Independent Variables Essay

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Independent variables are the presumed causes whose effects are measured via changes in the values of dependent variables. For example, winning a state lottery (independent variable) might increase expenditures on luxury items (dependent variables). According to Cohen, Cohen, West, and Aiken (2003), there are four requirements for concluding that an independent variable (X) causes or influences a dependent variable (Y):

  1. X precedes Y in time (temporal precedence).
  2. Some mechanism whereby this causal effect operates can be posited (causal mechanism).
  3. A change in the value of X is accompanied by a change in the value of Y on the average (association).
  4. The effects of X on Y can be isolated from the effects of other potential variables on Y (non-spuriousness or lack of confounders). (p. 64)

It is important to note that (3) above represents a probabilistic view of causality that is particularly important in the social sciences. A change in an independent variable is likely to be associated with a change in the dependent variable, but the occurrence, size, and direction of the change may differ from one individual or occasion to the next. In social science research, multiple observations are used to address this problem.

Experimental research has long been considered the ”gold standard” in providing evidence of causality. In experiments, causal relationships are tested by manipulating the independent variable (or variables) of interest while keeping other factors constant.

Although such control is not possible in social science research, researchers can attempt to control or account for other potentially confounding variables. Thus, one might study the effect of mode of presentation of a political story (newspaper vs. television) in a study in which potentially confounding variables (e.g., sex and age of audience members) are controlled by random assignment to conditions, matching, or statistical procedures.

Bibliography:

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003) Applied Regression/Correlation Behavior Analysis for the Behavioral Sciences, 3rd edn. Lawrence Erlbaum Associates, Mahwah, NJ.

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