About Meghan Duffy

I am an ecologist at the University of Michigan. My research focuses on the ecology and evolution of infectious diseases, particularly in lake Daphnia populations.

Paired line plots (a.k.a. “reaction norms”) to visualize Likert data

In the past year, I’ve been working on several projects that used Likert-scale data (e.g., 1 = strongly disagree, 5 = strongly agree). And, in several instances, there were questions that it made sense to pair. As one example (which I blogged about in more detail earlier this month), for Morgan Rondinelli’s undergraduate thesis project on student mental health, we asked students whether they would think less of someone who sought mental health care and also whether they thought others would think less of someone who sought mental health care? In that case, I was curious not just about the aggregate percentages in the different categories, but also how individual views compared. So, being a good evolutionary ecologist raised on reaction norms (where genotypes are plotted in different environments, with the points for each environment connected by a line), I made a paired line plot:

plot with lines connecting student views asking about how others view seeking mental health care vs. how they feel. y-axis has amount of stigma from low to high. The lines generally go down, indicating more stigma held by others Note: the individual lines are gray and slightly transparent, so more common pairs of responses appear darker and thicker

This figure shows me that no students viewed themselves as more judgmental than the average: none of the lines go up. That’s not information that I could get from other ways of plotting the data (shown in my earlier post).

A different example comes from a project studying student views on climate change, which I’m working on with Susan Cheng and JW Hammond. We asked students the same questions at the beginning and end of the semester. To focus on one question, we asked students “Do you think climate change is happening” at the beginning of the semester and again at the end of the semester. The overall results were promising:

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Guest post: I am a scientist. Ask me what I do, not where I am from “originally”.

Note from Meghan: This is a guest post by Gergana Daskalova, a PhD student at the University of Edinburgh.

I recently attended the British Ecological Society Annual Meeting, one of the biggest scientific conferences in the calendar year of an ecologist. Over the course of just one day, I got asked where I am from 18 times. I counted because in just four years of attending conferences, meeting with seminar speakers and engaging in similar activities, I have been asked where I am from way too many times. When the pattern repeated itself on day one of the BES conference, I thought I could do the actual count on day two of the conference. I, like many other of my fellow conference goers, get these questions at a very high frequency probably because our looks or accents give away that “we are not from here”. Though it may seem like an innocent question –  where are you from? – it leaves me feeling like my fellow ecologists are more interested in why I stand out than why I belong.

To counter the question in a productive way and to get the focus back on my science, over the last year, I have made a point of replying that I am from the academic institution where I am doing my PhD. People always follow up with “No, I meant where are you from originally?” The problem is not that I want to hide where I am from, the problem is that in a professional scientific environment, where I am from shouldn’t matter. When people make general chat at conferences with a group of PhD students, most of them get asked what they do. When the conversation makes its way to me, I get asked where I am from. Followed by comments about my country of origin. Cool! Exciting! I’ve never been to that country. Why did you come here? What a poor country. Was it hard living there? The list goes on. Only just over half of the 18 people that asked me where I am from originally then went on to ask me about my work.

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Did the other reviewer notice things you didn’t? That doesn’t mean you did a bad job.

Reviewing is something that brings out my imposter syndrome, and I know I’m not alone. Being asked to review implies that someone views us as having expertise in a given area, which means that, if you screw up the review, you will reveal yourself as an imposter (or so our brains tell us). And, for journals that copy reviewers on the decision letter, one way to tell if you’ve messed up and are an imposter is by comparing your review to that of the other reviewer(s). Rarely, I’ve been unable to figure out which was my review, because the reviews were so similar. (Phew, not an imposter!) But what about when the other reviewer notes things I missed? Clearly that means I’m an imposter!

Not necessarily.

For a long time, I viewed it as a failure on my part if the other reviewer caught something I missed. I felt like it indicated that I hadn’t been careful or critical enough. If we aren’t super critical, we aren’t good scientists, right? (I’m being facetious. I don’t actually believe that being harsh = being a good scientist. And it is definitely not the case that the harshest review is the best review!) But what about cases where the other reviewer raises concerns or criticisms that seem important and insightful and constructive. If I missed those, I failed as a reviewer, right?

