Using SPSS To Run Analytical Strategies

Part B. Applying Analytical Strategies to an Area of Research Interest

1. 1. Briefly restate your research area of interest. (my area of interest is comparing 2 different types of couple therapy with each other as well as a control group that does not receive treatment)
   1. Pearson Correlation. Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R² while also taking into account the third variable problem and well as direction of causality.
   2. Spearman’s Correlation. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R² while also taking into account the third variable problem and well as direction of causality.
   3. Partial Correlation vs. Semi-Partial Correlation. Identify three variables for which you may be interested calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses using your variables and research example. Which would you use and why?
   4. Simple Regression. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R² tell you about the relationship between the two variables?
   5. Multiple Regression. Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would R² and adjusted R² tell you about the relationship between the variables?
   6. Logistic Regression. Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?