This week you focus on relationships, not the personal kind, but a kind that can influence your daily life nonetheless. A statistical relationship is a key concept researchers rely on when making predictions or showing how something might influence something else.
For example, what is the relationship between your salary and your years of education? If you learned that more education predicts that you may earn a higher salary, it makes sense that you would seek additional educational opportunities.

Here is another example. Do you think there is a relationship between salary and how fast you can run? Perhaps people who earn high salaries also run faster than people who earn lower salaries. If people who run fast earn more than those who run slowly, does that mean that if you train to run faster, you will earn a higher salary? Maybe, although it seems unlikely that running fast will influence your salary.

This raises an important question: Does a relationship necessarily show that one thing causes another? In the case of the running-versus-salary example, perhaps something else entirely predicts both running speed and salary. It may be that people who are more ambitious earn higher salaries and also run fast, which explains why people who earn more also run more quickly.
This week you will look at relationships through the lens of correlation coefficients. You will also look at making predictions through the use of regression analyses.

Objectives

Students will:

• Analyze use of regression
• Apply regression to research scenarios
• Generate hypotheses
• Evaluate one-tailed and two-tailed tests
• Implement correlation in SPSS
• Identify correlation coefficients
• Identify degrees of freedom
• Identify p value
• Evaluate hypotheses
• Evaluate significance of results
• Evaluate one-way and two-way chi-square tests
• Implement chi-square in SPSS
• Identify scales of measurement