Methodology: Draft/Free writing (part 6)
As the focus of my topic has shifted a bit, I’ve gone back and modified my methodology section a little bit. I’ve only posted the last bit that I’ve rewritten, rather than the whole thing.
Methodological Approach
The methodology used in this study will be a population study. To provide context for the findings, other methods will be utilized. The exact method for conducting the population study will differ depending on the site being examined. Therefore, most of the methodology used in this study will briefly be explored inside specific chapters. A detailed explanation for all methodologies for all sites can be found in Appendix 1: Site Specific Methodologies.
Despite the specific methodologies for different sites, there are several broad methodologies that are used as a component of population study. The type often is dependent on the type of fan being looked at. The first involves measuring the population of viewers. The second involves determining the size and characteristics of the population that identify with a club, athlete or league. The third involves measuring population shifts over time. To a degree, a fourth method of predictive analysis will be utilized to determine if the answers discovered using the three previous methods can predict the size and shape of Australian sport going into the future.
Determining the population of viewers is generally very simple. A site is visited and a number is recorded. In the case of Alexa, website viewers would be determined by recording the rank of the site around the world and in Australia. If a site has enough visitors, Alexa may provide some demographic data about site viewers. Twitter viewership involves recording the total number of Tweets using specific keywords. For Wikipedia, viewers are determined utilizing Wikipedia article statistics provided at http://stats.grok.se/ . For YouTube, total viewers are determined by recording information about a video and the number of views for that video. This particular method is used to determine the total interest in an athlete, club or league. No assumptions regarding classification of fan type can be made as looking at a webpage, tweeting about a topic or watching a video are largely passive activities that do not imply a person wants to be publicly identified as supporting the club. The point of using this methodology is to determine the reach for a league, club or athlete.
Population size and characteristics involve determining what people want to be publicly connected to an athlete, club or league. On Facebook, this is determined using Facebook’s advertisement page at https://www.facebook.com/ads/create/ . People who like fan pages or list a topic as an interest on their profile appear in those results. Different keywords and demographic characteristics are chosen and the results are then recorded. On LiveJournal, profile interests and community membership are used to determine who identifies with an athlete, club or league. Once that is determined, demographic information on their profile is recorded. On Twitter, profile information is recorded for people who follow selected accounts on the site. On Yahoo!Groups, membership to a group dedicated to a club or athlete determines if a person identifies with them. Profile information is then pulled from the Yahoo!Group members list. On YouTube, this information comes from the profiles of those who upload video featuring an athlete or club. This methodology differs from viewers as the group being examined has taken active steps to express allegiance to the athlete, club or league by sharing that interest on public profiles.
Throughout the sections that required population size and characteristics information, there was a dependence on user listed locations to determine the geographic location of members of the Australian sport fan community. To provide consistency across all sites looked at, a list was developed that included user generated location, city, state and country. The city, state and country were determined based on intelligent guesswork. For example, as the focus of the research is Australia, if Melbourne was standing alone, the assumption was that the user meant Melbourne, Victoria, Australia and not Melbourne, Florida, United States. Spelling variations and nicknames were also used to determine location. For example, Brisvegas is a nickname for Brisbane, Queensland, Australia. Often patterns of cities were looked for assuming the standard pattern of city, state, country or country, state, city or city, country or city, state. To aid in processing location lists more quickly, when using an automated tool such as the one for Twitter, the user-generated list was supplemented with one created by the author. This list included all Australian cities listed using the patterns of postal code and city, state and city, country, and city, state, country. The completed list contains over 65,000 variants that were listed by the author or user created. This information can be found in Appendix 2: User locations to city, state, country.
The third methodology is a repeat of the two previous types. There is an additional step of gathering this data on multiple dates to check for historical patterns and determine if there are shifts in the composition of a fan community or for the audience interested in an athlete, club or league.
In order to provide framework for the data, the analysis will be done as a series of cases involving events that take place in Australian sport fandom. Most of these cases will involve the AFL or the NRL. As the cases evolve, the results will become predictive analysis: Do previous cases suggest patterns of predictable behavior in how fan populations respond to certain situations?
