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Social Network Analysis UCINET Social Network Analysis UCINET

UCINET--Introduction l UCINET—UCINET is produced by Analytic Technologies. It offers a very user-friendly, reasonably UCINET--Introduction l UCINET—UCINET is produced by Analytic Technologies. It offers a very user-friendly, reasonably priced software system for network analysis. l 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 l When you first open UCINET, set the default directory to a Starting UCINET l 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. l 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 l There are several ways to read network How to Read Data into UCINET l There are several ways to read network data into UCINET. I’ll review two basic methods using matrices, and using dl language. l UCINET can read in a matrix data—either saved in a text file, or saved in excel. l 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 l If those data are saved in a How to Read Data into UCINET l 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. l “Arizona. txt” is an example of such a file. l 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 l One way to double check that it How to Read Data into UCINET l 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. l After you’ve read in the data, an output screen will show you the data in matrix form. l 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 l An alternative is to read data in How to Read Data into UCINET l An alternative is to read data in using a standard excel file. An example of such an excel file can be found here. l In UCINET, go to Data, then Import, then Excel matrix. Be sure to tell UCINET whether the matrix includes row / column labels. l (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 l Often, the easiest way to read data How to Read Data into UCINET l 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. l 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). l 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 l Some researchers find excel files easier to How to Read Data into UCINET l 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. l 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 l Network files can be thought of in terms of Other Types of Data l Network files can be thought of in terms of matrices (whether or not they are actually read in as matrices (versus DL language)). l 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 l Reading in attribute files to UCINET is a bit Reading in Attribute Files l 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 “swnom 01”, a wnominate score). l Download the two excel files: srepub and swnom 01.

Reading in Attribute Files l Notice that the files are in matrix form—there are Reading in Attribute Files l 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. ) l 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”. l At that point, you should have srepub. ##h (and the companion file srepub. ##d) as well as swnom 01. ##h (and the companion file swnom 01. ##d).

Reading in Attribute Files l In order to create an attribute measure, go to Reading in Attribute Files l 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). l 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 l So, for “Vector is Row or Column? ”, choose Reading in Attribute Files l So, for “Vector is Row or Column? ”, choose column. For “Which Row/Col”, choose 1. l 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 l So, under “Method”, select “Exact Matches” l And the Reading in Attribute Files l So, under “Method”, select “Exact Matches” l And the output data set defaults to srepub. Column-1. It may be useful to rename it to something that makes more intuitive sense, such as “sameparty”

Reading in Attribute Files l Next, let’s look at ideology as an attribute (swnom Reading in Attribute Files l Next, let’s look at ideology as an attribute (swnom 01). l 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 l However, it no longer makes sense to choose “exact Reading in Attribute Files l 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 l And, it may make sense to name the output Reading in Attribute Files l And, it may make sense to name the output file something that makes intuitive sense, such as diffideol. l Okay, now what can we do with UCINET?

UCINET l As noted in previous discussions, UCINET has a terrific tutorial. You can UCINET l 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 l UCINET also allows the possibility of a regression analysis, using a QAP Procedure l UCINET also allows the possibility of a regression analysis, using a QAP procedure. l The QAP Procedure will be the focus of our next discussion.