Filed Under (criteria, Methodology, program evaluation) by Molly on 18-08-2015

I just got back from a road trip across Southern Alabama with my younger daughter.southern alabama We started from Birmingham and drove a very circuitous route ending in Mobile and the surrounding areas, then returned to Birmingham for her to start her second year at Birmingham-Southern College.

As we traveled, I read a book by Bill McKibben (one of many) called Oil and Honey: The Education of an Unlikely Activist. It is a memoir, a personal recounting of the early years of this decade, which corresponded with the years my older daughter was in college (2011-2014). I met Bill McKibben, who, in 2008, is credited with starting the non-profit, 350.0rg, and is currently listed as “senior adviser and co-founder”. He is a passionate, soft-spoken man, who beleives that the world is on a short fuse. He really seems to believe that there is a better way to have a future. He, like Gandhi, is taking a stand.  Oil and Honey puts into action Gandhi’s saying about being the change you want to seegandhi and change. As the subtitle indicates, McKibben is an unlikely activist. He is a self-described non-leader who led and advises the global effort to increase awareness of climate change/chaos. When your belief is on the line, you do what has to be done.

Evaluators are the same way. When your belief is on the line, you do what has to be done. And, hopefully, in the process you are the change that you want to see in the world. But know it cannot happen one pipeline at a time. The fossil fuel industry has too much money. So what do you do? You start a campaign. That is what has done:  “There are currently fossil fuel divestment campaigns at 308 colleges and universities, 105 cities and states, and 6 religious institutions.”(Wikipedia, 350.0rg) (Scroll down to the heading “Fossil Fuel Divestment” to see the complete discussion.) Those are clear numbers, hard data for consumption. (Unfortunately, the  divestment campaign at OSU failed.)

So I see the question as one of impact, though not specifically world peace (my ultimate impact). If there is no planet on which to work for world peace, there in no need for world peace. Evaluators can help. They can look at data critically. They can read the numbers. They can gather the words. This may be the best place for the use of pictures (they are, after all, worth 1000 words).  Perhaps by combining efforts, the outcome will be an impact that benefits all humanity and builds a tomorrow for the babies born today.

my two cents.



Filed Under (Methodology, program evaluation) by Molly on 03-03-2015

This is a link to an editorial in Basic and Applied Social PsychologyBasic and applied social psychology cover. It says that inferential statistics are no longer allowed by authors in the journal.

“What?”, you ask. Does that have anything to do with evaluation? Yes and no. Most of my readers will not publish here. They will publish in evaluation journals (of which there are many) or if they are Extension professionals, they will publish in the Journal of Extension.JoE logo And as far as I know, BASP is the only journal which has established an outright ban on inferential statistics. So evaluation journals and JoE still accept inferential statistics.

Still–if one journal can ban the use, can others?

What exactly does that mean–no inferential statistics? The journal editors define this ban as as “…the null hypothesis significance testing procedure is invalid and thus authors would be not required to perform it.” That means that authors will remove all references to  p-values, t-values, F-values, or any reference to statements about significant difference (or lack thereof) prior to publication. The editors go on to discuss the use of confidence intervals (No) and Bayesian methods (case-by case) and what inferential statistical procedures are required by the journal. Read the rest of this entry »

Filed Under (Data Analysis, program evaluation) by Molly on 07-11-2013

I had a topic all ready to write about then I got sick.  I’m sitting here typing this trying to remember what that topic was, to no avail. That topic went the way of much of my recent memory; another day, perhaps.

I do remember the conversation with my daughter about correlation.  She had a correlation of .3 something with a probability of 0.011 and didn’t understand what that meant.  We had a long discussion of causation and attribution and correlation.

We had another long conversation about practical v. statistical significance, something her statistics professor isn’t teaching.  She isn’t learning about data management in her statistics class either.  Having dealt with both qualitative and quantitative data for a long time, I have come to realize that data management needs to be understood long before you memorize the formulas for the various statistical tests you wish to perform.  What if the flood happens????lost data

So today I’m telling you about data management as I understand it, because the flood  did actually happen and, fortunately, I didn’t loose my data.  I had a data dictionary.

Data dictionary.  The first step in data management is a data dictionary.   There are other names for this, which escape me right now…know that a hard copy of how and what you have coded is critical.  Yes, make a back up copy on your hard drive…have a hard copy because the flood might happen. (It is raining right now and it is Oregon in November.)

Take a hard copy of your survey, evaluation form, qualitative data coding sheet and mark on it what every code notation you used means.  I’d show you an example of what I do, only they are at the office and I am home sick without my files.  So, I’ll show you a clip art instead…data management    smiley.  No, I don’t use cards any more for my data (I did once…most of you won’t remember that time…), I do make a hard copy with clear notations.  I find my self doing that with other things to make sure I code the response the same way.  That is what a data dictionary allows you to do–check yourself.

Then I run a frequencies and percentages analysis.  I use SPSS (because that is what I learned first).  I look for outliers, variables that are miscoded, and system generated missing data that isn’t missing.  I look for any anomaly in the data, any humon error (i. e. my error).  Then I fix it.  Then I run my analyses.

