Guest Blog: Collaborative Research in Odum School of Ecology

Note from Chao: John and I investigated the research focus and collaboration of faculties at Odum School of Ecology. I posted the analyses on the diverse research areas a few days ago. John has graciously agreed to analyze the collaboration network and write this guest blog. Here are what he has to say about the collaborative research in Odum School of Ecology.

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Diverse Research Areas in Odum School of Ecology

John Vinson and I had an interesting conversation on how to visualize the collaboration and research focus among labs on our drive back from Atlanta. One idea is to classify labs based on similarity in research focus. We can look at the journals each lab published in, assuming that people publish in the same journal share more similar research interests. Another idea is to look at the collaboration structure. We can examine the number of coauthored paper each faculty have with another faculty. This blog post will be about the first idea. John will write a guest blog on the collaboration network. Stay tuned!

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The Practice of Plotting Everything Against Everything Else

I have seen the following scenario a lot in ecological research. After we collected bunch of data from field, we often explore the data first. We plot response variables of interests against all the possible predictors we measured. Some may show a significant relationship. Some may not. Those significant (in a statistical sense) relationships end up in papers while those insignificant ones don’t. I have certainly done things in this fashion before. But as I reflected the approaches of doing research in general, I start to feel bad about doing this.

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Proportion of Female Graduates from Odum School of Ecology Increases Over Time

When I was searching an email last night, I came across an old email calling for graduate student symposium speaker nomination. A list of Odum School of Ecology alumni was attached. I decided to analyze how the gender proportion of graduates changes over time. Here are what I found.

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Dealing with Overdispersion in Generalized Linear Models

I talked with my fellow graduate student Rachel Smith this morning on analyzing her species abundance data. The goal is to use environmental variables to predict the abundance of species. One particular concern she has is overdispersion. That is, the variance of data is more than predicted/constrained by the model. The problem of overdispersion often occurs in logistic regression or poisson regression.  Here are some thoughts I have on dealing with overdispersion.

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Why Is R2 Not a Measure of Association Strength?

One of the most common mistakes I saw when teaching simple statistics in introductory ecology lab is a misinterpretation of R2 from a linear regression. Students tend to use R2 as a measure of association strength. That is, a high R2 indicates a strong correlation. Although we have emphasized to students that R2 is a goodness of fit measure, not a measure of association strength, I have never found a convincing way to explain to students why. John Vinson and I had a discussion on this issue today and this is an attempt to explain why.

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A Complete Work Flow of Fitting a Simple Linear Model

I recently discussed with one of my fellow graduate students testing linear contrasts from a two way ANOVA model. He was a little confused about implementing it in R and interpreting some of the model outputs. As I discussed in an earlier post, we often learn statistical methods piece by piece without the complete workflow in class. Hence, this is the first of a series of blog posts I intend to write to show examples of complete workflow of data analysis. For those familiar with statistics, this is very simple. But I hope this is useful for those who are not so familiar with it.

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The Ideal Introductory Statistics Course for Ecologists

There has been a lot of discussions on offering an introductory statistics course as part of the graduate students’ curriculum at Odum School of Ecology over the years. A few different kinds of introductory statistics courses were offered in recent years. I heard quite a few discussion on the strengths and weakness of these courses. When I served as the graduate student representative in the graduate program committee two years ago, the discussion on offering a introductory statistics course often pop up in the conversation. I have been wondering what is the ideal statistic course for ecologist.

To be clear, we as ecologists face complex data. Almost none of the data we encounter in real research are the standard textbook examples. Expecting one or two classes to solve all these problems is unrealistic. But can we design an introductory statistics course to better prepare us for these complex problems ? If we were to require one statistics course for all ecology graduate students, how should that course be?

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Why Do Faculties and Students View Presentation Differently?

I gave a talk at the Graduate Student Symposium at Odum School of Ecology last week. Each talk was judged by several faculties and students. Each judge has a judging form, consisting of 13 scoring items. Each item has a score from 1 to 5. The maximum score is 65. The 13 scoring items are grouped into three categories: content, presentation and delivery. Each judge will identify himself/herself as faculty, post doc or student. I got 9 judging forms back, 4 from faculty, 1 from post doc and 4 from student. I noticed some differences between how faculties and students judge the talk. Here are some more exploration.

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