It must be that time of year because I was asked to speak at two different schools in downtown Los Angeles this week, one elementary school and one middle school. The Perfect Jennifer probably won the coolest teacher award for getting her younger sister, a world champion in mixed martial arts and subject of a made for TV movie this summer to come talk for career day.
However, after the mobs of autograph seekers had departed, there were still plenty of questions for the old mom, just as there were at the elementary school in MacArthur Park (yes the same of disco song and gang fame).
Here are some of my favorite questions and the answers that I gave.
Q. Were you always a math genius?
I was not a particularly good student. I got in trouble a lot for fighting and I wasn’t all THAT interested in school. I think I started being interested in math when I was in the sixth grade just because the math teacher (Sister Marion) was really nice and some of my other teachers were really mean. I mean, really mean, like throwing stuff at me. It’s true, I was an annoying child, but still. Since I liked her, I liked her class, so I studied harder for it and did better.
Q. Is your mother proud of you?
Yes, I believe she is. I’ve gotten a lot of education, started a company that does good work, been a teacher and been able to take care of my children well, so I would say, yes, she is proud of me.
Q. What do you dislike about your job?
I really had to think about this one and for a long time I could not think of anything. Then, The Perfect Jennifer reminded me that sometimes I have to go to North Dakota in the winter. That is the one thing I don’t like about my job, when I have to go somewhere it is really cold because I hate cold weather.
Q. What was your Plan B?
I had to think about that, too, for a while. I finally said that I really like being a statistician and the work that I do and if it doesn’t work out, if the grant that I’m working on now doesn’t get funded, if my game I’m working on now doesn’t sell then I think I will just try again. It’s like my daughter Ronda (who spoke earlier in the morning) said. Someone asked her in an interview once,
“You’ve won every match so far in your career with the arm bar in the first round. What are you going to do if you try the arm bar on someone one day and it doesn’t work?”
“Well, I guess in that case, I’d probably try again.”
(In fact, if you saw her last match, that is exactly what she did.) So, I said, I think my Plan B would be to try again to succeed as a statistician.
Q. What do you like about your job?
Everything. I like traveling. I like working with really smart, nice people which is all I work with any more, because if they are jerks, I just turn down the contract and don’t work with them. I like the fact that every project is something new, sometimes it’s seeing if a program works, some days it’s trying to catch fraud, other days it is teaching a class. I like the fact that I don’t have to get up before 10 o’clock in the morning.
Finally I told them,
If you don’t remember anything else I said or that anyone else said today, remember this, because it took me a long time to figure it out. Don’t EVER believe that other people are smarter than you, that they have some special kind of math brain that they can get it and you can’t, that everyone knows more than you. If they do know more than you it is just because they worked at it longer and harder and if you work long enough and hard enough you will get to the same place. Don’t believe you need to be a certain race or age or look a certain way to start a technology company and be successful. It just is not true. I used to think that way, that people who are really good at math were not people like me, certainly none of the math professors I had in college or people I saw on television talking about starting companies looked like me. None of that matters. Now I write the sort of things that I could not imagine even understanding when I was young and I toss it off like it’s nothing and it IS nothing because I’ve been doing it for twenty years. Math, martial arts, programming – anything – you just bang away at and you get it eventually. Why do you think they call it hacking?
Last week I wrote a bit about how to get an exploratory factor analysis using Mplus. The question now, is what does that output MEAN ?
First, you just get some information on the programming statements or defaults that produced your output:
INPUT READING TERMINATED NORMALLY
Exploratory Factor Analysis ;
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 730
Number of dependent variables 6
Number of independent variables 0
Number of continuous latent variables 0
Observed dependent variables
Q1F1 Q2F1 Q3F1 Q1F2 Q2F2 Q3F2
Row standardization CORRELATION
Type of rotation OBLIQUE
This tells us we our analyzing all of the data as one group, and not, for example, separate analyses for males and females. We have 730 records, six variables, all of which are continuous and listed above. The maximum likelihood method (ML) of estimation is used and the default rotation, GEOMIN, which is an oblique method, that is it allows the factors to be correlated.
Here we have a list of our eigenvalues
RESULTS FOR EXPLORATORY FACTOR ANALYSIS
EIGENVALUES FOR SAMPLE CORRELATION MATRIX
1 ……… 2 ……… 3 4 5
________ ________ _____ ________ ________
1.866 1.262 0.866 0.750 0.716
EIGENVALUES FOR SAMPLE CORRELATION MATRIX
In this case, you could go ahead with the eigenvalue greater than one rule, but let’s take a look at a couple of other statistics. First, we have the results from the one factor solution. Here we have the chi-square testing the goodness of fit of the model
Chi-Square Test of Model Fit
Degrees of Freedom 9
We want this test to be non-significant because our null hypothesis is there is no difference between the observed data and our hypothesized one-factor model. This null is soundly rejected.
Let’s take a look at the Chi-square for our two-factor solution
Chi-Square Test of Model Fit
Degrees of Freedom 4
You can clearly see that the chi-square is much smaller and non-significant.
Let’s take a look at two other tests. The Root Mean Square Error of Approximation (RMSEA) for the one-factor solution is .115, as shown below. We would like to see an RMSEA less than .05 which is clearly not the case here.
RMSEA (Root Mean Square Error Of Approximation)
90 Percent C.I. 0.095 0.137
Probability RMSEA <= .05 0.000
For the two factor solution, our RMSEA rounds to zero, as shown below
RMSEA (Root Mean Square Error Of Approximation)
90 Percent C.I. 0.000 0.049
Probability RMSEA <= .05 0.954
Clearly, we are liking the two-factor solution here, yes? The eigenvalue > 1 rule (which should not be TOO emphasized) points there, as does the model fit chi-square and the RMSEA.
In their course on factor analysis, Muthen & Muthen give this very nice example of a table comparing different factor solutions using the data
They also like the scree plot, which I do, too. I also agree with them that one should never blindly follow some rule but rather have some theory or expectation about how the factors should fall out. I also agree with them in looking at multiple indicators, for example, scree plot, chi-square, RMSEA and eigen-values.
Many people have commented how ironic it is that I’m writing computer games these days because I’m one of the least playful people you’ll meet.
I have a confession to make, although confession is perhaps the wrong word because I don’t feel the least bit bad about it.
Playing with small children bores me.
Don’t get me wrong – I love my children and grandchildren and I would do anything for them. I taught my children to read, took them to soccer/ judo/ track/ swim practice , to piano/ bassoon / guitar/ drum lessons and ballet / tap/ hip-hop classes. I worked thousands of hours of overtime to pay for camps in Europe, in marine biology, private universities.
And yes, I went to the park, played with my little ponies, pushed children on swings, threw them up in the air (and caught them – any problems they have are NOT because they were dropped on their heads at a young age no matter how much their behavior during adolescence might lead you to believe otherwise). I read The Perfect Jennifer her favorite book – Where the Wild Things Are – so many times that I still have it memorized years after she finished graduate school.
AND YET …. when I hear those women rave about sitting down with their children and eating carrot sticks while they played with my little ponies together were the most fulfilling moments of their lives, I think to myself,
What? Are you fucking kidding me?
And apologies to the nice man at SAS Global Forum who reminded me that some people read my blog at work and asked me if I could not swear quite so much. I did post four days in a row on factor analysis and no swearing was involved, so I made a good faith effort, I really did.
Seriously, though, that’s what fulfills you? My little ponies?
Because as I was listening to my granddaughter talk about my little ponies what was going through my head was how I could use a statistical test for the difference in sample proportions to prove that a set of data I was asked to analyze was fraudulent. I’ll probably post about that next week. I was also intrigued by the very simple way the Muthuens had demonstrated comparison of competing factor solutions by using a table showing the chi-square, RMSEA and presence/ absence of Heywood cases.
When my four-year-old granddaughter told me she wanted to be a princess when she grew up I told her,
Princesses suck and I hate princesses. They’re useless and they don’t DO anything.
To which my darling daughter number one responded that “we” don’t say “hate” and “we” don’t say “suck” and I believe she muttered under her breath something about it being a wonder that she turned out normal with a mother like me. Obviously, this is a new meaning of the word “we” that doesn’t include the other person.
I am certain that I muttered under my breath, “Well, it’s true. They DON’T do anything useful.”
As penance I was forced to go to Disneyland and visit the Pavilion of Princesses. My granddaughter ADORED it. I was bored out of my mind by the princesses but the radiant look on her face DID make it worth taking a day away from work and paying Disneyland the equivalent of the median annual income in many countries for seven of us to eat churros and buy random pink crap bearing the stamp of useless women a.k.a. princesses.
The truth is, as much as I truly loved my children – and I had three under age five while working on my PhD – at the end of each day, when they were all asleep, I sighed deeply, sat down and read books on multivariate statistics and matrix algebra and was satisfied with life. I did NOT wish they would wake up so we could dress up like princesses.
There you have yet another of the 55 things I have learned in (almost) 55 years – you can be bored to death by Curious George, Strawberry Shortcake and every other thing designed to appeal to people with the mind of a three-year-old and still be a good mother.
It reminds me of a story I heard about someone who had a son who was crazy about baseball. The father bought season tickets, attended every home game and when the team made the World Series he flew to whatever city it was being held in to attend the games. When someone said to him,
I never knew you loved baseball so much.
I don’t. I think baseball is the most boring game ever invented. But I love MY SON that much.
Previously, I discussed how to do a confirmatory factor analysis with Mplus. What if you aren’t sure what variables should load on what factor? Then you are doing an exploratory factor analysis. Really, you should probably do the exploratory factor analysis first unless you have some very large body of research behind you saying that there should be X number of factors and these exact variables should load on them. If you’re analyzing the Weschler Intelligence Scale, you probably could skip the exploratory step. For everyone else …. here is how you do an exploratory factor analysis with Mplus.
TITLE : Exploratory Factor Analysis ;
Data: FILE IS ‘values.dat’ ;
VARIABLE: NAMES ARE q1f1 q2f1 q3f1 q1f2 q2f2 q3f2 ;
ANALYSIS: TYPE = EFA 1 3 ;
ESTIMATOR = ML ;
When no rotation is specified using the ROTATION option of the ANALYSIS command, the default oblique GEOMIN rotation is used.
The fourth statement is new. Like the other statements, you need to follow the ANALYSIS key word with a colon and end each statement in the command (or if you are familiar with SAS, think of it as a procedure) with a semi-colon.
TYPE = EFA 1 3 ;
Requests an exploratory factor analysis with a 1 factor solution, 2-factor solution and 3-factor solution. Of course, depending upon your own study, you can request whatever solutions you want. This is really useful because often in an exploratory study you aren’t quite sure of the number of factors. Maybe it is two or maybe three will work better. Mplus gives you a really simple way to request multiple solutions and compare them. I’ll talk more about that in the next post.
ESTIMATOR = ML ;
requests maximum likelihood estimation.
If you are interested in factor analysis at all, there is a really good video on the Mplus site. Far more of it discusses exploratory and confirmatory factor analysis – methods, goodness of fit tests, equations, interpretation of factor matrix – than Mplus, which as you can see, is pretty easy, so even if you are using some other software the video is definitely worth checking out.
Being able to find SPSS in the start menu does not qualify you to run a multi-nomial logistic regression.
This is the kind of comment statisticians find funny that leaves other people scratching their heads. The point is that it’s not that difficult to get output for some fairly complex statistical procedures.
Let’s start with the confirmatory factor analysis I mentioned in my last post. Once you get past the standard stuff that tells you that your model terminated successfully, the number of variables and factors, you see this:
Chi-Square Test of Model Fit
Degrees of Freedom 8
The null hypothesis is that there is no difference between the patterns observed in these data and the model specified. So, unlike many cases where you are hoping to reject the null hypothesis, in this case I certainly do NOT want to reject the hypothesis that this is a good fit. As you can see from my chi-square value above, this model is acceptable.
Another measure of goodness of fit is the root mean square error of approximation (RMSEA).
RMSEA (Root Mean Square Error Of Approximation)
90 Percent C.I. 0.000 0.046
Probability RMSEA <= .05 0.973
An acceptable model should have an RMSEA less than .05. You can see above that the estimate for RMSEA is .011, the 90 percent confidence interval is 0 – .046 and the probability that the population RMSEA is less than .05 is 97.3%. Again, consistent with our chi-square, the model appears to fit.
…………………Estimate S.E. Est./S.E. P-Value
Q1F1 1.000 0.000 999.000 999.000
Q2F1 1.828 0.267 6.833 0.000
Q3F1 1.697 0.235 7.231 0.000
Q1F2 1.000 0.000 999.000 999.000
Q2F2 1.438 0.291 4.943 0.000
Q3F2 1.085 0.191 5.687 0.000
Here are the unstandardized estimates. By default the first variable for each factor is constrained to a value of 1, so, of course, there is no real standard error, probability or standard error of estimate. It isn’t really an estimate, that was set. Let’s look at the other two. Since they are unstandardized the more useful measure for us is the estimate divided by the standard error of the estimate, for example 1.828/ .267 . This is done for us in the column under Est. / S.E. and in that case comes out to 6.833. You interpret these values in the same way as any z-score, with 1.96 as the critical value, and you can see in the last column that all of my variables loaded on the factor hypothesized with a p-value much less than .05.
The next thing I look at is the residual variances. At this point my only concern is that I *not* have a residual variance that is negative. It makes no sense that you would have a negative variance because (among other reasons) variance is a sum of squares and squares cannot be negative. Also, in this case, the commonality is greater than 1, meaning you have explained over 100% of the variance in this variable by its relation to the latent construct. This also makes no sense. These are referred to as Heywood cases and explained beautifully here (even though the linked documentation is from SAS it applies to any confirmatory factor analysis).
The final thing I want to look at, for right now, anyway, is the R-squared
Variable Estimate S.E. Est./S.E. P-Value
Q1F1 0.142 0.032 4.473 0.000
Q2F1 0.475 0.065 7.256 0.000
Q3F1 0.438 0.061 7.123 0.000
Q1F2 0.174 0.045 3.883 0.000
Q2F2 0.376 0.078 4.827 0.000
Q3F2 0.179 0.044 4.057 0.000
You can see that the r-square is pretty decent overall. These are interpreted just like any other R-square values. I didn’t show the standardized factor loadings here but just take my word for it that the R-squared values are the standardized loadings squared. So this is the variance in q1f1, for example, explained by factor 1.
I started this whole thing working with Mplus to do a factor analysis and overall, I’d have to call it a pretty painless experience.
Someone had a question about factor analysis with Mplus and even though it is not a piece of software I work with normally, we aim to please at The Julia Group, so I downloaded the demo version and away I went.
It truly was, as my granddaughter says, easy-peasy lemon squeezie.
You might not think so, because the first thing you are confronted with is pretty much a blank window like this
1. Create a .dat file from the original file. The file was in a SAS format and I did not have SAS on the laptop I was working on (I’m in Cambridge, MA at the moment). What I did was
- Open the file in SPSS by, from the FILE menu selecting READ TEXT DATA and then selecting SAS as the format
- Ran this SPSS command from the syntax window to output a tab-delimited file with no header, which was the type of input Mplus would expect.
2. Type in this program to do a two-factor solution with the first three variables loading on the first factor and the next three loading on the second factor.
TITLE : Confirmatory Factor Analysis ;
DATA: FILE IS ‘/Users/annmaria/Documents/mplustest/values.dat’ ;
VARIABLE: NAMES ARE q1f1 q2f1 q3f1 q1f2 q2f2 q3f2 ;
MODEL: f1 BY q1f1 q2f1 q3f1 ;
f2 BY q1f2 q2f2 q3f2 ;
OUTPUT: standardized ;
3. Click the RUN button.
That is really all there was to it.
Okay, well that is easy if you knew what to type so let me explain a few things. If you know SAS or SPSS this will be easy.
Each of those things that I put in all capitals is a command in Mplus, analogous to a DATA or PROC step in SAS and a command in SPSS. They don’t need to be in all caps, I just did that for ease for the reader. They DO need to be followed by a colon and then end the statement in a semi-colon.
Title – pretty obvious, gives your output a title.
DATA: FILE IS — gives the path to locate your data.If your file is in the same directory as your program, you don’t need a fully qualified path and can just call it ‘values.dat’
VARIABLE: NAMES ARE
Give the names of your variables. You can specify a format but if you do not Mplus assumes they are in free format, which is the same as what SAS refers to as list format. You might want to note that if you are using the demo version you can only have a maximum of 6 independent and 2 dependent variables.
