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NN/LM Outreach Evaluation Resource Center

Simply Elegant Evaluation: Appreciative Inquiry at NN/LM MAR

KF brochure

Kate Flewelling is the Outreach Coordinator for the National Network of Libraries of Medicine’s Middle Atlantic Region (NN/LM MAR), which is located at the University of Pittsburgh Health Sciences Library.  For those unfamiliar with the NN/LM, libraries and organizations join the network to help promote health information access and use.  The program is funded by the NIH National Library of Medicine, and NN/LM MAR is one of eight regional medical libraries that coordinate a region of the network. These eight health sciences libraries partner with member organizations and sometimes fund their health information outreach activities.

After attending an NN/LM Outreach Evaluation Resource Center training session, Kate was inspired to conduct an Appreciative Inquiry (AI) evaluation project to explore how NN/LM MAR could attract health professionals into the NN/LM and provide them with useful services.  In March 2015, she conducted eight 30-minute interviews with health professionals. She recruited interviewees from among health professionals who served on a special NN/LM MAR advisory committee or represented health organizations that received funding from NN/LM MAR. She chose interviewees from three states where NN/LM MAR works (Pennsylvania, New York, and Delaware), with representation from organizations in both rural and urban areas. Her interview guide was modeled after one presented in an OERC blog post.

Kate agreed to talk with the OERC to share her experience using Appreciative Inquiry.

OERC: What motivated you to do an Appreciative Inquiry project?

Kate:I wanted to see why health professionals got involved with us and have been so committed to us. We wanted to come up with selling points to tell other potential health professional members why they should join our network.”

Kate also chose an AI approach because she wanted stories, not numbers.  She was working with a small but diverse group of representatives, so interviews seemed to be a better approach than surveys for getting unique perspectives. She also believed an AI assessment was simple enough to be completed in about a week. In fact, she completed all eight interviews in eight days.

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OERC: Did you believe you got responses that had an overly positive bias?

Kate: “I only asked people who loved us, but they also know us. So they have an idea of what we’re doing and a much broader understanding of what we do with outreach because they hear about the whole outreach program. But I got really good feedback. Not criticism, but stuff we could do to improve our services.”

In AI, it is not unusual to interview an organization’s champions, because they often can provide the most informed advice about improving a program. Kate understood that her interviewees had favorable opinions about NN/LM MAR, but she said her interviews still identified holes in their outreach efforts to health professionals. They provided good advice on how to attract other health professionals to the network.

OERC: What did you want to learn from the study?  

Kate: “The experience was great! It gave me good ideas.  I realized we weren’t using them [health professional colleagues] as much as we could. They told me ‘I will pass on whatever you need me to pass on.’  It gave me great ideas for how to use them, use their connections and develop target outreach materials and messages for special audiences.’”   

She realized that NN/LM MAR could send regular postings to the health representatives, just as they do to health sciences librarians.  The postings just needed to contain more context so that they were targeting a public health or clinical audience.

Kate: “The project also made me realize how far [NN/LM MAR’s] reach has gone in the past four years…It felt like, during our first and second year, throwing spaghetti on the wall to see if it was working with health professionals. But we were trying to make the connections. Now we know, for our most engaged people, what they value about their relationship with us.”

Most of the staff joined the NN/LM MAR in 2011, when University of Pittsburgh Health Sciences Library was awarded the contract to coordinate the Middle Atlantic Region. So the AI project was a member check with their public health and clinical health partners, to see how well the relatively new program was meeting their needs.  Before the AI project, Kate said she knew what NN/LM MAR staff was getting from their relationships with health professionals.  Afterwards, she understood what the health professionals were getting from NN/LM MAR.

OERC: How did you use the information you gathered?

Kate: “Just starting to talk to people at exhibits, I have a sense of what’s going to grab them.”

Kate developed a brochure targeted to health professionals with a summary of NN/LM selling points on the front (gleaned from her AI interviews) and resources of interest on the back. She plans to share the brochure with the other eight NN/LM regional medical libraries. She also believes the NN/LM MAR staff will tap into this information in the future when they plan programs for health professionals.

