Social Research Methods - Knowledge Base - Establishing Cause & Effect
The three criteria for establishing cause and effect – association, time ordering ( or temporal A central goal of most research is the identification of causal relationships, While the classic examples used to illustrate these criteria may imply that procedures whenever possible), careful data collection and use of statistical. The general amount of traffic in a city has a cause-effect and a reverse cause - effect relationship with the general number of roads built. When research is designed to investigate cause and effect relationships ( explanatory research) through the direct manipulation Implies that the same data would have been collected each Operational definition of the dependent variable.
Notice, however, that this syllogism doesn't not provide evidence that the program caused the outcome -- perhaps there was some other factor present with the program that caused the outcome, rather than the program. The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome -- a continuous relationship: It's possible that there is some other variable or factor that is causing the outcome.
This is sometimes referred to as the "third variable" or "missing variable" problem and it's at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity see single group threatsmultiple group threats or social threats -- each one describes a type of alternative explanation.
In order for you to argue that you have demonstrated internal validity -- that you have shown there's a causal relationship -- you have to "rule out" the plausible alternative explanations. How do you do that? One of the major ways is with your research design. Let's consider a simple single group threat to internal validity, a history threat. Let's assume you measure your program group before they start the program to establish a baselineyou give them the program, and then you measure their performance afterwards in a posttest.
You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat -- it's not your program that caused the gain but some other specific historical event.
For instance, it's not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General's latest report that happened to be issued between the time you gave your pretest and posttest.
How do you rule this out with your research design? One of the simplest ways would be to incorporate the use of a control group -- a group that is comparable to your program group with the only difference being that they didn't receive the program. But they did experience the Surgeon General's latest report. If you find that they didn't show a reduction in smoking even though they did experience the same Surgeon General report you have effectively "ruled out" the Surgeon General's report as a plausible alternative explanation for why you observed the smoking reduction.
Establishing Cause & Effect
In most applied social research that involves evaluating programs, temporal precedence is not a difficult criterion to meet because you administer the program before you measure effects. And, establishing covariation is relatively simple because you have some control over the program and can set things up so that you have some people who get it and some who don't if X and if not X.
Many public opinion surveys typically place considerable emphasis on defining the population of interest and drawing good samples from that population.
On the other hand, laboratory experiments often employ "convenience samples," such as intact college classes taught by a friend or in the College "subject pool". As a result, we may not know whom the subjects represent. A measure with high construct validity accurately reflects the abstract concept that you are trying to study. Since we can only know about our concepts through the concrete measures that we use, you can see that construct validity is extremely important.
It also becomes clear why it is so important to have very clear conceptual definitions of our variables. Only then can we begin to assess whether our measures, in fact, correspond to these concepts. This is a critical reason why researchers should first work with concepts, and only then begin to work on operationalizing them, if at all possible.
If we only use one measure of a concept, about the best we can do is "face validity," i.
Therefore, it is wise to use multiple measures of a concept whenever possible. Furthermore, ideally these will be different kinds of measures and designs. You might measure mathematical skill through a paper and pencil test, through having the student work with more geometric problems, such as a wood puzzle, and having the student make change at a cash register. Our faith that we have accurately measured her high math ability is stronger if she performs well on all three sets of tasks.
Construct validity is often established through the use of what is called a multi-trait, multi-method matrix. At least two constructs are measured. Each construct is measured at least two different ways, and the type of measure is repeated across constructs. For example, each construct first might be measured using a questionnaire, then each construct would be measured using a similar set of behavioral observation categories.
Typically, under conditions of high construct validity, correlations are high for the same construct or "trait" across a host of different measures. Correlations are low across constructs that are different but measured using the same general technique e. Sometimes, this is called "triangulating" measures. Under low construct validity, the reverse holds.
Correlations are high across traits using the same "method" or type of technique or measurement but low for the same trait measured in different ways. For example, if our estimate of a student's math ability was wildly divergent depending on whether we examined scores on the questionnaire, making change, or the wood puzzle, we would have low construct validity and a corresponding lack of faith in the results.
Be very skeptical of studies that totally equate their concrete measures with their constructs. For example, if you are convinced that biological factors cannot be overcome, you probably will not work with visually impaired children because you would believe that they could not compensate for their disabilities. Consider some different perspectives on causality: God or some type of Gods did it. Nature works with "an unseen hand". There are "rational laws" to be discovered and people are capable of discovering these.
Causal relations are an illusion; the universe is random and chaotic, and runs on entropy. Of course, none these perspectives nor the "means of proof" below are mutually inconsistent in the human cognitive process.