MODEL: This is my model (duh) and I am modeling two factors. The first factor I creatively named f1 and it is represented BY (notice the BY in the command) variables also creatively named q1f1 q2f1 and q3f1.
Similarly, I have a second factor named f2 ;
I added an OUTPUT statement with a standardized option because I wanted (surprise) standardized estimates. That statement is not required but as you’ll see in my next post on interpreting factor analysis data, you do want it.
I am intrigued by Mplus. It sort of assumes you have close to perfectly cleaned up data because I wouldn’t want to be doing a lot of data management with it, but for doing some relatively complex models – factor analysis, path analysis, structural equation modeling – it looks pretty cool.
Here are four more of Dr. De Mars 55 things I have learned in (almost) 55 years, and that is that there are four thing students should have learned in school but often didn’t.
1. Say what you mean. I don’t know who those teachers are who reinforce students for using longer words, longer sentences and writing more pages but I hope someone finds them and beats them senseless with The Elements of Style , which nearly a century after it was first published I still think is one of the best books on writing out there. When you write,
In the experiment under discussion we utilized two conditions in the manner such that one group of the subjects referred to in the preceding paragraph received no treatment, that is, they were what is referenced as the control group. The other group, that is the second group, which was the group receiving our treatment described in the section under procedures which follows is hereafter referred to as the treatment group. A treatment group is defined by Academic-Guy (2012) as …
Subjects were randomly assigned to either a treatment or control group.
You may think the first example makes you sound intelligent and well-educated but it doesn’t. It makes you sound like you learned English by watching the Power Puff Girls and imitating Mojo Jojo. People – clients, your boss – are busy, and grant applications have page limits.
2. Don’t be a pain in the ass. I wrote a post about this, Why the cool kids won’t hang out with you. In brief, no matter how smart you are, if you constantly run down your co-workers, flaunt the policies of your organization and are rude to your boss, at some point they will replace you with an equally smart person who is less of a pain. This may sound hypocritical because if you have been reading this blog for long you are well aware that I swear, don’t do mornings and, if I have to wear a suit, I charge extra. However, I work with clients that are cool with that.
Really points 1 & 2 generally reveal a person trying to prove that he or she is smarter than the other people in the room. That usually reflects an underlying insecurity. I have met some absolutely brilliant scientists and businessmen/women. None felt the need to try to impress me. I was already impressed when I met them, and I’m sure that was the reaction they got from almost everyone.
3. Mean what you say. If you say you will be in the office at 8 a.m., be in the office at 8. I tell clients I will be in by 9:30 or 10 if necessary because I know there is no way on God’s earth I am dragging myself out of bed at 7 a.m. It’s not happening. On the other hand, they know that if I say I will be in by 10, I will. If you say you can write programs in Perl or are experienced creating multi-media PowerPoint presentations, then when I ask you to do that, you should be able to do it. [I don't really need anyone to do either so if you are applying for our summer intern position, you don't need to mention these. It was just an example.]
4. Learn to code. It doesn’t matter what language. It’s absolute bullshit that once you know one programming language you know them all, but it is certainly true that once you have the idea of loops, arrays, properties, methods, classes, extend, functions and a few dozen other key concepts, it will be much easier for you to pick up a second, third or fourth programming language. The Perfect Jennifer is an amazingly great history teacher and she is in one of the minority of fields where you can not do any programming and have a decent, stable job. Did I mention she is amazingly great, and works an enormous amount of extra hours? However, if you are planning on going into consulting, management or a large number of other fields, knowing how to code will help you immensely. Even our Chief Marketing Officer, who only focuses on marketing, has done a little coding and has some idea of the constraints of developing a new product. I’m so convinced of the personal and professional value of learning at least a little bit of programming that I have gone back to requiring it in my statistics courses. Often students don’t learn to code because they underestimate themselves. They believe programming is done by people who are smarter, more focused or in some way better than them. That’s simply not true and learning to code will give them both more skills and more confidence.
So, those are four more things I have learned in (almost) 55 years and that I think any student graduating should learn as well.
