Social Network Analysis

Social Network Analysis
UCINET
UCINET--Introduction

UCINET—UCINET is produced by Analytic
Technologies. It offers a very user-friendly,
reasonably priced software system for
network analysis.

Throughout this discussion, we’ll use the
example of the cosponsorship network of the
58 legislators in the lower house of the
Arizona legislature, 2001.
Starting UCINET

When you first open UCINET, set the default
directory to a directory of your choice, by typing in
the directory name (into the space at the bottom
edge of the UCINET window). Note that the original
default directory is just the c:\ drive.

Note that UCINET produces many types of files—
and deleting any (before you are entirely done with
your analysis) may make it difficult to use some of
the others.
How to Read Data into UCINET

There are several ways to read network data into UCINET.
I’ll review two basic methodsusing matrices, and using dl
language.

UCINET can read in a matrix data—either saved in a text
file, or saved in excel.

So, in the case of the Arizona cosponsorship data that we
will use as an example, there are 58 legislators – and
therefore 58 X 58 = 3,364 dyads.
How to Read Data into UCINET

If those data are saved in a text file (with 58 rows,
and 58 “cosponsorship frequencies” listed on each of
those rows), they can be read in as “raw data” into
UCINET.

“Arizona.txt” is an example of such a file.

Download this file. Then, in UCINET, click on “Data”
and “Import” and “Raw”. The text box should allow
you to search for the file you’ve downloaded, and to
read it into UCINET.
How to Read Data into UCINET

One way to double check that it is “correct” is to look at the
number of rows and columns that are automatically filled into
that dialogue box: it should be 58.

After you’ve read in the data, an output screen will show you
the data in matrix form.

You can also see the data in matrix form by going to Data,
clicking on Spreadsheets, then clicking on Matrix. This will
open up an emptry spreadsheet window. Click “File” and then
“Open” (or, control-O) to open the file. The file is in the default
directory (as specified in the lower row of the UCINET
program), and it is called “Arizona.##h”.
How to Read Data into UCINET

An alternative is to read data in using a standard
excel file. An example of such an excel file can be
found here.

In UCINET, go to Data, then Import, then Excel
matrix. Be sure to tell UCINET whether the matrix
includes row / column labels.

(The UCINET program will immediately remind you
of the name of the program—just as a safety doublecheck. Just click “ok”.)
How to Read Data into UCINET

Often, the easiest way to read data into UCINET is to
use DL language. A document with a set of
examples of DL programming can be found here.

When you use DL language, the default value of the
edge between each possible pair of nodes is
automatically set to 0 (that is, no connection).

The DL language is then used to specify the value of
all edges. Labels for the rows and columns of the
matrix can also be specified through DL language.
How to Read Data into UCINET

Some researchers find excel files easier to work
with—some find DL language easier. In the Arizona
case, the SAS program that I used actually creates a
matrix file that is relatively easy to transfer into DL
language.

DL language can also be useful if you need to read
in additional matrices. In the cosponsorship
example, I have an additional matrix file that lists the
number of “shared committees” on which each pair
of legislators serve.
Other Types of Data

Network files can be thought of in terms of
matrices (whether or not they are actually
read in as matrices (versus DL language)).

A second type of file is an “attribute” file,
which lists attributes for each node. So, I
have a set of “attributes” for each legislator,
including race, sex, partisanship, seniority,
etc.
Reading in Attribute Files

Reading in attribute files to UCINET is a bit
unwieldy. We’ll use two examples from the
Arizona legislative datasetparty (“srepub”,
coded 1 if Republican, 0 otherwise) and
ideology (measured by “swnom01”, a wnominate score).

Download the two excel files: srepub and
swnom01.
Reading in Attribute Files

Notice that the files are in matrix form—there are 58 legislators in this
network, so each of these two files has 58 rows and 58 columns. It is
really only the first column that matters—the other columns are just
filler columns (as UCINET works only with matrix files.)

Read the two files into UCINET, as you did with the cosponsorship file
previously. In this case, however, choose “node attribute file”
rather than “network adjacency matrix”.

At that point, you should have srepub.##h (and the companion file
srepub.##d) as well as swnom01.##h (and the companion file
swnom01.##d).
Reading in Attribute Files

In order to create an attribute measure, go to Data,
then click on Attribute. Browse to find the
“srepub.##h” that you imported into UCINET (after
downloading the parallel excel file).

The attribute information for Republican is in the first
column (recall what the excel file looked like—the
first column was 0/1 (0=Dem, 1=Repub), and the
other 57 columns were “99” to just fill out the matrix.
Reading in Attribute Files

So, for “Vector is Row or Column?”, choose column.
For “Which Row/Col”, choose 1.

Now, you need to specify how you want the attribute
to be constructed. In this case, it makes sense to
create an attribute that stands for exact matches—
two legislators who have the same code will be of
the same party (either both Republican or both
Democrat).
Reading in Attribute Files

So, under “Method”, select “Exact Matches”

And the output data set defaults to srepubColumn-1. It may be useful to rename it to
something that makes more intuitive sense,
such as “sameparty”
Reading in Attribute Files

Next, let’s look at ideology as an attribute
(swnom01).

As before, enter in the name of the file (browse to
find wherever you saved the ideology file that you
imported into UCINET, after downloading the
relevant excel file from the web.) And, enter in
“Column” and “1” to specify that the attribute info can
be found in the first column of data in the file.
Reading in Attribute Files

However, it no longer makes sense to
choose “exact matches”, since legislators will
rarely be exactly the same ideology—what
matters is how close they are in ideology.
So, in “Method”, choose “absolute
difference”—which will represent similarity
between two legislators’ ideologies.
Reading in Attribute Files

And, it may make sense to name the output
file something that makes intuitive sense,
such as diffideol.

Okay, now what can we do with UCINET?
UCINET

As noted in previous discussions, UCINET
has a terrific tutorial. You can use the
Arizona data to practice reading in files, and
calculating out commonly used descriptives
for networks and nodes (such as the various
centrality measures that we’ve discussed.)
QAP Procedure

UCINET also allows the possibility of a
regression analysis, using a QAP procedure.

The QAP Procedure will be the focus of our
next discussion.

Download Report