When doing research there are some important generic terms for variables that you will encounter:

• Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.
• Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.
• Predictor variable: A variable thought to predict an outcome variable. This is basically another term for independent variable (although some people won't like me saying that; I think life would be easier if we talked only about predictors and outcomes).
• Outcome variable: A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with 'dependent variable' for the sake of an easy life.

Variables can be split into categorical and continuous, and within these types there are different levels of measurement:

• Categorical (entities are divided into distinct categories):
• Binary variable: There are only two categories (e.g., dead or alive).
• Nominal variable: There are more than two categories (e.g., whether someone is an omnivore, vegetarian, vegan, or fruitarian).
• Ordinal variable: The same as a nominal variable but the categories have a logical order (e.g., whether people got a fail, a pass, a merit or a distinction in their exam).
• Continuous (entities get a distinct score):
  • Interval variable: Equal intervals on the variable represent equal differences in the property being measured (e.g., the difference between 6 and 8 is equivalent to the difference between 13 and 15).
  • Ratio variable: The same as an interval variable, but the ratios of scores on the scale must also make sense (e.g., a score of 16 on an anxiety scale means that the person is, in reality, twice as anxious as someone scoring 8).