social network analysis - virginia tech · network analysis pp. 340 ... bb b. 05‐mar‐13 7...

25‐Mar‐13

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Short Course in

Social Network Analysis

held at Sokoine University of Agriculture

11‐15 March 2013

Instructors:Dr. Keith M. MooreMs. Jessie Gunter

Virginia Polytechnic Institute and State University Blacksburg, Virginia

Synopsis

This week-long short-course is designed to introduce participants to the theory, applications, and methods of social network analysis (SNA). SNA is currently becoming a popular approach to analyzing a wide range of social and biophysical relationships. However, it has a long history and has evolved along various disciplinary pathways. This workshop will introduce basic SNA terminology and concepts (egos and alters, structural equivalence/roles, network density, degree and betweenness centralities, etc.). Participants will become familiar with various theoretical perspectives and approaches. The potentials of these modes of explanation will be investigated through review of real world applications and workshop exercises. The workshop will also address SNA research methodologies, including how to design survey instruments and develop sampling frameworks, through data collection and analysis, and the development of visual presentations. Workshop exercises will create a learning-while-doing environment.

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Learning Objectives

Having completed this short course, participants will be able to:

1. Understand and use basic social network analysis (SNA) terms and concepts.

2. Explain how alternative approaches to SNA address different types of problems.

3. Compare and contrast different applications of SNA.4. Understand and apply basic SNA research tools.

Supplemental Readings

Marian, Alexandra and Barry Wellman (2011) Social Network Analysis: An Introduction. In: J.P. Scott and P.J. Carrington (eds) The Sage Handbook of Social Network Analysis pp. 11‐25.

Freeman, Linton C. (2011) The Development of Social Network Analysis – with an emphasis on recent events. In: J.P. Scott and P.J. Carrington The Sage Handbook of Social Network Analysis pp. 26‐39.

Granovetter, Mark S. (1973) The Strength of Weak Ties. American Journal of Sociology 78(6) pp. 1360‐1380.

Scott, John (2012) Chapter 3: Analyzing Relational Data. In: Social Network Analysis pp. 41‐62.

Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, Giuseppe Labianca (2009) Network Analysis in the Social Sciences. Science 323 (13 February 2009) pp. 892‐5.

Bodin, Orjan and Beatrice Crona (2009) The role of social networks in natural resource governance: What relational patterns make a difference? Global Environmental Change 19 pp. 366–374.

Matsaert, Harriet, Zahir Ahmed, Faruqe Hussain and Noushin Islam (2007) The dangers of writing up: a cautionary tale from Bangladesh. Anthropology Matters Vol 9 (2) pp. 1‐12.

Hanneman, Robert A. and Mark Riddle (2011) excerpts from: Concepts and Measures for Basic Network Analysis. In: J.P. Scott and P.J. Carrington The Sage Handbook of Social Network Analysis pp. 340‐346, 356‐369.

Short‐CourseOutline:SocialNetworkAnalysis

Monday, 11 March 2013

9:00 Introductions

9:30 Social Network Analysis – some theory and definitions

10:15 BREAK

10:30 Introduction to basic concepts and definitions: node, ties, attributes versus relations, paths,

dyads, triads,

11:30 Questions/discussion/review

LUNCH

14:00 Introduction to Gephi: examples and exercises of edges table, matrix and analysis of a particular

case. Show graphic results based on tabular analysis; and vice‐versa

15:00 BREAK

15:15 Examples and exercises

16:30 End

Tuesday, 12 March 2013

9:00 Review and preview

9:30 Structural equivalence; patterns of relations, strengths of ties, and structural holes; centrality

and centralization, density and distance; etc.

10:15 BREAK

10:30 Patterns of relations: continued. Applications of social network analysis

11:30 Questions and review

LUNCH

14:00 Generating network measures of graphs

15:00 BREAK

15:15 Examples and exercises

16:30 End

Wednesday, 13 March 2013

9:00 Generating network measures and designing graphic presentations

10:15 BREAK

10:30 Designing network graphic presentations continued

12:00 LUNCH

14:00 Research objectives, sample and questionnaire design

15:00 BREAK

15:15 Design a SNA survey and questionnaire in small groups of 5 or 6

16:30 End

Thursday, 14 March 2013

9:00 Continue small group survey and questionnaire design

10:15 BREAK

10:30 Small groups present SNA survey and questionnaire designs

LUNCH

14:00 Generating network visualizations

15:00 BREAK

15:15 Alternative methods and resources

16:30 End

Friday, 15 March 2013

9:00 Quiz and course evaluation

10:00 BREAK

10:30 Individual consultations for research projects

05‐Mar‐13

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DAY 1

Social Network Analysis:

