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UCINET Visualization and Quantitative Analysis Tutorial

Session 1 – Network Visualization

Session 2 – Quantitative Techniques

An Overview of UCINET (6.437)

Transferring Data from Excel (From Tab ConCoInfo)

Transferring Excel Matrix Data into UCINET

Step 1. Copy data from Excel Step 2. Open spreadsheet editor in UCINET Step 3. Paste into spreadsheet editor in UCINET Step 4. Save as “info”

Button To Open Spreadsheet Editor

Transferring Attribute Data into UCINET (From Tab: ConcoAttr)

Step 1. Copy data from Excel Step 2. Open spreadsheet editor in UCINET Step 3. Paste into spreadsheet editor in UCINET Step 4. Save as “attrib”

Button To Open Spreadsheet Editor

Opening NetDraw For Visualization

Step 1. Click The NetDraw Button To Open

Opening Data in NetDraw

Step 1. File > Open > Ucinet dataset > Network Step 2. Choose network dataset (info.##h)

Opening Data in NetDraw

Step 1. Click - open folder icon Step 2. Choose network dataset (info.##h), then click OK.

Initial Visual in NetDraw

Dichotomizing in NetDraw

Step 1. Click Relations Tab Step 2. Select “Greater Than” Operator Step 3. Insert The Number 3 Or Use The Plus Button To Get To 3

Using Drawing Algorithm in NetDraw

Step 1. Choose = option on tool bar

Using Attribute Data in NetDraw

Step 1. Click - open folder icon Step 2. Choose attribute dataset (attrib.##h), then click Open. Step 3. Click “OK” On Matching Box And “X” Out Of Attribute Editor. Step 4. May need to re-set tie strength levels and click lightning bolt again.

Choosing Color Attribute in NetDraw

Step 1. Select “Nodes” Step 2. Select “Region” Step 3. Place a check mark in the color box

Selecting Nodes in NetDraw

Step 1. Default is all groups selected. To remove one group, e.g. group 2, remove check from box

Selecting Egonets in NetDraw

Step 1. Select “Ego” Button On ToolBar Step 2. Ensure Geodesic distance FROM/TO ego is <= 1 Step 3. Select “BM” Step 4. De-Select “AR” Step 5. Select “All” Button and “X” Out Of Ego Net Viewer

Changing the Size of Nodes in NetDraw

Step 1. Properties > Nodes > Symbols > Size > Attribute-based Step 2. Select gender and make minimum node size 8 and maximum 16

Changing the Shape of Nodes in NetDraw

Step 1. Properties > Nodes > Symbols > Shape > Attribute-based Step 2. Select attribute, e.g. hierarchy

Changing the Size of Lines in NetDraw

Step 1. Properties > Lines > Size > Tie strength Step 2. Select minimum =1 and maximum = 5

Changing the Color of Lines in NetDraw

Step 1. Properties > Lines > Color > Node attribute-based Step 2. Select Region attribute, then choose within, between or both Step 3. Select Properties > Lines > Color > General to return to black lines

Deleting Isolates in NetDraw

Step 1. Select Iso option on the toolbar Step 2. Select ~Nodes button to bring back removed nodes (click on “Okay” in

pop-up box)

Resizing and Re-centering in NetDraw

Step 1. Layout > Move/Rotate Step 2. Select “Center” option

Saving Pictures in NetDraw

Step 1. File > Save diagram as > Jpeg Step 2. Choose file name, e.g. “Example Jpeg File For Powerpoint”

Session 1 – Network Visualization

Session 2 – Quantitative Techniques

The survey data that we collect is usually valued data. Although we can use valued data in UCINET we prefer to take different cuts of the data. For example, we may want to examine the data where people only responded “strongly agree” to a question. To do this we dichotomize the data i.e. convert it to zeros and ones where one means strongly agree and zero means any other response.

Dichotomizing Valued Data

Step 1. Transform > Dichotomize Step 2. Choose input dataset (info.##h)

Step 3. Choose cut-off op. and value (e.g. GE and 4) Step 4. Specify output data set (Info_GE_4)

Measures of Network Connection

• Density – Shows overall level of connection within a network. – We can also look at ties within and between groups.

• Distance – Shows average distance for people to get to all other

people. – Shorter distances mean faster, more certain, more

accurate transmission / sharing.

Network Connection Centrality

Cross Boundary Analysis

Density

• Number of ties, expressed as percentage of the number of pairs • Dense networks have more face-to-face relationships

Low Density (25%) Avg. Dist. = 2.27

High Density (39%) Avg. Dist. = 1.76



Quantitative Analysis: Density

Step 1. Network > Cohesion > Density > Density Overall Step 2. Input dataset “Info_GE_4”



Density of this network is 8%.

Distance

Average number of steps to reach all network participants Lower scores reflect a group better able to leverage knowledge

Short average distance Long average distance



Quantitative Analysis: Distance

Step 1. Network > Cohesion > Geodesic Distance (old) Step 2. Input dataset “Info_GE_4”



Average Distance is 3.545

Measures of Centrality

Degree Centrality: How well connected each individual is.

Betweenness Centrality: Extent to which individuals lie along short paths.

Closeness Centrality: How far a person is from all others in the network.



Quantitative Analysis: Degree Centrality

Step 1. Network > Centrality and Power > Degree



Quantitative Analysis: Degree Centrality

Step 1. Input dataset “Info_GE_4” Step 2. Choose whether to treat data as symmetric. I almost always select no. If you

choose “no” it will calculate separate figures for the people you go to and the people that come to you.



