Researchers have their own vocabulary or communicating among themselves and with outsiders. The following briefly defines some of the more popular terms you are likely to encounter in behavioral science studies.
A variable is any general characteristics that can be measured and their changes in amplitude, intensity or both. Some examples of OB variables are job satisfaction, employee productivity, work stress, ability, personality and group norms.
A tentative explanation of the relationship between two or more variables is called a hypothesis. The statement that participation in college athletics leads to a top executive position in large corporation is an example of a hypothesis. Until confirmed by empirical research, a hypothesis remains only a tentative explanation.
A dependent variable is a response that is affected by an independent variable. In terms of the hypothesis, it is the variable that the researcher is interested in explaining. Referring back to our opening example, the dependent variable in my friend’s hypothesis was executive succession. In organizational behavior research, the most popular dependent variables are productivity absenteeism turnover, job satisfaction, and organizational commitment.
An independent variable is the presumed cause of some change in the dependent variable. Participating in varsity athletics is an independent variable in a hypothesis. Popular independent variables studied by OB researchers include intelligence, personality, job satisfaction, experience, motivation, reinforcement patterns, leadership style, reward allocations, selection methods, and organizations design.
You may have noticed that job satisfaction is frequently used by OB researchers as both as dependent and an independent variable. It merely reflects that the label given to a variable depends on its place in the hypothesis. Increases in job satisfaction lead to reduced turnover. Job satisfaction is an independent available. However, in the statements Increase in money leads to higher job satisfaction. Job satisfaction becomes a dependent variable.
A moderating variable abates the effect of the independent variables on the dependent variable. It might also be thought of as the contingency variable: If X (independent variable) will occur, but only under conditions Z (moderating variable). To translate this into a real life example, we might say that if we increase the amount of direct supervision in the work area (X), then there will be a change in worker productivity (Y) but this effect will be moderated by the complexity of the tasks being performed (Z).
A hypothesis by definition implies a relationship. That is, it implies a presumed cause and effect. This direction of cause and effect is called causality. Changes in the independent variable are assumed to cause changes in the dependent variable. However, in behavioral research, it’s possibly to make an incorrect assumption of causality when relationships are found. For example, early behavioral scientists found a relationship between employee satisfaction and productivity. They concluded that a happy worker was a productive worker. Follow up research has supported the relationship. The evidence more correctly suggests that high productivity leads to satisfaction rather than the other way around.
It is one thing to know that there is a relationship between two or more variables. It is another to know the strength of that relationship. The term correlation coefficient is used to indicate that strength and is expressed as a number between –1.00 (a perfect negative relationship) and +1.00 (a perfect positive correlation).
When two variables vary directly with one another, the correlation will be expressed as a positive number. When they vary inversely that is, one increases as the other decreases the correlation will be expressed as a negative number. If the two variables vary independently of each other, we say that the correlation between them is zero.