Comments | differenceblog: Queer @ Work

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Queer @ Work

Jun 06, 2007 08:30

Gay men and women have many challenges in the workplace. Some are similar; some are different. Chung and Harmon (1994) suggest that gay men are more likely to be interested in traditionally feminine careers than straight men, although they found that masculinity/femininity was not a predictor (as determined by BSRI). Adams et al (2005) did not ( Read more... )

lesbian, gay, eve adams, sexual orientation, women's work, latino, work, homosexuality, cultural differences, ethnic differences, barry chung, workplace, stereotypes, charmine härtel, gender roles, homosexual, bem sex role inventory, careers, lgbt, glbt, raymond nam cam trau, queer, lenore harmon, bsri

Comments 5

beowabbit June 6 2007, 13:42:16 UTC

Adams interviewed eight people; Nam Cam Trau and Härtel interviewed five.
Wow, I should seriously go into this field! You don’t have to have actual data to publish! I guess you get to footnote stuff “¹ These three guys we know, personal correspondence, 2002.” :-)

differenceblog June 6 2007, 13:46:14 UTC

Oh, it's data -- it's "qualitative" data. You do "content analysis" on "interviews".

The research has to start somewhere. When I put together my research proposal, I can cite these guys in my literature review, and then I don't look as much like I'm pulling stuff out of thin air.

astrogeek01 June 6 2007, 14:34:17 UTC

Sometimes, if you've got three photons, you can manage to say something useful in astronomy.

Small numbers statistics does not mean no statistics... ;)

randomguy3 June 12 2007, 17:07:51 UTC

That's not really the same. In a deterministic system, you (in theory) only need a sample of one. People are not deterministic, though, except as part of a large population.

Statistics tell you something about the population, not about an individual. Sure, you can get a probability of something being true of a particular person, but that assumes that you fix a certain number of attributes and randomly select a person from everyone having those attributes.

There are things called confidence intervals. Usually you want a 95% confidence interval. If a statistic is true with a 95% confidence interval, that basically says that in 95% of the populations the sample could have been take from (remember - you know nothing about the population you haven't sampled), the statistic will be true. [Caveat: this may be slightly incorrect if I am misremembering my A-level stats]. You need a large sample (both in real terms and in comparison to your population) to get that ( ... )

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