It certainly seems plausible that as inflation increases, more employers find that in order to meet costs they have to lay off employees. So it seems that inflation could, at least partially, be a cause for unemployment.
But both inflation and employment rates are occurring together on an ongoing basis. Is it possible that fluctuations in employment can affect inflation? If we have an increase in the work force i. So which is the cause and which the effect, inflation or unemployment? It turns out that in this kind of cyclical situation involving ongoing processes that interact that both may cause and, in turn, be affected by the other. This makes it very hard to establish a causal relationship in this situation.
What does this mean? Before you can show that you have a causal relationship you have to show that you have some type of relationship. For instance, consider the syllogism:. If you observe that whenever X is present, Y is also present, and whenever X is absent, Y is too, then you have demonstrated that there is a relationship between X and Y. I don't know about you, but sometimes I find it's not easy to think about X's and Y's.
Let's put this same syllogism in program evaluation terms:. Or, in colloquial 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:. Just because you show there's a relationship doesn't mean it's a causal one.
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? Once an association has been established, our attention turns to determining the time order of the variables of interest.
In order for the independent variable to cause the dependent variable, logic dictates that the independent variable must occur first in time; in short, the cause must come before the effect. This time ordering is easy to ensure in an experimental design where the researcher carefully controls exposure to the treatment which would be the independent variable and then measures the outcome of interest the dependent variable.
In cross-sectional designs the time ordering can be much more difficult to determine, especially when the relationship between variables could reasonably go in the opposite direction.
For example, although education usually precedes income, it is possible that individuals who are making a good living may finally have the money necessary to return to school.
Determining time ordering thus may involve using logic, existing research, and common sense when a controlled experimental design is not possible. In any case, researchers must be very careful about specifying the hypothesized direction of the relationship between the variables and provide evidence either theoretical or empirical to support their claim. The third criterion for causality is also the most troublesome, as it requires that alternative explanations for the observed relationship between two variables be ruled out.
Another well-known example is the relationship between the number of fire fighters that respond to a fire and the amount of damage that results — clearly, the size of the fire determines both, so it is inaccurate to say that more fire fighters cause greater damage.
Though these examples seem straightforward, researchers in the fields of psychology, education, and the social sciences often face much greater challenges in ruling out spurious relationships simply because there are so many other factors that might influence the relationship between two variables.
Cause and effect is one of the most commonly misunderstood concepts in science and is often misused by lawyers, the media, politicians and even scientists themselves, in .
Establishing Cause and Effect. A central goal of most research is the identification of causal relationships, or demonstrating that a particular independent variable (the cause) has an effect on the dependent variable of interest (the effect).
A cause and effect research paper is custom written by the writers at Paper Masters and will explore the cause and effect of any type of phenomena you need. As one of our frequent research paper topics, below we outline how to write a cause and effect paper. Establishing Cause & Effect. Establishing a Cause-Effect Relationship. How do we establish a cause-effect (causal) relationship? Typically the most difficult criterion to meet is the third -- ruling out alternative explanations for the observed effect. That is why research design is such an important issue and why it is intimately linked to.
Apr 25, · Cause and Effect Essay Topics. Updated on April 10, Virginia Kearney. more. What effect has cancer research had on stopping cancer deaths? What causes people to get cancer? Okay so cause and effect essays are written in different formats, but let me say in my college profession for the essays to be written were to be in APA Reviews: 1 A GUIDE TO WRITING A CAUSE AND EFFECT RESEARCH PAPER What is a Cause and Effect Research Paper? You’re probably wondering what this “freshman research paper .