# Correlational Methods Of Analysis – res726 (3 credits)

This course focuses on statistical approaches to analysis and reporting when examining bivariate and multivariate relationships among variables. Students will explore the underlying assumptions, advantages, limitations and appropriate application of correlation/regression based approaches to data analysis. The course will cover basic correlation methods, simple and multiple regression techniques, and advanced multivariate procedures including factor analysis and structural equation models. Depending on the students’ needs, other approaches may be covered. Competency A: Examine different models of multiple regression and how those models address different research questions. Competency B: Apply the fundamental statistical concepts in multiple regression analyses. Competency C: Analyze the relationship among ANOVA, regression, and correlation. Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. Competency E: Conduct data analysis for regression and correlation research. Competency F: Present results of correlation and regression analysis and interpretation.

This graduate-level course is 8 weeks. This course is available to take individually or as part of a degree or certificate program. To enroll, speak with an Enrollment Representative.

### Data Analysis Using Multiple Regression and Correlation

• Competency E: Conduct data analysis for regression and correlation research. [Wk 5]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 5]
• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 5]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 5]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 5]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 5]

### Diagnosing and Solving Regression Problems

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 6]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 6]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 6]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 6]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 6]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 6]

### Multiple Regression and Correlation Models

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 7]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 7]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 7]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 7]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 7]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 7]

### Longitudinal Regression Methods and Generalized Linear Models

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 8]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 8]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 8]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 8]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 8]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 8]

### Fundamental Statistical Concepts in ANOVA, Regression, and Correlation

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 1]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 1]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 1]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 1]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 1]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 1]

### Regression and Correlation

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 2]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 2]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 2]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 2]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 2]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 2]

### Data Visualization and Assumption Checking

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 3]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 3]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 3]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 3]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 3]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 3]

### Scales, Transformations, and Variables

• Competency A: Examine different models of multiple regression and how those models address different research questions. [Wk 4]
• Competency B: Apply the fundamental statistical concepts in multiple regression analyses. [Wk 4]
• Competency C: Analyze the relationship among ANOVA, regression, and correlation. [Wk 4]
• Competency D: Evaluate common data visualization strategies used in correlation and regression analysis. [Wk 4]
• Competency E: Conduct data analysis for regression and correlation research. [Wk 4]
• Competency F: Present results of correlation and regression analysis and interpretation. [Wk 4]