> our research showed that quality of leadership received virtually no mention when people described meaningful moments at work, but poor management was the top destroyer of meaningfulness
Spolsky:
> Administrators [code name for managers] aren’t supposed to make the hard decisions. They don’t know enough. All those super genius computer scientists that you had to recruit from MIT at great expense are supposed to make the hard decisions. That’s why you’re paying them. Administrators exist to move the furniture around so that the people at the top of the tree can make the hard decisions.
I follow an imaginary and totally-not-backed-by-science model whereby I view managers as a force multiplier on their teams. The number can be anything between 0 and 10. In this model, a PHB[1] hovers between 0.2 and 0.8 (it depends on how well the team can ignore him/her), whereas the average manager hovers somewhere between 0.8 and 1.2; their effect on their teams is neutral, give or take. Good managers are between 1 and 3 -- they can double or triple their team's output -- whereas excellent managers are 3 and up. Such managers also tend to possess very strong leadership skills.
Using your system, as a manager, if you're not above 1 you should work hard to never ever go below 1.
That's the point Spolsky is making, I think. It's a fair point and rooted in a lot of evidence, but somewhat limited, because as you say, there can be great managers whose experience and insight multiply their potential.
What types of things can a good manager do that increases an output by triple? That seems incredibly high to me. The little bit of research that's been done looking at how managers increase employee output have found the vast majority of force multiplication comes from training.
Being a project manager I have to agree. You seem to do your job best when you act as the assistant of the others, not when you treat them like they are your assistants.
And as funny as it sounds in contrast to the 'Murican Movie Manager, from that kind of attitude even shy people can gain power. When you work for others well being they are more eager to consider your opinion and follow your guidance as well. (That just because of that second quote making it sound like the manager should be the engineers "bXXch", it's not the case)
I like that attitude very much and I think it can be extended to everyone. As someone who's straddling the line between developer and manager I've sometime found that difficult but try to be as supportive as possible. The project managers where I work all seem to think developers are children that need a babysitter. The lack of respect is infuriating.
In the words of Lao Tzu: "A leader is best when people barely know he exists, when his work is done, his aim fulfilled, they will say: we did it ourselves."
This just sounds like Taylorism is responsible for a lot of disgruntled employees. But most corporations hardly care about unhappy employees outside leadership positions unless it starts to actually impact the stock value somehow. Then comes the superficial means of raising morale and the studies for why people are so disengaged, and the most serious of questions and suggestions are never raised.
Workers often blame their superiors for lack of motivation or inspiration, but that wouldn't account for the good workers who show up to work inspired already. They find it before work, and if they can't find it, they move on.
They don't stick around and dig for it. That's why you want people who have other options to work for you, and when you work for others, you want to know you could be doing something else. Meaning is defined from the choices we exercise. Why here instead of there is what is meaningful.
No one can tell you how to live your life. Similarly, no one can tell you why you're doing your job. You need to figure that out yourself, and seeking it should involve finding other jobs, not just repeatedly digging at the same spot.
Good teachers and good managers alike can help you immensely if you already have your reasons. They provide a synergy. And for that, they need you to be inspired and be passionate. Synergy fails if either is void. So if you show up inspired to a boss who isn't, that's a sign you may need to revisit your choices also.
Eventually meaning always comes. Maybe it finds us. But if you want to make the most of it, never wait and never settle. All one needs is the courage to move on.
This article strikes a chord for me. I also read a book sometime back, called Flow [1] by Mihaly Csikszentmihalyi which draws similar conclusion and is good, quick read.
And then I feel sad, because all the effort to seek that personal connect with work at my current workplace, have been taken away by a disconnected and immature middle management, which only works for meeting the goals and deadlines given to them...