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Improving Your Data Storytelling in 30 Days

Here are some more great techniques to help with telling a story to report your evaluation data so it will get the attention it deserves.

Friends at campfire telling storiesJuice Analytics has this truly wonderful collection of resources in a guide called “30 Days to Data Storytelling.” With assignments of less than 30 minutes a day, this guide links to data visualization and storytelling resources from sources as varied as Pixar, Harvard Business Review, Ira Glass, the New York Times, and Bono (yes, that Bono).

The document is a checklist of daily activities lasting no longer than 30 minutes per day. Each activity is either an article to read, a video to watch, or a small project to do.

The resources answer valuable questions like:

  • What do you do when you’re stuck?
  • How do I decide between visual narrative techniques?
  • Where can I find some examples of using data visualization to tell a story?

Soup Up Your Annual Reports with Calculator Soup

Summer is annual report time for our organization. Sometimes when I’m putting together my bulleted list of accomplishments for those reports, I feel as though our major wins get lost in the narrative. So I recently turned to an online calculator to help me create better metrics to talk about our center’s annual wins.

One of our objectives for the year was to increase participation in our evaluation training program. We developed new webinars based on our users’ feedback and also increased promotion of our training opportunities. The efforts paid off: training session attendance increased from 291 participants the previous year to 651 this year. Now that is a notable increase, but the numbers sort of disappear into the paragraph, don’t they? So I decided to add a metric to draw attention to this finding: Our participation rate increased 124% over last year’s attendance. Isn’t “percent increase” a simpler and more eye-catching way to express the same accomplishment?

Doing this extra analysis seems simple, but it takes time and gives me angst because it usually requires manual calculation. First I have to look up the formula somewhere. Then I have to calculate the statistic. Then I calculate it again, because I don’t trust myself. Then I calculate it again out of pure obsessiveness.

That’s why I love online calculators. Once I find one I like and test it for accuracy, I bookmark it for future use. From then on, I let the calculator do the computation because it is infinitely more reliable than I am when it comes to running numbers.

One of my favorite sites for online calculators is Calculator Soup, because it has so many of them. You may not ever use 90% of its calculators, but who knows when you might need to compute someone’s age from a birth date or convert days to hours. The calculators also show you the exact steps in their calculations. This allows you to check their work. You also can find formulas that you then can apply in an Excel spreadsheet.

One word of advice: test a calculator for accuracy before adopting it. I always test a new calculator to be sure the designers knew what they were doing. For Calculator Soup, I can vouch for the percent change and the mean/median/mode calculator. If I use any others at that site, I’ll test them as well. I’ll create an easy problem that I can solve manually and make sure my result matches the calculator’s.

If you want to see what Calculator Soup has to offer, check out their calculator index here.

Random array of simple arithmetic formulas

A Rainbow Connection? The Evaluation Rainbow Framework

Once you start looking online for help with your evaluation project, you will find a veritable storm of evaluation resources out there. So many that it can be confusing how choose the best ones for your needs.  But don’t worry, once you’ve looked at this online tool you will find the rainbow of hope that follows the storm (okay that was pretty cheesy – stay with me, it gets better).

A group of evaluators from all over the world created a website called BetterEvaluation.org for the purpose of organizing and sharing useful online evaluation resources. The framework they created to organize resources is called the Rainbow Framework because it divides the world of evaluation into seven different “clusters” which are delineated by rainbow colors.  Each cluster is then broken down into a number of tasks, and each task broken down into options and resources.

Here is an example of the Rainbow Framework in action.  By clicking on the yellow “Describe” category, the image opens a window on the right that lists seven tasks: 1) Sample; 2) Use measures, indicators, or metrics; 3) Collect and/or retrieve data; 4) Manage data; 5) Combine qualitative and quantitative data; 6) Analyze data; and 7) Visualize data.

