Category Archives: program evaluation

Need to know? Nice to know?

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What is the difference between need to know and nice to know?  How does this affect evaluation?  I got a post this week on a blog I follow (Kirkpatrick) that talks about how much data does a trainer really need?  (Remember that Don Kirkpatrick developed and established an evaluation model for professional training back in the 1954 that still holds today.)

Most Extension faculty don’t do training programs per se, although there are training elements in Extension programs.  Extension faculty are typically looking for program impacts in their program evaluations.  Program improvement evaluations, although necessary, are not sufficient.  Yes, they provide important information to the program planner; they don’t necessarily give you information about how effective your program has been (i.e., outcome information). (You will note that I will use the term “impacts” interchangeably with “outcomes” because most Extension faculty parrot the language of reporting impacts.)

OK.  So how much data do you really need?  How do you determine what is nice to have and what is necessary (need) to have?  How do you know?

  1. Look at your logic model.  Do you have questions that reflect what you expect to have happen as a result of your program?
  2. Review your goals.  Review your stated goals, not the goals you think will happen because you “know you have a good program”.
  3. Ask yourself, How will I USE these data?  If the data will not be used to defend your program, you don’t need it.
  4. Does the question describe your target audience?  Although not demonstrating impact, knowing what your target audience looks like is important.  Journal articles and professional presentations want to know this.
  5. Finally, ask yourself, Do I really need to know the answer to this question or will it burden the participant.  If it is a burden, your participants will tend to not answer, then you  have a low response rate; not something you want.

Kirkpatrick also advises to avoid redundant questions.  That means questions asked in a number of ways and giving you the same answer; questions written in positive and negative forms.  The other question that I always include because it will give me a way to determine how my program is making a difference is a question on intention including a time frame.  For example, “In the next six months do you intend to try any of the skills you learned to day?  If so, which one.”  Mazmaniam has identified the best predictor of behavior change (a measure of making a difference) is stated intention to change.  Telling someone else makes the participant accountable.  That seems to make the difference.

 

Reference:

Mazmanian, P. E., Daffron, S. R., Johnson, R. E., Davis, D. A., & Kantrowits, M. P. (1998).   Information about barriers to planned change: A Randomized controlled trail involving continuing medical education lectures and commitment to change.  Academic Medicine, 73(8).

 

P.S.  No blog next week; away on business.

 

 

 

Quantative data analysis

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Quantitative data analysis is typically what happens to data that are numbers (although qualitative data can be reduced to numbers, I’m talking here about data that starts as numbers.)  Recently, a library colleague sent me an article that was relevant to what evaluators often do–analyze numbers.

So why, you ask, am I talking about an article that is directed to librarians?  Although that article is is directed at librarians, it has relevance to Extension.  Extension faculty (like librarians), more often than not, use surveys to determine the effectiveness of their programs.  Extension faculty are always looking to present the most powerful survey conclusions (yes, I lifted from the article title), and no you don’t need to have a doctorate in statistics to understand these analyses.  The other good thing about this article is that it provides you with a link to an online survey-specific software:  (Raosoft’s calculator at http://www.raosoft.com/samplesize.html).

This article refers specifically to three metrics that are often overlooked by Extension faculty:  margin of error (MoE), confidence level (CL), and cross-tabulation analysis.   These are three statistics which will help you in your work. The article also does a nice job of listing the eight recommended best practices which I’ve appended here with only some of the explanatory text.

 

Complete List of Best Practices for Analyzing Multiple Choice Surveys

1. Inferential statistical tests. To be more certain of the conclusions drawn from survey data, use inferential statistical tests.

2. Confidence Level (CL). Choose your desired confidence level (typically 90%, 95%, or 99%) based upon the purpose of your survey and how confident you need to be of the results. Once chosen, don’t change it unless the purpose of your survey changes. Because the chosen confidence level is part of the formula that determines the margin of error, it’s also important to document the CL in your report or article where you document the margin of error (MoE).

3. Estimate your ideal sample size before you survey. Before you conduct your survey use a sample size calculator specifically designed for surveys to determine how many responses you will need to meet your desired confidence level with your hypothetical (ideal) margin of error (usually 5%).

4. Determine your actual margin of error after you survey. Use a margin of error calculator specifically designed for surveys (you can use the same Raosoft online calculator recommended above).

5. Use your real margin of error to validate your survey conclusions for your larger population.

6. Apply the chi-square test to your crosstab tables to see if there are relationships among the variables that are not likely to have occurred by chance.