Some Theory and Definitions

Sustainable Agriculture and Natural Resource Management CRSP

Office of International Research, Education, and Development

Social network analysis stems from several scientific traditions, but two stand out:

Social‐anthropological – study of small groups and the structure of their relationships

society is not an aggregate of individuals, but a structure of interpersonal ties (social structure)

Mathematical – socio‐metric analysis and graph theory

this structure can be mapped

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In the early 20th century, the German social theorist Simmel contributed significantly to the development of social network analysis

In particular he pointed out the distinction between:

• the form of social relations (network structure) and

• their content (categories, attributes)

Sociometry – the designing of socio‐grams or social fields constituted of points connected by paths

allows us to visualize the relationships and explore them in an intuitive way

Graph Theory – allows us to rigorously test hypotheses about the abstracted parameters generated in the map or social field

• a graph is a set of lines (paths) connecting points

• graph theory consists of a body of mathematical axioms and formulae that describe the properties of the patterns formed by the lines

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Network Components

Nodes – individuals, organizations, other meaningful entities, and things

these are seen as actors,

Ties – the relationships between nodes

bound together in some meaningful fashion

these may be strong or weak

The identity of a node is

Structural

• an individual, group, organization, event, or other entity

• that exists in an array of other nodes, a node or social agent cannot exist in isolation

A node is variously called:

a point, a vertex, an actor, a role, or an agent

What is a node ?

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The identity of a tie is

Relational, defined by inter‐agent subjectivitiescharacterized by four types of ties:

Similarities – group membershipRelations – kinship, other role relationsInteractions – behavior‐basedFlows – exchanges and transfers

A tie is variously called:

an edge, a line, an arc, a path, an interaction, a relation, an encounter, a link, a connection, an attachment or an exchange

What is a tie ?

A Network of Nodes and Ties

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Dyad

Triad

Number of

Nodes Ties

2 1

3 6

4 10

5 15

6 21

7 28

8 36

Narratives, stories, and discourses

• Stories describe how the nodes are tied to each other

• rationalizing certain behaviors and structures expected of that position

• A social network is a network of meanings, discursive frameworks, and cultural idioms.

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Strength and quality of ties

Directionality – ties can be:

• positive (+) or negative (‐) relations; • either directed (with arrows) or undirected

(Directed ties or edges are sometimes called arcs)

In an undirected graph, the relation A to B is assumed to be the same as the relation B to A

A A A

Undirected Directed Balanceddirected

B B B

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Strength and quality of ties (2)

Balance – is the particular balance of signs or directionality

Transivity – if there is a tie between A and B and one between B and C, then in a transivity network A and C will also be connected

Homophily – the tendency of people to relate to people with similar characteristics (status, beliefs, etc.) leading to the formation of homogeneous groups

The strength, weight or value of ties can vary according to:• Frequency of interaction• Flows of information• Number of items exchanged• Perceptions of relation• Distance• Other

Network Boundaries

boundary problem – the difficulty of defining the population of actors to be studied through network analysis in a way which does not depend on a priori categories; in other words, the problem of delimiting the study of social networks which in reality have no limits

Realist boundaries: as perceived by participants (social organizations; groups, events, etc.)

Nominalist boundaries: use of a formal criterion to identify the boundaries that has some analytical significance but may not be socially organized

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Egocentric networks

An actor, the actors which it has relations, and the relations among those actors. A neighborhood or personal network.

Whole network

All actors within thedefined population.

