Q: What are the different kinds of variables and their uses?
In research, a variable is a measurable characteristic of an object. Values of a variable can ‘vary’ or be distributed across the set of all values that a variable can possibly have. There are different ways of categorizing variables for data analysis. For ease of understanding, we just indicate the following broad categorizations:
Qualitative and quantitative variables
- Quantitative data indicate amounts. There are two types of quantitative variables: discrete variables (counts of items, e.g., number of patients in a group) and continuous variables (e.g., weight of patients).
- Qualitative or categorical data refer to groupings. Three types of categorical variables are binary (yes/no outcomes), nominal (groups with no rank or order, e.g., species names and eye color), and ordinal variables (groups ranked in an order, e.g., scales in a survey).
Independent and dependent variables
An independent variable (IV) is a variable that is manipulated in an experiment to observe the effect on a dependent variable (DV), a variable that depends on an independent variable. As an example, consider a researcher wanting to know the effects of a drug on physiological responses. The drug is an IV, the dose of which can be varied. The physiological responses, e.g., heart rate and blood pressure, are the DVs.
There are certain other variables that can influence an experiment, such as confounding variables (variables that obscure the true effect of another variable in an experiment) and latent variables (variables that cannot be directly measured but can be inferred by measuring other parameters).
Coming to the uses of variables. Well, identifying which variables to study to answer your research question is crucial, and the type of variable will dictate the analysis approach and the appropriate study design.
Hope that helps. You may also find these following previous questions (by other researchers) of interest: