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NEO Shop Talk

The blog of the National Network of Libraries of Medicine Evaluation Office

Creating Partnerships that Work

Multiracial Businesspeople Stacking Hands

“Five guys on the court working together can achieve more than five talented individuals who come and go as individuals.” Kareem Abdul-Jabbar

When you’re working on an outreach project, you will almost certainly have some kind of partner organizations in the project.  Funders of outreach projects love to see partnerships, and sometimes they even require it.  When everything works like it’s supposed to, a partnership between organizations working on a joint outreach project can spawn better ideas, create a richer program, and improve reach.

But have you ever felt like you’ve made some bad decisions in your choice of partners? (I’m not talking about your sordid relationship history here).  It feels like a disaster when your plans fall apart because your partner organization had a completely different understanding of their role in the project, different priorities, or there were communication problems (okay, maybe I am).

Yesterday I was reviewing our Tools and Resources Guide for a major website update coming next week.  And I found some great resources for choosing and maintaining partners.

The Community Tool Box from the University of Kansas has a toolkit on Creating and Maintaining Partnerships.  This toolkit is made up of steps for partnering organizations to work through together. Here are some of the main categories, but the descriptions on the website are quite detailed:

  • Describe the problems or goals that have brought partners together in common purpose
  • Outline your partnership’s vision and mission, objectives
  • Re-examine the group’s membership in light of your vision, mission, and objectives
  • Describe potential barriers to your partnership’s success and how you would overcome them
  • Describe how the partnership will function and how responsibilities will be shared among partner organizations
  • Describe how the group will maintain momentum and foster renewal
  • If the partnership losing momentum, review current barriers to your success
  • If necessary, revisit the plan to identify and recruit new or additional members
  • When maintaining the partnership at its current level is no longer appropriate or feasible, consider other alternatives, including changing focus, adding new members, or even dissolving the partnership

The Urban Indian Health Institute (UIHI) has a Resource Guide: Establishing and Maintaining Effective Partnerships.  It is a one page document with an emphasis on building trust among partners.  Here are some of their characteristics of successful partnerships with an emphasis on learning and openness.

  • A common vision and collective commitment
  • Mutual trust and respect
  • Risks, resources and rewards are shared jointly
  • Opportunities for capacity building through learning exchanges
  • Openness to learning and teaching opportunities
  • Ground rules that create a safe space to address challenges
  • Acknowledgement of the differences between the partners
  • Flexibility

Balancing the emphasis on trust and respect, UIHI also has helpful guides on their Resources for Partnerships page for establishing Memorandums of Understanding (MOUs) so partners clearly understand what’s expected of each other.

You might take a look at these guides and think “they’re asking me to do a lot of work – I just want to do a few health information presentations at the local public library.”  While you may be correct, sometimes a small project grows into something bigger, and then suddenly you find yourselves writing a proposal for a grant from the National Library of Medicine.  When going into a partnership, large or small (or personal), it might be worthwhile to take a look at some of these these guides — even if you don’t do all the steps or complete an MOU, you will find something that can help you make sure your partnership is nourished and successful for as long as it needs to be.

 

The Appreciative Inquiry Holiday Challenge

hand writing something in to the notebook near christmas toys.

The holiday season is upon us, so I want to give our readers a holiday Appreciative Inquiry challenge.  This is a fun way to practice the Appreciative Inquiry interview. It also provides an opportunity for you and your family to plan a better-than-usual holiday season.  Finally, it gives everyone something to talk about other than politics. (You’re welcome.)

During the coming week, ask yourself and your loved ones the following three questions:

  • What was the best holiday experience you’ve ever had?
  • What made that experience so special? What did you value about it?
  • What could happen to make this year’s holiday season exceptional?

Here’s how I would answer the questions.  My favorite holiday was the one I had as a child, traveling to Arizona to spend Christmas with extended family.  For a kid from Western Pennsylvania, Tucson was exotic.  Christmas lights on saguaro cactuses. Luminarias.  Tree ornaments from Mexico. The best part, though, was a trip to the Catalina mountains.

What I valued about that holiday was the differentness of the setting and seeing how those from another part of the country celebrated the holiday. I also liked the bright sunny days outdoors.

It’s a little too late to book a trip to Arizona for the holidays, but I can still seek out places close by that have a different take on holiday decorations. As for enjoying the outdoors, I live in a place that offers lots of opportunity on that front. My husband and I can easily fit in a hike and a trip to Helen, a Bavarian Alpine village in the North Georgia mountains.

