Brokerage & Local Position Lab

Post date: Mar 23, 2014 5:54:45 PM

For this lab we will use only one network and one attribute dataset:

KRACK-HIGH-TECH (KHT) & HIGH-TEC-ATTRIBUTES (HTA)

This is a dataset collected by David Krackhardt from managers of a high tech company. KRACK-HIGH-TECH is a stacked dataset containing three directed, dichotomous matrices which represent ADVICE, FRIENDSHIP, and REPORTS_TO ties among 21 managers within the company. HIGH-TEC-ATTRIBUTES contains four attributes for each of the 21 actors, including each manager’s age (in years), tenure with company, level in corporate hierarchy, and department.

NOTE: I use the abbreviations KHT and HTA to refer to the full filenames (KRACK-HIGH-TEC and HIGH-TEC-ATTRIBUTES) in the lab for brevity.

1) Structural Holes using UCINET and NetDraw with KHT and HTA

a. If you have not already done so, unpack (Data | Unpack) the KHT dataset to get the three adjacency matrices FRIENDSHIP, REPORTS_TO, ADVICE.

b. Run Network | Ego Networks | Structural Holes on the FRIENDSHIP data. From the output, who appears to have the largest Effective Size?

c. Visualize the FRIENDSHIP in Netdraw to visualize it.

d. When you ran structural holes in UCINET it automatically saved the output in a dataset called “FREINDSHIP-SH” (unless you changed the name). Load FRIENDSHIP-SH as an attribute file in Netdraw and use the effective size attribute (EffSize) to size the nodes on the graph. What information does this convey in the graph?

e. Again using the “Nodes” tab in the control region, select the density attribute (Desnity) and click on the “size” checkbox to resize the nodes based on Density. What happened? Why?

2) Transitivity and Clustering in UCINET using KHT

a. Run Network | Cohesion | Clustering Coefficient on the FRIENDSHIP data.

b. By default, this procedure creates a file called ClusteringCoefficients. Open that file in the UCINET spreadsheet (click on the grid icon to bring up the UCINET spreadsheet). Select the data and label from the column labeled “Clus Coef” and copy it. Now, open the StructuralHoles dataset created in step 1 in the data grid. Increase the number of columns by 1 by changing the value in the box under “Cols:” in the “Dimensions” section of the window, click in the empty label cell (grey cell at top) for the new column and paste the data you just copied. Save this dataset with a new name (e.g., HolesAndClusters) using File | Save As.

c. Now run Tools | Similarities on this new dataset to find correlations between the variables. Which of Burt’s structural holes measures is the most like and the most opposite clustering coefficient? Why?

d. Run Network | Cohesion | Transitivity on the KHT (KRACK-HIGH-TEC) stacked dataset (NOT on FRIENDSHIP). What does this tell you? Given that transitivity is about “Closure” (or lack of structural holes), which of the three relations has the most and the least closure?

3) E-I Index with UCINET using KHT & HTA

a. Run Network | Cohesion | E-I Index on the FRIENDSHIP data, partitioning the data based on department (which is in column 4 of the HIGH-TEC-ATTRIBUTES dataset). Looking at the individual E-I index statistics, who has the most homophilous ties (more concentrated within the same department), and who has the most heterophilous (most concentrated outside department) ones?

b. Rerun E-I index using the same partitioning, but instead of using the FRIENDSHIP dataset, use the stacked KHT (KRACK-HIGH-TEC) dataset. Bearing in mind that you ran this on a stacked dataset, what do you think these results tell you? How could you find out?

c. Display (using the “D” icon) the KHT stacked dataset. Rerun the E-I index on the individual dataset that is displayed first from this command and compare the results to the results from step b. Is this what you thought was happening?

4) Brokerage with UCINET using KHT & HTA

a. Run Network | Ego Networks | G&F Brokerage roles on the FRIENDSHIP data, again using the department attribute (Column 4 of HTA) for your partition vector.

b. Open KRACK-HIGH-TEC in Netdraw and, using the “Rels” tab in the control region, display only the FRIENDSHIP relation. Compare this visualization with the results from step a. It may help to color or shape the nodes by department. Can you find at least one example of each kind of brokerage for Actor 5? (Remember, direction counts in brokerage, so make sure you have the arrows on and visible.)

c. Running brokerage automatically created two datasets, one called BROKERAGE which is a two-mode matrix of actors by brokerage roles. Run Tools | Scaling/Decomposition | Correspondence on the BROKERAGE dataset. Interpret the picture you get.

d. Because this dataset is actors by brokerage roles, it (like most output from UCINET routines) can be used as an attribute file. Load BROKERAGE as an attribute file in NetDraw (making sure the FRIENDSHIP relation is open and displayed first). Now, size the nodes by the various brokerage roles (Consultant, Representative, etc.). Does doing this help identify the different kinds of brokerage roles people play in the network?

e. Rerun the brokerage routine using the REPORTS_TO data instead of the FRIENDSHIP data, still partitioning based on the department attribute. Thinking about the relationships, what can you tell about the actors based on this output.

f. Re-run brokerage on the REPORTS_TO data, but this time use the Level attribute (Column 3) to partition that data. How does this data compare to the previous output? Why is it different?

5) Ego-Net Strength with UCINET and NetDraw using KHT

a. Run Network | Ego Networks | Egonet composition | Continuous alter attributes specifying the ADVICE dataset you previously unpacked from KHT for the Input Network Dataset. For the Input Attribute dataset, specify HIGH-TEC-ATTRIBUTES and select the column labeled “Tenure.”

b. This procedure gives information about each actors’ egonet with respect to their access to “Tenure”. (This is a newer routine which reads the column labels from the attribute file so you do not have to look up the column numbers.) If we say that getting advice from people who have worked for the company longer is more likely to lead to success, which people are most likely to succeed based on these results?

c. Go back to NetDraw, load KHT and ensure that the ADVICE relation is being displayed.

d. Now load the dataset just created (by default it was called ADVICE-EgoStrength) as an Attribute file in NetDraw. Size the nodes based on both the “Sum” and the “Avg” (Average) values calculated. How do the results differ? Which do you think is more likely to lead to success?