Basic Approaches to Research
Nonexperimental Research
Much research can be categorized as being either experimental or nonexperimental in nature. In nonexperimental research (also called nonmanipulative or correlational research), the investigator simply studies the association between two or more naturally occurring variables. A naturally occurring variable is one that is not manipulated or controlled by the researcher; it is simply observed and measured (e.g., the age of insurance salespersons).
The insurance study described previously is a good example of nonexperimental research since you simply measured two naturally occurring variables (i.e., goal difficulty and amount of insurance sold) to determine whether they were related. If, in a different study, you investigated the relationship between IQ and college grade point average (GPA), this would also be an example of nonexperimental research.
With nonexperimental research designs, social scientists often refer to criterion variables and predictor variables. A criterion variable is an outcome variable that might be predicted by one or more other variables. The criterion variable is generally the main focus of the study; it is the outcome variable mentioned in the statement of the research problem. In our example, the criterion variable is Amount of Insurance Sold.
The predictor variable, on the other hand, is that variable used to predict or explain values of the criterion. In some studies, you might even believe that the predictor variable has a causal effect on the criterion. In the insurance study, for example, the predictor variable was Goal Difficulty. Because you believed that Goal Difficulty positively affects insurance sales, you conducted a study in which Goal Difficulty is identified as the predictor and Sales as the criterion. You do not necessarily have to believe that there is a causal relationship between Goal Difficulty and Sales to conduct this study. You might simply be interested in determining whether there is an association between these two variables (i.e., as the values for the predictor change, a corresponding change in the criterion variable is observed).
You should note that nonexperimental research that examines the relationship between just two variables generally provides little evidence concerning cause-and-effect relationships. The reasons for this can be seen by reviewing the study on insurance sales. If the social scientist conducts this study and finds that the agents with the more difficult goals also tend to sell more insurance, does that mean that having difficult goals caused them to sell more insurance? Not necessarily. You can argue that selling a lot of insurance increases the agents’ self-confidence and that this, in turn, causes them to set higher work goals for themselves. Under this second scenario, it was actually the insurance sales that had a causal effect on Goal Difficulty.
As this example shows, with nonexperimental research it is often possible to obtain a result consistent with a range of causal explanations. Hence, a strong inference that “variable A had a causal effect on variable B” is seldom possible when you conduct simple correlational research with just two variables. To obtain stronger evidence of cause and effect, researchers generally either analyze the relationships between a larger number of variables using sophisticated statistical procedures that are beyond the scope of this text, or drop the nonexperimental approach entirely and, instead, use experimental research methods. The nature of experimental research is discussed in the following section.
Experimental Research
Most experimental research can be identified by three important characteristics:
Participants are randomly assigned to experimental and control conditions.
The researcher manipulates one or more variables.
Participants in different experimental conditions are treated similarly with regard to all variables except the manipulated variable.
To illustrate these concepts, assume that you conduct an experiment to test the hypothesis that goal difficulty positively affects insurance sales. Assume that you identify a group of 100 agents who will serve as study participants. You randomly assign 50 agents to a “difficult-goal” condition. Participants in this group are told by their superiors to make at least 25 cold calls (sales calls) to potential policyholders per week. The other 50 agents assigned to the “easy-goal” condition have been told to make just five cold calls to potential policyholders per week. The design of this experiment is illustrated in Figure 1.2.
After 12 months, you determine how much new insurance each agent has sold that year. Assume that the average agent in the difficult-goal condition sold $156,000 worth of new policies while the average agent in the easy-goal condition sold just $121,000 worth.
It is possible to use some of the terminology associated with nonexperimental research when discussing this experiment. For example, it would be appropriate to continue to refer to Amount of Insurance Sold as being a criterion variable because this is the outcome variable of central interest. You could also continue to refer to Goal Difficulty as the predictor variable because you believe that this variable will predict sales to some extent.
Notice, however, that Goal Difficulty is now a somewhat different variable. In the nonexperimental study, Goal Difficulty was a naturally occurring variable that could take on a variety of values (whatever score participants received on the goal difficulty questionnaire). In the present experiment, however, Goal Difficulty is a manipulated variable, which means that you (as the researcher) determined what value of the variable would be assigned to both participant groups. In this experiment, Goal Difficulty could assume only one of two values. Therefore, Goal Difficulty is now a classification variable, assessed on a nominal scale.
Although it is acceptable to speak of predictor and criterion variables within the context of experimental research, it is more common to speak in terms of independent and dependent variables. The independent variable (IV) is that variable whose values (or levels) are selected by the experimenter to determine what effect the independent variable has on the dependent variable. The independent variable is the experimental counterpart to a predictor variable. A dependent variable (DV) is some aspect of the study participant’s behavior that is assessed to reflect the effects of the independent variable. The dependent variable is the experimental counterpart to a criterion variable. In the present experiment, Goal Difficulty is the independent variable while Sales is the dependent variable. Remember that the terms predictor variable and criterion variable can be used with almost any type of research, but that the terms independent variable and dependent variable should be used only with experimental research.
Researchers often refer to the different levels of the independent variable. These levels are also referred to as experimental conditions or treatment conditions and correspond to the different groups to which participants can be assigned. The present example includes two experimental conditions: a difficult-goal condition and an easy-goal condition.
With respect to the independent variable, you can speak in terms of the experimental group versus the control group. Generally speaking, the experimental group receives the experimental treatment of interest while the control group is an equivalent group of participants who do not receive this treatment. The simplest type of experiment consists of just one experimental group and one control group. For example, the present study could have been redesigned so that it consisted of an experimental group that was assigned the goal of making 25 cold calls (the difficult-goal condition) and a control group in which no goals were assigned (the no-goal condition). Obviously, you can expand the study by creating more than one experimental group. You could do this in the present case by assigning one experimental group the difficult goal of 25 cold calls and the second experimental group the easy goal of just 5 cold calls.