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

CAT | Social Network Analysis

Hi, I’m Maryann Durland, an independent evaluator and Social Network Analysis (SNA) practitioner. In this post I will address the requirements for doing an SNA application, particularly in evaluation, and which we could also call the standard for an application. I will use the early literature that formed and grounded SNA thinking and that continues to be relevant.

Early on in the history of defining SNA, Linton C. Freeman, described four requirements for completing a social network analysis in his book, The Development of Social Network Analysis: A Study in the Sociology of Science:

  1. A structural perspective
  2. Empirical data
  3. Graphics
  4. Mathematical models with analysis

I believe and promote, particularly in evaluation applications, that these are still the requirements for meeting the standard for doing SNA. Evaluations using SNA are distinct from research on SNA theories and measures, which may have different requirements.

In evaluation applications structural perspective means that we can define relationships within the program and these relationships create a structure through which information flows, resources are found, barriers are identified, spaces are found that need connections, and so on. Data is the existence or non-existence of a relationship between two elements. Empirical data refers to verifiable data collected on the relationship between any two elements, also called the members of a set.

Just like traditional data collection, we collect relational data through a variety of methods from observations to surveys about experiences. The data we collect populates matrix cells, indicating the presence or degree of a relationship between two members. Graphics indicate that we can visualize the network, results and/or analysis in graphs such as sociograms. Mathematical models with analysis allow us to calculate SNA measures which are measures of the network, not attributes assigned to individuals. Models called algorithms or a set of procedures, are as much a description of the relationship as they are the algorithm for how to calculate a measure.

Lesson Learned: Clearly, these four requirements delineate a specific methodological basis that is different from traditional quantitative and qualitative analysis. These requirements mean evaluators must think differently, ask questions for a different purpose, and conceptualize an evaluation differently.

Rad Resource: Early literature on SNA sought to develop what we could call standards for applications and one of the most important resources is the work of Linton C. Freeman. Freeman’s work continues to set the standards for SNA applications and the reference for the requirements.

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.

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Hello, my name is Jayne Corso and I am the community manager for American Evaluation Association and the voice behind the AEA Facebook page.

If you manage a company Facebook page, you might have noticed a drop off of “likes” recently. Facebook has begun removing memorialized and voluntarily deactivated accounts from Pages’ like counts. This change ensures that data on Facebook is consistent and up-to-date—but could mean a drop for your analytics. Although some Pages might lose “likes,” they could also gain a more accurate way to track their followers. I have compiled a few tips for tracking your analytics and gaining more visibility for your page.

Rad Resource: Take advantage of Facebook “Insights”

Facebook offers Page Insights after at least 30 people have liked your Page. Use this tool to understand how people are engaging with your Page. With this tool, you can see your Page’s growth, learn which posts have the most engagement, find demographic information about your audience, and identify when your audience is using Facebook.  This data is available for free and can easily be customizable for time frame and downloaded to excel.

Rad Resource:  Use Google Analytics to track effectiveness

Tracking your analytics through Google allows you to see how many people are coming to your site from social networks, understand the website pages they are most interested in, and gain a better understanding for how your audience is engaging with your web content.  To find this information, enter your Google analytics account and go to “Acquisitions”. From here you can look at the performance of your social networks as an overview or look more specifically at referrals, activity, and user flow. All of this data allows you to gage the effectiveness of your social campaigns.

Hot Tips: Increase your Facebook likes

Finally here are a few simple tips for increasing the likes on your Facebook Page—hopefully you can make up for any followers you lost when Facebook made their changes.

  • Add the Facebook icon to your website, so visitors know you have a presence on the social network (Place the icon high on the website page, near your navigation)
  • Add the Facebook icon to your email communication or blog to reiterate your presence on Facebook to your subscribers
  • Cross promote your Facebook page on your other social media sites. You may have followers on Twitter that have not liked your Facebook page or didn’t know you had a Page

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 from Florent and Margaret in Sydney! We are two seasoned evaluators from ARTD Consultants, an Australian public policy consultancy firm providing services in evaluation, research and strategy. As more Australian government services and programs are delivered through partnerships, evaluators need to find better partnership evaluation methods. Faced with the challenge of evaluating partnerships, we quickly realised that there are a number of methods out there: partnership assessment surveys of varying types, social network analysis, collaboration assessment, integration measure, etc.

But which one should we choose? Having looked at a number of these we felt that choosing one would not enable us to see what was really happening at all levels of the partnership.

So, in our most recent partnership evaluation, we combined some of these methods to get a more complete picture of the partnership. The three we chose were: a partnership survey (adapted from the Nuffield Partnership Assessment Tool), an integration measure (based on the Human Service Integration Measure developed by Brown and colleagues in Canada) and Social Network Analysis (using UCINET). The diagram below represents our conceptual framework, with each method looking at the partnership at a different level: overall, between organisations and departments, and between individuals.