Once you’ve talked with your family, make a list of everyone’s ideas for a great holiday and check them off as they happen. You could even do this as a group on a private Facebook page.  Or go old school and put a written list on your refrigerator door.  See if Appreciative Inquiry doesn’t add some sparkle to your holiday season this year.

Happy Thanksgiving, everyone.

 

 

A Chart Chooser for Qualitative Data!

Core Values Word Cloud Concept

When people talk about data visualization, they are usually talking about quantitative data. In a previous post, we explained that data visualizations help people perform three primary functions: exploring, making sense of, and communicating data.  How can we report qualitative data in a way that performs those same functions?

We just got some exciting news from the EvergreenData blog that they have developed a Qualitative Chart Chooser. Seriously–it’s a work of art. Actually two works of art because they have two different chart chooser drafts to choose from.

The way it works is this: you think about the story you want to tell with your data, maybe about how something improved over time because of your awesome project. Then using the chart chooser, you look at the “show change over time” category, and then you could select a timeline, before-and-after “change photos,” or a histomap (what’s a histomap?  Take a look at this one).

This chart chooser is a very cool tool. But I wouldn’t wait until it was time to report findings to use it. One thing that we at the NEO suggest is that when you are first planning your project, you should think about the story or stories you want to tell at the end of your project. Maybe when you’re thinking about the story you want to tell, you could look at all these different qualitative charts in the chart chooser.  Which ones would you like to use? Do you want to tell the story of how your program aligns with the goals of your institution (you could try indicator dots)? Or maybe you want to show how the different parts of your project work together as a whole (a dendrogram might work). By looking at these options before you design your evaluation plan, you can be sure that you are gathering the right data from the beginning. Backing up even further in your planning process, if you are having trouble trying to decide what story or stories you want to tell, this Qualitative Chart Chooser can give you ways to think about that.

Here is some more information on qualitative data visualization and storytelling from NEO Shop Talk:

Qualitative Data Visualization, September 26, 2014

More Qualitative Data Visualization Ideas, December 18, 2014

Telling Good Stories About Good Programs, June 29, 2015

DIY Tool for Program Success Stories, July 2, 2015

 

Participatory Evaluation, NLM Style

Road Sign with directional arrow and "Get Involved" written on it.

This week, I invite you to stop reading and start doing.

Okay, wait. Don’t go yet.  Let me explain. I am challenging you to be a participant-observer in a very important assessment project being conducted by the National Library of Medicine (NLM).

The NEO is part of the National Library of Medicine’s program (The National Network of Libraries of Medicine) that promotes use of NLM’s extensive body of health information resources.  The NLM is devoted to advancing the progress of medicine and improving the public health through access to health information. Whether you’re a librarian, health care provider, public health worker, patient/consumer, researcher, student, educator, or emergency responder fighting health-threatening disasters, the NLM has high quality, open-access health information for you.

Now the NLM is working on a long-range plan to enhance its service to its broad user population.  It is inviting the public to provide input on its future direction and priorities. Readers, you are a stakeholder in the planning process. Here is your chance to contribute to the vision. Just click here to participate.

And, because you are an evaluation-savvy NLM stakeholder, your participation will allow you to experience a strength-based participatory evaluation method in action.  Participatory evaluation refers to evaluation projects that engage a wide swath of stakeholders. Strength-based evaluation approaches are those that focus on getting stakeholders to identify the best of organizations and suggest ways to build on those strengths. Appreciative Inquiry is one of the most widely recognized strength-based approaches. The NEO blog have posts featuring Appreciative Inquiry projects here and here.

While I have no idea if the NLM’s long-range planning team explicitly used Appreciative Inquiry for developing their Request for Information, their questions definitely embody the spirit of strength-based assessment. I’m not going to post all of the question here because I want readers to go to the RFI to see the questions for themselves. But as a teaser, here’s the first question that appears in each area of inquiry addressed in the feedback form:

 “Identify what you consider an audacious goal in this area – a challenge that may be daunting but would represent a huge leap forward were it to be achieved.  Include any proposals for the steps and elements needed to reach that goal. The most important thing NLM does in this area, from your perspective.”

So be an observer: check out the NLM’s Request for Information.  Notice how they constructed a strength-based participant feedback form.

Then be a participant: take a few minutes to post your vision for the future of NLM.

Mean, Median or Mode–What’s the Difference?

