A key part of biomedical research involves observing, manipulating, and tracking changes in different things, such as clinical outcomes, patient characteristics, or disease characteristics. In statistical research, these are called variables.<\/p>\n\n\n\n
When you conduct statistical analysis in your study, especially inferential analysis, you will usually have two types of variables: explanatory and response variables.<\/p>\n\n\n\n
In biomedical research, an explanatory variable (also called an independent variable) is a variable that is observed, manipulated, or controlled by the researcher in order to understand its effect on the response variable.<\/p>\n\n\n\n
The response variable (also called a dependent variable) is the variable that is being measured or observed in order to determine the effect of the explanatory variable. The response variable is expected to change in response to changes in the explanatory variable.<\/p>\n\n\n\n
To determine which variables are your explanatory variables and which are response variables, you need to consider your research objective.<\/p>\n\n\n\n
For example, if you want to find out whether vitamin C supplementation improves lipid profile, your explanatory variable would be vitamin c supplementation and your response variables would be HDL cholesterol, LDL cholesterol, VLDL cholesterol, triglyceride, and total cholesterol levels. Depending on your study design, you may be able to manipulate your explanatory variable, such as by giving different groups of participants different amounts of vitamin C supplements.<\/p>\n\n\n\n
Explanatory and response variables can easily be presented visually. For instance, you can create a graph with the explanatory variable plotted on the X-axis and the response variable plotted on the Y-axis. Depending on whether your variables are continuous or categorical, you will be able to create a scatterplot, line graph, or bar graph.<\/p>\n\n\n\n
It is important to carefully define both the explanatory and response variables in any biomedical research study in order to ensure that the study is well-designed, and the results are valid.<\/p>\n\n\n\n