Gomez

Lesson learned #1: A key benefit of combining partnership assessment methods is that it enables you to look at the partnership at different levels. Adding in-depth interviews or other qualitative methods to the mix will allow you to explore further and drill down into underlying mechanisms, perceptions of what works for whom, experiences of difficulties and suggestions for improvement.

Lesson learned #2: Partnerships are abstract/ intangible evaluation objects and evaluations of partnerships often lack data about what is happening on the ground. Adding methods to quantify and substantiate partnership activities and outcomes will make your evaluation more robust and the findings easier to explain to stakeholders.

Lesson learned #3: Combining methods sits within the good old mixed-methods tradition. Various metaphors are used to describe the benefits of integrated analysis in mixed-methods research (see Bazeley, 2010). In this case, the selected methods are combined ‘for completion’, ‘for enhancement’ and as ‘pointers to a more significant whole’.

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 Jayne Corso. I am a Community Manager for the American Evaluation Association (AEA) and I manage AEA’s social media presence (@aeaweb). This past week at Evaluation 2014, our event hashtag (#eval14) took on a life of its own and racked up a total of 5,601 tweets thanks to your postings, retweets, and replies. In this post, I’d like to share a few exciting trends and stats that we noticed over the course of the week.

The following data points reflect Tweets sent with the #eval14 hashtag from Monday, October 13 – Sunday, October 19

Impact: 3,707,835—This is the potential number of times someone could have seen #eval14 hashtag on their Tweet stream.

506 Contributors—This number refers to the number of twitter users that sent tweets or retweets using the #eval14 hashtag.

Our twitter community was very active throughout the conference, especially on Friday, October 17. (Click image to enlarge) 

AEA tWITTER GRAPH

Thank you to all of our 506 twitter contributors! You helped AEA keep the conference relevant on twitter and we loved seeing your original tweets. (Click image to enlarge) 

Most Active Contributors:

  1. @StrongRoots_SK
  2. @KatHaugh
  3. @Broadleafc
  4. @InnoNet_Eval
  5. @KimFLeonard

Most Popular Contributors:

  1. @Education_AIR
  2. @TechChange
  3. @NPCthinks
  4. @BillNigh
  5. @FSGtweets

Aea top tweeters

 

Here are a few great tweets we collected from this week’s festivities. Thank you for helping AEA take over twitter for Evaluation2014!

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Hello, my name is Jayne Corso and I work with Dan McDonnell as a Community Manager for AEA. As a frequent social media user, and one of the voices behind @aeaweb, I am always searching for new tools that can organize my social media feeds and help me stay up-to-date on the latest conversations, topics, and hashtag surrounding the evaluation community.

HootSuite is my primary tool for monitoring industry news and evaluating our social media posts. The ease of access to industry information that that this tool provides makes research much more effective – and easy!

Rad Resource: Manage your Social Media Accounts Through the HootSuite Dashboard

Each HootSuite user has a personal dashboard, which can be customized to fit posting or research needs. The dashboard can manage multiple platforms including: Twitter, Facebook, LinkedIn, and WordPress— creating separate tabs for each platform. Each tab can be customized with ‘streams’, (feeds, keyword searches, lists, etc.) so you can curate the most relevant information on one screen.

This is a great way to see how the evaluation community is engaging with @aeaweb’s daily Tweets. The different streams help better identify good times to share posts, what content is most popular, and the best ways to present information. Using these insights, AEA seeks to better connect with the evaluation community on Twitter and other social media channels.

Here is a quick-and-easy guide to adding tabs and streams to your dashboard

Rad Resource: Using Hashtags and Keywords to Follow the Conversation

HootSuite is an excellent resource for staying connected with other evaluators on social media and joining evaluation-related conversations. Add streams to your dashboard that follow keywords or hashtags and HootSuite will search social platforms for the most recent and relevant posts. This is where you come in – jump in, and say hello! Offer your thoughts, insights, and experience to add value to one of the many conversations that are happening. You may just meet some new friends!

Choosing your hashtags depends on the topics you are interested in, be it evaluation (#eval), data visualization (#dataviz), or even helpful excel tips (#exceltip). Hashtags also allow you to follow along with industry events like AEA’s Evaluation 2014.  By adding #Eval14 as a stream to your dashboard, you’ll receive the most recent and updated information tweeted and what other evaluators are saying about the event.

Want to learn more? Here’s a helpful resource from Fresh View Concepts on how to set up your HootSuite Dashboard.

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 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.

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