Getting Acquainted

Post date: Jun 26, 2013 11:15:4 AM

Before You Start

This exercise assumes you have access to the standard datasets that come with UCINET. These are automatically installed on your computer when you install UCINET. The default location is c:\program files\analytic technologies\ucinet 6\datafiles. For various reasons, you might prefer to copy this folder to a new location. For example, you might copy the Datafiles folder to your root directory, so that the new pathname would be c:\datafiles. This is a nice short path name! Whatever you decide to do, just remember where you put it and adjust the instructions below accordingly.

Setting Your Default Folder

It is *extremely* wise to start each UCINET session by setting the default folder to the folder containing the data you want to work on. Here’s how you do it.

1. Start up UCINET
2. On the main window, press the “file cabinet” button on far right at the bottom of your screen. Navigate to your datafiles folder, such as

c:\program files\analytic technologies\ucinet 6\datafiles

make sure to double click on it to select the folder.

Display some Data

In the menu, select Data|Display. At the “dataset:” prompt, press the ellipses (“..”) button to access file menu. Select a dataset called Campnet.

Note that what you see might be campnet.##h or campnet.##d. This is normal. The way UCINET stores data, the logical dataset CAMPNET is physically stored as two separate files, campnet.##h and campnet.##d, both of which are necessary. But you can refer to the dataset as campnet.##h or campnet.##d or just campnet: the program doesn’t care. It will be accessing both files no matter what.

Then press “ok”. You should see something like this:

DISPLAY

-----------------------------------------------------------------------

Width of field: MIN

# of decimals: MIN

Rows to display: all

Columns to display: all

Row partition:

Column partition:

Input dataset: C:\Program Files\Analytic Technologies\Ucinet 6\datafiles\campnet

1 1 1 1 1 1 1 1 1

1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8

H B C P P J P A M B L D J H G S B R

- - - - - - - - - - - - - - - - - -

1 HOLLY 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0

2 BRAZEY 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0

3 CAROL 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0

4 PAM 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0

5 PAT 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

6 JENNIE 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0

7 PAULINE 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

8 ANN 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0

9 MICHAEL 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0

10 BILL 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0

11 LEE 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

12 DON 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0

13 JOHN 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1

14 HARRY 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0

15 GERY 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1

16 STEVE 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1

17 BERT 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1

18 RUSS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

----------------------------------------

Running time: 00:00:01

Output generated: 06 Jul 03 22:21:41

Univariate Stats

Let’s run some basic statistics on the campnet dataset. Go to Tools|Univariate Stats. Where it says “Input Dataset:” type CAMPNET. Where it says “Which dimension to analyze” enter “Matrices” (use the down button to select matrix). Then press OK. The result should be:

UNIVARIATE STATISTICS

--------------------------------------------------------------------------------

Dimension: MATRIX

Diagonal valid? NO

Input dataset: C:\Data\DataFiles\campnet

Descriptive Statistics

1

-------

1 Mean 0.176

2 Std Dev 0.381

3 Sum 54.000

4 Variance 0.145

5 SSQ 54.000

6 MCSSQ 44.471

7 Euc Norm 7.348

8 Minimum 0.000

9 Maximum 1.000

10 N of Obs 306.000

Statistics saved as dataset C:\Data\DataFiles\campnet-uni

----------------------------------------

Running time: 00:00:01

Output generated: 11 Jul 04 23:31:57

The mean of the matrix is .176. Since the matrix consists of 1s and 0s, you can interpret this mean as the number of 1s divided by the number of cells in the matrix (306). In other words, it is the proportion of cells that have value of 1.

Note that the stats you see on the screen are also saved as a file called campnet-uni. Most UCINET procedures produce two different kinds of outputs. One is the textual data that you see on the screen (reproduced in step 3 above). The other is a dataset (in this case called campnet-uni) saved on the hard disk.

Similarly, most procedures have two kinds of inputs: (a) a dataset (campnet), and (b) a set of choices of parameters, such as dimension = matrices.

