I finished reading the much-discussed Emily Glassberg Sands study on sexism in the theatre this weekend (and even had a phone conversation with Sands afterward). I must report, however, that the conversation grew "contentious" (as Sands herself put it) as I pressed her on issues regarding the final section of her paper - the part devoted to Broadway productions.
In a nutshell, as I read her paper, I began to have doubts about her data - both her sample of "female-written shows" (which seemed to include musicals which had a female book writer, but a male composer and lyricist), and her "proxy" for the profitability of the shows she was considering. It hadn't really come clear to me when I viewed the PowerPoint presentation that Sands gave in New York that she didn't actually have any hard data about the profitability of these shows (although on a second look, you could probably construe this from the fine print of her tables, and she may have mentioned it during the actual proceedings).
But this issue struck me as key to her argument that producers had ended the runs of female-written shows "earlier" than those of male-written shows. After all, a simple explanation for this might be that producers ended the female-written shows because they weren't as profitable as the male-written shows - perhaps they had higher production costs, for example, which cut into their weekly revenues (or a major star left the production, etc.).
But Sands didn't have this data (which in the text of her study she admits openly - it's hard to come by). Instead, she estimated the variable of production cost by a "proxy" of show type (musical, straight play, one-person show, and "exception"). I couldn't make out from her study, however, how accurate these proxies might be - and I wondered, frankly, if the method she was using was truly valid given that she lacked this hard data.
I therefore felt I was looking at a study in which the final section relied on a possibly questionable sample and a problematic proxy for a central variable. My conversation with Sands, however, yielded little real insight into these issues - perhaps, as she often reminded me, because I'm not as trained in statistical analysis as she is (my "training" goes no further than the standard introductory college course, taken some twenty years ago).
So I'm throwing open an invitation to any reader with a sophisticated statistics background - would you care to more thoroughly fisk Ms. Sands's thesis? I can email you her study, which I've downloaded (contact me at email@example.com), or you can find it here. If my doubts about her methodology turn out to be ill-founded, of course, I'll happily publish those findings. (It's entirely possible, too, that if hard data on profitability were available, it would back up Ms. Sands.) In the meantime, I've emailed Ms. Sands my questions in the hopes that the explanation which eluded us in conversation can be found via printed text.
I'll be following up later this week, so stay tuned . . .