AEA365 | A Tip-a-Day by and for Evaluators

TAG | Social network analysis

Hi, I’m Barbara Klugman. I offer strategy support and conduct evaluations with social justice funders, NGOs, networks and leadership training institutions in South Africa and internationally. I practice utilization-focused evaluation, frequently using mixed methods including outcomes harvesting and Social Network Analysis (SNA).

Rad Resource: For advocacy evaluation, SNA can help identify:

  • how connected different types of advocacy organizations are to each other;
  • what roles they play in relation to each other such as information exchange, partnering for litigation, driving a campaign, or linking separate networks;
  • if and how their positioning changes over time in terms of relative influence in the network.

The method involves surveying all the groups relevant to the evaluation question, asking if they have a particular kind of relationship with all other groups surveyed. To illustrate the usefulness of SNA, the map below illustrates an information network of the African Centre for Biodiversity, a South African NGO.  In the map, each circle is an organization, sized by the number of organizations who indicated “we go to this organization for information” – to answer one piece of the evaluation question, regarding the position and role of the evaluand in its field, nationally and regionally. Of the 55 groups advocating for food sovereignty in the region who responded, the evaluand is the main bridger between South African groups and others on the continent. It is also a primary information provider to the whole group alongside a few international NGOs and a few African regional organizations.

As another example, an SNA evaluating the Ford Foundation’s $54m Strengthening Human Rights Worldwide global initiative distinguished changes in importance and connectedness before the initiative and after four years, among those inside the initiative (blue), ‘matched’ groups with similar characteristics (orange), and five others in Ford’s portfolio (pink). It shows that the initiative’s grantees and notably those from the Global South (dark blue) have developed more advocacy relationships than have the matching groups (see larger size of nodes and more connections). However, the largest connector for advocacy remains Amnesty International – the big pink dot in the middle, demonstrating its continuing differential access to resources and influence relative to the other human rights groups.

 

Hot tips:

  • Keep it simple: As surveys ask about each organization, responding takes time, so ask only about roles that closely answer the evaluation questions regarding the network. For example, “my organization has engaged with them in advocacy at a regional forum”; “my organization has taken cases with them”
  • Work with a mentor: While SNA software like Gephi is open access, making sense of social network data requires statistical analysis capacity and SNA theory to extract meaning accurately.

Lesson Learned:

  • Consider whether or not to show names of groups as your tables or maps will surface who is ‘in’ and who is on the outside of a network in ways that might have negative consequences for group dynamics or for individual groups, or expose group’s negative perceptions of each other.

Rad resources:

Wendy Church, Introduction to Social Network Analysis, 2018.

 

The American Evaluation Association is celebrating APC TIG Week with our colleagues in the Advocacy and Policy Change Topical Interest Group. The contributions all this week to aea365 come from our AP TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Hello evaluation community, I am Johanna Morariu, Director of Innovation Network. Do you like getting something for free? Me too! Using Twitter data of the use of #eval13 I will demonstrate how to get started with network analysis in NodeXL. Morariu 1

Were you at Evaluation 2013 in Washington, DC? Thousands of evaluators converged to talk methods, data, and analysis. It was awesome! And what better way to celebrate an evaluation conference than with more evaluation?!

Social Network Analysis (SNA) is a dataviz approach for data collection, analysis, and reporting. Networks are made up of nodes (often people or organizations) and edges (the relationships or exchanges between nodes). The set of nodes and edges that make up a network form the dataset for SNA.

Cool Trick: There are four basic steps to creating a social network map in NodeXL: get NodeXL, open NodeXL, import data, and visualize. Let’s get started with our analysis of #eval13 SNA! Click here for the interactive step-by-step NodeXL instructions.

Morariu 4

 

 

Lessons Learned: Alright, let’s look at that #eval13 data!Morariu 2

The data covers about 8 days (from Tuesday, October 15 @ 7:19p ET through Wednesday, October 23 @ 5:13p).

There were 433 nodes, or 433 Twitter users tweeting with #eval13.

What does that mean in relation to conference attendance? More than 3,000 people attended the conference, so for about every seven people at the conference there was one person who tweeted about the conference. Pretty cool!

There were 2264 edges, or 2264 interactions between Twitter users using #eval13. (Of those, 925 were unique edges.)

Who’s at the center of the #eval13 network? For those of you active on #eval13 these names are likely very familiar! Morariu 3

  1. Ann Emery (@annkemery)
  2. Chris Lysy (@clysy)
  3. Allison Titcomb (@allisontitcomb)
  4. Innovation Network (@innonet_eval)
  5. Jane Davidson (@ejanedavidson)

What do you see when you look at the #eval13 network? To do your own analysis of the dataset, access it here.

Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org . aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

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Hi, I’m Maryann Durland, Co-Chair of the Social Network Analysis TIG and owner of Durland Consulting, where we specialize in social network analysis. In this post, I show how to enter data into UCINET for a network where you are measuring multiple types of relationships.

An NGO recently wanted to understand how a network of community organizations connected and what each saw as the strengths of the network members. This resulted in 15 different types of collaborative relationships based on the different activities that the network as a whole engaged in. Collaborative activities included collecting data, drafting reports, presenting reports, lobbying, recruiting other members, networking, and planning for the future.

UCINET accepts a variety of data formats. One common approach is to load data from an excel file(.xls, .xlsx, .csv) into a DL format. DL stands for “data language,” and the DL statement at the beginning of the file describes the data, including the number of nodes, labels, and the type of format. UCINET provides a menu-driven process for uploading data and converting to DL. In UCINET, select Data>Import Excel>DL-type formats and copy and paste your data into the DL Editor or open your Excel file using File>Open Excel file. Under Data format, select the appropriate format and other details about your data. For my NGO project, I selected Edgearray1 (ego alter rel1 rel2) to match the layout of my data, which was in the following order: the chooser (ego), the chosen (alter), and the type of collaborative activities (rel1, rel2, …). Though I have column labels, I do not need the labels chooser and chosen,and these will be deleted when I save the file.

Durland 1

Now you will have all relationships saved in one file. When you run measures for the network, you will run them on all relationships at the same time. To visualize the network, you can pull up the data in NetDraw (the network visualization program packaged with UCINET) and code for each relationship and show all of the relationships together in one sociogram or separately:

Durland 2

In the map above, the color of the arrows provides information on the type of collaborative activities reported by community organizations. For example, member 2 reported that they draft reports with member 3 (blue arrow), and member 3 reported they collect data with member 1 (red). The black arrow means a member said that they worked on lobbying efforts together. If members indicated multiple relationships, then the arrows are gray.

Rad Resource: UCINET 6 User’s Guide includes a section on importing data that illustrates how to format various DL files—explore here to learn about the possibilities!

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Greetings! I am Simon Geletta – Program Co-Chair for the SNA-TIG and associate professor of public health atDes Moines University in Iowa. In this post, I illustrate how I used the open-source softwareGephi to conduct a social network analysis of hospital visitation patterns in Iowa.

Hospital utilization patterns are important to healthcare services research because they relate to the concept of access. Using SNA revealed more insight than other more “traditional” analytic approaches such as statistical summary tables and GIS maps, which provide insight on abstract (the statistical approach), or spatial patterns (the GIS approach). SNA allowed me to look at patterns of hospital use in the network, which shed further light on patient access to healthcare services.

Lesson Learned: Getting Data into Gephi. Originally, my data were stored in an Excel spreadsheet. To import the data, I used Gephi’s “Data Laboratory” interface (shown below). In my analysis, I used hospital locations and patient locations as nodes. The edges linked the patient location nodes to the hospital location nodes.

Galetta 1

Hot Tip: You can import data into Gephi from a range of standard data formats, including spreadsheets and delimited files (comma separated, tab delimited, etc).

Lesson Learned: Visualizing Data in Gephi. In Gephi, I used the “Overview” window to visualize and analyze my data. To get an intuitive view of the hospital visitation network, I selected theForce Atlas 2 layout option, which balances speed and precision.

Galetta 2

Hot Tip: Gephi includes 12 layout settings – fit for a wide array of network sizes and complexity. After applying layout settings, you can also tweak your layout to make it less cluttered.

Lesson Learned: Analyzing Data in Gephi. I used the modularity analysis function to identify communities within the network. The analysis revealed five closely knit hospital visitation patterns or communities that reflected geographic characteristics (i.e., most visits were to hospitals closest to the visitors), and organizational characteristics (larger hospitals attracted more patients – not only from their immediate locality but also patients that were further away from their localities.)

Galetta 3

Once the communities were delineated, the “Overview” window also allowed me to filter the network by the community partitions and evaluate each community as a separate network. You can export and use measures from Gephi for modeling and hypothesis testing using other statistical software.

Rad Resource: For more information on the modularity analysis function, check out this article byV. D. Blondale and colleagues.

