Module 9: Conducting Correlational Research

Question 1

Magnitude

Answer 1

the strength of the relationship. This is determined by the correlation coefficient (from -1.00 to +1.00)

Question 2

Scatterplot

Answer 2

can be used to make a visual representation of the relationship between two variables

Question 3

4 basic patterns of scatterplot

Answer 3

• positive correlation
• negative correlation
• no relationship (intelligence and weight)
• curvilinear relationship

Question 4

Positive correlation

Answer 4

shows that an increase in one variable is related to an increase in the other and vice versa
- smoking and cancer

Question 5

Negative correlation

Answer 5

indicates that an increase in one variable is accompanied by a decrease in the other variable. This correlation represents an inverse relationship
- mountain elevation and temperature

Question 6

Curvilinear relationship

Answer 6

A correlation coefficient of 0 indicates no meaningful relationship between two variables. However, it is also possible for a correlation coefficient of 0 to indicate a curvilinear relationship. The strong positive relationship depicted in the left half of the graph essentially cancels out the strong negative relationship in the right half of the graph.
- memory and age

Question 7

Causality

Answer 7

refers to the assumption that the correlation between two variables indicates a causal relationship

Question 8

Third-variable problem

Answer 8

results when a correlation between two variables is dependent on another (third) variable.
Using partial correlation to remove the third variable.

Question 9

Partial correlation

Answer 9

It is possible statistically to determine the effects of a third variable by using a correlational procedure known as partial correlation, which involves measuring all three variables and then statistically removing the effect of the third variable from the correlation of the remaining two.

Question 10

Restrictive range

Answer 10

A variable that is truncated and has limited variability

Question 11

Person-who argument

Answer 11

Arguing that a well-established statistical trend is invalid because we know a “person who” went against the trend