Paolo asks for an example to clarify the post below. Let’s see if I can do that. The network picture is an information network, drawn by the actor “parboiler”, located in the middle of the picture. This is one of a number of interviews we want to look at.
The table shows the degree centrality values for this network. As you can see, the interview partner has an extremely high centrality value, because she knows most about her own links. Because of that it doesn’t make sense to compare her with the rest of the list. What does make sense though is to look at the rest of the actors she talks about, seeing e.g. that consumers have a higher degree centrality than their family (what ever that means in this specific case). Now if you are asking 20 par-boilers, you will find that (most likely) most will put themselves in a central position of their own information network, but still, some will just have a few information sources, while others have many more. Maybe you are interested in their absolute numbers of information sources, then you will compare the in-degree, out-degree or degree centrality (combination of in- and out-degree).
But if you want to know how connected they are as compared to the connection potential within their specific network, you look at the normalized centralities (last three collumns). They basically show: What percentage of the possible links (being linked to everyone in the network) does an actor have. The fact that the normalized degree centrality for parboiler is higher than 100% looks strange but stems from the fact that they add the normalized in-degree and the normalized out-degree. If you have collected non-network data (about the income, education, age or family situation of the women for example) you could then develop some ideas about why some women are better networked than others. Or what the effects of being well connected are. Maybe women with higher education seem to have more information links. Or women who don’t have husbands (because the ones with husband rely on the husband for most external information).
Obviously, the fact that two things appear together doesn’t tell you, whether one causes the other. Just like the old story of the ambulances at the site of an accident. Someone observed that the more ambulances you can count at the site of an accident, the more people died in the accident. So the logical solution was: Don’t send these ambulances and no one will die.