Quantitative research, or how many people is enough?

Analyzing quantitative methodology

The paper I’m looking at is Social Networking Sites: Their Users and Social

Implications — A Longitudinal Study by Petter Bae Brandtzæg. The paper looks at how people are affected by the use of so called SNS’s (Social Networking Sites). It tries to examine whether users of social media are more or less antisocial and lonely than people who do not use social media. The paper uses a longitudinal study, that is, a study that looks at how the data changes over time, in conjunction with a survey that gathered quantitative data from the respondents.

What stood out to me when I read the paper was the drastic lessening of responses they got over time. From about 2000 participants in the beginning of the study it had dropped to 708 or about 35% of the original respondents (the study was conducted three waves over the course of three years). While I was aware that a drop off in participation is expected, I wasn’t expecting it to be quite so steep. This, to me, stresses the importance to make sure to start with a large research material. I would also suspect that this makes it important to control that the dropout haven’t shifted the demographics significantly.

I fail to see any major flaws in the methodology at work here. However, I’m actually unsure if this means that are none, or is just an indication of my own lack of comprehension of what the author is doing. I understand the data collection process, but I’m having trouble following what they’re doing to analyze the data. Similar to a discussion we had during last themes seminar on not finding theory in a paper because of being overwhelmed by data, I’m wondering if I’m blinded by the sheer amount of numbers and diagrams here. The one possible flaw I can see is in the selection of the participants. The author have chosen data from Norway, a very computer heavy and internet savvy country. This might make the conclusions not applicable to a less computer dominated society.

Reflections on Drumming in Immersive Virtual Reality

Do you move differently in someone else's body? That is the question the researchers are trying to find the answer to. They did this through motion recording of people drumming in a virtual reality setting, where the all white participants were given either a white or a black avatar.

What did I learn? That it is really difficult to read someone’s quantitative research methodology and understand what it is they did. Although I had no trouble grasping how the study was done or what the purpose was, I really didn’t understand how they had analyzed their results. I am not a statistician, which is good since the numbers and deviations just confused me. This is quite similar to the issues I had with the other paper, which I have detailed above.

This leads me to believe that one of the downsides of using quantitative methods is that it’s harder for someone not in the know to understand why the conclusions were drawn. Other limitations may include a difficulty in distinguishing anomalies from rare behaviour. This is not based on the essay, but instead just a speculation on my part. I would argue that when using quantitative methods, if you see something out of the ordinary it’s harder to know what to deal with. If you were to use a more qualitative approach you could always ask the subject, or something along those lines, but that is not possible in this scenario. Benefits would include things such as a, in a way, more neutral result. Quantitative methods leave less room for the researcher's bias to be made apparent, since you don’t really draw conclusions in a way that leaves much room for speculation. It is perhaps also in a way more giving for future research. If the data and analytical methods are clearly presented, it would be fairly simple for another research team to use your results as a accessible jumping off point.

So what about qualitative methods? Well, some things would be what I accused quantitative of not being. It is often easier for a layman or someone not as deep in the field to view and understand what you have done. It is able to present a fuller picture than just data points can, and leave more room for interpretation and adaptation. This is not necessarily a good thing, however, since the same argument could be made as a weakness.