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Independent and Dependent Variables
In most of the experimental designs, the use of both dependent and independent variable is necessary.
Dependent variable refers to the variable that that the researcher believes might be influenced or modified by various factors such as treatment or exposure. Sometimes, dependent variables are also use as the representation of the variable that the researcher is trying to predict. In the simplest term, dependent variables are the variables with which the values in various treatment conditions are compared (http://www.childrens-mercy.org/stats/definitions/dv.htm,2006).
Independent variable, on the other hand, is the exact opposite of the dependent variable. This is the variable that has the ability to influence the researcher's outcome measure. Independent variables can be the values that the researcher him/herself controls. It can also be used to represent some demographic factors such as age or gender. At most times, independent variables are coined as the 'hypothesized cause' (http://www.childrens-mercy.org/stats/definitions/dv.htm,2006).
In an experiment research, keeping everything constant, except on the independent variable, is very important because this ensures the objectivity and precision of the report. If the numbers or the data will not be constant, the results will not be paralleled and will surely have discrepancy on its accuracy.
Meanwhile, experimenter bias is a big threat to the accuracy and/or precision of the report's result. An example of the threat is not being able to identify all the factors that might affect the result. An experiment can be said biased if he/she has already an expected outcome in the end, hence, he/she will just focus on that outcome and what will provide such outcome. In turn, all other factors may be already neglected and will not be taken into consideration.
Works Cited
Independent and Dependent Variable." 2006. [online] available at: http://www.childrens-mercy.org/stats/definitions/dv.htm
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