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

CAT | Social Network Analysis

Jeneen R. Garcia

Jeneen R. Garcia

My name is Jeneen R. Garcia. I’ve been a full-time evaluator at the Independent Evaluation Office of the Global Environment Facility (GEF IEO) for the last seven years. The GEF is the largest multilateral funder of environmental programs worldwide. Because the programs we evaluate almost all take place in complex social-ecological systems, we constantly need to seek out new methods for dealing with complexity.

One of the methods we’ve used is Social Network Analysis (SNA). In one evaluation, we wanted to assess the role of the GEF in increasing interactions among environmental actors at the regional level. Two things made this system complex:

1) the work of these many actors intersected, but they had no hierarchical structure, and

2) interventions took place at multiple scales, which ultimately shaped interactions at the regional scale.

It’s hard to keep track of what everyone says they’re doing and who they’re doing it with. By mapping the relationships among actors, SNA allowed us to see how well-connected the actors in the region are, and which ones are key to keeping the network well-connected.

Because it was an impact evaluation, we also needed some sort of counterfactual to compare our observations with. The big problem was, it is practically impossible to “randomly select” a region that is comparable to any other, much less find a high enough number of them to ensure statistical robustness. In this case, we were looking at the South China Sea, a region with several territorial conflicts, and which GEF has funded for > 20 years. How could we find a region to compare with that?

Hot Tips

  • Instead of looking outward, we created a scenario of the same region without GEF’s presence. We did this by redoing the SNA with the same set of actors except the GEF. The result was, without GEF support, some actors that were important at the country level became disconnected from the regional discussions.
 SNA diagram with and without GEF

(click for larger image)

  • We did not rely on this analysis alone to assess the impact of GEF funding in the region. We triangulated it with field visits, interviews at multiple scales, document reviews, environmental monitoring data, global databases, and satellite images, among others. A wide range of evidence sources and methods for analysis is your best defense against data gaps in complex systems!

Rad Resources:

To find out more about which SNA measures were used to come up with our findings, you can check out this paper that I wrote up on the analysis.

You can also see how this analysis fits in with the larger impact evaluation by reading the full report.

For more on the basics on the basics of SNA and how it can be used in evaluation, you can explore this Prezi I made. It includes links to evaluations, software, and other resources related to SNA. (CAVEAT: I delivered that presentation to a Spanish-speaking audience and haven’t translated it yet. My apologies to the non-Spanish speakers!)

The American Evaluation Association is celebrating Social Network Analysis TIG 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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My name is Rebecca Swann-Jackson, a Senior Research Associate at the Center for Research and Evaluation on Education and Human Services at Montclair State University.  I currently manage evaluation projects focused on teacher preparation and development, and educational programs and community-based initiatives serving urban children and families.  In the evaluation of an urban teacher residency program, I recently used social network analysis (SNA) to examine the relationship between support for novice teachers and retention (i.e., staying in their schools and/or the profession).

Social network analysis is an innovative method used to understand relationships. As relational models, networks show both structure (who and what) and process (how and why) at the same time. Further, you can obtain a more complete picture by combining quantitative (outsider view) and qualitative measures (insider view) of the structure and process.

SNA diagram

Hot Tips: These tips are especially relevant for those who want to try out mixing quantitative SNA with qualitative methods.

The network survey will help to construct the ‘who and what’ relational network. To use the network for evaluation purposes, you also might consider using qualitative methods to investigate ‘how and why’ wonderings. Interrogate, or question, your models; what do you want to know? Ask questions of the relationships and connections you see (and don’t see!).

In the case of the evaluation of the urban teacher residency program, I was curious about:

  1. How does each supporter do their job?
  2. Why do novice teachers reach out to these people for support?
  3. Why do novice teachers reach out to these people for these specific types support?

Tip 1: Have a data party to engage respondents in interpretation and questioning: Reconvene your survey respondents. Distribute copies of the network model with the identifiers removed. Have them identify the questions they have about the model. Ask participants which node they think represents them and ask them to explain their decision-making.