When you click on a specific task, a page listing a variety of Options and Resources will open, like this:Rainbow Framework Options

 

BetterEvaluation made eight 20 minute “coffee break” webinars in conjunction with AEA that you can watch for free on the BetterEvaluation website. Each webinar describes a cluster, and there is one overview webinar.  The webinars are two years old, so the actual image of the rainbow looks a little different from the webinar video, but the content is still relevant.  Here is a link to the webinar series: http://betterevaluation.org/events/coffee_break_webinars_2013

The Rainbow Framework does more than just organize resources. Here are some reasons you might want to use this Framework.

1) Help designing and planning an evaluation

2) Check the quality of an ongoing evaluation

3) Commission an evaluation – will help formulate what’s important to include when you commission an evaluator and then when you assess the quality of the proposals

4) Embed stakeholder participation thoughtfully throughout the evaluation

5) Develop your evaluation capacity – lifelong learning – to fill in gaps of knowledge.

So, somewhere over the rainbow, your evaluation skies may be blue…

How to Write a Mission Statement Without Losing Your Mind

Mission statements are important. Organizations use them to declare to the world how their work matters. They are the North Star for employees, guiding their efforts toward supporting organizational priorities.  And mission statements are important to evaluators, because evaluation methods are ultimately designed to assess an organization’s value.  Having those values explicitly stated is very helpful.

Yet most of us would rather clean out the office refrigerator than participate in a mission-writing process. Now imagine involving 30 people in the writing process. Make that the refrigerator and the microwave, right?

That’s why I am so enthusiastic about the Nonprofit Hub’s document A Step-By-Step Exercise for Creating a Mission Statement, which the authors promise  is a tool “for those who want to skip the nitpicking, word choice arguments or needing to create the elusive ‘perfect mission statement.’”

I won’t go into details about how their process works, because the guide lays it out elegantly and concisely. You can read through the process in five minutes, it is so succinct.   I’ll just tell you what I like most:

  • The exercise reportedly takes 1-2 hours, even though you are engaging up to 30 stakeholders in the process.
  • Stories comprise the foundation of the mission statement: people start by sharing stories about the organization’s best work.
  • The individuals do group qualitative analysis on the stories to begin to understand the organization’s cause, activities, and impact.
  • Small groups draft mission statements, with instruction to write short, simple sentences. In fact, 10- word sentences are held up as an ideal. The small groups share back with the large group, where big ideas are identified and discussed.
  • The actual final wording is assigned to a small task force to create after the meeting, which prevents wordsmithing from dampening the momentum (and the mood).
  • In the end, everyone understands and endorses the mission statement because they helped develop it.

This exercise has potential that reaches beyond development of mission statements.  It would be a great exercise for advisory groups to contribute their ideas about future activities. Their advice will be based on your organization’s past successes.  The stories generated are data that can be analyzed for organizational impact.  If you are familiar with Appreciative Inquiry, you’ll recognize the AI influence in this exercise.

The group qualitative analysis process, alone, could be adapted to other situations (see steps 1 and 2).  For example, a small project team could use the process to analyze stories from interviews, focus groups, or even written comments to open-ended survey questions.

Even if mission statements are not on your horizon, check out the Nonprofit Hub’s document. There might be something you can adapt for future planning and evaluation projects.

Cover sheet for the Nonprofit Hub's "A Step-by-Step Exercise for Creating a Mission Statement" exercise instructions

Getting Started in Evaluation – Evaluation Guides from the OERC

New to the world of evaluation? What is your boss talking about when she says she wants you to measure outcomes, not outputs?  What is an indicator? How many responses should you get from your surveys?

Sometimes people think evaluation is just the form that you fill out at the end of a class or event. But in fact evaluation can start at the beginning of the project when you do a community assessment and evaluation includes building support for your project from your stakeholders. And it continues through making an evaluation plan as part of your project, gathering data, analyzing data, and reporting the data back to the stakeholders in a way that it is useful.  Here is a model that the CDC uses to describe the evaluation framework:

CDC Framework for Program Evaluation

The Outreach Evaluation Resource Center (OERC) has a series of three booklets entitled Planning and Evaluating Health Information Outreach Projects that guide people through the evaluation process, from needs assessment to analyzing data.  While focusing on “health information outreach” this series of books can be used to learn how to do evaluation for any type of project.