Ever since reading http://strikemag.org/bullshit-jobs/ I have been thinking about this. One problem with this is the definition of 'meaningful', it is not very concrete. But I think the phenomenon is true - more and more work feels like doing something unnecessary (bullshit). I don't agree with Graeber explanation why this happens - which is basically a grand conspiration by all business owners to give worthless work to people so that they don't revolt. I think it is the result of the growing complexity of our work - we encounter more and more rules that we suspect to be stupid. Many of the rules are stupid indeed - based on some random past events, but many are not - they are just covering some cases that we have never thought of. It is easier to assume that the rules are stupid than admit incompetence. But as the complexity grows less and less people understand the rules, the bad rules are not revised and it all goes in a vicious circle.
I've seen the scenario where every time something goes wrong a new rule is handed down from on high, eventually you get to the point where you aren't allowed to prepare food in the kitchen or use the shower to have a shower or more importantly you aren't allowed to do the work you are there for.
People in power almost always try to increase their level of control, essentially centralizing the power structure of the entity. At some point of time the entity is completely inefficient and collapses under its own weight (rules, bureaucracy).
You can see this everywhere where power can be centralized: companies, organizations, nation states.
Ironically, successful leaders these days often do the opposite of leading, they decentralize, making the company much more efficient (assuming there are still sub-entities that can/want to be efficient).
Once thing that always strikes me with those studies, and a lot of other medical, economical or psychological studies and tryouts are the sample size...
A study that interviews 135 people? And this is seen as significant? It seems to me saying something like "we tried this on 150 people, and we saw that 60% of them X" proves nothing because of how small the sample size is.
There's a place for qualitative research. Work like this could help structure bigger studies, suggest new hypotheses, and refine interview techniques.
The sample size you need depends on the strength of the effect in the population. For instance you wouldn't need a sample size of thousands to conclude cyanide capsules were not a safe, effective remedy for tooth ache. Or that people don't like being bullied at work.
This work wasn't statistically focussed, so it's not as crucial, but if you interview 135 people under a good sampling scheme and get very consistent responses about what matters to them it can't be dismissed as meaningless out of hand.
I would expect plenty of room for change in our understanding of meaningfulness as more work is done though...
It's a pretty large sample for qualitative research, actually.
When you're building an app, a good way to spot problems is to run a user experience test. Sit down at the side of random potential users for half an hour each. Observe how they're using the product, and let them walk you through what they were thinking when you find it interesting.
Past a certain number of users, major UI problems will surface again and again from test to test, alongside marginal - and arguably less important - tidbits of information that let you identify new UI issues. When you reach that stage you know you should stop. It kicks in at around a half dozen users in my experience - and is very rarely over a dozen.
Well, this specific one is not about statistics and hard data, but about personal experiences and feelings. Perhaps larger numbers would simply add points that are too specific to include in a generalisation. Or perhaps it takes too much time to interview 1000s one on one :)
As small as the sample was, I think it was quite relatable.
In statistics, what matters far more than the size of your sample is the method of your sampling.
This is where lay and academic understandings of statistics diverge dramatically. There's far more discussion of how to ensure an appropriate sample size (determined generally by the independent variables you're tracking and how you plan on subsetting the sample) and tests for randomness. Which is why the time to call a statistician into your study isn't when you want to run algos over your data but before you even begin collecting data.
Oh, and large sample statistics is a thing. You need to have a minimum number of observations before large-sample methods are generally considered accurate, described by n.
n = 30.
If (and that's a crucial if) your sampling is random, you can draw tremendous inferences from small datasets. This is why many national-level polls rely on samples of about 300 individuals. The key isn't the size, it's making sure those 300 are really, really random. Goof that and you end up with a "Dewey Defeats Truman" headline. Or Trump as the GOP candidate.
(Both owe a lot to how phones are used -- in 1948, landline phones were still a sufficient luxury item that it skewed phone-polling methods. In 2016, cord-cutting is having similar effects.)
I had the interesting experience a while ago of looking at data as it came in generating an estimate of a population of 2.2 billion individuals, based on a sample of 50,000 (Google+ activity, relying on a sitemap file of profiles, where sitemaps are restricted to 50,000 entries per). Well within the first 100 records, the long-term trend of ~8-10% (my final value was 9%) of profiles showing any public activity was established. Given web polling and delays introduced (I tried, and succeeded, to avoid tripping any bot-denial mechanisms), the data rolled in slowly, so I simply set my stats script to loop over the incoming files every minute or so.
