“Between the reality and final description resulting from the analysis of the data are two processes that can be thought of as acting like polarizing filters placed on the lens of a camera: They allow only a limited amount of what comes into them to pass through, and they shape what manages to pass through in their own peculiar way. These 'filters' are the processes of measurement and analysis of the data. Both processes interact with the data in important ways.” (Richards 1985:112-113)
In accordance with Richards, my argument is that during the collection and preparation of data for social network analysis certain interferences can occur that could finally lead to the resulting network data set not representing the reality anymore. A complex reality is simplified again and again until it fits into one or more matrices in a computer file.
Until the results of the analysis are on the table, the researcher has to decide on many factors that can influence the final results of the study. Therefore, it is a justified question to ask whether these results still have anything do to with the reality.
This thesis seeks to investigate some of these factors. I refer to them as size reduction and transformation processes. This includes the planned and targeted choice of certain methods on the one hand, as well as the unintentional occurrence of interferences on the other hand. Often these processes are inevitable, sometimes they are chosen because they are convenient and fast to realize.
Size reduction refers to all processes that somehow involve the loss of data. In this particular study, biased and unbiased nonresponse, forgetting, the omission of unimportant nodes, and random sampling will be investigated. Transformation refers to changes in the way network-analytical data is contained in the matrix. In particular, symmetrization, dichotomization and collapsing actors will be discussed and investigated. Transformation leads to a simplification and therefore information is lost as well, but in a different way as with size reduction processes. The reason for this manifold choice of processes and methods is what they have in common. A researcher is sooner or later forced to consider them when doing social network analysis. But the decisive factor for this choice was that all processes can be investigated in a similar way.
This study utilizes a simulation technique to test to which extent the size reduction and transformation processes influence the results of social network analysis. When you
face nonresponse in a study, the main problem is that you do not know what those interviewees would have answered. There is no way to know if the results of your study are in danger. When you ask people about their ties to others and they forget to tell you some of them, you have the same situation. You need to rely on your data, and there is no way to test if this is reasonable in that particular case. At some point you have to draw a line and exclude less important actors from your study in order to avoid a long or even infinite data collection process. Another solution for dealing with large populations discussed in the literature is to draw a sample. But again you do not know whether the conclusions derived from a study based on a sample would be the same conclusions you would derive from a study based on a full survey of the whole population. The only way to know this would be to do a survey of the whole population and to compare your results to those of the sample afterwards. But as soon as you have the complete data you do not need the sample anymore and you do not need to test whether a sample would have been different.
That is the idea of this study: A complete data set is taken and compared to different versions of the data set in which nonresponse, forgetting, sampling, or other interferences have been introduced. This is a simulation because the complete data set is not the reality either. It is only defined to be the point of reference for the comparison. It is also a simulation, as, for example, no real nonresponse is investigated: The answers of certain actors are removed from the data set to simulate nonresponse. Simulating transformation is done simply by comparing a transformed data set to the original.
Afterwards, some of the most common network-analytical measures are calculated and compared to the results from the original data set. Then it can be seen whether the size reduction and transformation processes harm the results. Its simulation methodology is the reason why this thesis refers to the “size reduction and transformation of data sets”
in its title. Still, I expect that the results can be generalized and also applied to real sampling, nonresponse and other processes.
This study utilizes policy networks for the simulation. Therefore, the terminology is chosen accordingly (for example the word “actor”), although it should be possible to generalize the results of this study beyond the area of policy networks. The discussion will be based on one-mode whole-network designs with data collection based on questionnaires. Neither egocentric networks nor two-mode data will be discussed in detail.
In chapter 2, network analysis and its methods will be presented shortly. It will only include those methods and measures that are dealt with in this study. Chapter 3 will present the current literature on the topic of methodological problems in network analysis and will then show what has already been investigated. The particular research question of this study with all the influencing factors and size reduction and transformation processes will be discussed in chapter 4. Chapter 5 will describe the analysis done for this study and its results. Finally chapter 6 will shortly summarize the results and discuss their consequences.