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Question

In which of the following statistical analysis we have more than one dependent/ outcome variables?

The correct answer is
Multi-variate analysis of variance

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.

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