Basic Concepts in Research
1.6 Causality and Correlation
participants from different populations using different experimental procedures and different measures of the dependent variable. Findings that hold up under a wide range of circumstances (like the“distributed over mass practice”finding) are termedrobust. Oftentimes a researcher, who has uncovered an interesting finding, will go on to conduct a series of related studies in which they system- atically alter populations, settings, and related variables in order to establish the robustness of their finding. This is oftentimes referred to as conducting aline of research.
Internal and external validity are only two among many different kinds of validities. They are all important to the research process, and much fuller treat- ment of these concepts can be found in various methodology textbooks.
and sufficient reason forYcoming about. After all, there may be many other rea- sons whyYhas occurred or can change in amount. When we say“XcausesY,” we are merely saying that if we placeXin this particular situation, we will get more Ythan if we had not placedXthere. This understanding of causality is indeed helpful, but it is clearly limited. Saying that the utilization of a mental imagery technique can increase one’s pain tolerance is not saying that it is needed for any amount of pain toleration, nor it is to say that this is the only means of chan- ging pain toleration. Rather, this causal statement is merely saying that if one uses a certain form of mental imagery (X), one can expect to experience more pain tolerance (Y) than if one had not. Finally, causality statements in the behavioral sciences do not necessarily imply that anything whatsoever is known about the much more profound question as towhyreality is such that if we placeXin this situation, we will get moreY.
Nonetheless, the termcause, if understood modestly, can be appropriately used in the behavioral and social sciences. However, other modest phrases are also employed to designate the causal influence of one variable upon another. For example, some causal relationships might be described in phrases like“Xincreases the probability thatYwill occur”or“Xtends to bring about a change in the occurrence ofY.”In fact, many different action verbs can be found in the social and behavioral science literature.
Scientists highly value discovering causal relations because they help to bring about a deeper sense of understanding. Recall that this is the highest goal of the researcher (see Section 1.2). It is critical to see, however, thatcausalityis not the only way in which a relationship between two variables can be described. If two different variables tend to change reliably in association with each other, they are said tocovary. Variables that covary arecorrelatedvariables. This more lim- ited description of relationship meets the second goal of the researcher: corre- lation (or prediction, association). Height and foot size, for example, tend to covary. They are correlated: taller people usually have larger feet, and smaller feet tend to be attached to shorter people. When data are gathered nonexper- imentally (i.e. without manipulation), two variables may be observed to covary, and this covariation can be quantified. And yet, this covariation does not imply that a causal relationship exists between the variables, let alone the exact nature of a causal relationship (for instance, mightXcauseY, orYcauseX, or might some other variable,Z, cause bothXandY?).The manner in which the data are collected will determine the type of interpretation allowed. Causal relations can only be established in an experimental setting when a manipulated variable is observed to influence another variable. Methods of gathering data without the use of manipulation are calledcorrelational designs. (These designs as well as experiments, and quasi-experiments, comprise the three most frequently used designs that employ statistical analysis.) A prototypical example of a cor- relational design would be the survey. There is no independent variable, nothing is being manipulated, and no causal statements can be made. Data is gathered
simply as it presents itself to the researcher. In this way, it is correct to say that data gathered from“correlational designs do not imply causation.”(This topic will be covered in much greater detail in Chapter 15.)
Research on clinical depression demonstrates how variables can be found to covary without knowing the precise causal relation between them. Depression has many characteristics, and the reasons why people become depressed are many: The phenomenon is not exhaustively understood. One view maintains that people feel depressed because of negative thinking. They are pessimistic, are self-critical, and do not praise themselves when they do something well.
This perspective strongly implies that these cognitions have some causative role in depression. However, it is also quite possible that when people become depressed, they are more likely to think in a negative fashion. In Figure 1.1, question marks reflecting this interpretive problem are drawn above the arrows between negative thinking and depression.
However, even though negative thinking and depression may correlate, it is possible that neither variable causes the other. They may covary because some third variable, like “loss of control” for example, causes both negative thinking and depression. The question marks over the arrows in Figure 1.1 pointing from loss of control to negative thinking and depression reflect this possibility.
Unfortunately, some important research questions are, for ethical, logical, or logistical reasons, not amenable to experimentation. The relationship between negative thinking and depression is a good example. Even if we discovered a means by which researchers could manipulate“depression,”it would seem to be unethical to do so. Likewise, it is hard to imagine logistically how one could manipulate, for a sustained period of time, the degree of negative thinking a par- ticipant experiences. Some questions, it appears, are restricted to merely a cor- relational analysis.
Loss of control
Negative
thinking Depression
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Figure 1.1 “Negative thinking”and“depression”are correlated, but which one causes the other? It is also possible that a third variable,“loss of control,”causes both“negative thinking”and“depression.”
1.6 Causality and Correlation 25