Strategies

One way to begin analyzing data like this is to come up with a way to visualize information along different axes. For example, you could create a chart in Microsoft Excel that lists times of the day along the horizontal or x-axis and the numbers of different things on the vertical or y-axis (such as number of words typed and/or read or instances of media consumption versus production). Experiment with different variables to see whether interesting trends emerge.

You can also do what researchers call “coding” of data, which involves creating categories. For example, you could look at what media you chose for different purposes. Did you tend to use Twitter for talking to certain categories of people but the phone for others? Did the length of sentences you wrote change based on whom you were writing to, what medium or program you were using, or what time of day it was? You would “code” this data by looking at each chunk of communication (an e-mail, a tweet, a phone conversation) and categorizing it in a certain way. If you were looking at whom you talked with on Twitter, you might simply count what social relationship they had with you (friend, classmate, relative, and so on). Then you could look for trends across those categories.

You’ll probably not be able to show any definitive trends in your analysis because your data size is so small — content analysis studies often examine hundreds or thousands of pieces of media. But for this informal study you can and should be open to thinking about possible trends.

3

Data analysis such as this is usually a recursive process where you sift through the raw data until you think you spot what might be a trend. Then you come up with concrete rules that you think will prove or disprove that trend. Sometimes you go into the analysis with a vague (or not so vague) hunch that you then test. There’s not a single best method for doing this; it pays to experiment and be open to new ideas as you work.