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.
The dataset has 3 networks (advice, friendship and co-worker) but you
only need to analyse ONE NETWORK. I will upload the datasets separately
(file name: LazegaNet.txt and Lazatt.txt)
Instructions

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 the
perspectives 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
Guidelines
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
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.
Marking
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%