Limitation of Research Data Blog Post – Well-behaved women rarely make (scientific) history

Why is there such a great gender disparity in the STEM field? Many different researchers and authors list sex differences in spatial skills and cognitive abilities as the reason to why there are more men than women in the STEM field. In Melissa Hines’ essay, “Do Sex Differences in Cognition Cause the Shortage of Women in Science,” it is proved sex differences in cognition are not the cause of the lack of women in the science field nor is it linked with hormones or genetics. Hines states that expectations and behaviors of society’s reaction to the lack of women in the STEM field are better reasons to explain the lack of women in the STEM field. After reading several articles on this topic, I have started to question whether my original views regarding sex differences and their role in workforce gender disparity are correct or valid. If cognitive sex differences do not seem to be linked to hormones or genetics, are we taking correlated data to assume causation? Hines’ essay makes me realize there is not enough proof regarding more men being in the STEM field due to cognitive sex differences. This is due to the inability for researchers to look at the larger picture in relation to possibly contradicting data presented. Instead of taking in all the data that is presented, they only look at specific pieces of data. This leads to many assumptions that should not be made as the reasoning behind sex differences in cognitive abilities.

Retrieved from sott.net

Researchers wrongly assume that cognitive sex abilities are genetic by looking at small pieces of data in detail instead of looking at the larger picture. An example of such is how researchers make the assumption that gender differences automatically conclude a link to genetics. However, this assumption should not be made because if so, we should see the same results across all races, economic background, and age. But this cannot be found, as this problem is usually seen in people in higher socioeconomic families. Hines shows how sex differences in height have a standard deviation of 2.0 while cognitive sex differences have a standard deviation of 0.9 units. This proves that there isn’t that great of a difference between male cognitive abilities to that of female in comparison to other traits. However, researchers tend to take the standard deviation in cognitive sex differences as large because a standard deviation of 0.8 units or above is seen as large. They tend to take this “large” standard deviation and link it to higher test scores in men and lower test scores in women. However, when we look back and compare that value to the sex differences in other traits such as height, we can see that the differences are actually very small. Why is it that we take such a comparatively small difference in cognitive abilities and conclude that it can have such a great reaction in the work force? This is because researchers only looked at the small pieces in the puzzle, not the bigger picture. They have cherry-picked research to show causation where it does not exist.

In addition, Hines also asserts that biases are often present in these research projects. From the selected group of people used for research on whether cognitive abilities linked to hormones, Hines shows how the results prove that the data correlates. However, it cannot be proved that sex differences are hormonal and that men have greater cognitive abilities because there is a selection bias in who gets chosen for these research projects. Oftentimes, the pool of candidates used for these research projects are people of higher intelligence. By using a limited variety of people, the data received will seem to correlate; however, if we take people with median intelligence levels, the intelligence test scores vary and tend to show different trends. In David Lubinski and Camilla Persson Benbow’s essay “Sex Differences In Personal Attributes For the Development of Scientific Expertise”, they show a graph (Ceci and Williams, 81) showing girls in the median IQ score band are scoring higher on tests than that of boys. The data taken if a greater variety of people were included in the research would probably show something different. The data many researchers use correlates due to the selection bias, but with insufficient diversity in research participants, it is hard to conclude that the data proves causation.

In conclusion, it is improper of researchers to conclude that cognitive sex differences are the cause of the lack of females in the STEM field because their research is cherry-picked and includes biases. Researchers must look at the larger picture to eliminate assumptions wrongly made and come to stronger conclusions that can be made from using all data found.

Sources:

Ceci, Stephen J., and Wendy M. Williams. Why Aren’t More Women in Science?: Top Researchers Debate the Evidence. Washington, DC: American Psychological Association, 2007. Print.

Rachel Lantz says:

October 8, 2015 at 8:17 pm

Hi Rachel! I enjoyed the fact that you bring up the point that this data regarding cognitive sex differences, or lack thereof, is full of biases. As potential scientists, we all must be able to understand when we are making underlying assumptions. I would be interested to know if you do not believe that cognitive differences lead to the underrepresentation of women in science, what else could you attribute to the lack of women in scientific fields?

Yang Yang says:

October 8, 2015 at 8:58 pm

Hi, Rachel! It is always a pleasure to read your logical essay. There is bias in every scientific research, such as selection bias, experimenter effect, subject effect, etc. In the articles of the book, evidence for a biological structure in brain for sex difference is inconsistent, but researchers tried to make a conclude. And the research result is misleading because it ignores opposite data. We should be wary of some research result because it may be biased by researcher who intend to conclude his or her hypothesis.