Session 2 got done today. We covered a serious lot of ground, even though it may not seem so at first glance.
Doing basic survey design principles, construct basics *and* questionnaire design all in one go in 110 minutes... each of those topics would merit at least a whole session all by itself ideally...
Update:
1. There will be a group homework assignment for this class. As you can guess it will involve your designing a survey around a construct of interest and programming it into any web survey software. This will be a group assignment. I will put up details later after these 5 rush-days of teaching are done...
Today's individual assignment is filling up this survey. You will have until tomorrow evening 10 pm to so. But then, tomorrow, again, there will be more surveys to fill...
2. Recall the mini-caselet we discussed in class today. Some of you asked for more info.
The 'Moneyball' approach to hiring CEOs
It was the lesson of the best-selling book-turned-movie, Moneyball: Don’t throw money at big-name baseball players or judge future performance by purely physical attributes. Assess them, instead, by more relevant measurements, like their on-base percentage.Wharton professor J. Scott Armstrong and Philippe Jacquart of EMLYON Business School in Écully, France, say the same principles can be applied to choosing corporate executives. In a recent paper, they challenge the popular belief that higher pay leads to selecting chief executive officers who will outperform their lower-compensated counterparts.
[...]Instead of throwing money at “superstars,” companies would be better served by using quantifiable measures to pick the right CEO, according to recent Wharton research.
Well, that should go some distance in answering whether Moneyball principles could be applied to hiring for more managerial positions. But reassuringly, the hires will all be human only. Machines still cannot hope to do a CEOs job. Yet.
3. Another article from a recent Economist issue and (ominously?) titled "The future of jobs" has this to say:
A new wave of technological progress may dramatically accelerate this automation of brain-work. Evidence is mounting that rapid technological progress, which accounted for the long era of rapid productivity growth from the 19th century to the 1970s, is back. The sort of advances that allow people to put in their pocket a computer that is not only more powerful than any in the world 20 years ago, but also has far better software and far greater access to useful data, as well as to other people and machines, have implications for all sorts of work.Well, tech 'progress' cannot be stopped I guess. But its the distribution of reward that had the Economist (and consequently me too) all worried. Only. See below.[...] Ten years ago technologically minded economists pointed to driving cars in traffic as the sort of human accomplishment that computers were highly unlikely to master. Now Google cars are rolling round California driver-free no one doubts such mastery is possible, though the speed at which fully self-driving cars will come to market remains hard to guess.
Even after computers beat grandmasters at chess (once thought highly unlikely), nobody thought they could take on people at free-form games played in natural language. Then Watson, a pattern-recognising supercomputer developed by IBM, bested the best human competitors in America’s popular and syntactically tricksy general-knowledge quiz show “Jeopardy!” Versions of Watson are being marketed to firms across a range of industries to help with all sorts of pattern-recognition problems. Its acumen will grow, and its costs fall, as firms learn to harness its abilities.
The machines are not just cleverer, they also have access to far more data. The combination of big data and smart machines will take over some occupations wholesale; in others it will allow firms to do more with fewer workers. Text-mining programs will displace professional jobs in legal services. Biopsies will be analysed more efficiently by image-processing software than lab technicians. Accountants may follow travel agents and tellers into the unemployment line as tax software improves. Machines are already turning basic sports results and financial data into good-enough news stories.
Jobs that are not easily automated may still be transformed. New data-processing technology could break “cognitive” jobs down into smaller and smaller tasks.
4. How do the economic spoils get split up in the coming years? Who gets what share of the prosperity pie? And why?
Yet some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently. They start from the observation that, across the rich world, all is far from well in the world of work. The essence of what they see as a work crisis is that in rich countries the wages of the typical worker, adjusted for cost of living, are stagnant. In America the real wage has hardly budged over the past four decades. Even in places like Britain and Germany, where employment is touching new highs, wages have been flat for a decade. Recent research suggests that this is because substituting capital for labour through automation is increasingly attractive; as a result owners of capital have captured ever more of the world’s income since the 1980s, while the share going to labour has fallen.At the same time, even in relatively egalitarian places like Sweden, inequality among the employed has risen sharply, with the share going to the highest earners soaring.
So who might be the winners and losers in what is surely coming? Here's a clue
There will still be jobs. Even Mr Frey and Mr Osborne, whose research speaks of 47% of job categories being open to automation within two decades, accept that some jobs—especially those currently associated with high levels of education and high wages—will survive (see table). Tyler Cowen, an economist at George Mason University and a much-read blogger, writes in his most recent book, “Average is Over”, that rich economies seem to be bifurcating into a small group of workers with skills highly complementary with machine intelligence, for whom he has high hopes, and the rest, for whom not so much.
5. The good news? The future for bright Business analytics people who combine non-standardized inputs (such as those from exploratory and/or qualitative work on the demand side) with machine intelligence is bright. When all is done, chances are you will belong to that select group. Change is coming whether we like it or not. The best we can do is to be better prepared. And that we are already doing...
OK, this part stretched longer than I intended. Will update and complete this post (or maybe have a second post to continue).
For now, I'll sign off. See you in class today for session 3 (Qualitative Research and Experimentation basics).
Sudhir
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