Happy 5th Workiversary To Me!


There have been a few times in my life when I decided to do something and then, as the thing approached I thought “What have I done? This is too big and too scary and too hard and I’m totally not going to be able to handle this!”. Moving across the country to do a PhD. Play in a hockey game that lasts for 10 days. Do an MBA part-time while still working full-time. Accepting my current job. As it turned out, all of these were things that I could handle and are things of which, as it turns out, I’m extremely proud! It’s almost like being scared that I’ve bitten off more than I can chew is a sign that I’m about to do something awesome.

My job prior to my current job was fun and I learned a lot and I met some great people, some of whom I’m still good friends with (Hi Heather!). But after 5 years in that job, I’d hit a pay ceiling, I’d learned all that I could learn, and so I wasn’t feeling challenged any more. And then a co-worker of mine told me about a job posting she’d seen that she thought I might be interested in. It was a job doing the same type of work (evaluation in healthcare), but taking it to the next level. A leadership position where I’d get to run a team of evaluators to conduct an evaluation of a massive, multi-organization, multi-year project that has the chance to change the face of healthcare in the region. I was excited by the possibilities this job entailed, so I applied and I got the job. And a few days after I handed in my resignation at my old job I thought “Oh my god, what have I done? I know how to do my old job really well. But there’s so much I don’t know about this new job – I have to learn a whole new area of healthcare AND I’ll be the boss of people and that’s a whole new ballgame for me. What if I can’t do it?” What I should have realized then was, just like the PhD, just like the Longest Game, and just like my MBA, that fear was a sign of a great challenge and I’d shown over and over again that I can rise to a challenge.

The last five years have been really interesting. I’ve learned a tonne about health informatics, about applying complexity concepts to the evaluation of an ever changing project, about governance, about managing people, about managing data when you have a large group of people creating and using a huge dataset, and that’s not even getting into what I’ve learned in terms of the findings of the evaluation so far!

I’ve had the opportunity to collect data from 13 healthcare facilities and counting, I’ve built my team up from 2 to 11 evaluators (all of whom are pretty fantastic, I must say), and I’ve presented my work across Canada, as well as in the US and Australia.

And even after five years, I’m not bored. I honestly feel like we are just getting things rolling and we are improving our processes at every step, and I’m learning so much from all the amazing people on my team, and we are producing information that is actually getting used by decision makers. And there’s so much more still to come.

This is not to say that it’s been easy, or that I will be easy going forward. In a recent presentation I gave about the project at the Canadian Evaluation Society conference, I used this image to represent my experience:

I also often reference that MC Escher painting where the stairs are going up but also going down at the same time as representing what it’s like to work on the project I’m working on. (I can’t put the image here on the blog because I don’t have copyright permission, but here’s a link to the Wikipedia page on it where you can see the image)

But honestly, it’s kind of OK with me. The real world is messy and things don’t always work out how you planned them, but you learn a lot by going along for the ride.

Image sources:

Cross posted on my other blog.

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Evaluator Competencies Series: Contributing to the Evaluation Profession

And the final reflective practice competency is:

1.8 Engages in professional networks and activities and contributes to the evaluation profession and its community of practice.

There are several ways that I engage in this competency:

  • involvement with the Canadian Evaluation Society (CES)
  • teaching evaluation
  • mentoring my evaluation team
  • social media involvement

Involvement with CES

I first got involved with CES back in 2010, when I was looking to find my way in this profession. The national conference was being held in Victoria, so I volunteered for the conference as I figured it would be a good way to meet other evaluators and learn about the field. And was I ever right – the evaluation community was so welcoming and I met people there that I’m happy to call friends and colleagues to this day.

For the next several years, I went to the CES national conference when I was able to attend, but then in 2015 the BC & Yukon chapter decided to host a one-day conference of its own, and that’s when my involvement really took off. I volunteered to be the conference program chair for that conference – and also volunteered to be a program co-chair for the national conference which was scheduled to be held in Vancouver in 2017. That role was a tonne of work, but it was also a lot of fun, as I got to work with two delightful fellow evaluators, Sandra Sellick and Wendy Rowe. I really enjoy and get a lot from conferences (both in the content I learn and in the networking opportunities they provide) and I know from experience that they take a lot of effort, so I think that volunteering for conferences is an important way that I can contribute to the profession and its community of practice.

Also in 2015, I joined the CESBCY council as a member at large, later transitioning into the VP role when the VP stepped down. In 2017, I became the chapter president. I’m really proud of the work the chapter is doing – we are hosting a lot of professional development events (e.g., one day conference, various workshops and webinars) and meetups that serve the evaluation community.

This year I also coached a student case competition team at the CES national conference – and that was a really rewarding way to support new evaluators in our community!


Another way that I feel that I contribute to the evaluation profession is by teaching evaluation. I’ve taught evaluation courses at both SFU and UBC, and I’ve supervised practicum students from SFU, UBC, and UVic. And several of my students have gone on to work in evaluation (right now, I have three of my former practicum students and two of my previous evaluation course students working as evaluators on my team!)


And speaking of my team, I currently have 10 evaluation specialists working on my team and a big part of the work that I do as the leader of the team is to mentor and support them. This is another way that I am working to contribute to the future of our profession.

Social Media

Another way that I’m involved in evaluation professional networks is online. There’s the #EvalTwitter hashtag that a lot of us connect through. There’s even a monthly #EvalTwitter tweetup on the last Thursday of every month (at 5:30-6:30 pm Pacific time). And through#EvalTwitter I learned about Eval Central, an online forum that “aim[s] to encourage positive and fruitful discussion among culturally diverse evaluators from around the globe.” So I recently joined that and am eager to see what kind of conversations happen there.

social media

Image credits:

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Evaluator Competencies Series: Self-Awareness and Reflective Thinking

I didn’t write a blog posting in my series last Sunday – the weekend was busy and time got away from me! But it’s now this Sunday night and I’ve got cup of tea and I’m ready to reflect on reflective thinking!

1.7 Uses self-awareness and reflective thinking to continually improve practice.

Spot of Tea

Often I do my my reflective thinking over a a cup of tea – whether sitting on my own to do some reflective writing, or chatting with colleagues (As an aside, if you want to read some brilliant thoughts on reflective practice, check out Carolyn Camman’s fabulously titled blog posting “The coffee is largely metaphorical“). I’m an external processor and I find that I tend to come up with a lot of my great “a ha!” moments when I write my thoughts down or talk to a friend or colleague. I also don’t have a great memory, so when I have an insight, I need to write it down to cement it in my brain (or the very least, so I can look it up again later.)


I write a lot of reflections as I go about my work. Whether I’m collecting data, analyzing data, in a meeting, or whatever activity I might be doing, if I have an “a ha!” moment, I write it in my reflective journal (which for the project I’m currently working on is typed up and saved on a shared drive with the rest of my team’s reflections, as these “a ha!” moments are about the content of the evaluation that we are working on together). A reflection might be about a pattern I’m noticing in the data, or a connection I’m making between different parts of the evaluation, or a surprise that I wasn’t expecting, or thoughts on some of our longer-term evaluation questions. My general rule is “if it’s interesting enough for me to want to tell my team about this cool thing I saw or thought of, it should write it down as a reflection). This improves my practice because it helps me to identify things that are important to the interpretation of the data, which allows me to develop accurate and comprehensive evaluation findings.

I also keep some separate reflections that are more for myself than as part of the evaluation data. For example, since I’m the team manager, if I have reflections that are about my work as a manager, and I might not want to share those with the team right away – especially if I’m trying to work through a challenge or figure out a way to be a better team manager. Some of those reflections might become things that I do want to talk about with my team later, but sometimes I need some time and space to work through stuff first. This helps improve my practice because being an effective leader will help my team be effective in its work.

Team reflections

Speaking of my team, we’ve taken to having group reflection sessions after we complete any big chunk of work where we debrief on:

  • what worked well
  • what didn’t work well
  • how might we have done things better
  • what can we glean from what worked well/didn’t work well to improve our practice for our next task

These are some pretty standard evaluation type questions, but we’ve definitely been able to continually improve our practice by doing this reflection together.

For example, in our first big round of data collection, we didn’t do nearly enough documentation of our data analysis. And with having a big team of people all working on different pieces of the data analysis, it meant that we had a lot of files that we’d all named in different ways, with our spreadsheets set up in different ways and often not very well labelled. So when it came time to write up our findings, it was quite difficult to find the data we needed, and we sometimes had to reproduce some of the analyses to ensure we had the correct data. So my big lessons learned for future rounds of data collection were:

  • we needed standardized naming convention that we all used
  • we needed all steps of analysis clearly documented so that another person could pick up the file and understand exactly what was done (without having to sift through formulas and pivot tables to figure out what it all meant)

These seem like pretty basic things – and they are – but this was the first time for all of us working on a big team. We each had our own individual naming conventions and ways of setting up our analyses in our spreadsheets that had served us well working as individual and what we hadn’t realized was how many different ways people could do the same task! Since the project is being implemented in a phased approached, we are now entering a period of time where our work will be a bit cyclical (collect baseline data for a site, monitoring data at the time of implementation, collect post-implementation data 3-6 months later, and repeat for the next site). And I can see that we are getting better and better each time because we’ve been reflecting on how we do our work and finding ways to be more efficient and more effective.

Another reflection that I shared in a team reflection session recently was something that I think links to the “self-awareness” part of the competency. Working in healthcare, even as a non-clinician, you get exposed to situations and information that can be quite emotional. For example, even when doing a chart audit, you get exposed to stories of serious illnesses/injuries and deaths. Or when interviewing healthcare workers who are exposed to traumatic situations, you also get exposed to those traumatic situations. As human beings, this can bring stuff up for us (like similar illness, injuries, patient journeys, and deaths of loved ones, for example) and it’s important to be kind to ourselves when stuff like this gets to us. I am extremely lucky that I work in a large team made up of kind and caring colleagues, so we know that we can go to each other if we need to debrief, or if today is just not a good day for us to do that particular observation or interview. Being aware of situations that might bring up things for me and being aware of my emotions as I’m experiencing them can help me to manage those, ask for help when I need it, and thus help to ensure that they don’t negatively effect the work. It can also help me to be empathetic to my colleagues and the people I interact with as I do my work.

In addition to reflection with my team of evaluators at work, I am also part of a co-op inquiry group that meets monthly to reflect on a particular topic (for us, it’s “boundaries in evaluation”) and that has been an amazing experience to hear the reflections of a group of evaluators from different sectors and locations – I have left every meeting having expanded on ideas I’ve been having and having learned new ideas or perspectives from my colleagues that have resonated with me.


Teaching is a fantastic opportunity to reflect. Whenever I prepare to teach an evaluation course, I’m dedicating time to stepping back and thinking about the big picture of evaluation – what it is and how to do it well. I find it also brings me back to the basics and it gives me the opportunity to think about whether there are ways that can improve what I’m doing. I use a lot of storytelling and examples when I teach – I’ve had many students tell me that they really appreciate that I do that because I tell them “what really happens, as opposed to what the textbooks tell you it’s going to be like”. But it also helps me because, again, it gives me an opportunity to think about how I’ve done my work, how it links to concepts, theories, standards, etc. and how I might do my work in the future.

In addition to getting back to basics, I also like to tell students about whatever the “hot topics” are in the field at the time, which means that I have to keep abreast of what the hot topics are, and typically do a bit of research to be well versed enough in the topic to discuss it with the class. This is an opportunity for me to identify gaps in my knowledge and do some learning.

Another aspect of teaching that I think is reflective is that students tend to ask really great questions. And since they are coming from a different perspective, sometimes those questions are things that I haven’t thought about before, which forces to me to reflect situations from a different angle. Sometimes they ask questions that I do not know the answer to – when that happens, I tell them that I’ll go do some research and get back to them. This links to that notion of self-awareness – knowing the limits of my knowledge, having the confidence to say “I don’t know that right now, but I will find out”.


And finally, this blog is something that I’m using as part of my reflective practice now. I’m glad that I decided to write this blog series on the evaluator competencies as a way to provide some structure and timeline to get me in the habit of reflecting here on a regular basis 1Last Sunday notwithstanding.. I’m finding it quite useful to spend a bit reflecting on the extend to which I have each of the competencies and areas for each where I can continue to learn and grow.

Image Source:

  • Pot of tea photo posted on Flickr by Jack with a Creative Commons license.