Tip 2: Investigate questions through qualitative inquiry with key nodes. In my evaluation, I used focus groups to further understand the nature of key nodes’ roles. I interviewed the key nodes to learn more about their day-to-day operations.

Lessons Learned: Combining SNA with qualitative methods provided a more holistic understanding of the relationship between support and retention. Learning how people perceived the network and the content and meaning of ties between individuals was essential to understanding network patterns as well as evaluating program implementation and outcomes.

Rad Resources:

Nvivo by QSR – Enabling Qualitative Social Network Analysis https://youtu.be/8cUBQSWgGqg

Robert Wood Johnson Foundation – Using Social Network Analysis in Evaluation https://www.rwjf.org/en/library/research/2013/12/using-social-network-analysis-in-evaluation.html 

The American Evaluation Association is celebrating Social Network Analysis TIG 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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Bethany Laursen

Bethany Laursen

Hello, everyone! I’m Bethany Laursen, principal consultant at Laursen Evaluation & Design, LLC and doctoral student at Michigan State University. I love sense making tools, don’t you? I need help untangling complex data into meaningful findings! Social network analysis (SNA) is one of those tools, and it can do a lot more than its name indicates if you know how to hack it.

SNA is fundamentally network analysis, and you can study almost anything as a network. In fact, if you’re a systems thinker like I am, you probably do this already!

Hot Tip: All you need to hack SNA is at least one set of nodes and one set of edges. Stuck? A few inspiring questions include: What is flowing in my network? What do I care about? What is easy to measure?

Here are some basic examples:

Nodes Edges
Bus stops Bus routes
Grants Shared objectives
Land preserves Wildlife migrations
Accounts Fund transfers
Activities Causes

 

Level 2 hacking adds more edges to make a multiplex graph. For example, we might track shared personnel as well as shared objectives among grants. Level 3 hacks add another set of nodes to create 2-mode networks, such as bus stops with ATMs within one block. Combining levels 2 and 3 gets you to level 4—a multiplex, two-mode network (!). There are more secret levels to discover if you create new nodes and edges out of your original ones using the analytic transformations available in SNA software.

For example, I once turned a simple information-exchange network into a two-mode expert-expertise network, and then—through a co-affiliation transformation in UCINET—I ended up with an awesome group concept map of everyone’s shared expertise, where the nodes were expertise types and the edges were people recognized as those experts. How cool is that?

Figure 1: An expertise network made of areas of expertise connected by people who have those expertises. From Laursen 2013.

Figure 1: An expertise network made of areas of expertise connected by people who have those expertises. From Laursen 2013.

Lesson Learned: You can make intangible, complex constructs visible and interpretable by re-purposing SNA.

Lesson Learned: It’s fun to play with the possibilities of SNA, but in the end, you need to have a purpose for the information you generate. Having a good question to answer is half the secret of sense making tools.

 

Rad Resources: Here are some methods and tools that re-purpose SNA:

The American Evaluation Association is celebrating Social Network Analysis TIG 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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My name is Sophia Guevara, MLIS, MPA.  I am a co-chair for the Social Network Analysis Topical Interest Group at the American Evaluation Association.   My co-writer is Simon Geletta, who was a past program chair of the SNA TIG. Simon is a professor of public health at the Des Moines University.

In this post we would like to introduce a software called “ORA”.  ORA is an extremely flexible network analysis tool that is ideal for creating, manipulating and analyzing networks and network structure from data that are stored in a number of different ways and formats (e.g., as a set of relational tables stored in a database or in a spreadsheet, as an n-dimensional matrix etc.) It allows visual as well as statistical analysis capabilities on both static social networks and dynamic social networks that can vary over time and/or space.

