A variable or theme can represent an attribute of a person, place, thing, or idea.
Variables can be quantitative (numeric) or qualitative (categorical, non-numeric); alternatively, themes are all qualitative.
Themes are typically descriptive in nature and, therefore, more complex than variables to analyze.
Analysis variables can be created to summarize one or more of the original variables. Through data reduction approaches, themes can also be transformed into variables, if desired.
Examples of analysis variables derived from original variables designed to characterize intervention exposure and impact include:
Many types of variables or themes may be included in quantitative and qualitative data analysis reflecting populations or samples, interventions, outcomes, and contextual conditions. See the table below for examples of variables in each of these categories.View the Resource Table
Consider how your variables may align with the following categories:
Independent variables - Variables related to populations or subpopulations, interventions, and contextual factors;
Dependent variables - Also known as "outcome variables"; and
Confounding variables - Variables related to populations or subpopulations and contextual factors.