 # Tutorial on Introduction to biostatistics

Variable classification according to their nature

a.      Independent variables

Variables that can either be manipulated by the researcher or which are not outcomes of the study but still affect its results are called independent variables. A good example is age of the subjects, which can be an independent variable that may affect a study outcome variable such as subject survival.

b.      Dependent variables

The outcome variables defined as part of the research process are termed dependent variables and these will be affected by the independent variables under study. An example of a dependant variable can be the number of people who developed a particular disease in a cohort study.

Variable classification according to their type

a.      Quantitative variables

Variables that can be measured numerically are called quantitative variables. These variables can be further classified as continuous and discrete variables. A continuous variable could take any value in an interval. Examples of continuous data are findings for measurements like body mass, height, blood pressure or serum cholesterol.

Discrete variables will have whole integer values. Examples are the number of hospitalizations per patient in a year or the number of hypoglycemic events recorded in a diabetic patient over 6 months.

b.      Qualitative variables

Variables, which cannot be measured numerically, are called qualitative variables. An example is gender.

Variable classification according to the scale of measurement

a.      Nominal Scale variables

Nominal scale measurements can only be classified but not put into an order, and mathematical functions cannot be performed on them. Gender is an example for this sort of variable as well.

b.      Ordinal Scale Variables

These variables can be put into a definite order, but the difference between two positions in the ordinal scale does not have a quantitative meaning. Essentially, this scale is a form of ranking. An example is the military hierarchy, where a general outranks a colonel who in turn outranks a captain. Though there is a clear series of ranks, the relationship is not numerical.
c. Interval Scale Variables

In an interval measurement scale, one unit on the scale represents the same magnitude of the characteristic being measured across the whole range of the scale, i.e. the intervals between the numbers are equal. However, the ratio between set of two numbers in the scale are not equal because an interval scale lacks a true zero point.

Temperature in Fahrenheit would be a perfect example for interval scales because though we can add and subtract degrees (70° is 10° warmer than 60°), we cannot multiply values or create ratios (70° is not twice as warm as 35°).

d.      Ratio Scale Variables

Ratio scale variables will have all the properties of interval variables with the ratio between two numbers in the ratio scales being identical. Ratio scales have an absolute or zero point. For example, a 100-year old person is indeed twice as old as a 50-year old one