Five stars ratings with shadow on white

Last week I taught the class Finding Information in Numbers and Words: Data Analysis for Program Evaluation at the SCC/MLA Annual Meeting in Galveston, TX.  There is a section of the class where we review some math concepts that are frequently used in evaluation, and the discussion of mean, median and mode was more interesting than I expected.  Mean, median and mode are measures of “central tendency,” which is the most representative score in a distribution of scores.  Central tendency is a descriptive statistic, because it is one way to describe a distribution of scores. Since everyone there had run across those concepts before, I asked the group if anyone knew of any clever mnemonics for remembering the difference.  Several people responded, both in class and afterwards (thanks Margaret Vugrin, Julia Crawford and Michelle Malizia!) Here are a couple of memory tools for you:

Mean: Turns out those mean kids are just average (mean = average)

Median: Just like the median in the road: if you line the values up in order, the median is the number “in the middle”

Mode: Mode has an “O” sound, and O is the first letter in “Often.”  It is the value on the list which occurs most often.

Or

Hey diddle diddle, the Median’s the middle; you add and divide for the Mean. The Mode is the one that appears the most, and the Range is the difference between.

Now that you can remember them, which one should you use?  I think a good way to think about it relates to the ratings you see when you’re trying to pick a hotel or restaurant.  I don’t know about you, but when I’m looking at a restaurant score, when I see 4 stars (out of 5), I’m not comfortable with that number without seeing the breakdown.  There’s a big difference between a 4 where the scores are spread out and one where the scores are heavily skewed towards 5.

Let’s say I’m looking at reviews for hotdog restaurants that have around 4.0 stars.  The first one I look at has a distribution of scores spread out relatively evenly from 5 stars (the most number of ratings) to 1 star (the fewest).  A mean (mean kids are just average) works well here.  To do this you add all the scores up and divide the sum by the number of responses to reach a mean of 3.9.

3-9-star-mean

Here is the chart for a similar hotdog restaurant with a slightly higher 4.2 star rating:

4-2-star-median

While the mean adds up to 4.2,  you can see in the chart that the scores are heavily skewed towards 4 and 5 stars, and it seems that 4.2 does not accurately describe the ratings of that restaurant (remember that central tendency is a descriptive statistic). However, if you use a (middle of the road) median with this kind of distribution, the result is a whopping 5.  To determine the median, you line all the results up in order and select the one in the very middle.  You might want to use a median when the distribution looks skewed.

This particular restaurant analogy doesn’t really work with mode. Mode is used for categories, which really cannot be averaged mathematically.  For example, if you want to know what is the most representative type of restaurant in your city, you might find out that your city has more hotdog restaurants than any other kind of restaurant (that would be awesome, right?).

I hope this helps. If you know any other mnemonics for mean, median, or mode, please send me an email at kjvargas@uw.edu and I will add them to the bottom of this post.

 

How I Learned to Stop Worrying and Love Logic Models (The Chili Lesson)

michelle

By Michelle Malizia, Director of Library Services for the Health Sciences, University of Houston

I’ll start with a full disclosure: I am a late convert to logic models. Many years ago, I worked in a department that, for a period of time, became governed by logic models. This experience made me fear… no, hate… logic models.  Several years later, through external workshops and assistance from the NN/LM Evaluation Office, I was introduced to the tremendous value of logic models.

My closest personal analogy relates to my feelings about chili. I grew up eating my mother’s chili, which basically consisted of cans of many different types of beans floating in a type of broth. I hated it. When I was 22 years old, I had no choice but to eat someone else’s chili. This chili had lots of ground beef and spices. It was delicious. Then it occurred to me, my mother’s chili was my only frame of reference for chili. I didn’t dislike chili – I disliked my mother’s chili.

And so it goes with logic models. Once I learned a different way to make and apply them, I became a dedicated user. I now design logic models whenever I plan a new service, activity or initiative.

In 2014, I was hired as the Director of Library Services for the Health Sciences at the University of Houston (UH). In 2017, UH will have its first medical library and my task is to plan the services for the new facility. Of course, I turned to logic models because they provided the framework of not only what I am planning but why I am planning each service and ultimately, how I will evaluate if I achieved the goal.