Somewhere in my testing I also ran a set of resamplings based on the data I'd ingested -- taking small sets of data from within the larger one and looking for any wildly anomolous trends. This would have suggested that the sitemap file itself wasn't randomly constructed, but eyeball tests of various aspects (including account age, region, and activity) strongly suggested it was. This spared me some more complex sample-generation.
(My conclusions were independently verified from a much larger sample of 500k profiles by Stone Temple Consulting and Eric Enge, a pretty heartening validation.)
________
Edit to add: another crucial point is that statistical error is governed by sqrt(n) -- SE = 1/sqrt(n). To halve your error, you've got to square your sample size. So if 30 doesn't work for you, you're looking at 900, not 60. Assuming sampling costs increase with n, reducing error gets expensive fast.
And for the pedants, SE is "standard error", as I'm aware.
It was like reading a description of Amazon. Thousands of talented and intelligent employees with thousands of ideas that could make or save godless amounts of money, 99% of which were shot down after they bubble their way up through a leadership telephone game which eventually totally misrepresents the idea, and the remaining 1% of ideas get the resource strangle after some VP decides his pet project needs all the resources and allocates his budget accordingly.
Amazon is a place where intelligent ambition is rewarded with plenty of money, but never used for anything but implementing other people's ideas.
And the French have "raison d'être", literally, "reason for being". Maybe part of the problem is that the language of business does not have a strong idiom for this.
in the language of business its called "profit motive" and it excludes almost all of human existence except for the part where you have more stuff at the end of the day than at the beginning.
Readit News
Spolsky:
> Administrators [code name for managers] aren’t supposed to make the hard decisions. They don’t know enough. All those super genius computer scientists that you had to recruit from MIT at great expense are supposed to make the hard decisions. That’s why you’re paying them. Administrators exist to move the furniture around so that the people at the top of the tree can make the hard decisions.
http://avc.com/2012/02/the-management-team-guest-post-from-j...
[1]https://en.wikipedia.org/wiki/Pointy-haired_Boss
Using your system, as a manager, if you're not above 1 you should work hard to never ever go below 1.
That's the point Spolsky is making, I think. It's a fair point and rooted in a lot of evidence, but somewhat limited, because as you say, there can be great managers whose experience and insight multiply their potential.
And as funny as it sounds in contrast to the 'Murican Movie Manager, from that kind of attitude even shy people can gain power. When you work for others well being they are more eager to consider your opinion and follow your guidance as well. (That just because of that second quote making it sound like the manager should be the engineers "bXXch", it's not the case)
e.g.: "cache:http://sloanreview.mit.edu/article/what-makes-work-meaningfu... (without the quotes)
They don't stick around and dig for it. That's why you want people who have other options to work for you, and when you work for others, you want to know you could be doing something else. Meaning is defined from the choices we exercise. Why here instead of there is what is meaningful.
No one can tell you how to live your life. Similarly, no one can tell you why you're doing your job. You need to figure that out yourself, and seeking it should involve finding other jobs, not just repeatedly digging at the same spot.
Good teachers and good managers alike can help you immensely if you already have your reasons. They provide a synergy. And for that, they need you to be inspired and be passionate. Synergy fails if either is void. So if you show up inspired to a boss who isn't, that's a sign you may need to revisit your choices also.
Eventually meaning always comes. Maybe it finds us. But if you want to make the most of it, never wait and never settle. All one needs is the courage to move on.
I would attribute blame to a manager who is a void however.
And then I feel sad, because all the effort to seek that personal connect with work at my current workplace, have been taken away by a disconnected and immature middle management, which only works for meeting the goals and deadlines given to them...
1. Flow: http://www.goodreads.com/book/show/66354.Flow
People in power almost always try to increase their level of control, essentially centralizing the power structure of the entity. At some point of time the entity is completely inefficient and collapses under its own weight (rules, bureaucracy).
You can see this everywhere where power can be centralized: companies, organizations, nation states.
