariate Analysis of Variance is limited to situations with only one dependent variable, making it unsuitable for the context of this question. Factor analysis, on the other hand, is used for data reduction and exploring the underlying structure of data, not specifically for comparing means across groups concerning dependent