Lesson Learned: Selecting an appropriate layout for your network is an iterative process. It is not an “exact” science but a combination of science, art, and aesthetics.

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Hello there! This is Jes Koepfler, Principal of UXR Consulting in Philadelphia, PA, and a PhD candidate at the University of Maryland in Information Studies, and Derek Hansen, Assistant Professor at Brigham Young University in the Information Technology department and one of the team members behind NodeXL. We are working with some of our colleagues at the Information Policy and Access Center at the University of Maryland to conduct an outcomes-based summative evaluation of an online civic engagement platform called ACTion Alexandria. As part of our multi-year, mixed-methods study, we’re using NodeXL to analyze social network data to address questions about community engagement through site participation.

Lesson Learned: We created the graph below using NodeXL. It shows the 5 key types of participation that visitors can engage in on the ACTion Alexandria website. In this graph, each dot represents a site visitor. Color represents how many activities site visitors have performed (e.g., orange=all 5 activities, dark blue=just 1 activity). Size also indicates the number of activities site visitors have performed. Each line represents an activity the site visitor engaged in on the website. The thickness of the lines (also referred to as edge thickness) shows the number of times a site visitor has performed an activity.

The graph highlights the following things about site participation:

  1. Most people only perform one activity, although a significant number perform two activities.
  2. Voting is by far the most popular way that people engage with the site.
  3. Blog posters post many times, but don’t tend to comment on each other’s blogs.
  4. People who engage with multiple types of activities are more likely to engage in them multiple times (i.e., have thicker lines).

Koepfler 1

Hot Tips:

  • Bi-modal networks: Social network analysis is commonly used to understand relationships between people, but it can also show interesting patterns of relationships between people and things (like activities on a website). These are called bi-modal networks.
  • Network graph aesthetics: Color, labels, line thickness, and size can all be used simultaneously to represent several types of information from your data in a network graph.
  • Manual layout: Network graphs are easier to read when as few lines overlap each other possible. In NodeXL, use the manual layout feature to ‘untangle’ any criss-crossing edges or to overlapping nodes.

Rad Resources:

  • Chapter 4: Getting Started with NodeXL, Layout, Visual Design, and Labeling of Analyzing Social Media Networks Using NodeXL provides a beginner-level, how-to guide for manipulating network graph aesthetics.
  • Check out the NodeXL graph gallery for ideas on ways to use network graph aesthetics to tell different stories with your data.

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Hello all, I’m Castle Sinicrope, Webmaster for the Social Network Analysis TIG since 2011 and Policy Analyst at Social Policy Research Associates, a research, evaluation, and technical assistance firm in Oakland, CA. For newcomers to social network analysis, picking the right software package can be a daunting task. Here’s an overview of four commonly used tools for visualizing and analyzing networks:

NodeXL: A free, open-source spreadsheet add-in for Microsoft® Excel 2007 and 2010, NodeXL integrates with social media tools like Twitter and Facebook, allows users to generate maps, and calculates common network statistics.

Strengths: Beginner friendly, active on-line support community

Cost: Free

Rad Resources: Marc Smith’s previous AEA365 post, co-authored how-to guide, Analyzing Social Media Networks with NodeXL: Insights from a Connected World, and YouTube introduction to NodeXL.

Gephi: Hailed as the “Photoshop for graphs,” Gephi is a free, open-source standalone program for interactively exploring and visualizing networks and complex systems. Gephi includes common statistics for social network analysis and supports main file formats from other software programs.

Strengths: Powerful and flexible visualization tool

Cost: Free

Rad Resources: Be enthralled by the dazzling Introducing Gephi video and learn with (and from) their online community support forum.

UCINET/NetDraw: Comprehensive and well-established, UCINET is an all-in-one program that supports advanced social network analyses and network visualization through an accessible user interface.

Strengths: Calculates sophisticated network statistics

Cost: $40 (students), $150 (faculty), $250 (corporate). Faculty and corporations can purchase site licenses at a discount. NetDraw, UCINET’s program for visualizing network data, can be downloaded for free here.

Rad Resource: Hanneman and Riddle’s Introduction to Social Network Methods, a comprehensive on-line textbook that introduces basics of social network analysis through UCINET.

R: Not for the faint of heart, R does not have a typical user interface. To succeed with R, you need to be comfortable with programming and writing your own code. The reward? Flexibility and ability to clean your data, visualize your networks, and generate network statistics, all in the same program.

Strengths: Very customizable and powerful

Cost: Free

Rad Resource: Stanford University’s R for Social Network Analysis website includes step-by-step instructions for getting started with SNA in R.

