Understanding Statistical Analysis with Multiple Dependent Variables

The question asks to identify the statistical analysis method that accommodates more than one dependent variable or outcome variable. Let's examine the options:

Analyzing Analysis Types

• Simple Regression: This method is used to examine the relationship between one dependent variable and one independent variable. It does not handle multiple dependent variables.
• Multiple Regression: This technique analyzes the relationship between one dependent variable and two or more independent variables. While it uses multiple predictors (independent variables), it still focuses on a single dependent variable.
• Multi-variate Analysis of Variance (MANOVA): MANOVA is specifically designed for situations where researchers want to study multiple dependent variables simultaneously, along with one or more independent variables (factors). The key feature is the presence of *multiple* dependent variables.
• Three-way Analysis of Variance (ANOVA): ANOVA, including a three-way ANOVA, typically investigates the effect of three independent variables (factors) on a *single* dependent variable. It does not inherently handle multiple dependent variables.

Identifying the Correct Statistical Method

Based on the definitions:

• Simple Regression: 1 Dependent Variable
• Multiple Regression: 1 Dependent Variable
• Three-way ANOVA: 1 Dependent Variable
• Multi-variate Analysis of Variance (MANOVA): More than 1 Dependent Variable

Therefore, Multi-variate Analysis of Variance (MANOVA) is the statistical analysis method used when there are multiple dependent/outcome variables.