1 Last Sunday notwithstanding.
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Evaluator Competencies Series: Transparency

1.6 Is committed to transparency in all aspects of the evaluation.

transparent adjective

a: free from pretense or deceit : FRANK

b: easily detected or seen through : OBVIOUS

c: readily understood

d: characterized by visibility or accessibility of information especially concerning business practices

Merriam-Webster Dictionary

To reflect on this competency, I decided to see what the definition of “transparency” is. I usually think of transparency in the sense of “sharing all the information”, which is a bit more extreme than one can actually be in an evaluation. For example, we have an ethical responsibility to maintain confidentiality for participants in our evaluations when they want their identity to be kept confidential. Sometimes we are working with proprietary information that the organization requires to be kept confidential. So as with so many things, being “transparent” requires a bit of nuanced thinking.

Glasswinged Butterfly

I used to work with someone who talked about her role in a communication chain in a hierarchical organization, where information came from the top and was cascaded down through the org chart. Sometimes, information was only allowed to be shared to a certain level – say, it could go from the VPs to the EDs to the Directors, but the Directors were not allowed to share it with the Managers – at least not yet. And this person’s (who was in a Director role) approach to it was to tell their managers “I do know this information but I am not allowed to share it with you at this time.” And then they would give the reason (e.g., “Leadership is planning to do X, but until it is signed off by the board of directors, it’s not official and so they do not want put this information out broadly in case the board does not sign off it on, as it could cause confusion.”). And then they would make a commitment to tell their managers as soon as they were allowed to. This approach stuck with me, because it was honest (they weren’t saying “I don’t know this information” when they really did know, which is an approach I’d seen others take in these types of situations) and it was as informative as it was possible to be given the situation – giving a reason why they weren’t allowed to share the information at that time, rather than just saying “I’m not allowed to say”. I find that not giving a reason usually results in people coming up with their own theories about what information is being kept hidden – and that ends up causing rumours and confusion. So I think that this (sharing what you can and being honest about what you can’t share and why) can be a useful approach to being transparent. Of course, there can be good reasons or bad reasons for not wanting to share information, so I think we also have a responsibility to think critically about the reasons why an organization might not want to share and to push back in situations where appropriate (e.g., if an organization wants to suppress evaluation findings because they think it makes them look bad, as I talked about last week, I’d push back on that).

I was interested to see that the definition of “transparent” isn’t just about making information accessible, but also making information “readily understood” and “free from pretence or deceit”. These are things I can get behind. Obviously, a credible evaluation should not include anything deceitful, but I think making information “readily understood” is something that is sometimes overlooked. There are so many ways that we can exclude people from evaluation by not being “readily understood” – whether that be by the way we design our evaluations, the ways we recruit participants, the methods we use, or the way the report the findings. There seems to be a lot of interest in the evaluation world around data visualization – i.e., presenting data in ways that actually convey the meaning of them. This is something that my team and I are actively working to get better at. And there’s interest in alternative reporting formats – i.e., not just handing over a 200 page report, but actually thinking about ways to report evaluation findings that work for those who are interested in those findings.

Glasswinged butterfly (Greta oto)

Something I see spoken about less often, but that I think about a lot, is the use of language. I’m a bit fan of clear, simple language when writing 1Though I will admit that I have a tendency to be wordy, I write complicated sentences, and I overuse footnotes to an excessive degree. because I think that if I’m writing something, I want people to understand it. I mean, isn’t that why I’m writing it? I try to avoid jargon (or at the very least explain any jargon that I use) and prefer to pick a simple word over an obscure one. But I often see writing that is full of jargon, and unnecessarily large and obscure words. Part of me thinks that people write this way in an attempt to look intelligent. And I have seen situations where people use jargon as a way to try to cover up that they don’t know what they are talking about (which becomes evident as soon as you start asking questions like “What do you mean when you use the word X?”) An even more cynical part of me thinks that people write like this in order to exclude other people, by making the knowledge they are ostensibly trying to “share” non-understandable by “others” who don’t have the same training/background as them. After all, knowledge is power and keeping knowledge away from others by making it not understandable to others is a way of holding onto power. Which to me, is another reason to make the effort to make my writing as clear and easy to understand as possible.

At any rate, I hadn’t really thought about making information “readily accessible” as being part of “transparency” before, but it makes sense when I think about it.

Image Sources:


1 Though I will admit that I have a tendency to be wordy, I write complicated sentences, and I overuse footnotes to an excessive degree.
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Complexity eStudy Notes – Session #3

My notes from the third and final part of the American Evaluation Association (AEA) eStudy course being facilitated by Jonny Morell.

  • Jonny answered my question from last week:what is the difference between conceptual use and metaphorical use
    • there are fuzzy boundaries
    • if you think about chaotic systems – “strange attractors” (a.k.a., chaotic attractors)
    • you can do the math to plot a fractal – that is a technical meaning of the word
    • a conceptual meaning of the word – I know it’s not random, but I can’t tell you from one instance to the next where it will be, but I can tell you where it won’t be. You aren’t using the mathematics, but you are using the concept. Conceptual is still grounded in the technical.
    • metaphorical use – a step further away – we have this concept of chaos – that means its unpredictable. Conceptual means you have to “stay close to the mathematical, without doing the math”.
  • he thinks that if you take complex behavoiur seriously, you’ll do better program design and evaluation
  • but not trying to convert everyone to complexity thinking for everything all the time

Unintended Consequences

  • he tends to think that unintended consequences are usually negative – any change perturbs a system, and even if some parts of a system aren’t working, it will mess up a system; it’s harder to make things work than to make things not work, so if you perturb a system, it’s more likely that bad things will come out of it
    • he’s heard this from many people with “broad and deep experience” whose work he respects
    • “Programs like to optimize highly correlated outcomes within a system. This is likely to result in problems as other parts of the system adapt to change.
    • Change perturbs systems. Functioning systems require many parts of “fit”, but only a few to cause dysfunction”
  • he recently read about some work that shows this might not be true! But he wants to read more about it.
  • there are always unintended consequences – and if they are good or bad is an important question!
  • examples of unintended consequences provided by an audience member. A medical school started at a northern university to promote more physicians to work in the north, but saw unanticipated consequences:
    • positive: changes in the community (e.g., more rentals, excitement in the community about the work being done at the university, culture of the community changed: a symphony was started in the community)
    • negative: other programs felt snubbed

Small Changes

  • “because of sensitive dependence, it may be impossible to specify an outcome chain”
  • e.g., sometimes programs evolve because of small things – e.g., because the program had time to do something that wasn’t in the original scope, or because the board agreed that something that wasn’t originally in scope still fit within the mandate


A neat example of how difficult it is to predict the future is shown in this letter from Rumsfeld to G.W. Bush.

  • “the commonly accepted view of logic models and program theory may be less and less correct as time goes on”
  • there is debate over whether there are “degrees of complexity” (or if something is either complex or it is not”
  • some think that even if you start with a simple system that can be reasonably represented by a logic model, over time it will transition to complexity behaviour (he doesn’t believe there are “degrees” of complexity, so it’s not that a simple system smoothly transitions to a complex one

Network Effects Among Programs

  • imagine you have one universe where:
    • two programs: one on malaria prevention and another one that is promoting girls education –> increased civic skills
  • and another universe where:
    • you have those two programs, but also other programs with goals around crop yields and road building = and all the programs interact with each other. E.g., if people are healthier (no malaria) and well fed (crop yield), you can work harder and increase economic development, which can feed back into the other programs, etc.
    • he thinks that this interconnected universe can have bigger effects over time
    • effectiveness can build over time with networked programs (whereas non-networked programs would just have the effect of the program and that’s it)
  • challenge: how do you evaluate this when programs (and evaluations) are generally funded for single programs (or at least within a single organization), but not across multiple programs in different areas
  • but there can be some programs that can spur change in all kinds of other areas of the system (e.g., ensuring everyone has a base level of education could –> increased civic engagement, increased health, increased economic development, etc.)

Joint Optimization of Unrelated Outcomes

verde amarelo
  • e.g., a program to try to decrease incidence and prevalence of HIV/AIDS
    • increase service –> decrease incidence and prevalence of HIV/AIDS
    • increase quality of service –> decrease incidence and prevalence of HIV/AIDS
    • decrease incidence and prevalence of HIV/AIDS –> better qualitity of life
  • this is a fine program model
  • all these outcomes are correlated
  • you pour a lot of money into this program – lots of people make career choices, intellectual capital goes there
  • so what happens to other things in the system?
  • less people, money, etc. to go to women’s health, other health services
  • so perhaps we see improvements in HIV/AIDS outcomes, but then you see worse outcomes in other areas of health
  • so instead of doing that, let’s jointly optimize unrelated outcomes
    • e.g., instead of trying to optimize just HIV/AIDS outcomes, but try to optimize health overall
    • of course, this is hard to convince people of this – how do you decide how much each different group gets
  • another example, you can drill people on reading to get them to do well on a test, but what if that makes them hate reading? Try to optimize that they do well enough on reading but also love reading
  • have you ever seen HUGE logic models – lots of elements and lots of arrows?
  • when you look at these, do you really think they are going to be correct? there’s lots of stuff that we don’t really know for sure; there are feedback loops that may or may not be true (feedback loops do tend to
  • famous picture of dealing with insurgent situation in Afghanistan – you look at it and think that it can’t possible be right on its whole – things like sensitive dependence, emergence, non-linear effects of feedback loops, etc., etc. aren’t accounted for here
  • it’s OK to have these big complex models, but it’s not OK to think that the whole model is true (even if you have data on every arrow within the model – because it doesn’t account for howcomplex systems behave). You can use the big model to look at pieces of it and think about how they relate to other parts of the model
  • he has a blog posting on “a pitch for sparse models” – if things happen in the “input” and “activity” realm, things will happen in the outputs/outcomes realm
  • he thinks that people can’t really specify the relationships in the level of detail that we usually see in big logic models (and he thinks it’s egotistical to think that we can do that).
  • but it’s not very satisfying to stakeholders to say “we can’t tell you anything about intermediate outcomes”
  • evaluators are complicit – we make these big models and stakeholders like it (and he says he is as guilty as anyone else at doing this)


  • if you push something out of place and there is an attractor present, it will go back
  • e.g., rain that falls all ends up in the river, push a pendulum and ultimately it will end up back in the middle, planetary motion – gravity holds planets in their orbits, kids like playgrounds – kids will end up there, animals go to the waterhole
  • “explains why causal paths can vary but outcomes remain constant”
  • attractors are useful because:
    • lets you conceptualize change in terms of shape and stability
    • insight about program behaviour outside of stakeholder beliefs
    • promotes technological perspective: what will happen, not why

How do you decide if you should use complexity thinking in a given evaluation?

  • more work to incorporate complexity into an evaluation (than, for example, basing an evaluation on a simple logic model)
  • the evaluator – and the evaluation customer – should think about whether the value that is added by doing so is worth the extra work

For Further Reading

Jonny provided an extensive reading list. Here are some that caught my eye and I’m planning to check out:

Image Sources

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Evaluator Competencies Series: Independent & Balanced Perspective

1.5 Provides an independent and balanced perspective in all aspects of the evaluation.

This is one competency that I have done a fair bit of thinking about. I believe that ito be a good evaluator, you need to bring a balanced perspective. I interpret this to mean that, as much as possible, you:

Social circles of Influence
  • go into an evaluation without a preconceived notion of whether the program/initiative that you are evaluating is achieving its goals/is having good effects/is having bad effects/is a success/is a failure. An evaluator should not be setting out to prove that the program works – they are seeking to find out if it “works” or to what extent it does or does not “work” or in what ways is does or does not work
  • go looking for both good and bad effects of a program
    • bring in as many perspectives as possible into the evaluation – what is considered “good” for one person or group might be considered “bad” by another person or group. Or which outcomes are most important might differ depending on who you ask.
Nimbusoft Aurora

When I first started working on the project I’m currently evaluating, people were referring to me as doing “benefits evaluation” and would say things like “Beth is going to be measuring the benefits of the project.” To which I would reply, “No, I’m going to be measuring outcomes. I’ll let you know if they are “benefits” or not.” (OK, so I may have said that last part in a little less snarky of a way, but you get my point. I was confused at first as to where this phrase of “benefits evaluation” came from, as I was new to the world of healthcare technology. After doing some digging, it appears that the phrase is often used by software vendors, who try to sell their products by highlighting all the purported “benefits” they are going to provide. (Sometimes they would also use the phrase “benefits realization” – as in, “Beth is going to be measuring the benefits realization” (to which I’d have a similar reply to the one I mention above)).

Also, there is an organization called Canada Health Infoway (CHI) that has a “Benefits Evaluation Framework” that is intended to be used by projects that are implementing healthcare technology, but it’s always bothered me because it makes the assumption that the technology will result in benefits and the evaluator just needs to measure them. (Or that if the expected benefits aren’t seen, you just identify what’s preventing it – such as people aren’t adopting the technology, so you increase adoption and then the benefits will happen). But we know that technology does not necessarily deliver the benefits that those selling those technologies claim they will deliver. Plus, the framework has nothing in it to allow you to look for unintended consequences, but we know that implementing technology is a complex endeavour and complexity is characterized by, among other things, the fact that we cannot predict what will come out of them. I saw a presentation by some people from CHI at the most recent CES conference and when I asked them about these issues, they conceded that their framework is missing these things.