Stay tuned for demo posts for NodeXL, Gephi, and UCINET later this week!

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

My name is Sophia Guevara, and I am the Program Co-Chair for the Social Network Analysis (SNA) TIG. I am currently pursuing my MPA degree at Wayne State University.

Like many graduate students, I spend much of my time studying traditional evaluation techniques for identifying and measuring the success of programs. I first learned about social network analysis by being mentored by a professional who did evaluation work at a large nonprofit.

For newcomers to SNA, knowing where to start can be intimidating. Based on my experience as a beginner, here are three tips to get you started:

Hot Tip #1: Get Involved and Learn from Others. One of the best ways to learn about SNA is to get involved in a community of practice. Each day, more and more evaluators are adding social network analysis to their toolkits, and many of us are learning together. I joined the AEA SNA TIG to learn from other AEA members who are active in the field and using SNA in their work.

Rad Resource: Join the expanding Network Weaving Facebook group to learn from other practitioners. Not only can you learn from others, ask questions, and interact with the growing group, but you can also discover innovative resources like Marc Smith’s NodeXL office hours via Google Hangout.

Hot Tip #2: Explore Online Resources. In addition to getting involved in learning communities, you can find a wealth of resources for SNA beginners on the web.

Rad Resource: This introductory slide show from Giorgos Cheliotis provides an overview and introduction to key concepts in SNA, including networks, tie strength, key players, and cohesion.

Hot Tip #3: Read Past Studies. One of the best ways to figure out how to bring SNA into your work is to learn from how other evaluators have used it. If you are an AEA member, you can find examples by searching the AEA journals for “social network analysis.” For example, New Directions for Evaluation has 19 articles, from 1992 to 2012 that incorporate and highlight SNA as an evaluation method, including the 2005 special issue on Social Network Analysis in Evaluation.

Rad Resource: Learn about how the Young Foundation mapped social networks to improve public service delivery in England (powerpoint summaryhere). This study examined relationships between residents and public service agencies and made recommendations for using networks to improve service delivery and communication.

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Welcome to the Social Network Analysis in Evaluation (SNA) TIG week on AEA365! My name is Stacey Friedman, and I am Co-Chair of the SNA TIG. I am the Associate Director of Evaluation and Planning for the Foundation for Advancement of International Education and Research (FAIMER), which works to improve education of health professionals around the world.

It is difficult to miss the increasing interest in “social networks.” Everything from social media to open office design concepts emphasizes social networks – patterns of relationships between people. Hand-in-hand with this is an interest among evaluation stakeholders in the “relationship” aspects of programs. Questions arise about, for example, the position of people or organizations in networks (e.g., who are key hubs of information and resources?), how individual characteristics (e.g., years of work experience, organization type) relate to network position, and the overall network structure (e.g., are there more collaborative relationships in the network over time?).

How can we as evaluators meet this need for insights into social networks? Social network analysis is an approach to studying networks of social relationships.

Hot Tip: If you search the AEA365 archives, you will find over 20 posts related to “social network analysis.” They note that using SNA in evaluation can help program stakeholders to:

–       Examine relationships among individual entities (people, organizations, etc)
–       See where individual entities stand in the network
–       Understand communication redundancies and inefficiencies
–       Reveal people who occupy key positions
–       Reveal clusters in the crowd
–       Foster collaboration
–       Visualize the development of collaborations
–       Study change/stability in network membership and structure over time

Hot Tip: SNA provides this information through a combination of both numeric data and visual graphics (sociograms). For example, it can provide numeric data about network “density.” Density indicates what proportion of all possible relationships in a network actually exist. So a density of 1 (or 100%) means that everyone in the network is connected to everyone else. And a density of 0.80 indicates that 80% of all possible relationships actually exist. This numeric information can be complemented by visual representations. For example, looking at the sociograms below, where each red dot represents an individual entity and each line represents a relationship – which one looks more dense?

Friedman 1 Friedman 2

 

 

 

 

Stay tuned this week for more about SNA-specific methodologies and tools – and how they can be applied within evaluations.

Rad Resource: Visit the SNA TIG website! Theresources page includes references, information about trainings, and more.

