VARIABLES While generating theories and hypotheses, we must know the importance of our collected data in testing hypotheses or deciding between competing theories. Therefore, we need to decide on two things: 1. What to measure? 2. How to measure it? Variables are data items whose values can change/vary between individuals or testing conditions (e.g. weight, RBC count, severity of disease, and tumor markers’ concentrations). On the other hand, the values of the other data type, Constants, are always the same.
Types of Variables Variables are usually classified according to two standards: 1. The causal relationship. 2. The level of measurement.
The Causal Relationship The variables are classified into two types with regard to causality: Dependent and Independent variables. In other words, you can think of them in terms of cause and effect: an Independent variable is the variable the researcher thinks is, or has a significant influence on, the cause, while a Dependent Variable is the effect, or a consequence thereof. For example, if you plan to study the effects of smoking on lung cancer:
The Independent Variable is a better fit to represent smoking. The Dependent Variable will be representing lung cancer, as it is dependent on smoking.
If there is a third variable in a study examining a potential cause-and-effect relationship, we call that variable a Confounding Factor. For example, a study investigating the association between obesity and heart disease might be “confounded” by age, diet, or smoking status. It's difficult to separate the true effect of the independent variable from the effect of the confounding variable. Therefore, in your research design, it is
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