Collecting and organizing data

Adjacency matrix

Ada Cora Jean Robin Helen Ella Alice

Ada 0 1 0 1 1 0 0

Cora 0 0 0 0 0 1 0

Jean 1 0 0 1 0 0 0

Robin 0 1 0 0 0 1 1

Helen 0 0 0 0 0 0 0

Ella 1 0 0 1 1 0 1

Alice 0 0 1 0 0 0 0

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From: Scott, Chapter 1 (2000)

Attribute data

• attitudes, opinions, and behaviors of agents

• properties, qualities, or characteristics that belong to them as individuals or groups

• typically analyzed with standard statistics as quantitative or categorical variables

Relational data

• contacts, ties, and connections

• group attachments and meetings that relate one agent to another

• typically analyzed using socio‐metric analysis and graph theory to analyze the structure of relations

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Ideational data

• meanings, motives, definitions and typifications of actions or agents

• typically analyzed through qualitative techniques such as:

• Discourse analysis

• Ideal type analysis (Weber)

• Symbolic interaction – dramaturgy (Goffman)

Moments of Enrollment and Translation in the constitution of social networks

1. Invocation of actors around a certain definition of a problem/issue

2. Imposition of identities and roles on actors relative to defined issue

3. Demonstration of a solution or critical information to the defined problem rationalizing empirical relationships between network components

4. Consolidation of socio‐material alliances and consensus concerning the ‘facts’

from: Callon, 1986

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Farmer Field Schools

A form of adult education based on experiential learning

Farmers observe and experiment with the modificationof the ecology of their crops, learning about:

- population dynamics- distinguishing pests and beneficials- crop damage-yield relationships

And gaining self-confidence and skills in how to:

- cooperate and communicate with peers- make more complex crop management decisions

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village rice farmers

subject matterspecialists

FSS Trainer

scientists

Farmer Field School Network

0

50

100

150

200

250

Control Exposed FFS

1990-91

1998-99

Pesticide Expenditures in Indonesia(in thousands of rupees inflation adjusted)

From: Feder, Murgai and Quizon, 2003

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village rice farmers

subject matterspecialists

FSS Trainer

scientists

Farmer Field School Pesticide Application Network

extension agent

rice cooperatives

pesticide companies

input suppliers

local traders

Principles for enhancing local networks of innovation

■ Negotiate decentralized exploration with centralized learning

■ Science, extension and innovation policies should be flexible to evolve with new information

■ Assess the extent of institutional interactions and power relations to establish effective governance structures

■ Evaluate knowledge flows between nodes and carefully select capable individuals to manage boundaries

■ Identify opportunities for interactive learning

■ Research and extension institutions must recognize they are not the central actors but play a supporting role

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What is gephi?Gephi is a free software that can be used to create

visualizations and to conduct exploratory data analysis.

http://vimeo.com/9726202

Downloading Java

You may need to download Java if your computer does not already have it installed.

http://www.java.com/en/download/index.jsp

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Downloading gephi

-Java must be installed first

-go to: https://gephi.org/

-click "Download gephi for windows"

-click "save file"

How to put network data into a database using Excel

Open Microsoft Excel. If it is not installed on your computer, GoogleDrive Spreadsheet can be used for free if there is an internet connection.

To enter data in a cell, simply click on the cell and type. Enter one observation (word or number, depending on your data) per cell.

We will start by entering a very small amount of data into an edge list in Excel. These pairs were generated randomly using another software, R.

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How to make an edge table in Excel

To create an edge table:

2 columns: Source and Target

Each row represents one directed connection. So, the name in the first column is the name of one of the survey respondents.

If person "J" identified "I" as a contact, enter "J" in the lefthand column and "I" in the righthand column. This does not mean that "I" also identified "J" as a contact.

How to make a nodes table in Excel

Make a list of all the nodes in the network.

Add attributes in separate columns.

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Save the Excel files as .csv

.csv stands for comma-separated values

It is a file format that saves tabular data in plain text form, which is understood by many different types of programs and software.

To save an excel file as a .csv, simply click the circular button in the top left corner of the page > Save as > Other Formats , and scroll down in the drop down menu next to "save as type" until you see CSV (Comma delimited).

Open the .csv files in gephiTo open the nodes table in gephi, click on "Data Laboratory" and then

"Import Spreadsheet" under the "Nodes" tab.

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Open the .csv file in gephi

Choose a CSV file to import by clicking on the box labeled "..." and double clicking on the .csv file that you saved in Excel

Click "Next".

Check the box next to each of the columns you want to import. Import the attributes as a string. Uncheck the box that says "Force nodes to be created as new ones".

Make sure the "Create Missing Nodes" box is checked. Click "Finish".

