When I went to the APS conference in San Francisco last year, I got to hear Janet Hyde talk about the gender similarities hypothesis. Broadly, she argues that most gender differences (i.e., men vs. women) in psychological variables tend to be small in size. She used meta-analysis — statistically summarizing the results of lots of published research — as a method of testing her hypothesis. I thought it was fascinating stuff and a great talk, so I wanted to incorporate some of her research into intro psychology. Since I’ve been sprucing up the intro psych section on sex and gender, I thought it would be interesting to include Petersen and Hyde’s (2010) meta-analysis on gender differences in sexual attitudes and behaviour. Broadly, it found that there are a few large sex differences (e.g., men watch more pornography and masturbate more than women), but consistent with her general argument, most differences were very small in size (i.e., smaller than d = .20) and men and women are more similar than different. However, the paper itself doesn’t present the results in a very PowerPoint friendly way, and certainly not intelligible to freshmen students:
So, with that in mind I decided to make a plot up to better visualize the data. The degree of uncertainty displayed by the 95% CI is important … but most in the class don’t actually have any statistics background. So I decided to teach them what “cohen’s d” was first, then create a graph that showed off those numbers. I also taught them that values less than .20 are essentially negligible, so I wanted to highlight that on the plot. I decided on a lollipop plot, which is a bit more appealing than a bar plot.
library(ggplot2) library(ggthemes) library(ggExtra) #Order the variables in Rank Order mydata$name <- factor(mydata$name, levels = mydata$name[order(mydata$d)]) #Create the Plot ggplot(mydata, aes(x=name, y=d, label=d)) + geom_point(stat='identity', fill="black", size=6) + geom_segment(aes(y = 0, x = name, yend = d, xend = name), color = "black") + geom_text(color="white", size=2) + labs(title="Gender Differences in Sexual Behaviour", subtitle = "Negative numbers = more common in women; positive numbers = more common in men", x = "", y = "cohen's d (effect size)") + ylim(-1, 1) + theme_bw() + geom_hline(yintercept = 0, linetype = "solid", color = "grey") + geom_hline(yintercept = -.20, linetype = "dashed", color = "grey") + geom_hline(yintercept = .20, linetype = "dashed", color = "grey") + coord_flip()
Overall, I think the plot turned out pretty clear and was a lot more appealing than just putting a table of numbers for a more dynamic presentation. The ordering from largest to smallest helps a lot to guide the eye, and the dotted grey lines show the range of “trivial” effect sizes. So there are some substantial gender differences, but the effect sizes are actually a LOT smaller than you’d expect! For context, a cohen’s d of ~.60 would look like this (using this site to generate this quickly):
So phrased another way, if you selected a random man and a random woman, there is ~67% chance that the man will watch more pornography than the woman (the “common language effect size“). That’s not insubstantial, but also probably a lot smaller than a lot of people would think! Many of the effect sizes are vanishingly small. For a Cohen’s d of 0.20, there’s only a 55% chance that a randomly selected man will be more likely to engage in the behavior than a randomly selected woman. For that matter, men and women are actually more similar than they are different on all of these variables, if you consider the percent overlap among the distributions. Which broadly, is what Dr. Hyde’s point has been all along.
Code and data available on my OSF page.