When I started with my logic models, I was tempted to begin with the activity. I had to remind myself that it is more important to document what I hope to accomplish by that activity (i.e. outcome).  Think about it: Why do librarians teach PubMed classes? Why do librarians want to be embedded in a nursing class? Why do so many libraries provide liaison services? Many of you are probably thinking: “That’s easy, Michelle. We do those things to better serve our customers.” My response is:  How do you know those activities better serve your customers?  How can you prove it to your stakeholders?  That is the reason you should start with your outcomes rather than activities.

For example, my new library will be providing assistance with NIH Public Access Policy compliance. When I developed my logic model, I called upon my inner 3-year old to ask the question best asked by toddlers:  Why.  Because I have a creative side, I use the software Visio (a Microsoft Office software) to create my logic models. It allows me to visually see connections between activities. The chart below shows  a portion of my logic model.

NIH Public Access Policy Assistance Services logic Model One. Activities are conduct workshops and assistance with compliance. Outputs are number of workshops and number of consultations. Short-term outcomes are increase awareness of NIH Public Access Policy and increased knowledge of compliance specifics. Intermediate outcome is compliance and long-term outcome is UH retains and receives NIH grants.

 

As you can see, my long-term outcome for this activity was to ensure that UH retains and receives NIH Grants. If UH researchers don’t comply with the NIH Public Access Policy mandate, their current and future funding is in jeopardy. The intermediate outcome leading to the long-term outcome is increased compliance with the policy. In order to increase compliance, I need to make researchers aware of the policy and how to comply.  That’s when I was able to determine the best methods for me to accomplish those outcomes. For my university environment, the best way to achieve these outcomes is through workshops and consultations.

Now that I knew the “what and the why,” I needed to determine the how.  How would I know if I accomplished my goals? Again, I turned to Visio to visualize how I could assess if I achieved my outcomes.

My final step was to determine my measurable indicators. For example, in the case of workshops, my indicator was “% of workshop attendees who reported being more knowledgeable about how to comply with the policy.”  My target was 85% of attendees. To evaluate this outcome, I would use a pre- and post-test.

 

Evaluation plan for NIH Public Access Policy Assistance Services. Outputs are workshops and consultations, leading to short-term outcomes of increased awareness of NIH Public Access policy and increased knowledge of compliance specifics. The outcomes will be assessed with a pre-post test and follow-up questionnaire. The intermediate outcome is increased compliance, which will be assessed with a survey and/or other follow-up

My overall work with logic models led to a pleasant surprise. Mid-way through my process, UH Libraries adopted a new strategic plan. Strategic plans are usually written in terms of goals. Some of my colleagues tried to feverishly determine where their activities fit into the library’s overall goals. Because I had already determined my outcomes, it was easy to slot my activities into the library’s overall plan.

If you have had a previous bad experience creating logic models, try it again. Ask the NEO for assistance and look at their extremely helpful guides. Like me, you may finally realize that logic models are worth the time and energy. Remember, there are many different types of chili.  Find the one that you like best.

NEO note: The evaluation field has come a long way in discovering new, less painful approaches to creating and using logic models.  If, like Michelle, you had bad experiences years ago with logic models, you might want to give them another chance.  You can learn one approach through the NEO booklet Michelle mentioned, which is  Planning Outcomes-Based Projects (Booklet 3 in our Planning and Evaluating Health Information Outreach Programs series). For alternative approaches, check out our NEO Shop Talk blog entries  Logic Model for a Birthday Party and An Easier Way to Plan: Tearless Logic Models.

 

How Many Interviews Does It Take to Assess A Project?

A green piggy bank standing with a group of pink piggy banks to represent the cost effectiveness of individual interviews

FAQ from NEO users: How many interviews or focus groups do we need for our qualitative assessment project?

Our typical response: Um, how much money and time do you have?

At which point, our users probably want to throw a stapler at us. (Karen and I work remotely out of an abundance of caution.)

Although all NEO users are, in fact, quite well-mannered, I was happy to discover a study that allows us to provide a better response to that question.  A study published by Namey et al, which appears in the American Journal of Evaluation’s September issue, provides empirically based estimates of the number of one-to-one or group interviews needed for qualitative interviewing projects.  More specifically, their study compared the cost effectiveness of individual and focus group interviews. The researchers conducted an impressive 40 interviews and 40 focus groups (with an average of eight people per group). They then used a boot-strap sampling methodology, which essentially allowed them to do 10,000 mini-studies on their research questions.

They first looked at how many individual and focus group interviews it took to reach what qualitative researchers call thematic saturation. In lay terms, saturation means “Not really hearing anything new here.”  Operationally, it occurs when 80-90% of the information provided in an interview has already been covered in the previous interviews.