Anyway, early on at the organization I am working in, I went on a bit of a campaign against the word “benefits” – and when I explained to people why I was opposed to the word, everyone I talked to saw my points as valid and I rarely hear the word benefits used on the project anymore (Every once in a while, when a new contractor starts on the project, they’ll start saying “benefits realization” and inevitably someone will say “You better not let Beth hear you say that word!”). It might seem overly pedantic to be so concerned about a word, but words have power and using a work that frames an evaluation in a biased way can set up the evaluation to be designed with that bias. And even if you set up the evaluation to be more balanced than the word implies, people saying that they are “measuring the benefits” is not conveying the right message about what evaluation is all about.

I struggle a bit with the phrase “independent perspective” in this competency. I think maybe they are going for “unbiased” in the sense of “doesn’t have a vested interest in the program being seen as a success”. I know that I’ve had the experience of having people who are interested in the results of an evaluation (e.g., participants in focus groups or interviews, others that are affected by a program) assume that an evaluator (who is hired by management) is going to just try to make a program look good. The way I see it, while I may want a program to be successful – I wouldn’t be working on an evaluation for an organization or program whose goals I opposed – I don’t think that saying a program is successful when it is not would be helpful. Because wanting a program to actually be successful is different from wanting the program to look like it’s successful. If I, as an evaluator, put out an evaluation that says “this program is successful” (whether by cherry picking data that makes the program looks good or by designing the evaluation in a way that it only looks at the good stuff and ignores bad things that are happening), then not only is that being dishonest, but it’s also not helpful as then the program will continue to operate in a way that is not getting the promised results and/or is causing harm. The way that I, as an evaluator, can help to contribute to the goals of a program being achieved is by providing an honest and thorough assessment of the what’s going well (if anything) and what’s not (if anything).

the Jenga

But perhaps the word “independent” in this competency is more about “is not influenced by the stakeholders to change the results of the evaluation.” We’ve all heard stories of, and/or have experienced for ourselves, situations in which stakeholders don’t like the results of an evaluation and want them changed or suppressed. And I agree that it’s important for evaluators not to be influenced to change results. And I can also see that it can be a challenging situation, especially when there is a power differential between the stakeholder and the evaluator (and if the stakeholder is the one paying the evaluator (whether internal or external) and/or can influence future work opportunities for the evaluator, there is a power imbalance. Some strategies that I use to help prevent a situation like that include:

  • agreeing at the start of the evaluation about how widely the results of the evaluation will be shared. Getting this in writing at the start can give you something to go back to to say “we agreed to this”.
  • discussing the possibility that “negative” findings might come up with stakeholders and how you will deal with them (e.g., “negative” findings won’t be suppressed, but perhaps management would want to include a response to the findings about what they are doing to make improvements) – it’s much easier to discuss this and come to an agreement when the idea of negative findings is just an idea, rather than waiting until there is a real negative finding about the program that might bring up an emotional or defensive reaction
  • discussing the importance of transparency and credibility – I often point out that participants in the evaluation know what they said in interviews/focus groups/surveys, so if you remove findings that management doesn’t like (again, not only would that be dishonest) they will know that this evaluation report does not reflect the data they provided and you’ll lose all credibility.

The other challenge that I have with the word “independent’ is that it suggests that the evaluator is separate from the evaluand, but again, going back to complexity, we know that things are connected and the evaluator is just another agent in the system, interacting with all of the other agents. Evaluations have affects on their evaluands – for example, when people know something is being measured, they change their behaviour. When people know they are being watched (such as when an evaluator is conducting observations), they change their behaviour. Evaluators also often do things like helping programs clarify their goals – and thus the program is changed by its interaction with the evaluator. I don’t think this is a bad thing. But I do think it’s important to be aware of. In my work, I write reflections on the ways in which I see the evaluation work affecting the program and I try to include that in my interpretation/sensemaking.

Images Sources:

  • The image of the mutlicolour ovals and circles was posted on Flickr by Anne Adrian with a Creative Commons license.
  • The photo of a computer was posted on Flickr by Gareth Halfacree with a Creative Commons license.
  • The photo of the leaning Jenga tower was posted on Flickr by Ed Garcia with a Creative Commons license.
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Evaluator Competencies Series: Well-Being of Human and Natural Systems

1.4 Considers the well-being of human and natural systems in evaluation practice.

For this competency, I would say I’ve focused much more on the “human” than the “natural”. I see some overlap with the stuff I talked about last week, as considering the well-being of humans includes ethical concerns like, for example, maintaining confidentiality for participants in the evaluation.


But looking at this competency, it’s gotten me thinking about the well-being of human systems – which leads me back to my thoughts on learning more about equity, which I also mentioned last week. Within human systems, there are some groups that disproportionately receive benefits of programs/services/initiatives/systems than others – and similarly, some groups that disproportionately are harmed by programs/services/initiatives/systems. Coincidentally, in the AEA eStudy webinar last week, Jonny talked about how in systems there are often disproportionate distributions of benefits and that’s something that we as evaluators need to pay attention to and think about the values underlying our evaluations when we thinking about how we decide if a program/initiative is a “success”. I think Michael Quinn Patton also talked a bit about this on the Eval Cafe podcast episode that just came out this week on Principles-Focused Evaluation (PFE) , where he was talking about the difference between rules and principles using the example of “do no harm” – but if we are interested in making systems more equitable, then aren’t we technically “harming” the more advantaged by taking away some of the power/benefits that they currently have in order to distribute benefits more equitably. I think that if you care about equity, you’d say that in in the interest of fairness/justice, that’s OK and the rule of “do not harm” is actually too rigid 1It’s entirely possible that I’m misremembering that was where I heard that example – I’m also in the middle of reading the PFE book, and having been reading/listening to a bunch of other stuff, so I may be conflating things. My apologies to all if I’ve mixed that up.. Is having some people benefit good enough to say a program is successful, or should we be looking at who is benefiting -and who is not – and who is being harmed – and who is not – across the whole human system? And how do we include that in our evaluations?

