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NN/LM Outreach Evaluation Resource Center

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Meet the New NEO

Friday, April 22nd, 2016

Cindy Olney and Karen Vargas

Head’s up, readers.  Look for a name change to our blog on May 1.

That’s the day the NN/LM Outreach Evaluation Resource Center will be replaced by the new NN/LM Evaluation Office, a.k.a. NEO.  The NEO will have the same staff (Karen Vargas and Cindy Olney) and same location (headquartered in University of Washington Health Sciences Library) as the OERC; but it has a new and evolving role in the National Network of Libraries of Medicine (NN/LM).

This time last year, Karen Vargas (evaluation specialist) and I (acting assistant director) began writing a proposal for this new NN/LM office.  The University of Washington Health Sciences Library submitted our proposal as part of its larger one for a five-year cooperative agreement to fund the NN/LM Pacific Northwest Regional Medical Library. (Spoil alert: UW HSL won the award.  See the announcement here:  https://www.nlm.nih.gov/news/nlm-rml-coop-agreement-2016.html.)

Our new name reflects one of a number of changes in NN/LM’s funding, organization, and management.  Leaders of the NN/LM are re-envisioning what it means to be a national network of organizations that work together to advance the progress of medicine and public health through access to health information. The NEO staff will contribute our evaluation expertise to help the leaders focus on key outcomes and measure progress and accomplishments.

The vision set forth in our proposal is to influence NN/LM’s use of evaluation to engage and learn about its programs, make good decisions, and enhance the visibility of its successes. Our proposed strategies were organized around five main aims. First, we will support the NN/LM leadership’s ability to make data-driven decisions. Second, we will collaborate with the regional medical libraries to increase use of evaluation in their regions.  Third, we will provide quality evaluation training opportunities to build evaluation skills of network members.  Our fourth aim is to increase visibility of NN/LM’s program successes.  Lastly, we plan to provide new written materials about effective and emerging evaluation practices and trends.

The exact nature of our services will be determined by the needs of the NN/LM as we all develop new approaches to working together. We do know that the NEO’s scope will expand beyond health information outreach evaluation to include other areas, such as organizational development and internal services to users and clients. We also want to put more emphasis on evaluation use, both for decision-making and advocating program value to stakeholders. As a teaser, Karen and I plan to develop our own expertise in evaluation reporting, participatory evaluation methods, and digital story-telling. (In fact, Karen’s blog post next week will describe our  recent participatory evaluation experience at the Texas Library Association 2016 meeting.)

The most important news for our blog readers, though, is that our URL address will not change for the foreseeable future. So in spite of the name change that’s coming, you will still find our weekly blog posts here.  So “see” you next week.

 

Our Favorite Evaluation Blogs

Friday, April 15th, 2016

successful business woman on a laptop

We really don’t want you to stop reading our blog!  But April is a really busy month for us, so this week we’ll make sure you get your evaluation buzz by letting you know of some other great evaluation blogs.

AEA365 – This is the blog of the American Evaluation Association.  This blog shares hot tips, cool tricks, rad resources, and lessons learned by different evaluators every single day!

Better Evaluation Blog – Better Evaluation is an international collaboration to improve evaluation by sharing information about evaluation methods, processes and approaches. The blog has posts that provide new perspectives  about particular issues in evaluation.

EvaluATE – EvaluATE is the evaluation resource center for the National Science Foundation’s Advanced Technological Education program. Their blog has lessons learned, tips, or techniques on evaluation management, proposal development, evaluation design, data collection and analysis, reporting, and more.

Evergreen Data – Stephanie Evergreen writes a blog about data visualization.  For the record, she has written the book(s) on data visualization, Effective Data Visualization and Presenting Data Effectively.

Visual Brains – Sara Vaca writes about new techniques and ways of visualizing data, information, and figures to communicate evaluation findings and to improve evaluation use, but also for use in other stages such as planning and analyzing.

The OERC Is On The Road in April

Wednesday, April 6th, 2016

 

A young boy having fun driving his toy car outdoors.

The OERC staff will be putting on some miles this month. Karen and Cindy are slated to present and teach at various library conferences and meetings. If you happen to be at any of these events, please look for us and say “hello.”  Here is the April itinerary: 

Cindy will participate in a panel presentation titled “Services to Those Who Serve: Library Programs for Veterans and Active Duty Military Families” at the Public Library Association’s 2016 conference in Denver. The panel presentation will be held from 10:45 – 11:45 am, April 7. She and Jennifer Taft, who is now with Harnett County Public Library,  will present a community assessment project they conducted for the Cumberland County Public Library, described here in the November/December 2014 edition of Public Libraries.

Karen will conduct the OERC workshop “Planning Outcomes-Based Outreach Programs” on April 8 for the Joint Meeting of the Georgia Health Sciences Library Association and the Atlanta Health Science Library Consortium in Decatur, GA. This workshop teaches participants how to develop logic models for both program and evaluation planning.

 Cindy and Karen will facilitate two different sessions for the Texas Library Association’s annual conference, both on April 20 in Houston. One session will be a large-group participatory evaluation exercise to gather ideas from the TLA membership about how  libraries can become more welcoming to diverse populations. The second is an 80-minute workshop on participatory evaluation methods, featuring experiential learning exercises about Appreciative Inquiry, 1-2-4-All, Photovoice, and Most Significant Change methods.

Cindy will join the NN/LM Middle Atlantic Region and the Pennsylvania Library Association to talk about evaluation findings from a collaborative health literacy effort conducted with 18 public libraries across the state. The public libraries partnered with health professionals to run health literacy workshops targeted at improving consumers’ ability to research their own health concerns and talk more effectively with their doctors. The public librarians involved in this initiative worked together to design an evaluation questionnaire that they gave to participants at the end of their workshops. The combined effort of the cohort librarians allowed the group to pool a substantial amount of evaluation data. Cindy will facilitate a number of participatory evaluation exercises to help the librarians interpret the data, make plans for future programming, and develop a communication plan that will allow them to publicize the value of the health literacy initiative to various stakeholders. The meeting will be held April 29 in Mechanicsburg, PA.

In addition, Cindy will be attending a planning meeting at the National Library of Medicine in Bethesda in mid-April with Directors, Associate Directors, and Assistant Directors from the NN/LM. Our library, the University of Washington Health Sciences Library, will receive cooperative agreements for both the NN/LM Pacific Northwest Regional Medical Library and the NN/LM Evaluation Office, which will replace the OERC on May 1. (You can see the announcement here.)  We will let you know more about the NEO later, except to say that we will be moving into the same positions in the NEO that we hold with the OERC. You have not heard the end of us!

Although we will be on the road quite a bit, rest assured we will not let our loyal readers down. So please tune in on Fridays for our weekly posts.

 

What chart should I use?

Friday, April 1st, 2016

It’s time to put your carefully collected data into a chart, but which chart should you use?  And then how do you set it up from scratch in your Excel spreadsheet or Power Point presentation if you aren’t experienced with charts?

Here’s one way to start: go to the Chart Chooser at Juice Analytics.  They allow you to pick your chart and then download it into Excel or Power Point. Then you can simply put in your own data and modify the chart the way you want to.

They also have a way to narrow down the options.  As a hypothetical example, let’s say a fictional health science librarian, Susan, is in charge of the social media campaign for her library.  She wants to compare user engagement for her Twitter, Facebook and Blog posts to see if there is any patterns in their trends. Here are some fictional stats showing how difficult it is to find trends in the data.

Monthly stats of blog, Twitter and Facebook engagement

Susan goes to the Juice Analytics Chart Chooser and selects from the options given (Comparison, Distribution, Composition, Trend, Relationship, and Table).  She selects Comparison and Trend, and then also selects Excel, because she is comfortable working in Excel.  The Chart Chooser selects two options: a column chart and a line chart.  Susan thinks the line chart would work best for her, so she downloads it (by the way, you can download both and see which one you like better).  After substituting their data with hers, and making a couple of other small design changes, here is Susan’s resulting chart in Excel, showing that user engagement with both blog posts and Facebook posts shows a pattern of increasing and decreasing at the same time, but that Twitter engagement does not show the same pattern.

Line chart of Blog Twitter and Facebook engagment

By the way, the total time spent selecting the chart, downloading it, putting in the fictional data, and making chart adjustments was less than 15 minutes.  Is it a perfect chart?  Given more time, I would suggest adjusting some more of the chart features (see our January 29, 2016 post The Zen Trend in Data Visualization). But it was a very easy way to pick out a chart that allowed Susan to learn what she needed to from the data.

One thing I want to point out is that this is not a complete list of charts.  This is a good starting place, and depending on your needs, this might be enough. But if you get more involved in data, you might want to take a look at small multiples, lollipop charts, dot plots, and other ways to visualize data.  Check out Stephanie Evergreen’s EvergreenData Blog  for more chart types.

 

Get to Know a Community though Diffusion of Innovation (Part 2)

Friday, March 11th, 2016

two granddaughter whispering some news to their grandmother

It’s really simple. You sell to the people who are listening, and just maybe, those people tell their friends.“ — Seth Godin, marketer.

Diffusion of Innovation changed my approach to community assessment. I now focus primarily on identifying the following three things: the key problem that the program or product (the innovation) offers the target community; key community members who will benefit both from the innovation and promoting it; and the best channels for capturing the attention of the majority and laggard segments of the group.

I now purposely use the term “community” rather than “needs” assessment because you have to assess much more needs. You must learn the key components of an entire social system. A community could be faculty or students in a particular department, staff in a given organization, or an online support group of people with a challenging health condition. All of these groups fit my definition of “community” by virtue of their connectedness and ability to influence each other.

Key Evaluation Questions

No matter what type of evaluation I do, I always start with guiding evaluation questions. These questions lead my investigation and help me design data collection methods. Here are my most typical community assessment questions:

  • What problems can the innovation solve for the target audience?
  • What are their beliefs, lifestyles, and values; and will the innovation fit all three? (Marketers call these characteristics “psychographics.”)
  • Who in the group is most likely to want to use the innovation and talk about it to their friends? (These are the early adopters who fit a second category: opinion leaders.)
  • Who among the early adopters will want to work with the project team and how can we work with them?
  • Where are the best places to connect with community members?
  • What are the best ways to communicate with the larger majority?

Answering Evaluation Question

 I also have a series of steps that I usually take to gather information about my key evaluation questions.  This is my typical process:

  1. Talk to “advisors” about their ideas and their contacts. Start talking with people you know who are part of or had experience with a community. I call this group my “advisors.” They don’t have to be high-level officials, but they do need to have solid social connections. It helps if they are well liked within the target community. They will know about the daily lives of target community members, as well as the influential voices in the community. They also can help you gain access.
  2. Look at publically available data: Local media provides clues to the concerns and interests of your target community. In a town or neighborhood, newspapers and websites for television stations are good sources.  Inside an organization, you should look at public and employee relation publications to see what is on the minds of leaders and employees.
  3. Interviews with key informants: Get your advisors to recommend people they think would be early adopters and opinion leaders for your innovation. But don’t stop there. Early adopters are different from those in the “later adopter” segments. You need to talk with people from the other segments to understand how to get their attention and participation. The best way to find community members in the other segments is to ask for recommendations and introductions from the early adopters. This is called “snowball sampling.”
  4. Visit the community: More specifically, visit locations where you are most likely to connect with members of your target audience. Visit the venue of a health fair where you could exhibit.  Stop by the computer lab in an academic department where you might teach students. Check out the parking and building access at the public library or community-based organization that could host a consumer health information workshop. If your community is virtual, see if you can visit and participate with group members through their favored social media channels.
  5. Put ideas together, then present them to early adopters for feedback: If at all possible, bring together a group of early adopters and potential partners to listen to and respond to your ideas. Early adopters are the people that companies use for beta testing, so you can do the same. It may be the same people you interviewed or a different crowd (or a mix).

Other Tips

For the most part, I tend to rely heavily on qualitative methods for community assessment. Diffusion of Innovation describes how ideas spread through a system of relationships and communication channels. You need details to truly understand that system.  You need to talk to people and understand how they live. Interviews, focus groups, and site visits provide the most useful planning information, in my opinion. You may have to include quantitative data, though.  Library usage statistics might indicate the best branches for providing workshops.  Short surveys might confirm broad interest in certain services. In the end, a blend of mixed-methods gives you the best picture of a community.

The downside to mostly qualitative data collection methods is that you get an overwhelming amount of information. I like to use an information sheet that allows me to summarize information as I conduct a community assessment. A version of this worksheet is available in OERC’s Planning and Evaluating Health Information Outreach Projects Booklet 1: Getting Started with Community-Based Outreach (downloadable version available here.). See Worksheet 2 on page 19. You should see how the evaluation questions I posed above are related to this worksheet.

Final Thoughts

Seth Godin said ideas that spread are remarkable, meaning they are “worth making a remark about.” Use community assessment to find out why your innovation is remarkable and how to start the conversation.

Other Resources

If you want to see an example of a community assessment that used this process, check out this article in Public Libraries.

You also might be interested in  Seth Godin’s TEDtalk How to Get Your Ideas to Spread.

A Most Pragmatic Theory: Diffusion of Innovation and User Assessment (Part 1)

Friday, March 4th, 2016

Seven tomatoes in a row, increasing in maturity from left to right

If your work includes teaching or providing products or services to people, you are in the business of influencing behavior change. In that case, behavior change theories should be one of the tools in your evaluation toolbox. These theories are evidence-based descriptions of how people change and the factors that affect the change process. If you have a handle on these influences, you will be much more effective in gathering data and planning programs or services.

Today and next week, I’m going to talk about my go-to behavioral change theory: Diffusion of Innovations. It was introduced in the 1960s by communication professor Everett Rogers to explain how innovations spread (diffuse) through a population over time. The term innovation is broadly defined as anything new: activities, technologies; resources; or beliefs. There are a number of behavioral change theories that guide work in health and human services, but I particularly like Diffusion of Innovations because it emphasizes how social networks and interpersonal relationships may impact your success in getting people to try something new.

I use Diffusion of Innovations for most user or community assessment studies I design. Next week, we’ll talk about using these concepts to frame community or user assessment studies. This week, I want to cover the basic principles I found to be most helpful.

People change in phases

The heart of behavior change is need.  People adopt an innovation if it that solves a problem or improves quality of life. Adoption is not automatic, however. People change in phases. They first become aware and gather information about an innovation. If it is appealing, they decide to employ it and assess its usefulness. Adoption occurs if the innovation lives up to or exceeds their expectation.

Product characteristics influence phase of adoption

Five criteria impact the rate and success of adoption within a group. First, the innovation must be better than the product or idea it is designed to replace. Second, it must fit well with people’s values, needs and experiences. Innovations that are easy to use will catch on faster, as will technologies or resources that can allow experimentation before the user must commit to it. Finally, if people can easily perceive that the innovation will lead to positive results, they are more likely to use it.

Peers’ opinions matter greatly when it comes to innovation adoptions. Marketers will tell you that mass media spreads information, but people choose to adopt innovations based on recommendations from others who are “just like them.” Conversations and social networks are key channels for spreading information about new products and ideas. If you are going to influence change, you have to identify and use how members of your audience communicate with one another.

Migration of flock of birds flying in V-formation at dusk

Riding the Wave

Segments of a population adopt innovations at different rates. In any given target population, there will be people who will try an innovation immediately just for the pleasure of using something new. They are called innovators. The second speediest are the early adopters, who like to be the trendsetters. They will use an innovation if they perceive it will give them a social edge. They value being the “opinion leaders” of their communities.

Sixty-eight percent of a population comprise the majority.  The first half (early majority) will adopt an innovation once its reliability and usefulness have been established. (For example, these are the folks who wait to update software until the “bugs” have been worked out.) The other half (late majority) are more risk adverse and tend to succumb through peer pressure, which builds as an innovation gathers momentum. The last adopters are called the laggards, who are the most fearful of change. They prefer to stick with what they know. Laggards may have a curmudgeonly name, but Les Robinson of Enabling Change pointed out that they also may be prophetic, so ignore them at your own risk.

Next Step: Diffusion of Innovations and User/Community Assessment

Next week, I will show you how I develop my needs assessment methods around Diffusion of Innovation concepts. In the meantime, here are some sources that might interest you. Everett Rogers and Karyn Scott wrote an article specifically for the NN/LM Pacific Northwest Region that you can read here. Les Robinson’s article has an interesting discussion of the specific needs of the different population segments: Finally, If you want the classic text by Ev Rogers himself, here is the full citation.

Rogers EM.  Diffusion of innovations (5th ed). New York, NY: The Free Press, 2003.

Appreciative Inquiry of Oz: Building on the Best in the Emerald City

Friday, February 19th, 2016

Cartoon image of an Emerald City

“One day not very long ago, librarians came to the Emerald City from their libraries in all of the countries of Oz. They came to visit the Great Library of the Emerald City, and to petition the Wizard allow them to borrow books and other items at the Great Library. Their hope was to transport items from one library to another using the Winged Monkeys, who offered their skills for this task after they were set free and got bored.”

Thus begins the latest OERC project – an online class in Appreciative Inquiry (AI), offered through the MidContinental Region’s Librarians in the Wonderful Land of Oz Moodle ‘game’ (i.e. series of online classes worth game points and CE credits from the Medical Library Association).  The game is made up of several ‘challenges’ (online classes) for librarians offered by NN/LM instructors.

In OERC’s challenge, Building on the Best at the Great Library of the Emerald City: Using Appreciative Inquiry to Enhance Services and Programs, the Wizard of Oz makes a deal with the librarians.  He will allow interlibrary loan of the Great Library’s resources if the librarians will assess customer satisfaction of the Great Library’s services and find things to improve.  And students in the class will learn to use a qualitative data collection technique called Appreciative Inquiry to do this assessment.

Sometimes people avoid customer service assessment because they find the methods to be complicated and time-consuming. Negative feedback can be uncomfortable on the part of the listener and the speaker. Appreciative Inquiry, with a focus on identifying and building on organizational strengths, removes that discomfort. A number of OERC workshops touch on Appreciative Inquiry but this Librarians of Oz challenge allows you to practice the technique, something that the OERC has not been able to provide in the traditional webinar or workshop context.  Completing the class is worth 14 MLA CE credits.

The class is free, but in order to take it you will need to register for the game Librarians in the Wonderful Land of Oz .  If you don’t want to take the class, but would still like to learn more about Appreciative Inquiry, I recommend these earlier blog posts:

From Cindy and Karen’s perspective, one of the best parts of this experience is that we finally get the official title of Wizard.  Special thanks to John Game Wizard Bramble of the NN/LM MCR who made all this happen.

 

Logic Model for a Birthday Party

Thursday, February 4th, 2016

Cindy and I feel that logic models are wonderful planning tools that can be used in many life events to stay focused on what’s meaningful. This blog post is an example of such a logic model.

My daughter’s birthday is coming up this week and we are having a party for her. My husband and I have quite a few friends with children about the same age as our daughter (who is turning 3).  This means that we go to birthday parties and we have birthday parties, and we are looking forward to another 15 years or so of birthday parties.  Even though we live in the 4th largest city in the country, it’s a bit of an project to come up with a place for the party.  I could see this problem stretching out into future years of Chuck E. Cheese’s and trampoline parks. Not that there’s anything wrong with those places, but we realized that for us it was time to stop the train before we went off the rails.  Looking at my own childhood, my birthday parties growing up were all at my own house. So we decided to see if we could have a party at our house and just have fun.

To make sure we had a great event and kept our heads on straight (and had something to blog about this week), I created a logic model for my daughter’s birthday party. We needed an evaluation question, which is “is it possible to have a party of preschoolers at our tiny, not-that-childproofed-house without going crazy?”

So here is the event we have planned.

Birthday Party Logic Model

If you’re new to logic models, they are planning tools that you use from right to left, starting with long-term outcomes (what you hope to see out in the future), intermediate outcomes, and short term outcomes. Then you think of the activities that would lead to those outcomes, and then inputs, the things you need in order to do the activities. (For more information on logic models, take a look at the OERC Blog category “Logic Models“).

What I’ve learned from this process is that every time I would come up with an idea about what we could do at the party, it would need to pass the test of whether or not it leads to the long-term outcome of being willing to throw parties in the house in the future – in other words if the party takes too much work or money (or it isn’t fun), we won’t remember it as an event we are likely to do again. For example, while we are inviting a person to our house to entertain the kids, we’re bringing our daughter’s music teacher from her preschool, so it should be fun for the kids that she knows from pre-school and everyone will know the music and can sing along.  Another activity that has high enjoyment and low effort is the dance party with bubbles. All toddlers love to dance, and we can make a playlist of all of our daughter’s favorite dance songs.  Adding bubbles to the mix is frosting.

The short term goals are our immediate party goals.  We would like the party to be fun for our daughter and for most of her friends (can we really hope for 100%?  Probably not, so we put 90%).  My husband and I may be a little stressed but we’re setting our goal fairly low at being relaxed 60% of the time (you’ll have to imagine maniacal laughter here).  Our intermediate goals are simply that we all can feel comfortable having our daughter’s friends over to our house in the near future. And the long term goal is to think this is a good idea to do again and again.

Wish us luck!

The Zen Trend in Data Visualization

Friday, January 29th, 2016

You may have noticed there is a data visualization bandwagon out there and the OERC bloggers are on it. We like to write about tools and resources that can help you communicate visually.  There’s a bit of self-interest here. We want to fuel this bandwagon because we personally prefer to review a one-page visual synopsis of data over a table-dense written report.

Unless, that is, we have to grapple with a poorly designed data visualization. These are displays with too many details; unnecessary icons; many variables piled into one chart. It can make your head spin.

That isn’t to say you have to be an artist to do good visual displays. Quite the contrary.  Most information designers adamantly state that data visualization is about communication, not art. However, you have to know how to design with a purpose.

To understand the basics of good design, you need to understand why humans respond so well to visual displays of data.  That is the topic of an excellent blog article by Stephen Few, a thought leader in the data visualization field. First, he advocates that data visualizations aid users in performing three primary functions: exploring, making sense of, and communicating data.  Evaluators would add that our ultimate goal is to help our users apply data in planning and decision-making. To that end, Few argues that data visualizations should be designed to support readers’ ability to perform these four cognitive tasks:

  1. See the big picture in the data
  2. Compare values
  3. See patterns among values
  4. Compare patterns

To demonstrate Few’s point, I am sharing a sample of the OERC Blog’s monthly site statistics.  The blog dashboard uses a table format to display total views per month. The table shows monthly view counts starting in June 2014, when we started tracking site statistics.

Table showing total page views per month for the OERC blog from June 2016 to Jan 2016.  The numbers generally get higher over time, but there is fluctuation month to month. It is difficult to see trendts

 

Slide3
Line graph of data shown in previous table (June 2014 to January 2015) It shows total page-view-per-months. The general upward trend is much more apparent than in the table. 2015 has more page views than 2014 for all months. There are some times during the year where there is a zigzag pattern, with high viewership one month followed by lower readership the next. This would suggests a need to look at topics or schedules of the blog readership.

Now here’s the same information presented in a line graph created in Excel.  You quickly see the big picture: Our page views are trending upward.  You can easily compare the direction of month-to-month traffic.  The chart allows comparison of monthly patterns in 2014 and 2015.  It’s the same data, but its almost impossible to see any of these findings in tabled data.

Dataviz design experts like Stephen Few are avowed minimalists. They hate chart junk, such as gridlines and data labels.  They have an affinity for small multiples, which are series of graphs displaying different slices of data.  (If you have never seen small multiples, here’s a post from Juice Analytics with good examples) In general, they do not include any element that will hinder users’ ability to make comparisons, find patterns, and identify pattern abnormalities that may be indicators of important events.  Needless to say, most data visualization luminaries are not big on decorative features like gas gauges and paper doll icons. These, too, are viewed as unnecessary distraction.

Yet, there is a lot of chart art out there and you may wonder why. There is a distinction between data visualizations and infographics.  Alberto Cairo, Knight Chair in Visual Journalism at the University of Miami’s School of Communication,  wrote that data visualizations are tools for interactive data exploration while infographics are visual displays that make a specific point.  I tend to think of data visualizations as having users, while infographics have readers. Chart art may be more legitimate in infographics because it supports the primary message or story.

Yet, Cairo admits that the boundary between infographic and data visualizations is fuzzy. He noted a trend toward infographics with two layers: a presentation layer, and an exploration one.  The infographics have an obvious primary message, but readers are also presented with opportunities to explore their own questions. One of my favorites from the Washington Post is an example of an infographic with both layers.

That said, Cairo still argues that data design principles hold true for both data visualizations and infographics.  There is no excuse to drown your readers in images or to distract them with bling.

Zen is in; bells and whistles are out. The good news is that simple data visualizations do not require sophisticated software or design skills.  That’s not to say that simple is the same as easy. Good data visualizations and infographics take a lot of thought. For the more interactive data visualizations, you must identify how your users will use your data and design accordingly. For infographics, you need first to clearly identify your central message and then be sure that every element has a supporting role.

Want to start developing good visual design habits? I recommend Presenting Data Effectively by Stephanie Evergreen (Sage, 2013).

 

 

New Outcome Measurement Resource for Public Libraries

Friday, January 22nd, 2016

Librarian and children looking at globe in library

About six months ago Public Library Association (PLA) initiated a service called Project Outcome, that I have been following with interest. An article entitled “Project Outcome – Looking Back, Looking Forward” by Carolyn Anthony, director of the Skokie Public Library, IL was recently published in Public Libraries Online that describes the successes of libraries using this service over the past 6 months.

Project Outcome is an online resource that provides evaluation tools that are designed to measure the impact of library programs and services, such as summer reading program or career development programming. It also provides ready-made reports and data dashboards that can be used to give libraries and stakeholders immediate data on their programs’ outcomes.  And Project Outcome provides support and peer sharing opportunities to address common challenges and increase capacity for outcomes evaluation.

Here are some of the things that make me the most excited about this service:

  1. Project Outcome has managed to create a structured approach for program outcome evaluation that can be used online by public libraries of all shapes and sizes by people who have not done outcome evaluation before.  Along with tools for collecting data, the resource has tutorials and support for libraries doing outcomes evaluation for the first time.
  2. Continued support and peer sharing as an integral part of the service means that PLA is building a community of librarians who use outcome evaluation.
  3. The stories that are shared by the peers as described in the article will increase the understanding that evaluation isn’t something forced on you from outside, but can be something that helps you to create a better library and enhance the meaning of your library’s programs.
  4. This process teaches librarians to start with the evaluation question (“decide what you want to learn about outcomes in your community”) and a plan for what to do with the findings. And the process ends with successfully communicating your findings to stakeholders and implementing next steps.
  5. Lastly, I love that Project Outcome and the PLA Performance Measurement Task Force are planning the next iteration of their project that will measure whether program participants followed through with their intended outcomes.  It will be very interesting to find out how this long term outcome evaluation comes out.

I’ll end with this statement from Carolyn Anthony, who said “the opportunity to spark internal conversations and shift the way libraries think about their programs and services is what outcome measurement is all about.”

Last updated on Saturday, 23 November, 2013

Funded by the National Library of Medicine under contract # HHS-N-276-2011-00008-C.