The researchers found that 80% saturation occurred after 2-4 focus groups or eight individual interviews. It took 16 interviews and five focus groups to reach 90% saturation. Note their estimates apply to studies that focus on one specific population.  If you want to explore the experiences of two groups, such as doctors and nurse practitioners, you would hold eight interviews per group to reach 80% thematic saturation.

For comparative cost assessment, the researchers used a formula that combined hourly rate of the data collector’s time, incentive costs per participant, and cost of transcription for recordings. They chose not to include cost for factors that vary widely, such as space rental or refreshments. Using more predictable costs made for cleaner and more generalizable comparisons.

Bottom line, they found individual interview methods cost 12-20% less than focus group methods.

Of course, many of us operate on shoestring budgets, so we are our own moderators and transcribers.  Even though most of us DIYers collect hourly wages, the cost for outsourcing these tasks is probably higher than for conducting them internally. Knowing this, the researchers looked at variations on moderator, transcriptionist, and incentive costs.  They also compared cost effectiveness of the two methods when lowering the standards for thematic saturation (i.e., aiming for 70% saturation instead of 80%). Across the board, individual interviews were more cost-effective than focus groups.

Cost is not always the only consideration when choosing between focus groups and individual interviews. Some assessment questions beg for group brainstorming, while others demand the privacy of one-to-one discussions. However, for many assessment studies, either method is equally viable.  In that case, cost and convenience will drive your decision. Personally, I often find individual interviews to be more convenient than focus groups, both for the participants and for me. It’s nice to know that the cost justifies using the more convenient approach.

The full article provides details on their methods, so it is a nice primer on qualitative analysis of interview transcripts. Here’s the full citation:

Namey E, Guest G, McKenna K, Chen M. Evaluating bang for the buck: a cost effectiveness comparison between individual interviews and focus groups based on thematic saturation levels. 2016 September; 37(3): 425-440.

 

Meet the NEO’s New Program Assistant Kalyna Durbak

Kalyna Durbak

I am pleased to introduce the NEO’s new program assistant, Kalyna Durbak, MLIS, who joined our staff on October 3.  Kalyna will be our go-to person for managing the NEO website, providing technical support with webinars, and helping with the “roll-up-your-sleeves” work involved in carrying out evaluation projects.

Kalyna began working for the UW Health Sciences Library in May 2016. Prior to joining the NEO, Kalyna was the Web Content Assistant on the team that created and promotes the Response & Recovery App in Washington (RRAIN), designed to provide emergency responders with quick access to disaster-management resources. It also provides local information such as weather alerts and traffic reports. Kalyna also provided web content and social media assistance for the Health Evidence Resource for Washington State (HEALWA), a portal that provides affordable online access to clinical information and health education resources. The portal is available to health professionals who are licensed through 23 state organizations. A 2015 evaluation study conducted by HEALWA showed that many health professionals eligible to use the portal are not aware of it.  Kalyna helped promote HEALWA through social media and exhibits.

Kalyna earned her MLIS degree from University of Illinois at Urbana–Champaign and a BA in History from University of Illinois at Chicago. She was an intern at the Smithsonian Ralph Rinzler Folklife Archives and Collections and the Rochester Institute of Technology Archives. A Midwestern native, she recently moved to Seattle with her husband because they were attracted to Seattle’s mix of urban and outdoor opportunities. To introduce herself to our readers, Kalyna agreed to answer a few questions about herself.

What made you want to pursue an MLS?

I always considered myself a “jack of all trades.” At school I did not excel in one subject, but rather did fairly well in most areas of study. I also fell in love with researching, and doing “deep dives” into different subjects. I figured that with an MLS, I could end up working in vastly different environments, help others with research, and pursue my dream of being a lifelong learner.

What made you want to join the NN/LM Evaluation Office?

I recently realized that I needed to strengthen my evaluation skills. Whether I am working or volunteering, I am constantly trying to solve issues concerning outreach and training. For most of my career, I just created solutions without ever thinking “How can I measure my success in solving this issue?” and “Are these solutions working the way I intended?” These questions are key in determining whether the solution is actually solving any problems, or just wasting time and energy.

What experience have you had with evaluation?

My experiences with evaluation come from managing social media accounts. Once I realized I had a whole dashboard of statistics to my disposal, I used them to set optimization goals in terms of posting times and types of content that resonate with my audiences.