Similarly, I think that paying attention to the unintended consequences of a program/initiative is a really important part of an evaluation. If we are only looking for the ways in which the program designers hoped that the program would be beneficial, but didn’t hold space in our evaluations to look out for ways that the program may cause harm, we aren’t really doing a very comprehensive evaluation.

As for considering the well-being of natural systems, this is an area that I have not done a lot of work. Like with the equity stuff I talked about last week, I think the types of evaluations that I do (in the healthcare sector), don’t have an obvious link to environmental issues like they would if I were doing evaluation work with organizations that are working directly in the environmental sector. But every program/initiative exists within, and interacts with, the natural world, and we are in the middle of a climate crisis. So I think it requires some time to reflect on how I can better consider the well-being of natural systems in my evaluation practice.


There are definitely ways that I try to do environmentally-responsive things in my day-to-day work – taking transit to the office instead of driving, not printing things unnecessarily, using a travel mug for my coffee every day, recycling and composting, not drinking bottled water. But honestly, these things are easy to do, and I don’t know how much impact my individual actions in these regards really have. And there are other things that I do that I know are harmful to the environment – flying to conferences and to sites to collect data, for example, have a huge carbon footprint.

And then there is the idea of how to incorporate considerations of the environmental impact of the programs/services I evaluate. For example, in the project I’m evaluating on switching from paper patient charts to electronic patient charts, one thing that could be evaluated is the saving of paper by going electronic vs. the vast energy costs of the server space required to go electronic. Is that something I could include in an evaluation, especially considering that isn’t really within the scope of evaluation per se? And how would you compare those two impacts? Clearly, this is a space where I have lots of room to grow.

Image credits


1 It’s entirely possible that I’m misremembering that was where I heard that example – I’m also in the middle of reading the PFE book, and having been reading/listening to a bunch of other stuff, so I may be conflating things. My apologies to all if I’ve mixed that up.
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Complexity eStudy Notes – Session #2

My notes from part 2 of the American Evaluation Association (AEA) eStudy course being facilitated by Jonny Morell.

  • one person commented that “measuring collective impact of small effects of multiple programs is almost always shot down by stakeholders in favour of measuring process outcomes” and Jonny talked about how evaluators are typically engaged to evaluate a single program, so if we tried to measure outcomes from other programs, we’d get shot down for wasting resources and going outside of the scope that we were hired for
  • this got me thinking about boundaries (something I’ve been reflecting on a lot lately as part of a group that I’m working with). The “scope” of a project is a boundary and it makes sense for a program to bound this scope to the work that their program is doing. They have limited funds and don’t want to spend them evaluating the broader system in which they are working. But the organization operates within that system and if change really does happen by a bunch of program contributing little bits of outcomes that all accumulate – how would we ever see that?
  • There are “collective impact” initiatives and I’ve seen evaluations of those, but those I’ve seen tend to be when a single funder is funding a bunch of programs and want to evaluate across all these programs that are setting out to improve some thing across all the programs.
  • But what about programs that aren’t linked through a collective impact project – what about just the way that all sorts of programs running in the world that affect similar things?
  • [Crazy idea: what if someone (like a philanthropic foundation) funded an evaluation of the impact of an entire system that relates to some issue – say, poverty, for example – with the freedom to go and investigate whatever programs/services/initiatives the evaluative process uncovers. Is anything doing something like this?]

Timing of Effects

  • we don’t often talk about “how long will it take?” for these effect in a program model to happen. So even if we say something is an “intermediate effect”, how long does that actually mean? Often effects don’t happen as soon as people expect, or soon as they would like.
  • also, sometimes things need to hit a tipping point, so you might not see effects for a long time, and then you see a big effect. This challenges people’s “common sense” feeling that things will be linear (you put in a bit of work, you get a bit of effect, you put in more work, you get a bit more effect).
  • “Success may mean that the rich get richer. In a very successful program, benefits may not be symmetrically distributed. evaluation methodology, straightforward. The politics and values? Not so much”
  • example: an agriculture program that leads to increase crop yield is expected to improve family standard of living. But does that get evenly distributed?
  • Looking at distributions is important!

Three ways to use complexity

  • e.g. thinking about a program as an organism evolving in an environment
  • instrumentally – would need to do a lot of math and specific data
  • conceptually – how might this program change? does it become more or less adaptable to its environment? does this program compete for resources from the environment with other programs? thinking about the program in this way changes how I think about the program
  • metaphorically – e.g. chaos has a very technical meaning, but it’s not useful in evaluatoin beacusae we never let chaos happen – we never let feedback loops go on uncontrolled. We intervene when things start to go off the rails. But the notion of chaos of repeated patterns that can’t be controlled or predicted
    • [I don’t understand that difference between conceptual and metaphorical use – going to post a question about this on the workshop discussion site]

Cross-cutting themes in Evaluation

  • whenever you are thinking about complexity, need to think about:
    • patterns
    • predictability
    • how change happens
      • without thinking about compleixty, 3 ways we think about change:
        1. from the outside: take a systems view; events in a program’s environment makes a difference
        2. expected causal relationships: identified model content in terms of elements and relationships
        3. traditional social science theories: usual paradigmatic stuff depending on your background (e.g., economics, sociology)
      • when you add complexity to the mix:
        • emergence – change cannot be explained by the behaviour of a system’s parts
        • sensitive dependence – small (sometimes random) changes can affect an entire trajectory over time. We usually think of a linear model – we only care about groups, we want a large n, we don’t want to see those little other things
        • limitations of models – models simplify, causal dynamics are going on that are unknown. (explicit or implicit models, quant or qual). Remember “all models are wrong but some are useful”. They help us identify things we care about and come up with methods, but need to remember that they aren’t perfect
        • evolutionary dynamics – think of programs as organisms evolving in a diverse ecosystem. Helps him to think of this as a metaphor
        • preferential attachment – on a random basis, “larger” becomes a larger attractor.
      • Jonny thinks its useful to think about each of these in an evaluation – you may not necessarily need to use them, but worth thinking about whether they could be useful

You can use simple methods to evaluate in situations of complexity

  • e.g., attendees of a program may affect their (non-attendee) friends. And their friends may also know each other. And the attendees may affect one another too. And maybe there are community-wide effects too. And maybe those effects might feedback and change the program too.
  • you could track the program over time (to see if there is a feedback loop from community to the program)
  • you could interview staff about their perceptions of needs
  • there are unpredictable changes in the community – you could do a content analysis of community social media; you could do open-ended interviews of community members
  • program theory – you can specify desired outcomes (and you can measure them); you can’t specify the path to the desired outcomes in the beginning -but you can track stuff and look at it post hoc
  • or you may decide that it is worth using fancy tools (such as agent-based or system dynamic modelling; formal network analysis)