Now you can see each of the nodes and their attributes listed in spreadsheet format.

You have many options in this screen: manually adding nodes, edges, columns...

Open the edge table in gephiNow open the edges table in

gephi by clicking "Import Spreadsheet" under the "Edges" tab.

Choose a CSV file to import, click "next".

Be sure to import as an edges table. Click "next".

Make sure the "Create Missing Nodes" box is checked. Click "Finish".

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Click "Overview" in the far left corner of gephi. Your screen should look like this....

Creating a graph in gephi

Now, there are several steps we can take to make the graph look better.

First, we can choose a layout for the graph. Select a layout from the drop-down menu and click "Run".

Try out several layouts, experimenting with them to see what happens when you check some of the boxes.

Fruchterman Reingold is a layout that works well with our small amount of data, but other layouts such as Force Atlas 2 may work better with larger datasets.

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Add node labels

Next, we can add node labels. Do this by clicking on the black "T" button on the bottom toolbar (1), and then click the picture of the clipboard with a wrench (2).

A box will appear with several options of boxes to check. In this case, we want to check the "ID" box because we want our nodes labeled according to their ID names from the dataset.

Add node colors, adjust size

Click on the drop-down menu for "Nodes" under the "Ranking" tab. Select degree to visually rank the nodes according to degree, or number of connections.

Select the small diamond to size the nodes according to their degree. Experiment with different minimum and maximum node sizes, clicking "apply" to see how the graph changes.

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Scale label size, other options1- Adjust

labels to be proportional to node size

2- Adjust overall node label size

3- More options

4- Manually move nodes (useful if there is overlap)

Screenshot, Preview, Save

If you wish to save a picture of the graph (for use in a paper, to send via email, or to print), you have 2 options.

1. Take a screenshot of the "Overview" screen and save it as a .png file or

2. Go to the "Preview" screen, where you'll need to make a few more adjustments.

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Preview SettingsSelect the way you want your graph to appear by

trying out different options in the drop-down menu at the top of the "Preview Settings" box, clicking "Refresh" to see previews each time you make a change.

Check boxes "Show Labels", "Proportional size", "Show edges", "Rescale Weight", and click "Refresh".

Continue exploring the options in the "Preview Settings" box, including changing the values of edge thickness and edge arrow size, until you are satisfied with the way the graph looks.

If you go back to make changes in the "Overview" mode, you will need to Refresh the "Preview" page and possibly make adjustments again to reflect the h

Save or export preview

To save your graph as a picture, click "Export" and choose your preferred file format.

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Day 2


“Actors do not behave or decide as atoms outside a

social context, nor do they adhere slavishly to a script

written for them by the particular intersection of

social categories that they happen to occupy. Their

attempts at purposive action are instead embedded in

concrete, ongoing systems of social relations.”

Mark Granovetter, 1985

The Problem of Embeddedness

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Structural equivalence – a matter of social roles

The structure of relationships can be compared both within and across networks .

A block is a set of structurally equivalent actors in a multiplex network.

Different classes of nodes share certain characteristic relations with each other

examples of such roles within:• a family (father, mother, daughter, son); • an industrial organization (manager, foreman,

assembly worker)• A salesperson and their client

a b g h

e f

c d i j

adapted from: Borgatti & Lopez‐Kidwell, 2012

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Network Theories: Network Flow Models

Structural holes generate:

• Information costs and benefits• Control opportunities

Weak ties can become bridges to increase:

• Information flow• Coordination

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Network Flow Models: Granovetter’s strength of weak ties

The stronger the tie between two people, the more likely that their social worlds will overlap – that they will have the same third parties in common.

Bridging ties – ties between people who are not connected to their other friends are potential sources of new ideas/information

Therefore, strong ties are unlikely to be the sources of new information

Network Flow Models: Burt’s structural holes

When A has contacts with alters that are not connected to each other; and

B has contacts with alters that are interconnected with each other

A has more structural holes than B. (or A has more non‐redundant ties than B).

Therefore, A is likely to receive more non‐redundant information at any given time than B.

