Dependent variables are the outcome measure in a study. The Dependent Variable is the effect, while the Independent Variable is the cause. The experimenter can independently vary the causal factor, the Independent Variable, in an experiment but the Dependent Variable depends on the way the participants react to the independent variable. The participants determine the values of the dependent variable while the experimenter decides on the Independent Variable. For example, the experimenter can determine which diners get a name introduction from a server OR a no-name introduction. The introduction is the independent variable. But the dependent variable, tip size, depends on the participants, each one decides how much to tip. The experimenter cannot directly control the tip size
Another type of study psychologists do are descriptive/correlational studies, where the researcher can't decide who is going to be in each group. For example, if studying the independent variable of Gender, the researcher CANNOT decide which person is going to be a woman or man like she can decide which diner is going to get a name introduction or no-name introduction. In correlational studies it is sometimes ambiguous which variable is the independent variable and which is the dependent variable. For example, suppose a study is done that shows the more hours student-athletes choose to practice the more they say they love the sport. A coach might say, "Practice makes the athlete love the sport so if you want to increase love of the sport require more practice." He is thinking Practice Time is the Independent Variable (actually it's a Pseudo Independent Variable since this is a correlational study) and Love of Sport is the Dependent Variable. BUT there is an alternative explanation (PRAH) for this data. It might be that the Love of Sport is the CAUSE and Practice Time is the EFFECT. This is an example of how which is the PIV and which is the DV is ambiguous in some correlational studies.
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