  • network structures can tell us a lot about relationships
  • even without doing fancy calculations, sometimes just looking at a network structure can be revealing
  • some evaluations, it is worth doing network analysis
  • fractal structures
    • example of a healthcare – primary, secondary, tertiary health care
    • primary clinics feed into secondary, secondary clinic feeds into a tertiary system – if the link between the secondary and teritary clinics breaks, the whole thing falls apart
  • fractal structure: unless you know the scale, you can’t tell how close or far away you are from it (e.g., snowflake, vascular system of the human body)
  • leads to robustness – if you only have one link (e.g., the only way to get into the tertiary clinic is referral from one secondary clinic, if that links breaks, the whole system is wrecked)

Competing Program Theories

  • we can have different, competing program theories
  • e.g. one theory might be that increasing air pollution controls and increased use of clean fuel sources –> decreased air pollution and increased economic growth (which is a theory that those who endorse more air pollution controls and promoting the use of more clean fuels might suggest)
  • but another theory might be that air pollution controls –> decrease air pollution, but increasing clean fuel sources –> increased cost of doing business –> slowed economic growth (which is a theory that those who opposed more air pollution controls and promoting the use of more clean fuels might suggest)
  • what would it take to activate one or the other program theory? it might be (a) small change(s). And it’s not really knowable/predictable what the events will tip the balance
  • in complex systems, small changes can lead to big results
  • simple programs can exhibit complex behaviours
  • so it’s always worth thinking about “might there be complex behaviours going on?”

How much do you need to know about complexity?

  • his argument by analogy:
    • how much do you know about a t-test?
    • if you know what it is appropriate for, that most people accept that p <0.05 as a level of significance, you can probably use the t-test reasonably – you can probably make sense of it
    • but there is lots more to know about the t-test – things like the distribution of data, underlying theory, there’s a whole argument about whether the level of 0.05 is really appropriate, central limit theorem, definition of degrees of freedom etc., etc.
    • do we need to know all of that deeper stuff to do a decent job of using a t-test? probably not.We’d be better off at doing it if we knew all the underlying stuff, but there’s not magical amount of stuff that we can say we “need” to know
    • he thinks it’s similar with complexity – knowing more is better, but hard to say how much is “enough”

Feedback Loops

  • “feedback loops can produce nonlinear behaviour”
  • but the nature of those feedback loops matters – things like how long the lag for a feedback is (shorter lag = quicker loops)
  • it was very interesting to see lags added into the a program logic model and see how that affected the overall timeline
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Evaluator Competencies Series: Ethics


1.3 Integrates the Canadian Evaluation Society’s stated ethics in professional practice and ensures that ethical oversight is maintained throughout the evaluation.

Like many evaluators, a lot of my knowledge of ethics comes from the research world. I recently completed the latest version of the TriCouncil’s online Course on Research Ethics, which was required by the organization I work for as the course has been updated since I originally took their ethics training a long, long time ago. A lot of the concepts from research ethics – informed consent of participants, do no harm, justice, etc. – are applicable to evaluation as well.

As for how I integrate ethics into my work and ensure that ethic oversight is maintained through the evaluation, a few things that I do include:

  • use the ARECCI Ethics Screening Tool to assess ethical issues when planning evaluations
  • create systems to protect the privacy of data that my team and I collect, such as only storing data on secure networks and using passwords to protect data
  • discussing ethical considerations, such as confidentiality, conducting rigorous evaluations, and reporting findings accurately and completely (just to name a few), with my team throughout the evaluation process
  • holding strong on my commitment to do my work ethically, even when it is challenging. I consider my integrity to be a very important part of being an evaluator. Without integrity, there would be no point to doing the work that I do.

One area of ethical considerations that I’m seeking to learn more about is equity in evaluation. Since I don’t work in an area where there is an obvious equity lens – such as there would be working with a non-profit that explicitly focuses on equity, for example – I find it challenging to see how my work links with equity. But inequities often stem from institutions and systems where power imbalances and institutionalized racism/sexism/ableism/and many other -isms are so embedded and are often difficult for someone with a lot of privilege (such as a straight, white, cis person such as myself) to see. So I figure that this is an area that I need to learn more about so that I can do better. Two great resources that I’ve heard about recently for learning more about equity and evaluation are Equitable Evaluation and We All Count.

The CES ethics statement is currently under review, as it is about 20 years old. I went to a session at the CES 2018 conference where they were consulting with evaluators to see if the statement needed some tweaks, or a complete overhaul. The group I was in felt it was the latter and I know there is a committee that is hard at work at revising that statement. I’m actually quite looking forward to seeing what they come up with – and I’m sure I’ll write a blog posting on it once it comes out – now that I’m on such a roll with writing here!

When I joined the Australasian Evaluation Society 1The year I went to their conference – it’s cheaper to join the society and pay the member conference registration fee than to just pay the non-member registration fee, so I joined., I had to attest to the fact that I would adhere to their ethical guidelines. I’m interested to see if CES will do the same with their new ethics statement when it’s released.


Image credits:


1 The year I went to their conference – it’s cheaper to join the society and pay the member conference registration fee than to just pay the non-member registration fee, so I joined.
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Complexity eStudy Notes – Session #1

Given my interest in complexity and evaluation, I decided to take the American Evaluation Association (AEA) eStudy course being facilitated by Jonny Morell. I’ve seen Jonny speak at conferences before and have learned some useful things, so figured I could learn a few things from him in this more extended session. Sadly, the live presentation of the eStudy conflicts with other meetings that I have, so I’m only going to be able to see part of the presentations live and will have to watch the other parts of the presentations after the fact from the session recording.

This is one of those blog postings that is probably more useful for me as a brain dump than it is for other people to read.

It was recommended that we check out a few of Jonny’s blog postings on complexity before the presentations.

Here’s one quote from his posting on complexity having awkward implications for evaluators that jumped out at me:

Contrast an automobile engine [not complex] with a beehive, a traffic jam, or an economy [complex]. I could identify each part of the engine, explain its construction, discuss how an internal combustion engine works, and what role that part plays in the operation of the engine. The whole engine may be greater than the sum of its parts, but the unique role of each part remains. The contribution of each individual part does not exist with beehives, traffic jams, or economies. With these, it may be possible to identify the rules of interaction that have to be in place for emergence to manifest, but it would still be impossible to identify the unique contribution of each part.