(in Granovetter’s language, A has more bridges than B)

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ego ego

Few structural holes Many structural holes

Adapted from: Halgin & DeJord, 2008


1. Information (or any resource) flows from node to node along paths consisting of ties interlocked through shared end points

The element of network paths is important

2. Flows in the network flow model

a. Whatever flows through the network may be damaged or changed en route, but it remains basically the same thing

b. Although translation from one idiom to another may occur

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Some additional theorems:

• Nodes with more ties have greater exposure to (i.e., more chances of receiving) whatever is flowing through a network

• Nodes connected to more central nodes will have greater exposure to the flows

• If connectedness of an ego’s alters matters so could other characteristics including non‐structural attributes, such as wealth, power or expertise.


Some additional theorems (con’t.):

• Nodes that are closest to all others should on average receive flows more quickly

• Nodes positioned along the best pathways between others may be able to benefit by controlling, filtering or coloring the flow (as well as charging rents)

• Nodes located in the same general ‘area’ (connected to the same nodes) will tend to hear the same things and therefore have equal access to opportunities provided by network flows.

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Centralities

In‐degree – the number of incoming (directed) lines to a node (below c has an in‐degree of 3; b is only 1)

Out‐degree – the number of outgoing (directed) lines from a node (below c has an out‐degree of 2; b is 3)

a b

c

d

e

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Measuring centrality

Semi‐walk – a sequence of lines connecting one node to another in a continuous chain (e‐c‐d‐b‐c‐a)

Walk – a sequence of directed lines between one node and another (c‐a‐d‐b‐c‐e)

Path – is a sequence of directed lines between one node and another that do not pass through the same node more than once (d‐b‐a‐c‐e)

Centralities (cont.)

Degree centrality – the number of a node’s in‐ and out‐degrees is the number of links that lead into or out of the node

Betweenness centrality – the number of shortest paths that pass through a node divided by all shortest paths in the network

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Centralities (cont.)

Closeness centrality – the mean length of all shortest paths from a node to all other nodes in the network. (It is a measure of reach: how long will it take to reach other nodes from a given starting node.)

Eigenvector centrality – a node’s eigenvector centrality is proportional to the sum of the eigenvector centralities of all nodes directly connected to it.

Eccentricity – the distance from a given starting node to the farthest node from it in the network

Density

Density is the ratio of actual relations or ties among a set of actors in a network to the maximum possible number of ties.

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Clique and cluster

Neighborhood – all nodes connected with ego (that is the ego and its alters)

Clique – a group of actors in which each is directly and strongly linked to all of the others (with a minimum of three nodes)

Modularity – a measure of strength of division between clusters in a network

Blocks

Partition – is a classification or clustering of the network nodes such that each is assigned to exactly one class or cluster; a partition maybe a structural property or an attribute

Blockmodeling: a technique for partitioning and graphically representing structurally equivalent actors

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Network Theories: Network Architecture Models

This model emphasizes the solidarities of mutual contact, nothing actually ‘flows’ along any pathway.

• Work may be done on behalf of another• Work may be done in complement or in concert with others

It is the alignment between nodes produced by the flow that yields the outcome

The case of authority relations (bureaucracy) is instructive

• Communication (flow) is involved, but it is the coordination, not the message that is the mechanism.

Network Theories: Network Architecture Models

Network Exchange Theory is associated with the architecture model.

• Position along the linkages between nodes determines network power

• While nodes interact and accumulate resources, resources do not travel along paths of the network –the rules of the game prevent it!

• Centrality measures in this case are useless in predicting outcomes

Centrality is a construct of the network flow model, and there are no flows here. But even without flows, paths do matter.

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Exchange Theory Game

Game 1: Participants arrange themselves in a linear structure of three (X‐Y‐Z) nodes.

The object of the game is for each participant (X, Y, or Z) to try to make the most and best exchanges to accumulate the most points. In each round, a pair (either X‐Y or Y‐Z) of participants must divide 24 points. Repeat for 6 rounds. In each round, Y will have to choose between dividing the 24 points with either X or Z. Repeat for 6 rounds.

X Y Z

Exchange Theory Game

Game 2:Participants arrange themselves in a linear structure of five (V‐W‐X‐Y‐Z) nodes.

The object of the game is for a participant (V, W, X, Y, or Z) to try to make the most and best exchanges to accumulate the most points. In each round, two pairs of either V‐W, W‐X, X‐Y or Y‐Z must divide 24 points within each pair. Repeat for 6 rounds. In each round, one participant will not be matched in a pair. Repeat for 6 rounds.

