Cause and Effect definition | Psychology Glossary | az-links.info
of identifying cause-effect relationships in large-scale natural resources surveys. menting a cause-effect study, the assumptions that are needed, and the. can be argued that the ultimate goal of most research is to identify cause and effect. reasoning to infer cause-effect relationships. In deciding whether a. Understanding exactly how causation is established in scientific research can help sift through the An education in cause and effect might help put this in perspective. Very few Are there more primary causes that explain the relationship?.
Resource text A principal aim of epidemiology is to assess the cause of disease.
However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer a cause-effect relationship exists.
That is, the observed association may in fact be due to the effects of one or more of the following: Chance random error Bias systematic error Confounding Therefore, an observed statistical association between a risk factor and a disease does not necessarily lead us to infer a causal relationship. Conversely, the absence of an association does not necessarily imply the absence of a causal relationship.
The judgement as to whether an observed statistical association represents a cause-effect relationship between exposure and disease requires inferences far beyond the data from a single study and involves consideration of criteria that include the magnitude of the association, the consistency of findings from other studies and biologic credibility .
The Bradford-Hill criteria are widely used in epidemiology as providing a framework against which to assess whether an observed association is likely to be causal.
Statistical Language - Correlation and Causation
Strength of the association. According to Hill, the stronger the association between a risk factor and outcome, the more likely the relationship is to be causal. Have the same findings must be observed among different populations, in different study designs and different times?
Specificity of the association. There must be a one to one relationship between cause and outcome. Temporal sequence of association.
Exposure must precede outcome. Change in disease rates should follow from corresponding changes in exposure dose-response.
Presence of a potential biological mechanism. It certainly seems plausible that as inflation increases, more employers find that in order to meet costs they have to lay off employees.
- Establishing Cause & Effect
- Establishing Cause and Effect
- Causation in epidemiology: association and causation
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.
Causation in epidemiology: association and causation | Health Knowledge
This makes it very hard to establish a causal relationship in this situation. Covariation of the Cause and Effect 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: 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: 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.
Australian Bureau of Statistics
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.