The issue of diversity has once again been raised in relation to online discussions of psychology. Others have talked about why it may happen and the consequences of it. I have nothing to add about those areas so I’m not going to discuss them. The purpose of this post is to analyse the diversity of my main contribution to social media discussions that I have total control over: the psychological methods blog feed. How many blogs by women are there? How many non-white authors? How many early-career researchers (ECR’s)? [note]Of course there are other diversity issues I could focus on but these are the ones that have been discussed the most frequently on social media.[/note]
Looking at gender first (simply because that was the issue that started this) I coded the gender of the authors of the blog to male and female. If it was a blog with a wide collection of authors I excluded them from the analysis (I’m looking at you JEPS). If there were multiple authors, I coded them individually (hence why the total number is greater than the number of blogs in the feed). So how many male and female authors are there? Compared to the very low bar set by a previous analysis it’s not terrible (mainly because n>0). But it could (should) be better. Here are my results:
For the ethnicity of the author (white or not) [note]Some may (fairly) take umbrage with the fact I’m only look at one ethnicity and bundling all the other ethnicities together, or the fact that “white” doesn’t properly encapsulate the differences between white Europeans, white Americans etc. I’m doing this for the sake of simplicity sake but if you think this is unfair then I’m very happy to discuss it.[/note] I judged whether they were white by their social media profiles and photos of that person. Again, if the blog contains posts by a large collection of authors then I excluded them and I for multiple authors I coded them individually. The results aren’t great:
Coding for whether the author was an ECR or not I used the definition provided by the Research Excellence Framework, which states an ECR is someone who has been hired to teach and perform research within the past 5 years (REF, 2014). To ascertain whether the blog author was an ECR I either: consulted their public CV’s; or asked them if they believed they qualified (according to the above definition). The descriptive statistics are:
So what does this tell us? That the vast majority of blog authors in my feed are male, white, and not ECR’s. Not particularly diverse. As I said, the purpose of this isn’t to show that I should be marched naked through the streets with a nun chanting “Shame!” behind me while ringing a bell. It’s to recognise I could do better and ask for your help. I want to increase the variety of voices people hear through my blog feed, so do you have any suggestions? The blogs don’t need to focus exclusively on psychological methods but they do need to discuss them. Feel free to comment on this post, contact me via Twitter (@psybrief) or Facebook (search PsychBrief on Facebook and you’ll find me), or send me an email (www.psychbrief@gmail.com). Any other names put forward are much appreciated. Please check the blog list (https://psychbrief.com/psychological-methods-blog-feed/) before adding to see if I haven’t already included it.
References
Ledgerwood, A. (2017). Why the F*ck I Waste My Time Worrying about Equality. [online] Incurably Nuanced. Available at: http://incurablynuanced.blogspot.co.uk/2017/01/inequality-in-science.html. Accessed on 25/01/2017
Ledgerwood, A.; Haines, E.; & Ratliff, K. (2015). Guest Post: Not Nutting Up or Shutting Up. [online] sometimes i’m wrong. Available at: http://sometimesimwrong.typepad.com/wrong/2015/03/guest-post-not-nutting-up-or-shutting-up.html. Accessed on 25/01/2017
Research Excellence Framework (2014). FAQ’s. [online] Available at: http://www.ref.ac.uk/about/guidance/faq/all/. Accessed on 25/01/2017
Code
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| Gender | |
| #Create a string called "BlogName" with all the names of the different blogs in it | |
| BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens") | |
| #Create a vector called "BlogGender" with a string of numbers to represent either female, male, or N/a | |
| BlogGender<-c(2,2,2,2,2,2,2,2,2,2,1,1,2,2,1,2,2,2,2,2,2,3,2,1,2,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2) | |
| #Turn BlogGender into a factor where 1 is labelled Female, 2 male, and 3 N/a | |
| BlogGender<-factor(BlogGender, levels= c(1:3), labels =c("Female","Male", "N/a")) | |
| #Create a data frame of the variable BlogName by the variable BlogGender | |
| Blogs<-data.frame(Name=BlogName, Gender=BlogGender) | |
| #Because I'm a peasant and can't work out how to create a graph straight from the data frame I created (though #I'm pretty sure I can't in its current form and don't know how to transform it into something that can be #mapped to a graph) I created one vector and one string with the number of male and female blog authors after #counting them up | |
| Gender<-c("Female", "Male") | |
| Frequency<-c(7,34) | |
| #Data frame of the vector and string | |
| Blogsdata<-data.frame(Gender,Frequency) | |
| #Graph object of the data frame with gender as the x axis and frequency as the y, coloured according to the variable Gender | |
| Gender_Graph<-ggplot(Blogsdata, aes(Gender, Frequency, fill=Gender)) | |
| #Put bars on my graph object and give it a title | |
| Gender_Graph+geom_bar(stat="identity")+ ggtitle("Number of female blog authors compared to male blog authors") | |
| Ethnicity | |
| BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens") | |
| Ethnlist<-c(1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1) | |
| BlogEthn<-factor(Ethnlist, levels= c(1:3), labels =c("White","Non-white", "N/a")) | |
| Ethn<-c("White", "Non-white") | |
| Frequency<-c(39, 2) | |
| Ethndata<-data.frame(Ethn,Frequency) | |
| EthnGraph<-ggplot(Ethndata, aes(Ethn, Frequency, fill=Ethn)) | |
| EthnGraph+geom_bar(stat="identity")+ ggtitle("Number of non-white blog authors compared to white blog authors") | |
| ECR | |
| BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens") | |
| ECRlist<-c(1,2,2,3,1,1,2,2,1,2,1,1,2,2,2,2,2,2,2,2,2,3,2,1,2,2,2,2,2,2,2,2,2,2,2,1,2,1,2,2,2,2) | |
| BlogECR<-factor(ECRlist, levels= c(1:3), labels =c("Yes","No", "N/a")) | |
| ECR<-c("Yes", "No") | |
| Frequency<-c(9, 31) | |
| ECRdata<-data.frame(ECR,Frequency) | |
| ECRGraph<-ggplot(ECRdata, aes(ECR, Frequency, fill=ECR)) | |
| ECRGraph+geom_bar(stat="identity")+ ggtitle("Number of non-ECR blog authors compared to ECR blog authors") |
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