ORA is versatile, as it is a “multi-platform” toolkit that can operate either in stand-alone mode, or as a service “plug-in” within a web architecture. With both a GUI version and batch mode version of ORA, it is noteworthy to mention that the batch mode version has been used with networks with 106 nodes.  ORA supports high dimensional network data (or “meta-network” data), including data that represent spatiotemporally dynamic network structure. Hence, while most SNA tools are capable of mapping single-mode or two-mode networks, ORA can handle n-mode networks – this makes it ideal for measuring and understanding network changes over time or through space.

A second powerful feature is its ability to visualize geo-spatial networks. The ESRI proprietary geographic “shape” file can be used together with network data to visualize relationships between entities over geographic space. Further, ORA outputs can also be export to Google Earth, or to KML files, thus enabling interoperation with third-party tools.

Finally, ORA is interoperable with a number of other SNA tools such as Pajek and UCINET. Further, its output can be consumed by a wide range of applications because they can be made to conform to CSV, TSV, XML, JSON and similar standards.

According to the Center for Computational Analysis of Social and Organizational Systems (CASOS) website, there is ORA-LITE which is limited to 2,000 nodes and a Pro version with no node limit available at Netanomics.com.  The Netanomics.com site invites visitors to access an article published in The Economist in 2015 that mentions the use of this software.

Rad Resource: ORA Google Group https://groups.google.com/forum/#!forum/ora-google-group

This Google Group provides information for those interested in “network science and network science tools”.  You can find more information on the page about training and purchasing tools.

The American Evaluation Association is celebrating Social Network Analysis TIG 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 Rebecca Woodland and I teach Introduction Social Network Analysis as an AEA pre-conference workshop and at UMass Amherst. Those who enroll in these courses come from all walks of the evaluation field and I feel quite fortunate to have the opportunity to learn alongside so many talented professionals.

What I’ve learned.

Whether people seek to conduct evaluation in health and medicine, education, environmental non-profits, or in any number of other settings and sectors, there are some common misconceptions that seem to exist. I too held many of these misconceptions when I first ventured into SNA for program evaluation.

Myth #1 – SNA is about Facebook, Twitter, etc. and other social networking sites.

A lot of people think that the “N” in SNA refers to “networking.” The reality is that there is no “ing” in SNA. Yes, online social networking can be analyzed using SNA, but SNA is not about the study of social networking per se. In the context of evaluation, SNA is most often about examining relationships (ties) between actors (people/organizations) and how a program’s “network” may enable or inhibit actor access to important resources.

Myth #2 – SNA is only about ties between people.

Perhaps because SNA has the word “social” in its title – it is widely assumed that SNA is exclusively about people. Evaluators are rightly concerned about program effects for individuals, but SNA is also a sophisticated way to examine ties between program resources, inputs, outputs, and outcomes. With SNA, we might examine ties between nonprofits and grant-making organizations, program activities and geographic regions, or policies and governmental groups, just to name a few.  An almost unlimited number of relationships between human AND/OR non-human actors can be examined through SNA.

Myth #3 –SNA is all about creating those cool pictures.

It is true that the production of colorful and dynamic sociograms is one of the most powerful aspects of SNA! However, effective SNA does not have to entail the creation of any pictures. SNA is predicated on matrix algebra – sociograms visually depict results of mathematical computations. Sometimes it is more important, accurate, and useful to tell the story of program cohesion and actor centrality using quantitative network measures and descriptive and inferential statistics.

Rad Resources:

If you find SNA intriguing – check out the following resources. Each text addresses SNA misconceptions and may help you to further incorporate this powerful approach into your evaluation practice.