7. Reading and reporting chi-square tests of cross-tab tables.

  • Use the .05 threshold for your chi-square p-value results in cross-tab table analysis.
  • If the chi-square p-value is larger than the threshold value, no relationship between the variables is detected. If the p-value is smaller than the threshold value, there is a statistically valid relationship present, but you need to look more closely to determine what that relationship is. Chi-square tests do not indicate the strength or the cause of the relationship.
  • Always report the p-value somewhere close to the conclusion it supports (in parentheses after the conclusion statement, or in a footnote, or in the caption of the table or graph).

8. Document any known sources of bias or error in your sampling methodology and in your survey design in your report, including but not limited to how your survey sample was obtained.

 

Bottom line:  read the article.

Hightower, C. & Kelly, S. (2012, Spring).  Infer more, describe less: More powerful survey conclusions through easy inferential tests.  Issues in Science and Technology Librarianship. DOI:10.5062/F45H7D64. [Online]. Available at: http://www.istl.org/12-spring/article1.html

Evaluation and creativity

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Creativity is not an escape from disciplined thinking. It is an escape with disciplined thinking.” – Jerry Hirschberg – via @BarbaraOrmsby

The above quote was in the September 7 post of Harold Jarche’s blog.  I think it has relevance to the work we do as evaluators.  Certainly, there is a creative part to evaluation; certainly there is a disciplined thinking part to evaluation.  Remembering that is sometimes a challenge.

So where in the process do we see creativity and where do we see disciplined thinking?

When evaluators construct a logic model, you see creativity; you also see disciplined thinking

When evaluators develop an implementation plan, you see creativity; you also see disciplined thinking.

When evaluators develop a methodology and a method, you see creativity; you also see disciplined thinking.

When evaluators present the findings for use, you see creativity; you also see disciplined thinking.

So the next time you say “give me a survey for this program”,  think–Is a survey the best approach to determining if this program is effective; will it really answer my questions?

Creativity and disciplined thinking are companions in evaluation.

 

Complexity and evaluation

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The topic of complexity has appeared several times over the last few weeks.  Brian Pittman wrote about it in an AEA365; Charles Gasper used it as a topic for his most recent blog.  Much food for thought, especially as it relates to the work evaluators do.

Simultaneously, Harold Jarche talks about connections.  To me connections and complexity are two side of the same coin. Something which is complex typically has multiple parts.  Something which has multiple parts is connected to the other parts.  Certainly the work done by evaluators has multiple parts; certainly those parts are connected to each other.  The challenge we face is  logically defending those connections and in doing so, make explicit the parts.  Sound easy?  Its not.

 

That’s why I stress modeling your project before you implement it.  If the project is modeled, often the model leads you to discover that what you thought would happen because of what you do, won’t.  You have time to fix the model, fix the program, and fix the evaluation protocol.  If your model is defensible and logical, you still may find out that the program doesn’t get you where you want to go.  Jonny Morell writes about this in his book, Evaluation in the face of uncertainty. There are worse things than having to fix the program or fix the evaluation protocol before implementation.  Keep in mind that connections are key; complexity is everywhere.  Perhaps you’ll have an Aha! moment.

 

I’ll be on holiday and there will not be a post next week.  Last week was an odd week–an example of complexity and connections leading to unanticipated outcomes.

 

Evaluation Cost Redux and other topics

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Evaluation costs:  A few weeks ago, I posted a summary about evaluation costs. A recent AEA LinkedIn discussion was on the same topic (see this link).  If you have not linked to other evaluators, there are other groups besides AEA that have LinkedIn groups.  You might want to join one that is relevant.

New topic:  The video on surveys posted last week generated a flurry of comments (though not on this blog).  I think it is probably appropriate to revisit the topic of surveys.  As I decided to revisit this topic,  an AEA 365 post from the Wilder Research group talked about data coding related to longitudinal data.

Now, many surveys, especially Extension surveys, focus on cross sectional data not on longitudinal data.  They may, however, involve a large number of participants and the hot tips that are provided apply to coding surveys.  Whether the surveys Extension professionals develop involve 30, 300, or 3000 participants, these tips are important especially if the participants are divided into groups on some variable.  Although the hot tips in the Wilder post talk about coding, not surveys specifically, they are relevant to surveys and I’m repeating them here.   (I’ve also adapted the original tip to Extension use).

  • Anticipate different groups.  If you do this ahead of time, and write it down in a data dictionary or coding guide, your coding will be easier.  If the raw data are dropped, or for some other reason scrambled (like a flood, hurricane, or a sleepy night), you will be able to make sense out of the data quicker.
  • Sometimes there are preexisting identifying information (like location of the program) that have a logical code.  Use that code.
  • Precoding by the location sites helps keep the raw data organized and enables coding.

Over the rest of the year, I’ll be revisiting survey on a regular basis.  Survey is often used by Extension.  Developing a survey that provides you with information you want, can use, and makes sense is a useful goal.

