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ADVANCED SOCIAL NETWORK ANALYSIS
Please read through and carefully follow these instructions and try to sketch an outline for a report.
The data set I’ve chosen is Lazega Law firm: An expanded version of the dataset for lawyers in a New England law firm. This has 71 lawyers, their advice, friendship, and co-worker ties as well as a number of attributes. I will upload the dataset separately.
Choose one of the themes from the list below which you feel most comfortable with:
· Degree-based effects
· Closure and connectivity
· Cohesion and embeddedness
· Balance, homophily and transitivity
and write a 3000 word (upper limit) report that addresses this topic from theperspectives of
- relevant network-related theory - use of an empirical Data set - appraisal of how theory, research questions and data fit together and support each other
You a meant to demonstrate that you can tie together some substantive theories with an empirical analysis, where the theories have been expressed in appropriate research questions, and subsequently draw conclusions about the extent to which data support your research questions. The report MUST include an empirical analysis. This does not have to be very advanced but you must employ some manner of quantitative evaluation; e.g. comparing some measure against a null-distribution of graphs (graphically or by using statistics); or fit a model (ERGM, SAOM or any other model).
The minimal requirement is that you form one research question based on theory that you then express in terms of prevalence of subgraphs or a particular structural pattern in data, and that you furthermore explore and test this on some data set.
Things you must do in your analysis of the dataset:
1. setting the number of nodes/actors
2. calculating the number of ties and density
3. calculating the number of Mutual, Assymetric, and Null Dyads
4. calculating the triad census
5. calculating the degree distributions
· When presenting and analysing the network make sure that you define the nodes, ties, and context, properly.
· Make sure you understand what the data set is and able to explain how it is collected.
The weight in the marking scheme is: Theory
Concepts 10% Usage 10% Motivation/Research questions 10%
Data analysis Correct 10% Grasp and command 10% Originality 10% Advanced 10%
Conclusions Interpretation 10% Evaluation 10%
Overall Clarity and structure 10%
Othe paper details:
Must be perfect in R-Project and R-Studio, 10 pages.
Citation Format is Harvard
No of Sources: 8 Sources
Must be familiar in R-Project and R-Studio
Note that the analysis of the data is very important.
Lastly, I selected 8 references to be covered.
My paper must be perfect no room for revision