It would seem like bias would be eliminated that way, but this is not the case. There can be associations found on both group and individual levels, and that can be misleading (Goodman, Buehler, & Koplan, 1990). A researcher who notices this kind of association can be led to believe that it is something taking place on a group level, where it should actually be attributed to the people in the group on an individual level. It is called ecological bias when this happens, but these studies still have their place. They are less expensive than many other types of studies, and they are also quicker, as they can often be conducted with information that has already been published by other researchers or with public records information (Yehuda & McFarlane, 1995). The cross sectional study is designed to gather information from a point in time, and from a specific population. It is like taking a snapshot to look only at that group at that chosen time, rather than at a group over a period of time. The study population is very well-defined, and the characteristics of that population are studied at a precise point (Goodman, Buehler, & Koplan, 1990). One example of this kind of cross sectional study is the exit polling that is done right after someone votes in an election. The people for this type of study are only contacted one time and specific information is gathered.

The study is very inexpensive but there can be problems with it. For example, in the case of disease this kind of study can detect something taking place at that moment but it cannot detect why it is taking place, how long it has been ongoing, or where it came from, which are all things that can be very important (Goodman, Buehler,...

If a person has changed since contracting that disease, this also cannot be seen with this kind of study. It works well for some kinds of information, but it will not work well for all applications.
The last of the studies in the case-control study. This study design involves choosing people who already have a specific disease (Neutra, 1990). It will then look at how many of those people have some other factor or behavior -- for example, how many lung cancer patients are (or were) smokers. It does not look at people who have other issues as well, though, because those can cloud the results (Goodman, Buehler, & Koplan, 1990). It puts too many factors into the mix. One problem with this kind of study is that of recall. If people do not remember the details of their disease very well, they can provide information that is not correct and not accurate when compared with the real-life scenario that the person went through. The study works well for rare diseases, however, because a group of people can be found who have the disease and then the contributing factors can be looked for. It ensures a good group that will actually provide important study information.

Bibliography

Goodman, R.A, Buehler, J.W. & Koplan, J.P. (1990). The epidemiological field investigation: science and judgment in public health practice. American Journal of Epidemiology 132: 9-16.

Neutra, R.R. (1990). Counterpoint from a cluster buster. American Journal of Epidemiology 132: 1-8.

Yehuda, R., & McFarlane, A.C. (1995). Conflict between current knowledge about posttraumatic stress disorder and its original conceptual basis. American Journal of…