New topic:  I’m thinking of varying the format of the blog or offering alternative formats with evaluation information.  I’m curious as to what would help you do your work better.  Below are a few options.  Let me know what you’d like.

  • Videos in blogs
  • Short concise (i.e., 10-15 minute) webinars
  • Guest writers/speakers/etc.
  • Other ideas

Aha! moments: Survey

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A few weeks ago I  mentioned that a colleague of mine shared with me some insights she had about survey development.  She had an Aha! moment.   We had a conversation about that Aha! Moment and video taped the conversation.  To see the video, click here.

 

In thinking about what Linda learned, I realized that Aha! Moments could be a continuing series…so watch for more.

Let me know what you think.  Feedback is always welcome.

Oh–I want to remind you about an excellent resource for surveys.  Dillman’s current book, Internet, mail, and mixed-mode surveys:  The tailored design method.  It is a Wiley publication by Don A. Dillman, Jolene D. Smyth, and Leah Melani Christian.  Needs to be on your desk if you do any kind of survey work.

Is the best action a quick reaction?

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You can control four things–what you say; what you do; and how you act and react (both  subsets of what you do).  So when is the best action a quick reaction and when are you not waiting (because waiting is an act of faith)?  And how is this an evaluation question?

The original post was in reference to an email response going astray (go see what his suggestions were); it is not likely that emails regarding an evaluation report will fall in that category.  Though not likely, it is possible.  So you send the report to someone who doesn’t want/need/care about the report and is really not a stakeholder, just on the distribution list that you copied from a previous post.  And ooops, you goofed.  Yet the report is important; some people who needed/wanted/cared about it got it.  You need to correct for those others.  You can remedy the situation by following his suggestion, “Alert senders right away when you (send or) receive sensitive (or not so sensitive) emails not intended for you, so the sender can implement serious damage control.” (Parenthetical added.)

 

Emails seem to be a topic of conversation this week.  A blog I follow regularly (Harold Jarche) cited two studies about the amount of time spent reading and dealing with email.  One of the studies he cites ( in the Atlantic Monthly), the average worker spends 28% of a days work time reading email.  Think of all the non-necessary email you get THAT  YOU READ.  How is that cluttering your life?  How is that decreasing your efficiency when it comes to the evaluation work you do?  Email is most of my work these days; used to be that the phone and face-to-face took up a lot of my time…not so much today.  I even use social media for capacity building; my browser is always open.  So between email and the web, a lot of time is spent intimate with technology.

 

The last thought I had for this week was the use of words–not unrelated to emails–especially as it relates to evaluation.  Evaluation is often referred to by efficacy (producing the desired effect), effective (producing the desired effect in specific conditions), efficiency (producing the desired effect in specific conditions with available resources), and fidelity (following the plan).  I wonder if someone would do an evaluation of what we do, would we be able to say we are effective and efficient, let alone faithful to the plan?

 

 

 

 

How much should an evaluation cost.

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AEA hosts an online, free, and open to anyone list serve called EVALTALK, managed by the University of Alabama.  This week, Felix Herzog posted the following question.

 

How much can/should an evaluation cost? 

 

This is a question I often get asked, especially by Extension faculty.  This question is especially relevant because more Extension faculty are responding to request for proposals (RFP) or request for contract (RFC) that call for an evaluation.  The questions arise about how does one budget for the evaluation.  Felix  compiled what he discovered and I’ve listed it below.  It is important to note that this is not just the evaluator’s salary, rather all expenses which relate to evaluating the program–data entry, data management, data analysis, report writing, as well as the evaluator’s salary, data collection instrument development, pilot testing, and the salaries of those who do the above mentioned tasks.  Felix thoughtfully provided references with sources so that you can read them.  He did note that the most useful citation (the Reider, 2011) is in German.

–           The benefit of the evaluation should be at least as high as its cost (Rieder, 2011)

–           “Rule of thumb”: 1 – 10% of the costs for a policy program (personnal communication from an administrator)

–           5 – 7 % of a program (Kellog Foundation, p. 54)

–           1 – 15 %of the total cost of a program (Rieder, 2011, 5 quotes in Table 5 p. 82)

–           0.5% of a program (EC, 2004, p. 32 ff.)

–           Up to 10% of a program (EC 2008, p. 47 ff.)

 

REFERENCES

EC (2004) Evaluating EU activities and practical guide for the Commission services. Bruxelles. http://ec.europa.eu/dgs/secretariat_general/evaluation/docs/eval_activities_en.pdf.

EC (2008) EVALSED: The resource for the evaluation of socioeconomic development. Bruxelles. http://ec.europa.eu/regional_policy/sources/docgener/evaluation/evalsed/downloads/guide2008_evalsed.pdf .

