- Used to determine relationship between two continuous variables.
- One variable plotted on x-axis, another on y-axis.
- Positive Correlation: Higher x-values correspond to higher y-values.
- Negative Correlation: Higher x-values correspond to lower y-values.
- Body weight and BMI
- Height and Pressure etc.
Linear vs Non-Linear Relationship
Variables changing proportionately in response to each other show linear relationship. Linear relationship is an abstract concept it depends what can be called linear and what can’t in a given context. A linear relationship may exist locally with a non linear relationship globally.
Method for understanding the relationship between two variables when at least one the variables is discrete. Example: Summary information about ages of active psychologists by demographics.
|Ages||(1) Total Active Psychologists||Active Psychologists by Gender||Active Psychologists by Race/Ethnicity|
|(2) Female||(3) Male||(4) Asian||(5) Black/ African American||(6) Hispanic||(7) White|
Discrete Variable(s): Demography: (1), (2), (3), (4), (5), (6), (7) Continuous Variable: Age
Cross-Tabulation Tables/ Crosstabs/ Contingency Tables
- Method for summarizing two categorical variables
- In practice, continuous variables may be at times summarized as categorical variables.
- Example: Age could be divided into categories as young, adult and senior citizen, etc. Income could be divided into categories as poor, middle class, upper middle class, wealthy, etc.
- A quantification of the linear relationship between two variables
- Ranges from -1 to +1
- Used for variables on an interval or ratio scale
NOTE: Correlation coefficient does not capture nonlinear relationships. Many nonlinear relationships might exist which are not captured (𝑟 = 0) by correlation coefficient.
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