V X Y ZW

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Network Measures in Gephi

Gephi has several built-in statistical measures that can help us understand more about networks. They can be found on the right side of the "Overview" page.

Using Gephi for quantitative analysis

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Built-in tools in gephi

On the righthand side of the "Overview" screen, there are several calculations that gephi can perform.

Clicking "run" will cause a window to appear with the results of the calculations for all of the nodes.

Built-in tools in gephi, continuedTo see numeric values for the measure you have run for each

individual node, go to the "Data Laboratory" tab. New columns will have been added automatically with the values.

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Average DegreeAn individual's "degree" is simply a measure of

how many contacts s/he has.

In a directed graph, indegree measures the number of incoming edges for each node.

Outdegree is the sum of a node's outgoing edges.

Indegree measures popularity, while outdegree measures expansiveness (Wasserman & Faust)

Run in gephi, look in "Data Laboratory" tab to see which nodes have the highest/lowest indegree.

CentralitiesTo calculate closeness centrality,

betweenness centrality, and eccentricity values, run "Network Diameter".

-Closeness centrality is the sum of the distances from one node to all other nodes in the graph (low sum = high closeness)

-Betweenness centrality is a measure of the extent to which a node acts as an intermediary between other nodes, bridging a structural hole (high betweenness can = high influence)

-Eccentricity is the length of the longest edge to a node (this can indicate the center(s) of a

graph)

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Centralities continuedA set of distribution graphs will appear once you click "run". Looking at

the graphs can help you understand the distribution of each of the measures. How many nodes have a betweenness centrality of 18?

Centralities continued

Go to the "Data Laboratory" tab to see values for individual nodes.

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ModularityA measure of the

strength of division between clusters, or communities

Run "Modularity" under Network Overview, and then set the rank parameterunder the Nodes tab to Modularity Class. Hit "Apply".

High modularity = communities in network are densely connected but network but communities are not well connected to each other

Partition

A partition is a classification or clustering of the nodes according to their attributes

After running Modularity (see previous slide), several options will appear under the Partition-Nodes tabs. Choose the one which you would like to apply to your graph. This can be an attribute of the graph from your nodes table.

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Eigenvector centrality

A measure of the importance of a node based on the importance of its connections.

Run the measure. You will be prompted to select "directed" or "undirected". Choose the appropriate option for your graph. The default number of iterations is 100 - this will be appropriate for smaller graphs.

Look in the data laboratory for individual node results.

Explore other options as needed

Tutorials can be found on the website www.gephi.org

Frequently Asked Questions can be found here:http://social-dynamics.org/gephi-faqs-and-answers/

A google search of your question will help you find other resources! For example, you could type into the search

bar:

import csv file gephiOR

how to import csv file in gephi

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Day 3

Research Design for


Overview of the SNA research process

1. Build on those who have gone before youa. Review the literatureb. Theoretically frame your research problem

2. Develop research questions or hypotheses

3. Determining data sourcesa. Identify the population of interestb. Defining network boundariesc. Establishing the sampling methodology

4. Data collection methodology (documents/survey)a. Decide what data to collectb. Design survey instruments/questionnaires

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Build on those who have gone before you

The International Network of Social Network Analysis has a website with links to journals, topics and people specialized in the field:

http://www.insna.org

We’ve discussed some theoretical frameworks and there are others. Be sure to apply the one or ones that most adequately frame the problem you are interested in.

Read the literature.

Develop research questions or hypotheses ‐ 1

Questions about how social networks affect other variables:

• How does an individual’s personal network affect that person’s ability to access certain kinds of resources?

• How do individuals gain access to social support through their personal networks?

• How does the structure of a given social network affect the formation (or maintenance) of collective norms?

• To what extent are particular behaviors, such as smoking or academic performance, influenced by one’s friendship ties to others?

adapted from Prell, 2012

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Questions about how other variables affect social networks:

• How does membership in an organization affect the likelihood of social ties forming amongst individuals?

• What role does geographical proximity play in the formation of social networks?

• Are actors who are similar to one another on some characteristic more likely to form a tie?

• Are two people more likely to form ties with one another, based on them sharing a common friend?



Questions about how social networks may affect one another:

• Do friends (relation 1) tend to offer advice (relation 2) on personal matters?