More Stats

Suppose we re-run the Univariate Stats procedure choosing different parameters. In particular, where we said “matrices” last time let us change it to “rows” this time. The output should be:

UNIVARIATE STATISTICS

--------------------------------------------------------------------------------

Dimension: ROWS

Diagonal valid? NO

Input dataset: C:\Data\DataFiles\campnet

Descriptive Statistics

1 2 3 4 5 6 7 8 9 10

Mean Std De Sum Varian SSQ MCSSQ Euc No Minimu Maximu N of O

------ ------ ------ ------ ------ ------ ------ ------ ------ ------

1 HOLLY 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

2 BRAZEY 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

3 CAROL 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

4 PAM 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

5 PAT 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

6 JENNIE 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

7 PAULINE 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

8 ANN 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

9 MICHAEL 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

10 BILL 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

11 LEE 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

12 DON 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

13 JOHN 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

14 HARRY 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

15 GERY 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

16 STEVE 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

17 BERT 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

18 RUSS 0.176 0.381 3.000 0.145 3.000 2.471 1.732 0.000 1.000 17.000

Statistics saved as dataset C:\Data\DataFiles\campnet-uni

----------------------------------------

Running time: 00:00:01

Output generated: 12 Jul 04 08:20:04

Each column of this matrix is a different statistic. For example, column 3 is the sum of each row in the Campnet matrix. In this case, each person has a sum of 3, indicating that they each have 3 outgoing ties.

Re-Displaying Data

Now run Data|Display on the dataset campnet-uni. The results should be the same as in the last step.

The Big D Button

Now we are going to display the CampAttr dataset (it might be called campattr2). But this time, use the shortcut icon on the toolbar that is labeled with a big “D”. The output should look like this:

DISPLAY

--------------------------------------------------------------------------------

Width of field: MIN

# of decimals: MIN

Rows to display: all

Columns to display: all

Row partition:

Column partition:

Input dataset: C:\Program Files\Ucinet 6\workshop\campattr

1 2 3

G R C

- - -

1 HOLLY 1 1 1

2 BRAZEY 1 1 1

3 CAROL 1 1 1

4 PAM 1 1 1

5 PAT 1 1 1

6 JENNIE 1 1 1

7 PAULINE 1 1 1

8 ANN 1 1 1

9 MICHAEL 2 1 2

10 BILL 2 1 2

11 LEE 2 1 2

12 DON 2 1 2

13 JOHN 2 1 2

14 HARRY 2 1 2

15 GERY 2 2 3

16 STEVE 2 2 3

17 BERT 2 2 3

18 RUSS 2 2 3

Bank Wiring Room Dataset

You start by going to Help|Help Topics and then clicking on “Standard Datasets”. Then click on “ROETHLISBERGER & DICKSON BANK WIRING ROOM” and read the description of the data.

Now display (again use shortcut key combination Ctrl-D) the Wiring dataset. Should look like this:

DISPLAY

--------------------------------------------------------------------------------

Width of field: MIN

# of decimals: MIN

Rows to display: all

Columns to display: all

Row partition:

Column partition:

Input dataset: C:\Program Files\Ucinet 6\datafiles\wiring

Matrix #1: RDGAM

1 1 1 1 1

1 2 3 4 5 6 7 8 9 0 1 2 3 4

I I W W W W W W W W W S S S

- - - - - - - - - - - - - -

1 I1 0 0 1 1 1 1 0 0 0 0 0 0 0 0

2 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 W1 1 0 0 1 1 1 1 0 0 0 0 1 0 0

4 W2 1 0 1 0 1 1 0 0 0 0 0 1 0 0

5 W3 1 0 1 1 0 1 1 0 0 0 0 1 0 0

6 W4 1 0 1 1 1 0 1 0 0 0 0 1 0 0

7 W5 0 0 1 0 1 1 0 0 1 0 0 1 0 0

8 W6 0 0 0 0 0 0 0 0 1 1 1 0 0 0

9 W7 0 0 0 0 0 0 1 1 0 1 1 0 0 1

10 W8 0 0 0 0 0 0 0 1 1 0 1 0 0 1

11 W9 0 0 0 0 0 0 0 1 1 1 0 0 0 1

12 S1 0 0 1 1 1 1 1 0 0 0 0 0 0 0

13 S2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

14 S4 0 0 0 0 0 0 0 0 1 1 1 0 0 0

------------------------------------------

Matrix #2: RDCON

1 1 1 1 1

1 2 3 4 5 6 7 8 9 0 1 2 3 4

I I W W W W W W W W W S S S

- - - - - - - - - - - - - -

1 I1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 W2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 W4 0 0 0 0 0 0 1 1 1 0 1 0 0 0