The American Evaluation Association is celebrating Social Network Analysis Week with our colleagues in the Social Network Analysis Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

Greetings, I am June Gothberg, Lead Curator for aea365 and Research Associate at Western Michigan University. As Lead Curator, I am always looking for ways to expand the knowledge of evaluators through hot tips, cool tricks, lessons learned, and rad resources. While working on my dissertation, I made a great find and thought I would share it with you. I was looking for a way to measure my variables measuring conversations between participants. In my search of the literature, I ran across the Linguistic Inquiry and Word Count (LIWC) software and discovered it’s multitude of uses.

Language Inquiry and Word Count Software

LIWC is a computerized text analysis program with Mac and Window versions. It calculates the degree to which people use different categories of words across texts, including emails, speeches, poems, or transcribed daily speech. A few of the most interesting include positive or negative emotions, self-references, causal words, as well as 70 other language dimensions. A new area in which LIWC is being used is social network analysis.

Lessons Learned:

  • Don’t be afraid to go outside your field. For example, the roots of modern text analysis are found in the field of psychology.
  • In general, LIWC categorizes words hierarchically. For example, insight is a subgroup of cognitive processes and anger is a subgroup of negative emotions. So, you must decide what level to measure.
  • LIWC offers a nice triangulation for analyzing data. It helped validate the rater/coder findings of my study in an unbiased manner.
  • Except for raw word count and words per sentence, all variables reflect the percentage of total words.

Hot Tips:

  • LIWC offers a truncated free online version. This is a good way to try-before-you-buy. You must supply the gender and age for the participant from which the text was derived.

LIWC free version

  • LIWC allows customized dictionaries of words and phrases. We are currently working on an evidence-based dictionary to identify words in speech as markers for resiliency and self-determination.
  • Read the manual! The manual explains how to deal with abbreviations, punctuation, numerals, contractions, time stamps, slang, nonfluencies, and filler words.
  • Use a transcriber who understands the manual. If transcriptionists follow the LIWC guidelines much time and effort is saved.
  • Use the option for batch processing.
  • Combine variables. If you have a certain variable of interest, you may move LIWC output into your statistical analysis software and combine variables. One of our variables of interest was participant feelings of a positive employment outlook. We combined positive emotion, future tense, and employment (posemo+future+work). We were then able to compare those who participated in a skills training session and those who did not.

Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org . aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

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Hello, my name is Todd Honeycutt and I’m a researcher at Mathematica Policy Research. We’ve used social network analysis (SNA) in several evaluations, and one challenge we’ve encountered is missing data. Even with high response rates on surveys, you can still have missing data from survey and item non-response. You can also have missing data by incorrectly defining network boundaries and membership (see this week’s AEA365 tips by Stacey Friedman and Russell Cole about this issue).

When we have missing data, we are making inferences from a partial network. Such results can be misleading, particularly if the data are not missing randomly.

Network data is about relationships—both to and from network members—and for each nonrespondent, there are many relationships about which you have no data. Consider a network with 10 organizations. If we have data about their communication with each other from all 10, then we would have data about 90 relationships [10 x (10-1)]. If only 8 of those 10 organizations responded to a survey, then we would have complete data about 56 relationships [8 x (8-1)] for measures that require a complete network (information both from and about each member), or 62 percent (56/90) of the network’s possible relationships. However, we also have partial data (about nonrespondents from respondents, but not vice versa), and so have data about 72 relationships (8 x [10-1]), or 80 percent of the network. These data can be used with measures that don’t require a complete network.

Hot Tip #1: The rule of thumb is to have data from 70 to 80 percent of your network members. However, when you have lower response rates, you should consider measures—such as indegree centralization—that are robust when data that are missing at random, and can be calculated for all network members, even nonrespondents.

Hot Tip #2: Consider using blockmodeling techniques for your analysis. You can include all network members, even nonrespondents, by using pre-specified conditions for members with missing data. [For more details, see Doreian, P., Batagelj, V., & Ferligoj, A. (2005.) Generalized Blockmodeling. Cambridge University Press, New York.]

Rad Resources: The following papers provide information on missing data in social networks:

  • Costenbader, E. and Valente, T.W. (2003). “The stability of centrality measures when networks are sampled.” Social Networks, 25, 283-307.
  • Huisman, Mark. “Imputation of missing network data: Some simple procedures.” Journal of Social Structure, 10(1), 1-29.
  • Kossinets, G. (2006). “Effects of missing data in social networks.” Social Networks, 28, 247-268.

The American Evaluation Association is celebrating SNA TIG Week with our colleagues in the Social Network Analysis AEA Topical Interest Group. The contributions all this week to aea365 come from our SNA TIG members and you can learn more about their work via the SNA TIG sessions at AEA’s annual conference. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

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