There are probably more steps than I’ve covered today.  These are the first steps that absolutely must be done BEFORE you do any analyses.  Then you have a good chance of keeping your data safe.

Filed Under (Methodology, program evaluation) by Molly on 12-06-2013

I have a few thoughts about causation, which I will get to in a bit…first, though, I want to give my answers to the post last week.

I had listed the following and wondered if you thought they were a design, a method, or an approach. (I had also asked which of the 5Cs was being addressed–clarity or consistency.)  Here is what I think about the other question.

Case study is a method used when gathering qualitative data, that is, words as opposed to numbers.  Bob Stake, Robert Brinkerhoff, Robert Yin, and others have written extensively on this method.

Pretest-post test Control Group is (according to Campbell and Stanley, 1963) an example of  a true experimental design if a control group is used (pg. 8 and 13).  NOTE: if only one group is used (according to Campbell and Stanley, 1963), pretest-post test is considered a pre-experimental design (pg. 7 and 8); still it is a design.

Ethnography is a method used when gathering qualitative data often used in evaluation by those with training in anthropology.  David Fetterman is one such person who has written on this topic.

Interpretive is an adjective use to describe the approach one uses in an inquiry (whether that inquiry is as an evaluator or a researcher) and can be traced back to the sociologists Max Weber and Wilhem Dilthey in the later part of the 19th century.

Naturalistic is  an adjective use to describe an approach with a diversity of constructions and is a function of “…what the investigator does…” (Lincoln and Guba, 1985, pg.8).

Random Control Trials (RCT) is the “gold standard” of clinical trials, now being touted as the be all and end all of experimental design; its proponents advocate the use of RCT in all inquiry as it provides the investigator with evidence that X (not Y) caused Z.

Quasi-Experimental is a term used by Campbell and Stanley(1963) to denote a design where random assignment cannot be made for ethical or practical reasons be accomplished; this is often contrasted with random selection for survey purposes.

Qualitative is an adjective to describe an approach (as in qualitative inquiry), a type of data (as in qualitative data) or
methods (as in qualitative methods).  I think of qualitative as an approach which includes many methods.

Focus Group is a method of gathering qualitative data through the use of specific, structured interviews in the form of questions; it is also an adjective for defining the type of interviews or the type of study being conducted (Krueger & Casey, 2009, pg. 2)

Needs Assessment is method for determining priorities for the allocation of resources and actions to reduce the gap between the existing and the desired.

I’m sure there are other answers to the terms listed above; these are mine.  I’ve gotten one response (from Simon Hearn at BetterEvaluation).  If I get others, I’ll aggregate them and share them with you.  (Simon can check his answers against this post.

Now causation, and I pose another question:  If evaluation (remember the root word here is value) is determining if a program (intervention, policy, product, etc. ) made a difference, and determined the merit or worth (i.e., value) of that program (intervention, policy, product, etc.), how certain are you that your program (intervention, policy, program, etc.) caused the outcome?  Chris Lysy and Jane Davidson have developed several cartoons that address this topic.  They are worth the time to read them.

A colleague asked an interesting question, one that I am often asked as an evaluation specialist:  “without a control group is it possible to show that the intervention had anything to do with a skill increase?”  The answer to the question “Do I need a control group to do this evaluation?” is, “It all depends.”

It depends on what question are you asking.  Are you testing a hypothesis–a question posed in a null form of no difference?  Or answering an evaluative question–what difference was made?  The methodology you use depends on what question you are asking.  If you want to know how effective or efficient a program (aka intervention) is, you can determine that without a control group.  Campbell and Stanley in their, now well read, 1963 volume, Experimental and quasi-experimental designs for research, talk about quasi-experimental designs that do not use a control group.   Yes, there are threats to internal validity; yes, there are stronger designs; yes, the controls are not as rigorous as in a double-blind, cross-over design (considered the gold standard by some groups).  We are talking here about evaluation, people, NOT research.  We are not asking questions of efficacy (research); rather we want to know what difference is being made; we want to know the answer to “so what”.  Remember, the root of evaluation is value; not cause.

This is certainly a quandary–how to determine cause for the desired outcome.  John Mayne has recognized this quandary and has approached the question of attributing the outcome to the intervention in his use of contribution analysis.  In community-based work, like what Extension does, attributing cause is difficult at best.  Why–because there are factors which Extension cannot control and identifying a control group may not be ethical, appropriate, or feasible.  Use something else that is ethical, appropriate, and feasible (see Campbell and Stanley).

Using a logic model to guide your work helps to defend your premise of “If I have these resources, then I can do these activities with these participants; if I do these activities with these participants, then I expect (because the literature says so–the research has already been done) that the participants will learn these things; do these things; change these conditions.”  The likelihood of achieving world peace with your intervention is low at best; the likelihood of changing something (learning, practices, conditions)  if you have a defensible model (road map) is high.  Does that mean your program caused that change–probably not.  Can you take credit for the change; most definitely.