Quantitative Analysis: Degree Centrality Network Connection Centrality


In-degree for HA is 7

Quantitative Analysis: Degree Centrality Network Connection Centrality


Average in-degree is 3.652

In-degree Network Centralization is 12.424%

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Opportunities exist to re-distribute relational load. Focus on ways to de-layer those in the top right quadrant (info access, decision rights, role) while also better leveraging those in the bottom quadrant

# People Each Person Seeks Information From

# Peo

ple R

eceiv

es In

forma

tion F

rom

High Info Sources

High Info Seekers

Integrators

“From whom do you typically seek work-related information?”

ScatterPlot Step 1: Save Text File

Step 1. Generate Degree Calc. Network > Centrality > Degree > Info_GE_4 Step 2. File > Save As > Degree Output Text



ScatterPlot Step 2: Save Text File

Step 1. Open Excel Step 2. File > Open > Txt > Degree Output Text Step 3. Step 1 (In Text Import Wizard) > Next Step 4. Step 2 (Pictured) > Insert De-Limiter Between Names and Number. Step 5. Step 3 Finish



ScatterPlot Step 3: Insert Columns Back In UCINET

Step 1. Open UCINET Spreadsheet Editor Step 2. Cut And Paste Relevant Headers And In/Out Degree Numbers Step 3. Save As A UCINET file titled, “Scatterplot”



ScatterPlot Step 4: Create Plot In UCINET

Step 1. Tools > Scatterplot Step 2. Click on open file folder to open “Scatterplot” Step 3. Play with options (e.g., uniform axis)



Cross-boundary Analysis

Density across boundaries: How connected are groups within themselves

and with other pre-defined groups. This view can be used for different boundaries. We have used the following in our research: • Function or other designation of skill or knowledge. • Geographic location (even if only different floors). • Hierarchical level. • Time in organization or time in department. • Personality traits. • Gender (interesting though may be inflammatory).

Brokers: Which individuals are the links between other groups. Brokers can

be beneficial conduits of information but they can also hold up the flow of information.



Cross-boundary Analysis

Information Network: Density as related to practice Please indicate how often you have turned to this person for information or advice on work-

related topics in the past three months (response of often or very often). Healthcare Government IT Oil & Gas Pharmaceuticals Industrial

Healthcare 17% 0% 0% 7% 38% 0%Government 0% 17% 0% 0% 0% 10%IT 0% 0% 0% 0% 0% 6%Oil & Gas 4% 0% 0% 19% 3% 8%Pharmaceuticals 35% 0% 0% 1% 49% 0%Industrial 1% 9% 9% 12% 1% 8%



Density Across Practice

Step 1. Network > Cohesion > Density > Old Density Procedure Step 2. Input dataset “Info_GE_4” Step 3. Click on “…” to select “Attrib” file for Row Partitioning. Arrow to end to select col 3. Step 4. Column Partitioning will automatically be filled in with the same text as the Row Partition. Step 5. Scroll all the way down in output file for density matrix.



Tip: Col 3 is the column that includes the practice attribute. You can select different columns for different attributes MAKE SURE TO USE THE “DENSITY /

AVERAGE VALUE WITHIN BLOCKS”

Broker Categories

Coordinator - This person connects people within their group. Ego

A B

Gatekeeper - This person is a buffer between their own group

and outsiders. Influential in information entering the group.

A

Ego

B

Representative - This person conveys information from their

group to outsiders. Influential in information sharing.

B

Ego

A



Quantitative Analysis: Broker Metrics

Step 1. Network > Ego networks > G&F Brokerage Step 2. Input dataset “Info_GE_4” Step 3. Partition vector “attrib col 2”

Tip: Col 2 is the column that includes the gender attribute. You can select different columns for different attributes



Additional Quantitative Analysis

•Symmetrization & Verification •Combining Networks

•QAP Correlation and Regression

Symmetrizing Data

• Bill says he communicated with John last week, but John doesn’t mention communicating with Bill

• Three options – take the conservative option, and put no tie between John and Bill

(minimum) – take the liberal option, and put a tie between John and Bill (maximum) – take the average, assigning a tie strength of 0.5 for the relationship

between John and Bill (average)

Bill John

Symmetrizing Data (Continued)

Step 1. Transform > Symmetrize Step 2. Input dataset “Info_GE_4”

Step 3. Symmetrizing method “maximum” Step 4. Output dataset “Info_GE_4-Sym”

Tip: See previous slide for how to choose the most applicable symmetrizing method.

Combining Networks

In the picture to the left you can see the information network.

In the picture below is the combined information and value network.

Combining Networks (Continued)

Step 1. Tools > Matrix Algebra Step 2. In the Enter Command box type “infovalue = mult(ArtCoInfo_GE_4,ArtCoKase)”

Tip: The new matrix “infovalue” can now be used for various visual and quantitative analysis.

QAP Correlation

Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > QAP Correlation (old) Step 2. 1st Data Matrix “ArtCoInfo_GE_4” Step 3. 2nd Data Matrix “ArtCoKase” (note that this file is already 1’s and

0’s so no need to dichotomize)

QAP Regression

Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > MR-QAP Linear Regression > Original (Y-permutation) method

QAP Regression (cont.)

Step 1. Enter dependent variable “ArtCoInfo_GE_4” Step 2. Enter independent variable “ArtCoKASE”

Adjusted R-Square of 0.133 indicates a moderate relationship between the two social relations. The probability of 0.000 indicates that it is statistically significant.

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