11–20 of 49 posts
Earlier quoted context omitted.
There was a story about Google and sex at the workplace, recently. So we are seeing both positive and negative articles about Google. While Google would like to portray itself as something akin to what Toyota was, naysayers would try to do the opposite.
This is the article on that topic: http://www.slate.com/blogs/business_insider/2013/09/20/sex_a...
As someone without a strong statistical background, this really sounds like "we got data that didn't agree with the point we were trying to make, so we tried a bunch of different ways to look at it until we found the one that matched our hypothesis".
Can someone explain to my why my reaction is wrong? I'm sure it probably is.
Earlier quoted context omitted.
There was a story about Google and sex at the workplace, recently. So we are seeing both positive and negative articles about Google. While Google would like to portray itself as something akin to what Toyota was, naysayers would try to do the opposite.
This is the article on that topic: http://www.slate.com/blogs/business_insider/2013/09/20/sex_a...
btw, the article tries to frame the issue as bad but if there are no sexual harassment suits, who cares? Google employees are smart enough to figure it out I suppose.
> “At first,” he says, “the numbers were not encouraging. Even the low-scoring managers were doing pretty well. How could we find evidence that better management mattered when all managers seemed so similar?” The solution came from applying sophisticated multivariate statistical techniques, which showed that even “the smallest incremental increases in manager quality were quite powerful.” As someone without a strong…
One of the big things I retained from my stats classes is the idea that, once you deviate from a pre-specified analysis technique, the strength of your conclusion is strongly diminished. Also, sophisticated statistical techniques are often less robust than simple ones. Maybe some other ideas apply that I can't think of off the top of my head.
On the other hand, the author may not have appreciated the statistical iffyness of that phrasing, and perhaps misrepresented the rigor of the actual analysis.
Earlier quoted context omitted.
This is the article on that topic: http://www.slate.com/blogs/business_insider/2013/09/20/sex_a...
Yes, I am sure the source really said that : "Inside Google, it's a Game of Thrones." /s btw, the article tries to frame the issue as bad but if there are no sexual harassment suits, who cares? Google employees are smart enough to figure it out I suppose.
Also, a lack of overt pressure does not mean there is no covert pressure being applied. Perhaps the new assistant knows what happened when the last one refused the advances and doesn't want to get demoted/fired. Placing subordinates in a position of apparent helplessness can be just as effective and harmful as real threats of violence, job loss or loss of status.
> “At first,” he says, “the numbers were not encouraging. Even the low-scoring managers were doing pretty well. How could we find evidence that better management mattered when all managers seemed so similar?” The solution came from applying sophisticated multivariate statistical techniques, which showed that even “the smallest incremental increases in manager quality were quite powerful.” As someone without a strong…
'For example, in 2008, the high-scoring managers saw less turnover on their teams than the others did—and retention was related more strongly to manager quality than to seniority, performance, tenure, or promotions. The data also showed a tight connection between managers’ quality and workers’ happiness: Employees with high-scoring bosses consistently reported greater satisfaction in multiple areas, including innovation, work-life balance, and career development.'
If their scores predicted those things, then they were measuring something real, regardless of whether they went looking for what they wanted or not. The question then becomes one of whether altering those scores alters the dependent variable or whether you've just created a correlation by doing evil to your numbers.
Which... they did look at their results down the line and I'd imagine they'd have looked at turnover, it'd seem really odd not to considering the other things they looked at and the obvious business case for doing so.
> “At first,” he says, “the numbers were not encouraging. Even the low-scoring managers were doing pretty well. How could we find evidence that better management mattered when all managers seemed so similar?” The solution came from applying sophisticated multivariate statistical techniques, which showed that even “the smallest incremental increases in manager quality were quite powerful.” As someone without a strong…
That's actually how one performs experiments and develops theories in the social (and managerial) sciences, such as economics: given data, and a hypothesis, and try to develop a model that fits both and offers demonstrable predictive power for future circumstances and datasets.
Of course, one's math might be wrong, and the model may still be falsified by future data. But that's what makes social science interesting.
We've seen a lot of articles on Google about the product side. This is the first I've seen in a while on their internal management. This is important for them to nail to avoid becoming the next IBM or Microsoft. (Both did well in their own ways, but Google aspires to be neither.) Are there any GOOG alums who would like to comment on the article? My impression is that data driven HR is a good start, but that it can le…