  • SNA: Methods and Applications by Wasserman & Faust
  • SNA: History, Theory and Methodology by Prell
  • Analyzing Social Networks by Borgatti, Everett, & Johnson
  • Social Network Analysis by Scott
  • SNA: Methods and Examples by Yang, Keller & Zhang
  • The SAGE Handbook of SNA

The American Evaluation Association is celebrating Social Network Analysis TIG 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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Maryann Maxwell Durland

Maryann Maxwell Durland

I am Maryann Maxwell Durland and I am part of the leadership team of the Social Network Analysis (SNA) TIG, along with Rebecca Woodland, and Sophia Guevara. One important distinction between our TIG and many others is that we are not a primary TIG for many members, but rather a “secondary” choice. Primary choices tend to be the area or field where we work or a specific interest such as the Disaster & Emergency Management Evaluation TIG. A secondary choice is of interest to an evaluator but generally not at the level of attending the business meeting or participating in TIG activities. Our leadership has been addressing this pattern and working to encourage evaluators to become more engaged in their secondary TIG’s, as well as providing resources that will address learning and professional development needs better. To inform this goal, in December 2017, we sent a survey to our members (300+) with a response rate of 12%.

Overall Results indicate that:

  • Half of the 38 members who responded are at the novice and beginner level; Another 13% rate themselves as experts
  • Based on respondents ranking of their confidence level with SNA concepts, we have a mix of members some who are very confident about concepts and a larger percentage who are not confident or have a little confidence.
  • Very few of respondents indicated confidence with software on a three point scale 11% with Gephi, 11% with R and 14% with UCINET. Respondents also listed 11 other software they use.
  • Over 70% of respondents have used SNA in describing a program as part of an evaluation and measuring/visualizing program outputs. Another 68% have used it in creating a final evaluation report, and 55% in engaging stakeholders.
  • The majority of respondents have not read common texts related to SNA concepts and several listed other texts they have read.

In the chart below are the specific concepts we asked about. Based on the survey results, the leadership team thought that we would address, from not confident to the very confident levels, the concepts that are foundational to doing SNA. All of our posts this week will discuss a variety of concepts and uses for SNA. I have started by illustrating how our TIG is addressing how we can be useful to our members. In addition, I’ll provide under Rad Resource, a short description of a classic book for first getting a sense of SNA.

Rad Resource:

John Scott’s  Social Network Analysis (3ed) gives a clear and readable overview of Social Network Analysis. Scott provides key definitions and an historical perspective, as well as covering key conceptual concepts. He includes brief examples in each chapter to further illustrate applications.

The American Evaluation Association is celebrating Social Network Analysis TIG 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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My name is Jessica Wakelee, with the University of Alabama at Birmingham.  As evaluators for our institution’s NIH Clinical Translational Science Award (CTSA) and CDC Prevention Research Center (PRC), one of our team’s tasks is to find ways to understand and demonstrate capacity for collaborative research.  This is a great need among the investigators on our campus applying for grant funding or preparing progress reports.  One of the tools we have found helpful for this purpose is Social Network Analysis (SNA).

To accomplish an SNA for a particular network of investigators, typically, we will collect collaboration data using a web-based survey, such as Qualtrics, unless the PI already has existing data such as a bibliography that can be mined.  We ask the PI to provide us with a list of network members, and send each one a survey asking them to check off collaborations they’ve had in the past 5 years with the other listed investigators. The most common collaborations include things like co-authored manuscripts, abstracts/presentations, co-funding on grants, co-mentorship of trainees, and other/informal scientific collaborations, but we also tailor questions to meet the interests of the investigator/project.  The result is a graphical depiction of the network as well as a variety of statistics we can use to provide context and tell a compelling story.

Hot Tip:

What are some of the ways we’ve found work best for describing translational research collaborations using SNA?

  • Reach of a center or hub to partners or clients
  • Existing collaborations among investigators, which can be compared at baseline and later time points
  • Increasing strength or quality of collaborations over time (i.e. pre-award to present)
  • Current/projected use of proposed scientific/technology Core facilities
  • Demonstrate multidisciplinary collaborations by including attributes such as area of specialty
  • Demonstrate mentorship and sustainability by including level of experience/rank

Lessons Learned:

  • To the extent possible, make the data collection instrument simple: Use check boxes and a single open text field for comments to provide context. This works well and minimizes the need for data cleaning/formatting.
  • While the software can assume reciprocity in identified relationships among investigators, having a 100% response rate allows for the most complete and accurate data. We have found it helps to have the PI of the grant send out a notice to collaborators to be expecting our survey invitation to boost the response rates.
  • Because we often prepare these analyses for grant proposals, it is important to allow time for data collection and avoid the “crunch time” when investigators are less likely to respond. The amount of time needed depends on the size of the network, but we find that about 4-6 weeks  lead time works well.