Again, not necessarily. The reason relates to something covered in a recent blog post by Stephen Heard, where he talks about finding reviewers. In it, he says he only uses one of the reviewers suggested by the authors, and explains that is because:

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Guest post: A tenure debacle and the invisible support network that kept me upright

Note from Meghan: This is a guest post from my colleague, Gina Baucom.

 

There has been a procedural error in your tenure case at the college level. I’m going to recommend that we stop your tenure case this year, and redo it again, from the beginning, next year.

This is not what you want to hear when you are going through tenure. Unfortunately, this is what I heard at a meeting the Dean called with me last January, even though the department vote on my tenure case had been unanimous and positive.

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I couldn’t make it in academia without my invisible support network

I recently got some good work news. (Hooray!) When I heard, one of the first things I did was text a group of friends who are also academics. They have become an essential source of support for me. I wanted to tell them the good news, yes, but I also wanted to thank them. I had almost given up on this thing over the summer—I wasn’t sure it was worth the time I was investing in it, and thought it didn’t stand much of a chance. They told me it was worth it and gave me the encouragement to go forward with it. So, without them, this good thing may well not have happened.

And that’s just one example of a time when I benefitted from my invisible support network. Both in Atlanta and here in Michigan, I’ve benefitted immensely from this behind-the-scenes support. These networks help with specific situations: Is it worth applying for this thing? What do I do about this tricky work situation? I think this behavior by person X seems not okay—am I being overly sensitive? What do you think of the wording on this really important email—is it too strong? Did I screw up when I did Y? I can’t decide between A & B—can you help me think them through? There’s also the general venting and commiserating and celebrating and checking in on each other. These support networks aren’t visible to outsiders, but they feel essential to my ability to do what I do.

It’s possible that the title of this post is an overstatement—maybe I could make it without my behind-the-scenes support networks?—but I’m really, really glad I don’t have to. I don’t want people who will agree with everything I say, but I do want people who I know will be supportive, even if they’re challenging me.

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Stigma associated with seeking mental health treatment: do students think others are judging them more than they actually are?

Last year, I supervised Honors Thesis research by Morgan Rondinelli related to mental health in two introductory science courses at Michigan (Bio 171 and Physics 140). Morgan’s survey included two common screeners, one focused on symptoms of depression (the PHQ-8*) and one focused on anxiety symptoms (the GAD-7). The survey also asked about previous diagnoses, stress mindset, resource usage and knowledge, barriers to seeking help, and demographic information. Here, I will briefly summarize some of our findings, but I will especially focus in on the area that seemed the most novel: student views on stigma associated with seeking mental health care.

The tl;dr answer to the question in the post title is: it seems possible.

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Bonus Friday link: NSF’s Bio Directorate removes proposal submission limit for 2019

For the US folks, NSF’s Bio Directorate had an important announcement yesterday, removing the limit on the number of proposals someone can submit as PI or co-PI in 2019. Here’s part of the announcement:

Having listened to community concern and tracked the current low rate of submission, and following extensive internal consultation, BIO is lifting all PI or co-PI restrictions on proposal submission for FY 2019, effective immediately.

BIO recognizes that it is important to track the effects of the no-deadline policy on proposal submission patterns, to ensure that a high-quality review process is sustained. Therefore, we are seeking approval from the Biological Sciences Advisory Committee to establish a subcommittee to assist in developing the evidence base for any future policy changes that may be needed.

I think this is great news! And I completely agree with Mike Kaspari:

With public engagement, it’s also okay to start small

Yesterday, I had a post about how it’s okay to start small when it comes to learning R or any other new technical skill. Today’s post takes that same “it’s okay to start small” message and applies it to public engagement.

