more myths, facts, and video games!

I am so uninformed about this topic, and I really think looking at different facts and myths is broadening my horizon in the world of video games!

Myth 5. Correlational studies are irrelevant.
Facts: The overly simplistic mantra, "Correlation is not causation," is useful when teaching introductory students the risks in too-readily drawing causal conclusions from a simple empirical correlation between two measured variables. However, correlational studies are routinely used in modern science to test theories that are inherently causal. Whole scientific fields are based on correlational data (e.g., astronomy). Well conducted correlational studies provide opportunities for theory falsification. They allow examination of serious acts of aggression that would be unethical to study in experimental contexts. They allow for statistical controls of plausible alternative explanations.



I feel as if I keep reiterating the same things, but every study should be taken into consideration! In order to accurately talk about this myth and fact I got a little bit of background information on what a correlation study actually consisted of, rhather then basing the blog off of my ignorance! On the IBM website, (http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.db2.udb.doc/admin/c0006909.htm), it actually explains what a correlation study is.

"Use the Correlation transformer to determine the extent to which changes in the value of an attribute (such as length of employment) are associated with changes in another attribute (such as salary). The data for a correlation analysis consists of two input columns. Each column contains values for one of the attributes of interest. The Correlation transformer can calculate various measures of association between the two input columns. You can select more than one statistic to calculate for a given pair of input columns.

The data in the input columns also can be treated as a sample obtained from a larger population, and the Correlation transformer can be used to test whether the attributes are correlated in the population. In this context, the null hypothesis asserts that the two attributes are not correlated, and the alternative hypothesis asserts that the attributes are correlated."

From this, I gathered that basically the person doing the test makes a hypothesis; (which is pretty standard in most testing, no matter what type). You create a table with 2 different sides. Its basically a test that makes a comparison between to different subjects. So for example, perhaps one side of the table could represent changes in behavior, whle the other side represented changes in the video game. The test will be able to accurately show how these 2 "subjects" related to each other. It is extremely important to see how these 2 things relate to each other in this study because it is exactly what is being tested! This test would definitely be successful, because it would make the 2 different sides more clear especially since they are easliy seperated and can be compared more clearly.