Booklet 1: Getting Started with Community-Based Outreach

  • Getting organized: literature review; assembling team of advisors; taking an inventory; developing evaluation questions
  • Gathering information: primary data; secondary data, and publicly accessible databases
  • Assembling, Interpreting and Acting: summarizing data and keeping stakeholders involved

Booklet 2: Planning Outcomes-Based Outreach Projects

  • Planning your program with a logic model to connect activities to outcomes
  • Planning your process assessment
  • Developing an outcomes assessment plan, using indicators, objectives and an action plan

Booklet 3: Collecting and Analyzing Evaluation Data

  • Designing data collection methods; collecting data; summarizing and analyzing data for:
    • Quantitative methods
    • Qualitative methods

The books can be read in HTML, downloaded as a PDF or physical booklets can be ordered for free from the OERC by sending an email request to: nnlm@u.washington.edu

Learn more about the CDC’s Evaluation Framework: http://www.cdc.gov/eval/framework/

 

 

Fast Track Interview Analysis: The RITA Method

If you want a systematic way to analyze interview data, check out the Rapid Identification of Themes from Audio Recordings (RITA) method described in Neal et al. (2014). This method skips the time-consuming transcription process, because you conduct your analysis while listening to the recordings.  Also, the process maintains nonverbal elements of your data (i.e., intonation), which are lost when interviews are transcribed. The authors presented a case in their article to demonstrate how to use the RITA method.

The five-step RITA process, briefly described below, is meant to be used with multiple raters:

  1. Develop focused evaluation questions. Don’t try to extract every detail from the recordings. Instead, write some focused evaluation questions to guide your analysis. For instance, you might want to know how participants applied lessons learn from a class on consumer health information or what services are desired by a specific type of library user.
  2. Create a codebook. Develop a list of themes by talking with the project team, reviewing interviewer notes, or checking theories or literature related to your project. For their sample case, the authors used eight themes. That’s probably is the upper limit for the number of themes that can be effectively used for this process. Once you have the list, create a codebook with detailed theme definitions.
  3. Develop a coding form. (See the figure below.) This will be used by all coders to record absence or presence of a theme in time-specified (e.g., 3 minute) segments of the interview. They listen to a time segment, mark if any themes were present, and then repeat the process with the next segment. (The article describes the process for figuring out the most appropriate time segment length for your project.) If you want, you can also incorporate codes for “valance,” indicating if comments were expressed positively, negatively, or in neutral tones.
  4. Have the coding team pilot-test the codebook and coding form on a small subset of interviews. The team then should refine both documents before coding all recordings.
  5. Code the recordings. At this stage, one coder per interview is acceptable, although the authors recommend that a subset of the interviews be coded by multiple coders and results tested for rater agreement.

RITA sample coding sheet (spreadsheet with themes in first column and time segments of 3-minute length in top row for recording presence of themes.

While the RITA process may seem time consuming, it is much more efficient than producing verbatim transcripts. Once the authors finalized their coding form, it took a team member about 68 minutes to code a one-hour interview. Because coded data was expressed in numbers, it allowed the authors to assess inter-rater reliability (agreement), which demonstrated an acceptable level of agreement among coders. Rater agreement adds credibility to your findings and can be helpful if you seek to publish your results.

While the RITA method is used with qualitative data, it is essentially a quantitative analytic method, producing numbers from text.  That leads me to my main concern. By reducing the data to counts, you lose some of the rich detail and subtle nuances that are the hallmarks of qualitative data. However, most evaluation studies use mixed methods to provide a complete picture of their programs.  In that spirit, you can  simply  keep track of time segments that contain particularly great quotes and stories, then transcribe and include them in your project report. They will complement nicely the findings from your RITA analysis.

Here is the full citation for the Neal et al  article, which provides excellent instructions for conducting the RITA process.

