Tag Archives: Biostatistics

Types of variables Part 2: Explanatory and Response variables

Disclaimer: This article is primarily intended for an online group of post graduate students in Community Medicine that I am involved with. The group was created to provide supplemental instruction to members on topics of common interest. Instruction is in bite-sized portions, since all members are busy PG students. Conceptual understanding is emphasized. Membership to that (Whatsapp) group is through invitation only. However, others interested in participating in the discussions and related activities in Google classroom may indicate the same by sending me a message on Facebook.

In the previous article, variables were classified based on their underlying scale of measurement. However, variables are also classified based on their relationship with the outcome of interest.

When a variable is suspected to influence the outcome, it is termed the explanatory/ independent variable.

When a variable is suspected to be influenced, it is termed the response/ outcome/ dependent variable.

Example: One wishes to determine the relationship between gestational age and fetal weight. Here, gestational age influences/ explains fetal weight; fetal weight is dependent upon gestational age. Therefore, the explanatory variable is gestational age, while the response variable is fetal weight.

In other words, the variable that is influencing is the explanatory/ independent variable, while the variable that is being influenced is the response/ outcome/ dependent variable.

Note: The terms independent variable and dependent variable are also used to describe relationships between variables. If the values of one variable are not influenced by another variable, it is independent of the other variable. However, if the values of one variable are influenced by another variable, the latter is said to be dependent on the former. The dependency is also described using the terms paired and unpaired variables: where dependency exists between values of variables, the variables are said to be paired, else they are unpaired.

Example: The scores obtained by boys and girls in a test are independent of each other (boys’ scores are not influenced by girls’ scores). However, scores obtained after a revision class are dependent on (influenced by) the scores obtained before the revision class (pre-post scenario).

Links to relevant previous articles:

Link to article on types of variables part 1:


Link to article on scales of measurement: