Content Analysis

Reading Summary
Content analysis is one of three methods that researchers use to discover the effects of media content. Content analysis allows for researchers to describe content, but it does not allow for inferences to be made about the effects of the content. Any description or summary must be purely objective. This is because there is a knowledge gap between the content and its effects that must be filled with context that mere analysis cannot provide. Content analysis is objective, systematic, and quantitative. Because of this, it is possible for multiple researchers to come to the same conclusion regarding a specific piece of content. Content analysis focuses on manifest content, which refers to tangible content that must be interpreted by a coder.

Outside Example
My example is of the common phrase “correlation does not imply causation.” During the reading, I was reminded of a site that graphed the correlation between events with seemingly no relation to one another. Though the number of people who drown in pools and the number of films Nicolas Cage appeared in are heavily correlated, they do not actually effect each other in any way. I hope.

Reading Connection
In the reading, it is repeated several times that content analysis cannot make any inferences about the data. This is because the analysis alone is not enough to work off of. If you were to interpret the data in the graph above, you might be led to believe that Nicolas Cage has a devout cult following that sacrifice themselves so that he may continue to make movies. Obviously this is not the case. This silly example goes to show that researchers should never try to interpret the data of content analysis without performing more tests.