Striking a balance in data collection

A big part of my research time is spent on violence against women, gender-based violence, domestic violence, and harmful traditional practices. Though sometimes all whipped into a category of “women’s issues,” I’ve argued before that these are problems that everyone should care about, that they exert severe effects on our health and well-being as a society, emotionally, physically and economically.

Currently, I’m mired in two data collection projects, both with various degrees of hopelessness. I’ll write more later about my time in Caracas, but suffice it to say for now that there simply isn’t data available on issues like the ones I mention above. Or if it is available, no one’s going to give it to me. No surveys, no police data, no statistics on hotline use, nothing. We don’t know anything.

Conversely, in a meta-analysis of programs for adolescent girls that I’m writing with a colleague, my coauthor came upon a study suggesting that in order to correctly assess prevalence of Female Genital Mutilation (FGM) we should submit randomly selected female villagers in rural areas to physical exams.

I was shocked and disgusted when she sent me the study. I don’t doubt for a minute that the most accurate way to gauge prevalence of FGM is to randomly select women and examine them, but seriously? I am astounded that no one thought through the psychological consequences of women who have already been victims of gender-based violence being examined by a foreigner who thinks they are lying about whether they’ve been cut.

These days, it’s a good reminder for me that in collecting data there is such a thing as too much, and such a thing as not enough. It’s all about striking a balance.

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