Ironically, successful leaders these days often do the opposite of leading, they decentralize, making the company much more efficient (assuming there are still sub-entities that can/want to be efficient).
A study that interviews 135 people? And this is seen as significant? It seems to me saying something like "we tried this on 150 people, and we saw that 60% of them X" proves nothing because of how small the sample size is.
what I am missing?
The sample size you need depends on the strength of the effect in the population. For instance you wouldn't need a sample size of thousands to conclude cyanide capsules were not a safe, effective remedy for tooth ache. Or that people don't like being bullied at work.
This work wasn't statistically focussed, so it's not as crucial, but if you interview 135 people under a good sampling scheme and get very consistent responses about what matters to them it can't be dismissed as meaningless out of hand.
I would expect plenty of room for change in our understanding of meaningfulness as more work is done though...
When you're building an app, a good way to spot problems is to run a user experience test. Sit down at the side of random potential users for half an hour each. Observe how they're using the product, and let them walk you through what they were thinking when you find it interesting.
Past a certain number of users, major UI problems will surface again and again from test to test, alongside marginal - and arguably less important - tidbits of information that let you identify new UI issues. When you reach that stage you know you should stop. It kicks in at around a half dozen users in my experience - and is very rarely over a dozen.
As small as the sample was, I think it was quite relatable.
Also a large amount of studies are done to show a potential area for further research which will involve larger populations when they are approved.
This is where lay and academic understandings of statistics diverge dramatically. There's far more discussion of how to ensure an appropriate sample size (determined generally by the independent variables you're tracking and how you plan on subsetting the sample) and tests for randomness. Which is why the time to call a statistician into your study isn't when you want to run algos over your data but before you even begin collecting data.
Oh, and large sample statistics is a thing. You need to have a minimum number of observations before large-sample methods are generally considered accurate, described by n.
n = 30.
If (and that's a crucial if) your sampling is random, you can draw tremendous inferences from small datasets. This is why many national-level polls rely on samples of about 300 individuals. The key isn't the size, it's making sure those 300 are really, really random. Goof that and you end up with a "Dewey Defeats Truman" headline. Or Trump as the GOP candidate.
https://en.m.wikipedia.org/wiki/Dewey_Defeats_Truman
(Both owe a lot to how phones are used -- in 1948, landline phones were still a sufficient luxury item that it skewed phone-polling methods. In 2016, cord-cutting is having similar effects.)
I had the interesting experience a while ago of looking at data as it came in generating an estimate of a population of 2.2 billion individuals, based on a sample of 50,000 (Google+ activity, relying on a sitemap file of profiles, where sitemaps are restricted to 50,000 entries per). Well within the first 100 records, the long-term trend of ~8-10% (my final value was 9%) of profiles showing any public activity was established. Given web polling and delays introduced (I tried, and succeeded, to avoid tripping any bot-denial mechanisms), the data rolled in slowly, so I simply set my stats script to loop over the incoming files every minute or so.
Somewhere in my testing I also ran a set of resamplings based on the data I'd ingested -- taking small sets of data from within the larger one and looking for any wildly anomolous trends. This would have suggested that the sitemap file itself wasn't randomly constructed, but eyeball tests of various aspects (including account age, region, and activity) strongly suggested it was. This spared me some more complex sample-generation.
(My conclusions were independently verified from a much larger sample of 500k profiles by Stone Temple Consulting and Eric Enge, a pretty heartening validation.)
________
Edit to add: another crucial point is that statistical error is governed by sqrt(n) -- SE = 1/sqrt(n). To halve your error, you've got to square your sample size. So if 30 doesn't work for you, you're looking at 900, not 60. Assuming sampling costs increase with n, reducing error gets expensive fast.
And for the pedants, SE is "standard error", as I'm aware.
Yup.
> To halve your error, you've got to square your sample size.
Nope. You've got to multiply it by 4. (It's the 2 that gets squared.) The square root of 1/120 is half that of 1/30.
Amazon is a place where intelligent ambition is rewarded with plenty of money, but never used for anything but implementing other people's ideas.
Ideally, your work should fall in the center. If only it was so easy though