Just as a physicist may secretly read his horoscope each morning, people may simultaneously invoke some, all, or none of these perspectives. Here are some different ways and means of "proof": Controlled experiments in which purported causal factors are manipulated systematically. Citing recognized authorities, such as Biblical or Quran scripture-or Sigmund Freud. Marshalling one's reasonable arguments as in a court of law or journalism.
Precedent as in a court of law. Reading traces in the environment Sherlock Holmes stories. Devine revelation in dreams, visions, bones, tea leaves, etc.
Statistically controlling various purported causal variables. Why do I bother with these different orientations? Because again causality is critical to the research enterprise! Much of science consists of ruling out alternative causes or explanations. While science is one form of knowing and one generic way of gathering evidence that either disconfirms or is suggestive of causality, it is not the only way of doing so.
The results of science may or may not be accurate, but without following "the rules" of science, most scientists do not believe one is "doing science. According to science rules, definitive proof via empirical testing does not exist. Science uses the term "proof" or, rather, "disproof" differently from the way attorneys or journalists do. Our measurements could be later shown to be contaminated by confounding factors.
A correlation could have many causes, only some of which have been identified. Later work can show earlier causes to be spurious, that is, both cause and effect depend on some prior causal often extraneous variable. Further, science at its best is a self-correcting process.
Another researcher can try to duplicate your results. If the results are interesting, in fact, dozens of researchers may try to duplicate the results. If something was awry with your study, the subsequent research projects should discover and correct this.
We use the rules of science in this course. While normal lung tissue is light pink in color, the tissue surrounding the cancer is black and airless, the result of a tarlike residue left by cigarette smoke. Lung cancer accounts for the largest percentage of cancer deaths in the United States, and cigarette smoking is directly responsible for the majority of these cases.
There are many topics where it is neither possible--nor desirable--to use the experimental method. To accept more correlational evidence it will help to examine the rules below. I have never understood how the numeric level of one's measures can have much to do with cause. After all, variables such as gender, nationality, and ethnicity can have profound casual effects and they are categorical variables.
Authors who make this mistake may also misunderstand causality.
This causal conclusion about smoking and lung cancer is based on correlational or observational evidence, i. There is no doubt that the results from careful, well-controlled experiments are typically easier to interpret in causal terms than results from other methods. However, as you can see, causal inferences are often drawn from correlational studies as well.
Non-experimental methods must use a variety of ways to establish causality and ultimately must use statistical control, rather than experimental control. The results of the Hormone Replacement Therapy experiments, released in the summer ofremind us of the great care that must be taken when designing nonexperimental research. Self selection of women into the original "hormone" non-experimental conditions implied that HRT prevented heart attacks and strokes among women.
Internal Validity - The Confidence in the Cause-Effect Relationship
In fact, when the topic was studied experimentally the reverse was true: HRT increased the risk of heart and circulatory disease among women. The discrepancy probably occurred because women who take better care of themselves may see a physician on a more regular basis, and thus be in better health to begin with. This self selection bias probably caused an erroneous and spurious correlation between HRT and women's health.
Some scientists mistakenly believe that large samples can establish causality. Just as numeric measures can't establish cause, neither can the size of the sample or population studied. Large numbers of participants can increase the stability of research results, but do not help to designate cause and effect.
Watch for some of these fallacies in establishing cause and effect in the research that you encounter. However, two variables can be associated without having a causal relationship, for example, because a third variable is the true cause of the "original" independent and dependent variable.
For example, there is a statistical correlation over months of the year between ice cream consumption and the number of assaults. Does this mean ice cream manufacturers are responsible for violent crime?
The correlation occurs statistically because the hot temperatures of summer cause both ice cream consumption and assaults to increase.
Thus, correlation does NOT imply causation. Other factors besides cause and effect can create an observed correlation. The effect is the dependent variable outcome or response variable. If you can designate a distinct cause and effect, the relationship is called asymmetric.
For example, most people would agree that it is nonsense to assume that contacting lung cancer would lead most individuals to smoke cigarettes. For one thing, it takes several years of smoking before lung cancer develops. On the other hand, there is good reason to believe that the carcinogens in tobacco smoke could lead someone to develop lung cancer. Therefore, we can designate a causal variable smoking and the relationship is asymmetric.
Two variables may be associated but we may be unable to designate cause and effect. These are symmetric relationships. For example, men over 30 with higher mental health scores are more likely to be married in the U. Marriage is a "buffer" protecting from the stresses of life, and therefore it promotes greater mental health. Perhaps the causal direction is the reverse.