Measures Of Relationship - Correlation

The video introduces measures of relationship in statistics, also known as correlation. It explains that researchers often study more than one variable to understand their dependence, defining variables, independent variables (stimulus manipulated by researcher), and dependent variables (outcome or response).

Correlation is defined as the relationship between two quantitatively measured or continuous variables. The degree or magnitude of this relationship is called the correlation coefficient, denoted by the small letter 'r'. Examples include the relationship between birth weight and gestational age, and smoking and lung cancer.

Positive correlation occurs when two variables change in the same direction; either both increase or both decrease. An example given is that as cholesterol levels increase, blood pressure also tends to increase.

Negative correlation describes a relationship where one variable increases while the other decreases. Examples include increased radiation exposure leading to decreased fetal birth weight, and advancing age leading to decreased memory levels.

The video provides several examples from the nursing field: overcrowding and pulmonary TB (positive correlation), economic status and health status (positive correlation), BMI and IQ (no correlation), aging and blood pressure (positive correlation), malnutrition and pulmonary TB (positive correlation), adiposity and cholesterol levels (positive correlation), and protein intake and calorie availability (positive correlation).

The video concludes by summarizing the basic concepts of correlation and correlation coefficient, mentioning that upcoming classes will discuss Pearson's correlation coefficient.