Sometimes, a colleague will ask about a recent public engagement activity my lab worked on. After I describe it, they sometimes say something like “I’d like to do more outreach work, but my lab isn’t as big as yours – I don’t have those people to help me!” Often, that is said with a sense of resignation that it won’t be possible for them to do outreach. Or perhaps the conversation centers around an upcoming NSF proposal, where a colleague is trying to figure out what they could propose for the broader impacts section, feeling like they want (or need) to propose something, but that there’s no way for them to do that if they are just starting out or haven’t done much public engagement in the past. In these conversations, my messages are:

  • it’s okay to start small, and
  • take advantage of existing opportunities.

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When learning R (or any other new task), it’s okay to start small: aim for improvement, not perfection

When I first thought about switching to R and doing reproducible data analysis, the idea was daunting. As a grad student, I couldn’t figure out how to even get my data into R. How would I figure out that plus mixed model analyses plus how to make figures in ggplot, with version control and a beautiful github rep for all of my work?! What I eventually accepted is: it’s okay to start small. Or, as a colleague of mine suggests: for any given project, aim to do one thing in R that you couldn’t before.

I’m not sure why I set the bar so high for initially learning R. When I was first learning how to knit (actually knit, with yarn and needles, not the R version of knit), I knit a square washcloth, not a sweater. So when learning R, why was I expecting I’d be able to start out with the coding version of knitting a sweater with multiple colors, a fancy pattern, and buttons?

File:Fair Isle knitwear geograph-3936603-by-Julian-Paren.jpg

Julian Paren / Fair Isle knitwear in the Shetland Museum / CC BY-SA 2.0 via wikimedia.org

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Guest post: Coding Club – trying to overcome the fear factor in teaching and learning quantitative skills

This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. In case you missed it, they wrote a wonderful guest post this summer on iPads and digital data collection in the field.

Ecology is a fast-paced science, with possibly hundreds of relevant papers published every week and new techniques and quantitative skills being developed all the time. It is easy to feel very behind and overwhelmed. The quantitative skills taught in undergraduate and graduate programs in ecology often lag behind those used in the literature. As ecologists at different stages of our academic careers, who have felt (and still do sometimes) pretty behind the eight ball in terms of quantitative skills, we wanted to do something about that for our students and peers. And that is how we came up with the idea of Coding Club.

How did it all begin?

Just about two years ago we had an idea. What if we set up an informal group and a website to teach key quantitative skills that could be useful to undergrads, grad students, postdocs, profs and ecologists working outside of academia? What if that website was built in a way that anyone could contribute tutorials or help to make the existing tutorials better? What if we taught people how to learn in their own working environment and how to develop their workflow using best practices in open science like version control from the very beginning? What if this content was aimed at people who felt afraid, anxious and behind in their own quantitative skills development. This was the beginning of Coding Club.

screen cap of the homepage for Coding Club; header says: Coding Club: A positive peer-learning community

The Coding Club website where we host all of our tutorials on data manipulation, data visualisation, modelling and more!

 

What is Coding Club?

Coding Club combines online and in-person resources to help teach quantitative skills to ecologists at all career stages. We have focused on trying to overcome “code fear” and “statistics anxiety”. Statistics anxiety – the worry about a lack of quantitative skills – and code fear – the fear of programming – can prevent people from learning. By building a sense of community around the development of skills, we hope to overcome the fear factor of ecology involving more code and math than people sometimes expect.

left panel shows six people posed, smiling at the camera; upper right panel shows a computer lab with people at work and someone at front; lower right shows three women talking and smiling

Part of the Coding Club team and snapshots of some of our workshops. Check out our team page for the full list of undergraduates, postgraduates and profs that have contributed to Coding Club! Photo credit for image on left: Sam Sills

 

Peer-to-peer teaching helps to reduce the fear factor

In Coding Club, we focus on peer teaching and interaction rather than having “trained experts” leading workshops as we feel people engage more when they are less intimidated. All of our teaching materials are developed by people who are actively learning data science skills at the same time as teaching them. We avoid hierarchy (though we love content on hierarchical modelling!) and encourage participation across different career stages from undergrad students through to PhD students, postdocs and staff. Moving away from the professor-student model and allowing everyone to engage as teachers and learners can be a pretty powerful way to break down barriers.