Source (emphasis mine)

This really is a challenge for evaluators! Imagine being hired to evaluate a program – your job is to answer “what happens as a result of this program?”, but you know that your program is just one part of a larger, complex system, so you can never really definitively say “this program, and this program alone, caused X, Y, and Z”, as you know that outcomes are affected by so many things that are outside of the control of the program. That is the situation that we evaluators find ourselves in all the time. That’s not to say that we can’t do anything, but just that we need to be thoughtful in how we try to determine what results from a program in the context of everything else in the system. Learning about complexity and systems thinking can help us do that.

I had a conflicting meeting during the first session, held on July 9, 2019, so I watched the recording afterwards. Here are my notes:

Rugged landscapes
  • people seems to think complexity is “mysterious” and “magic” – Jonny feels it is not
  • he feels that “most of the time you won’t have to use it at all”
  • if you learned thematic analysis or regression, you’d say “cool method, I’ll use it when it is needed and I won’t use it when I don’t need it”. He thinks complexity should be the same – use when it’s needed.
  • Two modes:
    • you might use complexity instead of another method (like you might say “thematic analysis is better than how I’ve been analyzing open ended survey data. I will use thematic analysis instead of what I was doing before”)
    • but you could also thinking about it like this: it can help you change how you conceptualize the problem, the data analysis strategies – “you begin to think differently about the world”
  • people seems to think that you need to use new fancy tools to apply complexity – and sometimes you do, but often you don’t – you can use familiar methods while applying complexity concepts
  • there’s no agreed upon definition of complexity – but he doesn’t worry about that
    • “systems” is a huge area (but he’s not that interested in it – though he did plug the AEA Systems TIG)
    • “complexity” also a huge area – and he thinks lots of the concepts are useful to evaluators
  • “I don’t know what complex systems are, but I know what complex systems do. I can work with that” – we can use that to make practical decisions on models, on methods, data interpretation, how to conceptualize the program.
  • He thinks that complexity is popular in evaluation today because there is a sense that programs aren’t successful and evaluators are the messenger (and people are shooting the messenger). And people think that maybe complexity can help explain why programs aren’t working.
  • The fact that everything is connected to everything else is true, but useless.” He wants to help us learn the “art” of getting a sense of what connections are worth dealing with and which aren’t. We need to “discern meaning within the fact that everything is connected to everything else.”
  • Cross cutting themes in complexity science
    • pattern
    • predictability – what can we predict and how well can we predict it
    • how change happens –
  • Complex behaviours that might be useful in evaluation ((Not everything that you’ll read about when you read about complexity is useful in evaluation:
    • attractors
    • emergence
    • sensitive dependence
    • unpredictable outcome chains
    • network effects among outcomes
    • joint optimization of uncorrelated outcomes
  • It’s hard to talk to people (like evaluation stakeholders) about complexity
    • if we show people a logic model or theory of change, they can understand how things they do in their program are believed to lead to outcomes they are interested in
    • but talking about things like a program might benefit a few people a lot and most people not at all, or network effects – these are things we aren’t used to talking to evaluation stakeholders about
    • it’s difficult to say to people that we might not be able to show “intermediate outcomes” on the way to long-term outcomes (because results aren’t so linear)
    • your program may have negative effects in the broader system (programs are siloed, so you are only working within your own scope and aren’t concerned (or incentivized to be concerned) about stuff outside of your program. If we throw all of our financial and intellectual resources into HIV, we’d make a lot of improvements with respect to HIV. But that pulls the resources away from prenatal care, palliative care, primary care, etc., etc., etc. You are “impoverishing” the environment for every other program – and those programs will have to adapt to that.
  • preferential attractors – e.g., snowflakes – the odds of a molecule attaching to a big clump is more than a little clump; same thing with business – you are more likely to attach to a bigger centre of money than a small one
Bee & Beehive
  • emergence is NOT “the whole is greater than the sum of the parts” – it’s about the WAY that the whole is greater than the sum of the parts. An engine is greater than the sum of its parts. But I could explain what the contribution of each of the parts is to the engine. That’s not the same for complex systems (like traffic jams, beehives, or economies) – you can’t explain the whole economy based on the contribution of each of its parts. Not just because we haven’t studied these enough – but because it is “theoretically impossible” to do so.
  • “Ignoring complexity can be rational, adaptive behaviour”
    • stovepipes are efficient ways to get things done
    • different programs have different time horizons
    • different organizations have different cultures
    • it takes resources to coordinate different programs/systems/organizations
  • Even if our stakeholders don’t buy into complexity, it’s still important for evaluators to think about and deal with
    • “if program designers build models that do not incorporate complex behaviour, they will:
      • miss important relationships
      • not be able to advocate effectively
      • not be effective in making changes to improve their programs
      • misunderstand how programs operate and what they may accomplish
    • these problems cannot be fixed in an evaluation, but it is still possible to evaluate the complex behaviours in their models”
    • e.g., he showed a logic model and talked about if you have a bunch of arrows leading into an outcome, are those “AND” or are they “OR” (i.e., do you need all of the outputs to happen to lead to that outcome, or do you only need one? Or only need some combo? He also added unintended consequences and about network effects.
    • the evaluator can still look at these complex behaviours – look for the data to support it. You can superimpose a complex model on top of the traditional logic model. You can do this even if the program stakeholders only see the logic model. You can show them the data interpreted based on their logic model, and then also show them how the data relates to the model that includes complexity (that might be what it takes to incorporate it).
    • He thinks more unintended consequences are undesirable and there are methods for measuring unintended consequences and they can be measured within the scope of an evaluation.
    • Jonny hates the “butterfly effect” because, in his world, he doesn’t see big changes happening super easily. He sees people making lots of policy/program changes, but the outcomes don’t change! His take on sensitivity to initial conditions is that you can run the same program multiple times and get different results each time because there are difference in the context of where its implemented and so you can’t necessarily replicate the outcome chain. But if the program is oeprating within an attractor, you might be able to get to the same ultimate outcome.
    • E.g., if you roll a boulder down a hill, you won’t be able to predict it’s exact path (e.g., might hit a pebble, wind might move it), but we know it will end up at the bottom of the hill because there is an attractor (gravity).
    • He’s not arguing to not measure intermediate outcomes, but we should think about these concepts [and maybe not be too overconfident in what we think we know about the outcome chain?]

Image Sources

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