What evaluation skills do you particularly hope to develop?

I am very interested in developing my outcome assessment skills. I am usually the big idea person of a group, and enjoy setting lofty goals. In the past, I have measured the success of an initiative based on the number of tasks my group completed for the project. What I want to do going forward is measure success by the initiative’s impact on the intended audience and community.

What other interests do you have?

I am very active in a Ukrainian Scouting Organization called Plast. Through scouting I found my love for the outdoors, and made countless friends all over the United States and around the world. When I’m not working on scouting activities, I find myself crafting. My favorite crafts include quilling, card making, and traditional Ukrainian embroidery.

When I am crafting or commuting to work, I listen to various nerdy podcasts. Some of my favorites include 99% Invisible, LibUX, and Reply All

What is the bravest thing you’ve ever done?

I took a hike with my husband down the Grand Canyon. I didn’t make it that far, because I’m afraid of heights. There was one foot between my body and a drop into the canyon, and that was not where I wanted to be. I had to turn back partway down. My husband said, “I love you. Do you mind if I keep going?”  So I had to walk back up the trail alone. Looking back, I’m glad I went through it.  Once I climbed up, I felt so proud of myself.

What’s in a Name? Convey Your Chart’s Meaning with a Great Title

Some of you may be working on conference posters and paper presentations for Fall conferences.  And some of those will probably include charts to demonstrate data representing a lot of hard work on your part.  In most cases you have minutes to use that chart to get your audience to understand the data.

Stephanie Evergreen has great advice for displaying chart data.  She literally wrote the books on it: Presenting Data Effectively and Effective Data Visualization.  Her recent blog post is about one of the simplest and most powerful changes you can make to effectively present your chart data: “Strong Titles Are The Biggest Bang for Your Buck.

What many of us do is present the data with a generic title, like “Attendance rates.” Then the viewer has to spend time working through the data and you hope that they see what you wanted them to.  What Stephanie Evergreen proposes (backed by persuasive research) is to give your charts a clear title that explains what the data shows. Your poster or paper is almost certainly making a point.  Determine how your chart supports the point of your presentation and state that in the title.  Here are some reasons why:

  • It respects your viewers’ time
  • It forces you to be clear about the point you want your data to make
  • It makes the data more memorable

Stephanie Evergreen’s post has some great examples of how a good title can really improve the impact of the chart.  In addition, here is an example from the NEO webinar Make Your Point: Good Design for Data Visualization.

Looking at this original chart, you might notice that in each activity the follow-up showed an increase over the baseline.  If you, the viewer, didn’t have a lot of time, that might be all you noticed.

Chart with title: Comparison of emergency preparedness activities from baseline to follow-up

With a simple change of title , you can see that the author of this presentation is highlighting the increased number of continuity of services plans.  This is designed to enhance the point of the presentation, and not waste the viewers’ time. Also, note that the title is left justified instead of centered.  Because the title is a full sentence, a left-justified format is easier to read.

Chart with title: The biggest improvement in emergency preparedness from baseline to follow-up was the number of network member organizations reporting that they had or were working on a service continuity plan.

So, while Shakespeare might have been correct when he wrote “What’s in a name? that which we call a rose / By any other name would smell as sweet,” what if the presenter was trying to show the fortitude of Texas antique roses to survive in harsh weather conditions, and the viewer only noticed how sweet the rose smelled?  Maybe the heading “A Rose” sometimes isn’t enough information.

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Six Degrees of Key Informants: Finding Interviewees for Community Assessment

Linking grid of the social networks of a young adults of various nationalitiesSix degrees of separation is a concept that describes social interconnectedness. The idea, popularized in a 1990s movie, is that each human on the planet could reach any other human, by way of friend-to-friend introduction, in six steps or less. For example, you are six (or fewer) people away from meeting your favorite celebrity you happen to adore. Research has explored this social interconnectedness and, while the actual maximum number of intermediaries may be in dispute, we do live in a small world.

If you are faced with doing a needs or community assessment, social interconnectedness should be comforting. Most good community assessments conducted for project planning involve interviewing key informants selected for their special knowledge about their communities. Key informant interviews can provide a quick source of detailed information important for planning. This tip sheet from USAID talks about the basics of conducting key informant interviews.

However, when Karen and I provide training on community assessment, we find our workshop participants feel daunted by the prospect of actually identifying key informants. So take heart, readers. Our social interconnectedness means good key informants are just a friend or two away.

