CSE Chapter 3. Cause and Effect
Apr 1, And although such relationships are, in the strict sense, merely very good human beings routinely, and necessarily, use them to assign cause and effect. is in practice not uncommon in scientific research as well. Cause and effect is the backbone of the scientific method; it drives everything We usually describe this relationship as positive, negative, or no relationship at. Cause and effect is a relationship between events or things, where one is the result of the other or others. Review the examples in this article to better.
Similarly, the general gas law fails for very low temperatures, since any gas will eventually start to liquify.
Establishing Cause and Effect
In general, it is not very clear what are the exact boundaries within which any given law can be applied; the boundary between Newtonian and quantum physics is fuzzy, which is a symptom of the fact that quantum gravitational phenomena are at present very poorly understood.
A third thing to notice is that physical laws cannot be proved; instead, they can fail to be refuted. The standard methodology of science calls for making hypotheses, and then trying to refute them with experiments. No single experiment can show that a physical law is true, because the law is supposed to apply to an infinite number of different situations. However, a single experiment can refute a hypothesis. A hypothesis that withstands more and more tests gradually comes to be more and more accepted.
Social Research Methods - Knowledge Base - Establishing Cause & Effect
And as already noted, even experiments that contradict a law may not serve to refute it totally, but only to restrict the range of phenomena within which it is considered to be usefully valid. If this is the situation for well-established and widely used physical theories, just think how bad things must be for the social sciences!
It is often said that good science is reductionist, and the success of the hard sciences is often given as an example that the social sciences should try to follow. A prime example is the reduction of chemistry to physics. However, if we look carefully at what really happens in chemistry, we will see that chemists are not doing a specialized kind of physics. On the contrary, they are using concepts at the level of chemistry, such as valance.
Since it is impossible in practice to solve Schroedinger's equation for any but the very simplest atoms, calculations in quantum physics cannot be used to do chemistry.
This situation is often described by saying that chemistry is an emergent level above physics, meaning that partial reductions are possible and can be very valuable when they occur, but concepts and theories that are distinctly chemical and not quantum mechanics are primary for the practical applications. This does not deny that reduction might be possible in principle, and most scientists believe that this is the case.
If we look at higher levels, such as biology, psychology, and sociology, we again see emergent phenomena, but it becomes progressively more difficult to support the belief that reduction to lower levels must be possible in principle, and indeed most social scientists today do not believe this.
So where does this leave us?
So we conclude that arguments in favor of technological determinism based on a claim that it is in some sense more scientific than alternatives are fatally flawed. Going a little further, I think we should conclude that it is very wise to be suspicious of simplistic principles and simplistic arguments in complex areas like the relationship between technology and society.
Technological determinism is a prime example of such a simplistic principle. But then, Why, given the deficiencies of technological determinism, do people find it so persuasive? Why is it so common in advertisements, newspaper and magazine articles, websites, and other places?
One answer is that causal explanations are built into our language. For example, the sentence John hit the ball. Readers want to understand this sentence, not in isolation, but as a part of a story, which might be about baseball, where the actor has an intention to perform the action, because of its consequences. That is, readers want to find a cause, e.
Linguistics has developed extensive theories of stories, which can add many interesting details to this discussion.
The narrative presupposition applies to any story, but especially to oral narratives of personal experience; it says that the order of clauses is the same as the order of the events that they describe unless there are explicit contrary indications this term was introduced by William Labov.
For example, if we hear John hit the ball. And we will further assume that the crowd cheered because John hit the ball.
This is an example of what I call the causal presupposition, which says that if possible, we should read the second event as caused by the first event. Note that the causal presupposition assumes the narrative presupposition. Here is a more complex example: John hit the ball, fell off his bike, and broke his arm.Cause and Effect
Here there are three narrative clauses, and as before, we automatically assume that the events they describe occur in the same order as the clauses, and furthermore that there are causal relationships between the first and the second event, and between the second and the third event.
Additional evidence concerning the narrative and causal presuppositions comes from studies of the Balinese language, in which the narrative presupposition is replaced by the default presupposition that, given clauses A, B in that order, the corresponding events happen concurrently, possibly with mutual interaction see papers by Alton Becker. In computer science terms, we might say that in English, the default semantic connection between subsequent clauses is ";" rather than " "whereas the opposite holds in Balinese.
Much more information on the theory of narrative can be found on the web through the links on the narratology page at the Media and Communication Studies site at the University of Aberdeen e. More details of my own approach can be found in the essay Notes on Narrative ; I hope that we can discuss narrative further later on in this course.
Let's put this same syllogism in program evaluation terms: This provides evidence that the program and outcome are related. 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.
Statistical Language - Correlation and Causation
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.