First of all, it should be pointed out that this study, that they are talking about, was conducted in southern spain. Perhaps if they had sampled women throughout europe it would be more valid, but ONLY the southern area of Spain? I really don't think that's a very valid sample for all women in the entire world. (Unless Southern Spain is the most internationally representative location in the word... or something.) I don't think the results can be extrapolated to the world. I know that lots of studies are done on college campuses and then extrapolated. But this study in particular deals with highly cultural issues. There is still a lot of variation on in the world on gender relations. I've heard, in the UK for instance (See this BBC "Quiz"), that women have a tougher time of it than in the US.
Also, I don't like that they jumped to a "queen bee" conclusion and plastered that all over the times online article. They do mention the other options, that men are percieved as better leaders by social bias in passing. It seems to me that there needs to be some clarification, is it a concious bias? or an unconcious bias? are women intentionally putting the women down? or do they just happen to rate the men higher? How much of this is social bias, and how much is getting ahead? But it turns out that this is a FICTIONAL company, the women don't work there, they are just evaluating candidates based on a description of the job and a CV.
So how could it be a queen bee issue at all since the women will absolutely NOT be competing with the candidate?
It isn't, the most IMPORTANT sentance in this entire article was the following, and it was buried at the bottom of the article:
They also assessed how likely they might be to receive promotion and were askedSo, the study is based ENTIRELY on stereotypes, because the participants in the study were TOLD to USE stereotypes in making their evaluations!!!!!!!!!!!!! It seems to me that a more valid conclusion than the Queen Bee conclusion would be that women have less positive stereotypical views of other women than men do. Or perhaps that men overcompensate for those views when evaluating women.
to take into account stereotypical traits of men and women such as sensitivity
or aggression.
It interesting to me that Dr. Helen picked up on the Queen Bee aspect, and didn't critically evaluate it, or look for other possible explainations. All that this study does is confirm what many people already believe about women, that we are mean and catty and hate eachother. Thank you Mean Girls. And you know what, that is true, some women are mean and catty and hate eachother. Some Men are mean and catty and hate eachother too, hello politics. It is just that when Men do it they are "competitive" and when Women are mean they are "catty."
All this study is doing is perpetuating stereotypes without giving us any real explaination for the phenomenon. Really, is that helpful? The matriarchy indeed, more like patriarchal views affecting how women view eachother.
UPDATE: Title fixed because I am blind to my own typos. Thanks Chris.
6 comments:
nice post and nice new blog front end
Thanks Kav, it seemed time for a change
Nice job tearing that study apart.
So is statistical analysis your profession or a hobby? I checked back at the beginning but couldn't find an explanation.
An interesting quote (which you've probably already heard) that I heard from a professor who taught statistics was, "Statistics don't lie. Liars use statistics." I didn't realize at the time how true those words were.
I think you misspelled your title Shi.
Ded,
It's my profession. I'm a big dork and I like it a lot.
I get frustrated by the fact that not a lot of people understand statistics and so what should be a useful tool ends up being used against the common good. (just like your quote)
My favorite stats quote is from The Importance of Being Earnest: "The number of engagements here seems to be considerably above the proper average that statistics have laid down for our guidance."
It's my profession.
That's cool.
I get frustrated by the fact that not a lot of people understand statistics and so what should be a useful tool ends up being used against the common good.
Hence the blog. Got it.
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