• Do colleagues (relation 1) tend to trust (relation 2) one another?


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Determining data sources

Identifying the population and determining boundaries:

Positional – using attributes of actors (memberships, occupants of well defined social roles, elites, village chiefs, etc.)

Relational – relies on knowledgeable informants or the network actors themselves to nominate additional actors for inclusion (i.e., snowball sampling, reputational, fixed list selecting)

Event‐based – relies on particular activities or events to locate a network’s boundary (markets, meetings, celebrations, etc.). A strongrationale for such selection is critical.

Data collection methodology

Determine whether to employ:• survey instruments (questionnaires, semi‐

structured interviews), • existing documentation (published reports, diaries,

archives, internet data bases, etc.), or • direct observation

Quantitative (fixed questionnaires) versus Qualitativedata collection (ethnographic interviews, focus groups, etc.)

Network construction:• Rosters• Name generators• Positional generators• Resource generators

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Questionnaire Design

Yes/No assessment of contact or more in‐depth qualification of the strength of contact (frequency, value of advice received, trust, etc.)

Questionnaire wording can be tricky: does friendship mean the same thing to everyone?

Length of questionnaire – building the network can be long and tedious

Ethical concerns

Identity and quality of network relations(within the agricultural production network)

What resources are accessed through interaction?

1. Advice 5. Pesticide 2. Information 6. Herbicide3. Seed 7. Tractor 4. Fertilizer 8. Other_______

Who Initiates the contact most of the time?

1. Always them 2. Mostly them 3. 50/50 4. Mostly me 5. Always me

Location and Events: Where do you interact?

1. Farm 5. NGO Office 2. Store 6. Community center3. Office 7. Farmer field day4. Market 8. Other________

12‐Mar‐13

6

Identity and quality of network relations(within the agricultural production network) – con’t.

Frequency: How often do you interact?

1. Weekly 2. Biweekly 3. Monthly 4. Seasonally 5. Yearly

Quality: Can you trust resources/info provided?

1. Always 2. Most of the time 3. Somewhat 4. Rarely 5. Never

Gender:

1. All male 2. Mostly male 3. 50/50 4. Mostly female 5. All female

People with which contact is made in order to

conduct agricultural production activities

(if no agricultural interaction, leave row blank)

a. What physical

resources are

exchanged

through

interaction?

b. What form of

information is

exchanged

through

interaction?

c. Who Initiates

the contact most

of the time?

d. Location and

Events:

Where do you

interact?

e. Frequency:

How often do

you interact?

f. Quality:

Can you trust

resources/info

from this

source?

g. Gender

0. None

1. Seed

2. Fertilizer

3. Pesticide

4. Herbicide/

Weedicide

5. Tractor

6. Crop

finance/loans

7.Vet services AI

8.Lamd

9. Cash 10.

Other_______

0. None

1. Advice or

consultation

2. Only

information

0. N/A

1. Always them

2. Mostly them

3. 50/50

4. Mostly

respondent

5. Always

respondent

0. N/A

1. Farm

2. Store

3. Office

4. Market

5. NGO Office

6. Community

center

7. Farmer field

day/event

8. Home garden

9. Collective

garden

10. Government

offices

10.

Other________

0. Never

1. Weekly

2. Biweekly

3. Monthly

4. Seasonally

5. Yearly

0. N/A

1. Always

2. Most of the

time

3. Somewhat

4. Rarely

5. Never

0. N/A

1. All male

2. Mostly male

3. 50/50

4. Mostly female

5. All female

1. Village/Subcounty chief5

2. Farmers

1. Neighbor/friend

1. Vendor in weekly market

1. Vendor in a shop in urban center

1. Vendor in a agro‐vet shop

1. Teacher in village

1. Minister/Priest/Imam in village

1. Government Extension agent

1. NGO/ Development Agent

1. Veterinary Service provider

1. Government Parastatals

1. Agricultural researcher

1. Agricultural/Micro Finance Representative

1. Tractor owner/ animal Traction owner

1. Leader of farmer organizations

1. Leader of women’s organization

1. Leader of youth organisation

1. Local Political leaders

1. Other to be determined

social network analysis - virginia tech · network analysis pp. 340 ... bb b. 05‐mar‐13 7...

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