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A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
Psychology is the scientific study of mind and behavior. [1] [2] Its subject matter includes the behavior of humans and nonhumans, both conscious and unconscious phenomena, and mental processes such as thoughts, feelings, and motives. Psychology is an academic discipline of immense scope, crossing the boundaries between the natural and social ...
Simple mediation model. The independent variable causes the mediator variable; the mediator variable causes the dependent variable. In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator ...
The independent variable is the time (Levels: Time 1, Time 2, Time 3, Time 4) that someone took the measure, and the dependent variable is the happiness measure score. Example participant happiness scores are provided for 3 participants for each time or level of the independent variable.
Manipulation checks are measured variables that show what the manipulated variables concurrently affect besides the dependent variable of interest. In experiments, an experimenter manipulates some aspect of a process or task and randomly assigns subjects to different levels of the manipulation ("experimental conditions").
For example, the psychologist Seymour Feshbach conducted an experiment to see how violence on television (the independent variable), affected aggression in adolescent boys (the dependent variable). He published his results in a paper called Television and Aggression in 1971.
The same is true for intervening variables (a variable in between the supposed cause (X) and the effect (Y)), and anteceding variables (a variable prior to the supposed cause (X) that is the true cause). When a third variable is involved and has not been controlled for, the relation is said to be a zero order relationship. In most practical ...
The information given by a correlation coefficient is not enough to define the dependence structure between random variables. The correlation coefficient completely defines the dependence structure only in very particular cases, for example when the distribution is a multivariate normal distribution .