Using Netdraw to create a 2-mode network is very similar to creating a 1-mode network except that now the data is in a different format.
First open Netdraw,
Go to File → Open → Ucinet Data Set
→ 2 Mode Network (Figure 42).
Figure 42: Open the Ucinet file in Netdraw
In the window that appears search for the name of the file to open (Figure 43).
Make sure the following options are selected:
File format: → Ucinet and
Type of Data: 2 mode network → OK.
Figure 43: Find the Ucinet file
The 2-mode network (Figure 44) chart appears showing how the actors are grouped around different information demands.
Figure 44: The 2-mode network
G Gi
Figure 45: Add the attributes The network map can be enhanced by adding
attribute data. This is done in the same way as before. The .txt files are Ucinet compatible.
Go to File → Open → Vna text file → Attributes (Figure 45).
Ucinet can also be used to create an attributes database. Both sets of nodes can be given attributes but only one attribute database can be used at a time.
A window will open from which to select the attribute file created in the same way as for a 1 mode network (Figure 46).
Make sure to choose:
File form: Vna and Type of data:
Node Attribute(s) → OK.
Figure 46: Find the correct file
Once this information has been added the node colour can be changed in exactly the same way as shown for 1 mode networks.
Go to Properties → nodes → color → attribute based (Figure 47).
Figure 47: Change the colour of the nodes
In the window that appears to the right of the screen select the attribute which will be used to colour the nodes. In the example in Figure 48 the different colours represent the number of cows owned by the producers interviewed.
Figure 48: Choose the colour of the nodes
Groups can be hidden depending upon their attributes to facilitate a more detailed analysis (Figure 49). As for the 1 mode network go to Nodes and unselect the group(s) of actors which should be hidden.
Figure 49: Hide groups of nodes
The programme also allows the colour of the lines to be changed to highlight the different communication media that have been suggested to receive each type of information.
Go to Properties → Lines → Color → Tie strength (Figure 50).
Figure 50: Change the colour of the ties
This function makes it possible to highlight very specific information. For example, if we only want to see what types of information people want to receive from the radio, go again to the window on the right: relationships and chose the number assigned to the radio (which for the purposes of this exercise is the number 6) and only these lines will appear.
Figure 51 shows that some of the information nodes are now isolated from the rest of the network. This means that no one has requested to receive those types of information from the radio. Different combinations of relationships can be selected depending upon the finality of the study in question.
Figure 51: Select tie variables
This new network can be given shape using the same layout tool as for a 1 mode network.
Figure 52 shows the new network structure when looking at requests for information through the radio.
Figure 52: Network structure showing only one type of tie
6. A flexible tool
As stated at the beginning of this text, the aim of this manual is to make social network analysis (SNA) widely available to people working in rural development and other sectors in which there are highly complex social networks, as these can either help or hinder the work of researchers and development projects, depending upon the relations they establish with these existing networks.
This manual can be adapted to the requirements of people and organisations working in many different circumstances. The potential variety of users in different sectors will be fundamental to updating the manual as this is a new tool whose applications still need to be explored and tested. Therefore each user should use this methodology according to their own needs and interests, identifying the how best way to use it to understand the complex social relationships that have an influence on local development. All contributions are welcome to continue streamlining and validating this methodology.
It is hoped that by simplifying SNA more users from different sectors will be able to access it as it is a flexible, practical tool that has a huge potential in the development and research sectors. It is still too early to come to any final conclusions and for the time being it is important to continue to apply this tool in a variety of different situations to further understand and evaluate both the potential and limitations of this methodology.
There is no single answer to explain the ‘why’ of poverty, nor the ‘how’ of development; understanding the combination of factors that lead to the success or failure of development projects requires flexible thinking and the ability to make constant adjustments. This manual seeks to help practitioners and researchers understand the complementarities and conflicts that exist among different actors and initiatives in the development sector in the hope that this tool will eventually take is place among the wide range of methodologies that are used to understand and encourage development processes.
Naturally those using this manual will have a lot of questions about SNA and the results it provides. This manual deliberately does not discuss how to carry out the analysis of results, so that each user is free to draw their own conclusions, reading the maps according to their own points of interest and highlighting the importance of seeing the same reality from different points of view. The concept that supports this idea is that visualising social networks can help people to understand how complex their environment is and use this as a first step towards making positive changes.
It is hoped that this manual will be a starting point which will enable users to understand the practical details of social network analysis, so that they will be free to use their imagination to explore how to use and adapt this tool to their own needs.
Good luck using this tool!