There are two ways to approach the collection of network data about your community – Secondary and Primary Data Collection.
When you work with pre-existing data to compile information about ties between organizations or individuals, you are using secondary data. You can look for existing information that shows links between residents and local schools, organizations, churches, or news outlets. Interpersonal ties might be gathered by seeing who appeared at the same event, who belongs to the same organizations, who collaborates on certain projects, and who has friendship or family relationships. There is also a vast amount of rich network data available on the Web. Friendships declared through online social network platforms like Facebook and hyperlinks between websites can be used to explore social and institutional connections. Lists of collaborative projects, partnerships or common funding sources may tell you something about the connections between local community organizations. Shared ownership, investments, market exchanges and strategic alliances will give you an idea about the ties between companies. Secondary data collection requires you to spend some time searching for reliable data that has already been collected, in some form or another, by others. It means giving a good look at information available on the internet or in other documents.
Any time you collect our own data on network relationships, you are using primary data. In some cases we simply cannot find enough available information without collecting it on our own. This often occurs when you are researching a group or topic that others have not looked at before. Primary data collection often involves administering a network questionnaire – a survey that asks participants to list their connections to others. You may, for example, ask respondents to name:
Get Participatory! The collection of primary network data offers an opportunity to include broader participation in your research. Perhaps you can enlist the help of those you would like to study in creating a network survey or in collecting the data about their networks of relationships.
If you are interested in studying inter-organizational networks, you may ask for a list of institutions that a local organization is partnering with or gets financial support from.
Different data collection strategies can be used depending on your research interests and available resources. We will look into two scenarios here:
Let's say the Metamorphosis Project was interested in studying the network relationships at a local community newspaper.
What is the structure of the network of relationships between people who work at the community newspaper?
If we were given access to the entire office, we could easily map the entire network. We would ask each staff member to describe their relationships with others at the newspaper. For instance, who do they talk to regularly? Who is their supervisor? Who do they go to for advice? Who do they never talk to? If we were to collect this information from every staff member, we could put together the individual pieces of data and compile this information to map out the overall network structure.
This approach is feasible when you are interested a relatively small, well-defined group. For example, perhaps you are interested in your own organization's network? Or the inter-organizational networks of community organizations that work on immigrants rights in your community? Or the networks of students participating in an after-school leadership program?
In this scenario, it may be helpful to include a roster in your network questionnaire. If you were studying your own organization, for example, you would have a list of all employees, with space for each person filling out the network survey to mark the names of people they collaborate with frequently. The roster will often remind your respondents about connections they would otherwise forget to mention.
2. You have access to a few members of the population you are interested in. You do not have a list of the whole population, or if you do, you lack the resources to widely distribute your network survey.
Under this scenario, it is still possible to use a survey and collect information about the individual social networks of members of your population (also known as ego networks). Each respondent (ego) can identify the people he or she is connected to (alters).
This approach will not provide you with information about the entire network. Instead, the data will likely consist of many separate small personal networks. This is only to be expected - if your respondents are randomly selected from a large population, they are not likely to know one another or have many friends in common.
Even though you won't have information about the entire network you are interested in, this approach may provide you with some important insights about the population under study. Ego networks will, for example, tell us a lot about the number and type of connections that individuals in your target group have. Back to our example, it might tell us that some homeless teens have large networks while others have small ones. It might tell you that certain female teens tend to associate with only females, while others associate with males and females. We might also see a few of the same connections pop up in different people's surveys, which suggests they might be an important person in the network of homeless teens – however, without the full network, it will be impossible to say just how central that person is.
One strategy that can be used in this approach is the so-called snowball sampling. For a snowball sample, you recruit a number of participants from the group you are interested in and ask them to identify their connections. At the next step you contact those connections (or ask the respondents to contact them for you) and ask them to participate in your survey. You can repeat this multiple times, collecting relationship information and using it to recruit new participants.
One field where this sampling strategy is well known is preventive medicine. Health studies will often use snowball samples to recruit members of at-risk populations.
Source: Valente, T. (2010) Social Networks and Health