7 W5 0 0 0 0 0 1 0 1 0 0 0 1 0 0

8 W6 0 0 0 0 0 1 1 0 1 1 1 1 0 1

9 W7 0 0 0 0 0 1 0 1 0 1 1 0 0 1

10 W8 0 0 0 0 0 0 0 1 1 0 1 1 0 1

11 W9 0 0 0 0 0 1 0 1 1 1 0 1 0 0

12 S1 0 0 0 0 0 0 1 1 0 1 1 0 0 1

13 S2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

14 S4 0 0 0 0 0 0 0 1 1 1 0 1 0 0

------------------------------------------

.. ..

(several more matrices)

Note that the Wiring dataset contains multiple networks, each represented as a binary matrix.

Now let’s run univariate statistics on the Wiring dataset, choosing “Matrices” as the dimension to analyze:

UNIVARIATE STATISTICS

--------------------------------------------------------------------------------

Input dataset: wiring (C:\Users\sborgatti\Documents\UCINET data\wiring

Output dataset: wiring-uni (C:\Users\sborgatti\Documents\UCINET data\wiring-uni

Dimension to analyze: Levels/Layers/Matrices

Diagonal valid: NO

Statistics

1 2 3 4 5 6

RDGAM RDCON RDPOS RDNEG RDHLP RDJOB

------- ------- ------- ------- ------- -------

1 Observations 182 182 182 182 182 182

2 Missing 0 0 0 0 0 0

3 Minimum 0 0 0 0 0 0

4 Maximum 1 1 1 1 1 20

5 Sum 56 38 26 38 24 49

6 Average 0.308 0.209 0.143 0.209 0.132 0.269

7 SSQ 56 38 26 38 24 621

8 Standard Deviation 0.462 0.406 0.350 0.406 0.338 1.827

9 Variance 0.213 0.165 0.122 0.165 0.114 3.340

10 MCSSQ 38.769 30.066 22.286 30.066 20.835 607.808

11 Euclidean Norm 7.483 6.164 5.099 6.164 4.899 24.920

11 rows, 6 columns, 1 levels.

-----------------------------------------

Running time: 00:00:01 seconds.

Output generated: 27 Jun 13 09:46:23

The result is a set of descriptive statistics for each matrix contained in the Wiring dataset.

Extracting Data

Let’s eliminate the isolates from a network – i.e., nodes that have no ties. Go to Data|Filter/Extract|Submatrix. Fill out the prompts as follows:

And obtain the following results:

EXTRACT

--------------------------------------------------------------------------------

Operation: DELETE

Rows: 2 13

Columns: 2 13

Relations: NONE

Input dataset: C:\Program Files\Ucinet 6\datafiles\wiring

Output dataset: wiring2

Matrix 1: RDGAM

1 1 1 1

1 3 4 5 6 7 8 9 0 1 2 4

I W W W W W W W W W S S

- - - - - - - - - - - -

1 I1 0 1 1 1 1 0 0 0 0 0 0 0

3 W1 1 0 1 1 1 1 0 0 0 0 1 0

4 W2 1 1 0 1 1 0 0 0 0 0 1 0

5 W3 1 1 1 0 1 1 0 0 0 0 1 0

6 W4 1 1 1 1 0 1 0 0 0 0 1 0

7 W5 0 1 0 1 1 0 0 1 0 0 1 0

8 W6 0 0 0 0 0 0 0 1 1 1 0 0

9 W7 0 0 0 0 0 1 1 0 1 1 0 1

10 W8 0 0 0 0 0 0 1 1 0 1 0 1

11 W9 0 0 0 0 0 0 1 1 1 0 0 1

12 S1 0 1 1 1 1 1 0 0 0 0 0 0

14 S4 0 0 0 0 0 0 0 1 1 1 0 0

…

What you have just done is to create a new dataset called Wiring2 that is just like the original except that it is missing the two nodes (I3 and S2).

UCINET Spreadsheet

Go to Data|Data Editors|Matrix Editors and open the Wiring2 dataset that you just created. Verify that I3 and S2 are gone.

Netdraw

Press the button to start NetDraw. Press the Open File button and load the Wiring2 dataset. You should see this diagram:

Visits: