Types of Variables in Statistics

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Types of Variables

Binary variable

Obsevations (i.e., dependent variables) that occur in one of two possible states, often labelled zero and one. E.g., “improved/not improved” and “completed task/failed to complete task.” Usually an independent or predictor variable that contains values indicating membership in one of several possible categories. E.g., gender (male or female), marital status (married, single, divorced, widowed). The categories are often assigned numerical values used as lables, e.g., 0 = male; 1 = female. Synonym for nominal variable. A variable that obscures the effects of another variable. If one elementary reading teacher used used a phonics textbook in her class and another instructor used a whole language textbook in his class, and students in the two classes were given achievement tests to see how well they read, the independent variables (teacher effectiveness and textbooks) would be confounded. There is no way to determine if differences in reading between the two classes were caused by either or both of the independent variables. A variable that is not restricted to particular values (other than limited by the accuracy of the measuring instrument). E.g., reaction time, neuroticism, IQ. Equal size intervals on different parts of the scale are assumed, if not demonstrated. Synonym for interval variable. An extraneous variable that an investigator does not wish to examine in a study. Thus the investigator controls this variable. Also called a covariate. The presumed effect in a nonexperimental study. The presumed effect in an experimental study. The values of the dependent variable depend upon another variable, the independent variable. Strictly speaking, “dependent variable” should not be used when writing about nonexperimental designs. Synonym for binary variable Variable having only integer values. For example, number of trials need by a student to learn a memorization task.

Categorical Variable

Confounding variable

Continuous variable

Control variable

Criterion variable Dependent variable

Dichotomous variable Discrete variable

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Dummy Variables

Created by recoding categorial variables that have more than two categories into a series of binary variables. E.g., Marital status, if originally labelled 1=married, 2=single, and 3=divorced, widowed, or separated, could be redefined in terms of two variables as follows: var_1: 1=single, 0=otherwise. Var_2: 1=divorced, widowed, or separated, 0=otherwise. For a married person, both var_1 and var_2 would be zero. In general, a categorical variable with k categories would be recoded in terms of k - 1 dummy variables. Dummy variables are used in regression analysis to avoid the unreasonable assumption that the original numerical codes for the categories, i.e., the values 1, 2, ..., k, correspond to an interval scale. Use: to place cases in specific groups.

Endogenous 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. A variable entering from and determined from outside of the system being studied. A causal system says nothing about its exogenous variables. The presumed cause in an experimental study. All other variables that may impact the dependent variable are controlled. The values of the independent variable are under experimenter control. Strictly speaking, “independent variable” should not be used when writing about nonexperimental designs. Synonym for continuous variable A variable that explains a relation or provides a causal link between other variables. Also called by some authors “mediating variable” or “intermediary variable.” Example: The statistical association between income and longevity needs to be explained because just having money does not make one live longer. Other variables intervene between money and long life....
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