There are, or so I have heard, people who are energized by parties, meet-ups and social events. I am not one of those people.
If I had my choice, I would never go to any gathering larger than our family dinners for the rest of my life. It’s not that I don’t enjoy talking to intelligent people nor that I don’t appreciate all of the great people that I get to work with in the course of the year – I really do. However, I have to confess, that is a fringe benefit. What I am most interested in doing is sitting at my computer solving problems. If there was some way to get anyone else to go to the meet-ups, demos, conferences and pitches, I would do it.
Most of our staff at The Julia Group is like that. When meet-ups or other networking opportunities there is more whining than taking a kindergarten class to church.
“Oh, man, do I *have* to go?”
“I just went last time.”
“Can’t I go next time?”
“Isn’t it somebody else’s turn?”
In fact, we DID hire someone, our new Chief Marketing Officer to handle these responsibilities because I got so tired of hearing the whining from everyone, including me. Now I only go when she tells me that I have to – and I still whine.
In my experience, most meet-ups will have from zero to one good point that is worth knowing. Usually that comes from whoever they have as a speaker, but not always. You’ll meet, if you are lucky, one interesting person with whom you wish to follow up, several people who want to sell you stuff and a couple of people who have an idea and are looking for someone to give them money so they can pay someone else to make it. Yet, I still go because that one point is worth hearing and the one person is worth knowing.
Here are five points I have learned from start-up meet-ups. Since you read my blog you can tell your CMO that you get to skip the next five (she probably won’t buy it, but it’s worth a try).
1. Cash is more than king. – From Jenny Q. Ta , founder of sqeeqee.com This advice from a highly successful founder confirmed what I have thought for years. At one point our company rented an office because I thought we should have one to look like a “real company”. Almost no one ever went there. Most of us work at home and we have people in several states. Now we Skype, FaceTime , email or meet in the office downstairs in my house. If we need a conference room, I rent one at the business center a half-mile away. Sometimes people are unimpressed that we still haven’t permanently moved out of the downstairs, but what we save on renting offices for a dozen people goes a long way to making sure we are in the black every month. If you have a healthy cash flow, you can get by without investor money for a long time.
2. Put off taking investor money as long as you possibly can – This is another good tip from Jenny Q. Ta The sooner in the game that investors come in, the more of a risk they are taking and the larger percentage of your business they are going to want.
I find it ironic that the two things that might impress a casual observer – paying for office space and getting angel investor money are the exact points that she argued against. (She’s not the only one, check Paul Hawken’s wonderful book Growing a Business). We have people putting in considerably more hours than they are getting paid for a share of the business – those are co-founders and that is the best investment we can get because not only is it equivalent to funds but it brings the talent with it.
3. Don’t believe everyone knows more than you. I heard this at a General Assembly start-up event and it is worth repeating. There was a time when I thought all of these people spouting so confidently that the target market for their product was in the hundreds of millions (it isn’t) or that the best choice for an application was Ruby (it wasn’t) knew so much more than me. Now I realize that many of them are just posturing. They’re either trying to sound confident for investors, or they just have a different world view than me. I’m a statistician. If I tell you we’ll make $5 million on a product I believe there is a greater than 50% chance based on the facts at my disposal. Others, if they say they’ll make $500 million are basing it on an assumed 5% chance and convinced they’ll make it with the right strategy.
4. Find a co-founder or two. I believe the optimal number of co-founders is three. More than that, you dilute decision-making too much. Less, and you probably haven’t covered all of the key skills.
The fifth and most important thing I have learned and I have heard it several times – most of success is just keeping working even when it’s hard and frustrating.
Speaking of which, I was taking a break from revising our first game to write this post but now I’m going to get some sleep and hit it in the morning.
(And there you have five more things I have learned in almost 55 years.)
Two questions I get asked occasionally are:
- Do I get paid to write nice things about software?
- Why don’t I write more about things I DON’T like?
1. No. The only one who pays for my blog is BlogHer – which is the ads you see – and they don’t seem to care what I write as long as people read it. The checks from them pretty much cover my Chardonnay bill. Incidentally, they pay WAY better than Google AdSense.
2. My original reason for writing this blog was to remind myself of stuff. Ever see that comic where the kid raises his hand and asks the teacher
“May I be excused from class? My brain is full.”
Here is a fact: A lot of stuff sucks.
There are over 1,000,000 books on Amazon. It’s a lot more productive to write about a book I read that was good than the 867,345 books on Amazon that are mediocre or worse. You’ll find something good much faster by looking for stuff that’s good than ruling out everything that sucks.
Whether you’re talking about a mobile app, a new suite of software or added functions or formats, there will be a lot that are boring, useless or just plain suck. It just seems really inefficient to waste my time writing about them unless the rise to a truly notable level of suckiness – and most things don’t, they don’t even excel at sucking and will fade out of the general consciousness, sinking under the weight of their own mediocrity.
I honestly don’t understand people who spend a lot of time writing about the stuff they DON’T like.
I highly recommend, The Dip, a short book by Seth Godin that lauds the value of quitting. I wrote about this at greater length on my other blog on judo and life, under the topic, “Know when to hold ‘em and know when to fold ‘em.” where, being the horrible mean old woman that I am, I suggested that giving up trying to make the Olympic team, going back to school and getting a real job might be a better path for some people.
Or in the words of not one, but two of my professors in graduate school, at two different institutions thousands of miles apart, the 19th thing I have learned in (almost) 55 years is
“Never play with a stacked deck.”
The deck might be stacked against you for a number of reasons. One of the professors who told me that was an African-American woman and at the end of the academic year, she left for another university. She was right that she would probably never get the job she wanted at that university. Her research wasn’t African-American studies – it was policy analysis, and she taught not multi-cultural something or other but statistics. She could have stuck around hoping to get tenure and make them see that she really was just as good, just as smart – or she could have gone to another university where they already knew that.
The other professor was white, male and vice-president of a major corporation who had come to teach in the MBA program for a year because he felt like it and he was rich and important, so there. We were glad to have him. He was a great professor. He pointed out there are times that you are not going to get what you want, because, say, the company was a family business and the owner’s son was going to end up as president no matter how wonderful you are. It could also be that there is an entrenched group and they are not going to support you in your job no matter what you do. They’ve worked together for twenty years and you just came in here because the boss hired you over them. One of the students asked,
“Isn’t that letting them win if you just give up and leave?”
The professor answered,
“Or, you could stay there for five years and fight them and maybe after five years, bring them around to recognize your contribution to the team and support you. In the meantime, you’ve wasted five years when you could have been working somewhere else where people got behind you and got the job done and been five years further ahead in your career. So tell me, what did you win?”
Sometimes, it’s not people that have stacked the deck against you. Maybe you have had too many injuries to come back and compete. That may sound hypocritical since I won the world championships with a knee missing all the cartilage and 2/3 the ligaments. The fact is, I was lucky and if I had taken one more shot that took out that last ligament, I would have been done not just competing but probably walking.
So, that brings me to my 20th thing,
Know what you are willing to risk.
In the case of competing, I was willing to risk never walking again without crutches. Thank God for the medical advances in knee replacements or I’d be on crutches now. Right now, I’m making half the money I could be making because I’m spending a lot of time on starting up 7 Generation Games and not taking any new consulting clients.
This might sound hypocritical again, because isn’t doing a start-up something for only young people? As Vivek Wadhwa said, isn’t it true that the average venture capitalist portfolio consists solely of white and Asian males barely old enough to shave? So isn’t this playing with a stacked deck?
Not at all. We may not get $10 million in venture capital but I’m okay with that (really). We have learned not to trade our lives for stuff. We’re pretty happy with life because we’ve learned not to want too much what we haven’t got.
We’re willing to risk some of our own funds and half (or more) of our time for two or three years to make this game happen. Looking at the progress we’ve made so far, the people we have working with us and the work we are all doing, I am pretty optimistic, but it’s a risk. If it doesn’t succeed, I will be disappointed, we all will. Then, we’ll pick ourselves up and after some swearing and possibly a martini or two, we’ll go on to the next idea, because we have learned that failure is never permanent and neither is success.
See how it all fits together – it’s like Legos.
Which brings me to my twenty-first thing I have learned …
You’ll be a lot happier in life if you don’t take yourself too seriously.