Rad Resource:

  • UCINET/NetDraw is the gold standard software for SNA, but there are free alternatives (e.g. “NodeXL”, an add-on to Excel) and free trials are available.

 

The American Evaluation Association is celebrating Translational Research Evaluation (TRE) TIG week. All posts this week are contributed by members of the TRE Topical Interest Group. 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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My name is Linda Stern, Director of Monitoring, Evaluation & Learning (MEL) at the National Democratic Institute.  One challenge to evaluating democracy assistance programs is developing comparative metrics for change in political networks overtime. The dynamism within political networks and the fluidity of the political environments make traditional cause-and-effect frameworks and indicators superficial for capturing context-sensitive changes in network relationships and structures.

To address these limitations, my team and I began exploring social network analysis (SNA) with political coalitions that NDI supports overseas. Below we share two examples from Burkina Faso and South Korea. 

Lesson Learned #1: Map and measure the “Rich Get Richer” potential within political networks

When supporting the development of political networks, donors often invest in the strongest civil society organization (CSO) to act as a “network hub” to quickly achieve project objectives (e.g., organize public awareness campaigns; lobby decision-makers). While this can be effective for short-term results, it may inadvertently undermine the long-term sustainability of a nascent political network.

In evaluating the development of a women’s rights coalition in Burkina Faso, we compared the “Betweenness Centrality” scores of members over time. Betweenness Centrality indicates the potential power and influence of a member by virtue of their connections and positions within the network structure.  Comparative measures from 2012 to 2014 confirmed a “Power Law Distribution” in which the number of elite members with the highest “Betweenness Centrality” scores (read power and influence) within a network tends to shrink, while those with modest or little power and influence within a network tends to grow or be “distributed” across the network.  This is known as the “Rich Get Richer” phenomenon within networks.

Lesson Learned #2: – Use “Density” metrics to understand actual and potential connectivity within a changing network

Understanding how a political network integrates new members is critical for evaluating network sustainability and effectiveness.  However, changing membership makes panel studies challenging.  In South Korea, to how founding and new organizations preferred to collaborate, compared to how they were actually collaborating, we used a spider web graph to plot the density of three kinds of linkages within the coalition: old-to-old; old-to-new; and new-to-new.  As expected, the founding organizations were highly connected to each other, as measured by in-group “density” of 74 percent.  In contrast, the new organizations were less connected to each other, with only a 27% in-group density score. We also found a relatively high in-group density (69%) of linkages between old and new members.  When we asked members who they preferred to work with, between-group density rose to 100%, indicating a strong commitment among founding members to collaborate with new members around advocacy, civic education and human rights initiatives.  However, the overlapping graphs indicated this commitment had not yet been realized.

Rad Resource – After grappling with UCINET software over the years, we finally landed on NodeExcel, a free excel-based software program.  While UCINET has more unique and complex features, for ease of managing and transformation of SNA data, we prefer NodeExcel.

The American Evaluation Association is celebrating Democracy & Governance TIG Week with our colleagues in the Democracy & Governance Topical Interest Group. The contributions all this week to aea365 come from our DG 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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My name is Kayla Brooks and I am the Monitoring and Evaluation Coordinator at One Earth Future Foundation. One of my primary responsibilities involves measuring the level of collaboration within the networks of our implementation projects. In this post, I discuss how to use social network analysis to measure collaboration and network effectiveness and provide evidence-based recommendations to your stakeholders.

Hot Tip #1 – Introduce program staff to social network analysis early and often.

Guide stakeholders through potential findings and uses of SNA. Inquire about what network information they need to help to move their program forward.