Coding Club covers a growing number of different quantitative skills

The Coding Club website contains a growing list of tutorials aimed at all levels of quantitative skills useful for ecologists and beyond. We cover topics from intro to advanced R tutorials, version control, data visualization to working with large datasets. We have a lot of R content but we don’t just do R! We are currently working on developing more tutorials using Python for process-based modelling and the Google Earth Engine for remote sensing analyses. We have been using the tutorials to teach in-person workshops at the British Ecological Society conference and at universities around the UK, but the tutorials are there online for everyone to use, provide feedback on or suggest revisions through GitHub. We are always looking for people to develop new content as well!

four badges, one for sharing quantitative skills, one for meta-analysis & bayesian statistics, one for spatial and population data, and one for pandas

A sample of the Coding Club tutorials, including a tutorial on how to make tutorials on GitHub. Data visualisation, mixed effects models, Stan models and more over here.

 

Quantitative learning should be active and not passive

We believe that the best way to teach coding and quantitative skills is through a problem-based approach that is question driven. We try to avoid approaches like ‘live coding’ as it encourages learners to be very passive with the subject matter and we believe this results in lower retention of the new material. To effectively learn a new skill, it is vitally important to know why you might want to learn that skill in the first place and to have a question that you want to answer to motivate you to learn. We also recognize that people learn in different ways and at different paces. In our in-person sessions, we encourage people to take as long or as little time as they wish to complete the tutorials. We believe this casual, non-compulsory and non-assessed nature of Coding Club also helps to reduce the fear and anxiety associated with quantitative skills.

 

Picture5

Coding our way towards finding out how population trends vary among different taxa, with cookies along the way. Not pictured: the standard error cookie. We forgot to make one, but of course we are all for reporting the uncertainty around effect sizes!

 

Quantitative skills are not hard – they just take some work to learn

We believe teaching quantitative skills is all about overcoming fear and building confidence. We try to avoid labeling skills as “hard” or “easy”, because we don’t want people labeling themselves as quantitative or not, or pre-judging the limits to their own capabilities. We aim to train people to be able to answer their own questions, resolve their own coding problems and seek out new skill sets independently. We are trying to teach people to train themselves beyond the timespan of a single workshop or course. Finally, we don’t think there is only one way to teach quantitative skills and promoting a diversity of approaches will reach the most people.

 

Coding Club has exceeded our expectations!

As of October 2018, the Coding Club website has received over 160,000 visits from over 73,000 unique IP addresses from over 180 countries. Our tutorials have been contributed by people from multiple universities (University of Edinburgh, University of Aberdeen, McGill University, Ghent University, Aarhus University) and used for quantitative training across several institutions so far (University of Edinburgh, University of Aberdeen, University of St Andrews, Queens University Belfast, Dartmouth College, Hebrew University, Calvin College, Centre for Ecology and Hydrology and more), and we are hoping to reach out further! If we can set up a network of people at universities and research institutes around the world who can work together to develop quantitative training from the ground up, then maybe we will all feel just a little less overwhelmed by our fast-paced discipline.

World map showing numbers of visitor, represented as blue dots. The dots are especially dark and big over the UK, but include visitors from around the world

The international audience of Coding Club – it’s been great to get feedback from people using our tutorials around the world!

 

The start of the new academic year feels like a fresh start. A chance to purchase some new office supplies, catch up on all the science missed over the summer, start a new work routine to enhance productivity and to set yourself some new challenges. Now that the term has started, maybe it is time for you to take the plunge and learn a new quantitative skill.

 

Are you a student or group of students wanting to increase your own quantitative skills? Are you someone who has a cool analytical technique that you want to share with your peers? Are you a prof. who wants to encourage your students and mentees academic development? Are you someone who feels like the quantitative training you got years ago is not enough for the ecological research today and want to brush up on your skills? Do you have thoughts on how we can improve quantitative training in ecology? If you answered yes to any of these questions, please comment below, check out the Coding Club website and get in touch if you are keen to join the team!