Kellogg Foundation.  (1984).  W. K. Kellogg Foundation Evaluation Handbook. <http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&sqi=2&ved=0CE8QFjAA&url=http%3A%2F%2Fwww.wkkf.org%2F%7E%2Fmedia%2F62EF77BD5792454B807085B1AD044FE7.ashx&ei=gUsAUIOBMIXaqgG6sdiqBw&usg=AFQjCNHorPDftVA4z54-Kx_d8WZT0p5eQg&sig2=0nQwUeUHCR0_4wuaGdwnBw>.

Rieder S. (2011) Kosten von Evaluationen. LEGES 2011(1), 73 – 88.

 

Recognizing the value of your evaluation work, being able to put a dollar value to that work, and being able to communicate it helps build organizational capacity in evaluation.

Lightbulbs and other insights

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Bright ideas are often the result of  “Aha” moments.  Aha moments  are “The sudden understanding or grasp of a concept…an event that is typically rewarding and pleasurable.  Usually, the insights remain in our memory as lasting impressions.” — Senior News Editor for Psych Central.

How often have you had an “A-ha” moment when you are evaluating?  A colleague had one, maybe several, that made an impression on her.  Talk about building capacity–this did.  She has agreed to share that experience, soon (the bright idea).

Not only did it make an impression on her, her telling me made an impression on me.  I am once again reminded of how much I take evaluation for granted.  Because evaluation is an everyday activity, I often assume that people know what I’m talking about.  We all know what happens when we assume something…….  I am also reminded how many people don’t know what I consider basic  evaluation information, like constructing a survey item (Got  Dillman on your shelf, yet?).

 

What is this symbol called?  No, it is not the square root sign–although that is its function.  “It’s called a radical…because it gets at the root…the definition of radical is: of or going to the root or origin.”–Guy McPherson

How radical are you?  How does that relate to evaluation, you wonder?  Telling truth to power is a radical concept (the definition here is departure from the usual or traditional); one to which evaluators who hold integrity sacrosanct adhere. (It is the third AEA guiding principle.)  Evaluators often, if they are doing their job right, have to speak truth to power–because the program wasn’t effective, or it resulted in something different than what was planned, or it cost too much to replicate, or it just didn’t work out .  Funders, supervisors, program leaders need to know the truth as you found it.


“Those who seek to isolate will become isolated themselves.” —Diederick Stoel  This sage piece of advice is the lead for Jim Kirkpatrick’s quick tip for evaluating training activities.  He says, “Attempting to isolate the impact of the formal training class at the start of the initiative is basically discounting and disrespecting the contributions of other factors…Instead of seeking to isolate the impact of your training, gather data on all of the factors that contributed to the success of the initiative, and give credit where credit is due. This way, your role is not simply to deliver training, but to create and orchestrate organizational success. This makes you a strategic business partner who contributes to your organization’s competitive advantage and is therefore indispensable.”  Extension faculty conduct a lot of trainings and want to take credit for the training effectiveness.  It is important to recognize that there may be other factors at work–mitigating factors; intermediate factors; even confounding factors.  As much as Extension faculty want to isolate (i.e., take credit), it is important to share the credit.

The other 95%

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Harold Jarche says that “most learning happens informally on the job.  Formal instruction, or training, accounts for less than 20%, and some research shows it is about 5% of workplace learning.” He divides learning into dependent, interdependent, and independent–that is, formal instruction like you get in school; social and collaborative learning like you get when you engage colleagues; and learning supported by tools and information.

As an evaluator, what do you do with that other 95%?  Do you read? Tweet? Talk to folks?  Just how do you learn more about evaluation?  I don’t think there is a best way.  I think that the individual needs to look what their strengths are (assets, if you will), where their passions lie, where their questions occur (and those may or may not be needs–shift the paradigm, people).  Sometimes learning emerges from a place never before explored.  A good example–I’ve been charged with the evaluation of a organizational change.  Although I’ve looked at references for organizational change, actually had a course in organizational behavior in graduate school, I haven’t really gone looking for answers…until this evaluation was assigned.  Then, at this years AEA annual conference, one of the professional development session captured much of what I’ve been puzzling– not that it will have answers; but maybe I’ll learn something, something I can take back with me; something I could use; perhaps even something in that 95%.  This professional development session (informal learning and interdependent) will afford me an opportunity for learning; for content I haven’t experienced.  I’d put it in the other 95%.

Social media falls into the category of the other 95%–it connects folks.  It provides information.  It builds community where one has not been before.  Can it take the place of formal education; no, I don’t think so.  Can it provide a source of information; possibly (it then becomes a matter of reliability).  My take away for today–explore other types of learning; share what you know.