The interview sampling approach I use is described in this article by Tiffany.  It is a participatory evaluation method that engages stakeholders in framing evaluation questions and recruiting interviewees. These stakeholders are your first key informants. You most likely will find them among members of the project team initiating the community assessment. It is likely that their interest in a community was sparked because someone on the team knew someone in the user community.

Those stakeholder key informants can, in turn, direct you to more key informants who can talk about their own and their peers’ needs, desires, opinions and lifestyles.  After each interview, you ask a key informant “Who else do you recommend that I interview?” and “What important information can that person add to my understanding about the community?” As a result, your key informants share in refining your community assessment inquiries. Their recommendations allow them input into the direction of your project plans. Because your key informants are likely to be opinion leaders in their communities, you can generate enthusiasm and possibly forge important partnerships, assuming they respond positively to your project.  Key informant interviews are your first step in building trust in the user community you’re assessing.

To find key informants  who can truly help you gather good project-planning information, be clear about the information you’re seeking.  That way, your stakeholders can refer you to the best  interviewees for your needs.  For guidance on the type of information you should gather in a community assessment, check out these NEO blog posts on Diffusion of Innovation Part One and Part Two.

Ideally key informants get something in return for participating in interviews. At the very least, key informants who are opinion leaders have valuable information about your project or organization to share with their peers. More significantly, your interviewees will assist you in bringing valuable services or resources to their communities.

I want to share two examples of projects where I used this approach to key  informant sampling.  A few years ago, I led a community assessment project for Cumberland County (North Carolina) Public Library and Information Center, which wanted to improve its service to the military community affiliated with Fort Bragg. (Public Libraries published an article about this project here.)Public librarian Jennifer Taft received funding from the State Library of North Carolina for this project and also participated in the community assessment process. We started by interviewing a cadre of her colleagues from the Fayetteville Community Blueprint Network, composed of representatives of local organizations that served military families. Jennifer and her colleagues had worked together to put on a community forum on post-traumatic stress. After each interview with her forum colleagues, I collected recommendations for other key informants. I did the same with my second wave of interviews. Our sample grew until we had a good sample of interviewees and focus group participants with experience-based perspectives on the military community. All worked in organizations that provided services to military families. Most also were members of military families (that is, service members, veterans or spouses).  The key informant interviews had an advantage beyond providing useful information. Relationships established in the interviewing phase provided the library with the contacts it needed to participate, for the first time, in on-post activities.

In a different project, I worked with the National Network of Libraries of Medicine South Central Regional Medical Library to explore how it could support public libraries in hurricane-prone counties. Our sampling process began with contacting librarians at the state libraries of Louisiana and Texas, both of which actively supported public libraries during Hurricanes Katrina and Ike. They introduced us to key informants from “further-in” libraries that valiantly helped waves of evacuees from communities that suffered direct hits.  Our contacts pointed us to libraries that were struck by the storms and restored services quickly in order to help their community members. After we completed our interviews, these librarians became valuable partners in helping us develop NN/LM resources. (You can read about the Gulf Coast library community assessment here in Public Libraries.

Still worried about locating good key informants?  I assure you, you can have faith in the interconnectedness phenomenon.  It has always worked for me, starting with my very first qualitative interviewing project. That project occurred in the 1970s when I was an undergraduate at Penn State. I was enrolled in an undergraduate class taught by an American folklorist.  Our extra credit assignment was to find three legends from our own families or social circles. In those pre-Internet days, most modern-day legends were ghost stories. I was immediately overwhelmed.  What friend of mine could possibly have a ghost story to share? Turns out, the first person I saw after class had a haunting tale. And so did the next person. Within days, I was 10 points closer to getting an A in the course. Everyone, it seemed, knew someone who had seen a ghost.

So remember, you are probably less than six people removed from a great key informant. Just get a handle on what you want to know in your community assessment, talk to anyone affiliated with the community, and you’re on your way.

And, if you know somebody who knows somebody who knows Kevin Bacon, kindly send their contact information to Cindy or Karen?

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Reference: One of the most well-known studies of interconnectedness was

published by Travers, Jeffrey, and Milgram, “An Experimental Study of the Small World Problem”, Sociometry 32(4, Dec. 1969):425–443

 

Last updated on Monday, June 27, 2016

Funded by the National Library of Medicine under Contract No. UG4LM012343 with the University of Washington.