Neal JW, Neal ZP, VanDyke E, Kornbluh M. Expediting the analysis of qualitative data in evaluation: a procedure for the rapid identification of themes from audio recordings (RITA). American Journal of Evaluation. 2015; 36(1): 118-132.

 

 

 

 

 

 

Designing Questionnaires for the Mobile Age

How does your web survey look on a handheld device?  Did you check?

The Pew Research Center reported that 27% of respondents to one of its recent surveys answered using a smartphone. Another 8% used a tablet. That means over one-third of participants used handheld devices to answer the questionnaire. Lesson learned: Unless you are absolutely sure your respondents will be using a computer, you need to design with mobile devices in mind.

As a public opinion polling organization, the Pew Center knows effective practices in survey research. It offers advice on developing questionnaires for handhelds in its article Tips for Creating Web Surveys for Completion on a Mobile Device. The top suggestion is to be sure your survey software is optimized for smartphones and tablets. The OERC uses SurveyMonkey, which fits this criteria. Many other popular Web survey applications do as well. Just be sure to check.

However, software alone will not automatically create surveys that are usable on handhelds devices. You also need to follow effective design principles. As a rule of thumb, keep it simple. Use short question formats. Avoid matrix-style questions. Keep the length of your survey short. And don’t get fancy: Questionnaires with logos and icons take longer to load on smartphones.

This article provides a great summary of tips to help you design mobile-device friendly questionnaires. My final word of advice? Pilot test questionnaires on computers, smartphones, and tablets. That way, you can make sure you are offering a smooth user experience to all of your respondents.

Many smart phones with application tiles on their touchscreens
Many smart phones with application tiles on their touchscreens

 

DIY Tool for Program Success Stories (Program Success Stories Part 2)

Last week, I wrote about program success stories. As a follow-up, I want to introduce you to a story builder tool available at the CDC Injury Prevention and Control web site. The story builder takes you through three steps to produce an attractive, well-written program success story. Each step offers downloadable Microsoft Word documents to walk you through the process.

Step 1: The worksheets are designed to gather and organize project information for your story. I think it would be interesting to use this step as a participatory activity. You could pull together your project team or a group of stakeholders to talk through questions in this worksheet. The discussion would help group members articulate the program’s value from their perspective.

Step 2: This step provides a story builder template to write your story, section by section. Each section has a field to develop a paragraph of your story, with some tips for writing in a compelling, user-friendly way. Each completed field prepares you for the final step.

Step 3: Here, you can download a layout template, where you transfer the paragraphs from your story builder template into the layout. Because this is a Word document, you can change background design, font, or even the size and placement of pictures and call-out quote boxes.

If you are thinking of trying your hand at program success stories, this story building web page provides some useful DYI tools to help you get started.

"What is Your Story" typed on paper in an old typewriter

Telling Good Stories about Good Programs

Sometimes our program successes are a well-kept secret, hidden deep in our final reports under pages of statistics, tables, and descriptive details. There is a way to shine a stronger light on positive program impacts: program success stories. These are short (1-2 page) narratives that are designed to educate policy makers, attract partners, and share effective practices among colleagues.

The Centers for Disease Control and Prevention deserves credit in leading a program success story movement within the public health sector. You can find lots of resources at the CDC’s website for developing program success stories. A quick Google search will turn up many success story web pages from public health departments, such as the three listed below:

If you want to create success stories for your program or organization, you need to start with a plan. You want to establish a routine to collect information in a timely manner. To get started, check out the CDC Division of Oral Health’s Tips for Writing an Effective Success Story. For more details, the CDC offers the workbook Impact and Value: Telling Your Program’s Story. The CDC Division of Adolescent and School Health also has a how-to guide for writing success stories: How to Develop a Success Story. Finally, you might find this Success Story Data Collection Tool helpful for organizing and writing your program story.  A data collection sheet could be particularly useful if multiple team members are involved in collecting success story data. The data collection tool is available in PDF or Word formats.

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Last updated on Saturday, 23 November, 2013

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