Hot Tip #2 – Use creativity to discover open-source data and collect it systematically.

Ideally, you should survey or interview most, if not all, actors in a network to collect information on their partners and relationships. However, surveys and interviews may not always be appropriate or feasible under some circumstances. Under those conditions, you can collect relationship data through open-source or internal project documents.

Hot Tip #3 – Make your data collection process manageable with the following good practices:

  • Compile an exhaustive list of relevant and specific keywords in partnership with program staff to help in your data search;
  • Perform foreign language searches if dealing with international networks; and,
  • Cross-reference your data with other existing evidence and discussions with program staff.

Hot Tip #4 -Perform regular updates and reviews of your data for accuracy and timeliness

If you are interested in measuring the change of your network over time, update your network data regularly. After each data collection update, verify the new data with stakeholders to ensure its integrity.

Rad Resource – If you are looking for affordable, easy-to-learn network analysis software, check out Gephi. Gephi is a point-and-click tool that both visualizes and analyzes network data in a single platform without requiring advanced programming skills.

Lesson Learned – Use surveys and interviews to uncover hidden details behind relationships.

Surveying or interviewing network participants can help to fill gaps where archival data falls short, such as participant motivations for being a member of the network or perceptions about the quality of relationships in the network. Surveying or interviewing network participants can also identify actors who do not share the objectives of others in the network, informing strategic decisions to perhaps dissolve certain relationships and improve network effectiveness.

Survey and interview data can also help you develop detailed profiles of stakeholders. Once important actors within the network are identified, it may be useful to assess their strategic importance through approaches like stakeholder mapping.

Good luck using social network analysis, and remember, it’s always helpful to start with a well-formulated plan that outlines why you are using it and how it will further your stakeholders’ goals.

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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My name is Sheena Horton, Senior Analyst at MGT of America, Inc., and Board Member for the Southeast Evaluation Association (SEA). It is no surprise that the use of data visualization in reporting and marketing is thriving. Studies have shown that humans process visual information better and faster than text. Data visualization can provide viewers with easily understood, actionable data and be more engaging to an audience in instances when the use of simple text can fall short.

Lesson Learned: Extend your use of data visualization beyond the workplace, and apply your data design skills to evaluating and strengthening your professional network, skills, and career profile.

Hot Tip: Conduct a social network analysis to evaluate your professional social network to identify your strong connections. Look for areas in your field, specialization, geographic location, or position type and level (e.g. managerial or mid-level) where you may need to build better connections. Note the networks where you can make contributions, and identify the best connections for conducting outreach to learn more about a specific area or skill.

Rad Resources: There are numerous data visualization tools available online to help you get started analyzing your social network. Socilab can provide you with a high-level overview of your LinkedIn network connections to jumpstart your analysis.

Hot Tip: Use data visualization to take stock of your hard and soft skills to determine the range of your strengths and to pinpoint skills to develop. A simple mind maphttp://www.mindmapping.com/ of your core skills can help you see where you can build upon your current skill set, or discover new skill areas to develop. Mind mapping adds focus to your professional development brainstorming, and helps to initiate an action plan.

Rad Resource: MindMeister is a popular and user-friendly mind mapping tool that can help you to start charting your skills quickly.

Hot Tip: Include data visualization in your resume, on LinkedIn, or on your professional website to showcase your skills and your career through visual storytelling. Determine where using data visualization can be useful based on your audience and the message you want to communicate. The visualization you select should display your data appropriately and engage your audience. A minimalist design works best; be careful not to go overboard. The key is to communicate your data simply and quickly.

Rad Resources: ResumUP and Vihttp://vizualize.me/zualize.me are good starting resources to experiment with framing your data and gathering ideas for display. A visualization that works for one person’s data may not work as well with your own.

The American Evaluation Association is celebrating Southeast Evaluation Association (SEA) Affiliate Week with our colleagues in the SEA Affiliate. The contributions all this week to aea365 come from SEA Affiliate 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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