Explore a complete list of types of variables used in statistics and research. This guide provides clear definitions and examples of essential terms, including independent and dependent variables, confounding vs. mediating variables, latent and manifest variables, categorical and continuous data, and exogenous vs. endogenous factors. Perfect for students and researchers needing a quick reference.
Types of Variables
Types of variables, independent variable, dependent variable, confounding variable, mediating variable, latent variable, categorical variable, continuous variable, exogenous vs endogenous, predictor variable, outcome variable, research methodology, statistics glossary.
| Types of Variables | Description |
|---|---|
| Binary Variable | Observations that occur in one of two possible situations/ states, such as zero and one. The possible outcome may be improved or not improved, true or false, completed or failed, pass or fail, etc. |
| Categorical Variable | A variable (predictor or independent variable) that contains values showing membership in one of several possible values/categories. For example, gender (male or female), marital status (single, married, divorced, widowed). Usually, categories are recoded into numerical values/labels, for example, 0 = male, 1 = female. Synonym for a nominal variable. |
| Confounding Variable | A variable that obscures the effect of another variable. A confounding variable is an unmeasured third variable that influences both the supposed cause (independent variable) and the supposed effect (dependent variable), creating a false association between them. For example, A study finds that ice cream sales are linked to drowning incidents. The confounding variable is hot weather, which causes people to both buy more ice cream and go swimming (leading to more drownings). |
| Continuous Variable | A variable that is not restricted to particular values (other than limited by the accuracy of the measuring instrument). For example, neuroticism, IQ, and reaction time. Equal-sized intervals on different parts of the scale are assumed, if not demonstrated. Synonym for interval variable. |
| Criterion Variable | The presumed effect in a non-experimental study. |
| Dependent Variable | The presumed effect in an experimental study. The values of the dependent variable depend upon another variable, the independent variable. The dependent variable should not be used when writing about non-experimental designs. |
| Dichotomous Variable | Another name of Binary variable. |
| Discrete Variable | A variable having only integer values. For example, the number of trials needed by a student to learn a memorization task. Number of students in a class. |
| Dummy Variable | A dummy variable is created by recoding categorical variables that have more than two categories into a series of binary variables (dichotomous). For example, marital status, if originally labelled as (1 = married, 2 = single, 3 = divorced, widowed, or separated), could be redefined in terms of two variables as follows Var1: 1 = single, 0 = otherwise Var2: 1 = divorced, widowed, or separated, 0 = otherwise For a married person, both Var1 and Var 2 would be zero. In general, a categorical variable with $k$ categories would be recorded in terms of $k-1$ dummy variables. |
| Endogeneous Variable | A variable that is an inherent part of the system being studied and that is determined from within the system. A variable that is caused by other variables in a causal system. An endogenous variable is a factor that is determined within the system or model. It is influenced by other variables (including exogenous ones) inside the system. For example, in the crop yield study, the crop yield itself is an endogenous variable: it is affected by rainfall (exogenous), soil quality, and fertilizer use. |
| Exogeneous Variable | A variable entering from and determined from outside of the system being studied. A causal system says nothing about its exogenous variables. An exogenous variable is an independent variable whose value is determined outside the statistical model and is not affected by other variables within the system. For example, in a study on crop yield, the amount of rainfall is an exogenous variable: it influences the crop yield, but the crop yield does not influence the rainfall. |
| Independent Variable | An independent variable is the factor that is deliberately changed or manipulated by the researcher to see what effect it has. For example, in a test to see if different amounts of sunlight affect plant growth, the amount of sunlight is the independent variable (you change it to measure its effect on the plant). |
| Interval Variable | It is synonym for continuous variable. |
| Intervening Variable | A variable that explains a relation or provides a causal link between other variables. It is called by some authors “mediating variable” or “intermediary variable”. For example, the statistical association between income and longevity needs to be explained because just havingmoney does not make one live longer. Other variables intervene between money and long life. People with high incomes tend to have bettter medial care than those with low incomes. Medical care is an intervening variable. It mediates the relationship between income and longevity. |
| Latent Variable | A latent variable that cannot be observed. It is hypothesized to exist in order to explain other variables, such as behaviours, that can be observed. A latent variable is a hidden factor that cannot be directly observed or measured. Instead, it must be inferred from other observable variables that we can measure. For example, “Intelligence” is a latent variable. We cannot directly measure it, but we infer it by observing measurable variables like test scores, problem-solving speed, and reasoning ability. |
| Manifest Variable | An observed variable is assumed to indicate the presence of a latent variable, also known as an indicator variable. We cannot observe intelligence directly, for it is a latent variable. One can look at indicators such as vocabulary size, IQ test score, writing ability, success in one’s occupation, ability to play complicated games well, and so on. |
| Manipulated Variable | A synonym for an independent variable. |
| Mediating Variable | It is a synonym for intervening variable. For example, parents transmit their social status to their children directly, but they also do so indirectly, through education: Parents’ Status -> Child’s Education -> Child’s Status |
| Moderating Variable | A variable that influences, or moderates, the relationship between two other variables and produces an interaction effect. |
| Nominal Variable | It is a synonym for a categorical variable. |
| Ordinal Variable | A variable used rank a sample of individuals with respect to some characteristics, but differences (intervals) and different points of the scale are not necessarily equivalent. For example, anxiety might be rated on a scale of “none”, “mild”, “moderate”, and “severe” with numerical values 0, 1, 2, 3. A patient with an anxiety score of 1 is ranked as less anxious than a patient with a score of 3, but patients with score 0 and 2 do not necessarily have the same difference in anxiety as patients with scores of 1 and 3. |
| Outcome Variable | The presumed effect in a non-experimental study. It is a synonym for the criterion variable, also called the output variable. |
| Polychotomous Variables | Variables that can have more than two possible values. The usual reference is to categorical variables with more than two categories. |
| Predictor Variable | The presumed “cause” in a non-experimental study. It is often used in correlational studies. For example, SAT scores predict first-semester GPA. The SAT score is the predictor variable. |
| Treatment Variable | It is a synonym for independent variable. |
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