Occasionally, a brave student will ask me,
When will I ever use this?
The “this” can be anything from a mixed model analysis to nested arrays. (I have answers for both of those, by the way.)
I NEVER get that question when discussing topics like filtering data, whether for records or variables, because it is so damn ubiquitous.
Before I headed out to be, literally, testing in the field (you can read why here) , I was working on an evaluation of the usability of one of our games, Fish Lake.
My next thought was that many students played the game for a very short time, got the first answer correct and then quit. I decided to take a closer look at those people.
First step: from the top menu select TASKS, then DATA, then FILTER AND SORT
Second step: Create the filter. Click on the FILTER tab, select from the drop-down menu the variable to use to filter, in this case the one named “correct_Mean” , select the type of filter in the next drop-down menu, in this case EQUAL TO and in the box, enter the value you want it to equal. If you don’t remember all of the values you want, clicking on the three dots next to that box will bring up a list of values. You can also filter by more than one variable, but in this case, I only want one, so I’m done.
Third step: Select the variables. Steps two and three don’t have to be done in a particular order, but you DO have to select variables or your procedure won’t run, since it would end up with an empty data set. I do the filter first so I don’t forget. I know the filter is the whole point and you’re probably thinking you’d never forget that but you’re probably smarter than me or never rushed.
If you click the double arrows in the middle, that will select all of the variables. In this case, I just selected the two variables I wanted and clicked the single arrow (the top one) to move those over.
Why include correct_mean, since obviously that is a constant?
Because I could have made a mistake somewhere and these aren’t all with 100% correct. (Turns out, I didn’t and they were, but you never know in advance if you made a mistake because if you did then you wouldn’t make it.)
I click OK and now I have created a data set of just the people who answered 100% correctly.
For a first look, I graphed the frequency distribution of the number of questions answered by these perfect scorers. To do this,
- Go to TASKS > GRAPH > Bar Chart
2. Click on the first chart to select it, that’s a simple vertical bar chart
4. Under APPEARANCE click the box next to SPECIFY NUMBER OF BARS. The default here is one bar for each unique data value, which is already clicked. Caution with this if you might have hundreds of values, but I happen to know the max is less than 20.
I thought I’d find a bunch answered one question and a few answered all of the questions and maybe those few were data entry errors, say teachers who tested the game and shouldn’t be in the database. When I look at this graph, I’m surprised. There are a lot more people who had answered 100% correctly than I expected and they are distributed a lot more across the number of questions than I expected. That’s the fun of exploratory data analysis. You never know what you are going to find.
SO, now what?
So, now what?
I want to find out more about the relationship among persistence and performance. To do this, I’m going to need to merge the answers summary data set with demographics.
I’m going to go back to the Summary Data Set I created in the last post (remember that one) and just filter variables this time, keeping all of the records.
Again, I’m going to go to the TASKS menu, select DATA then FILTER AND SORT, this time, I’m going to have no filter and select the variables.
Since the pop-up window opens with the VARIABLES tab selected, I just click the variables I want, which happens to be “correct_N”,” correct_mean” and “username”, click the single arrow in between the panes to move them over, and click OK at the bottom of the pop-up window. Done! My data set is created.
You can always click on PROGRAM from the main menu to write code in SAS Enterprise Guide, but being an old dinosaur type, I’d like to export this data set I just created and do some programming with it using SAS. Personally, I find it easier to write code when I’m doing a lot of merging and data analysis. I find Enterprise Guide to be good for the quick looks and graphics but for more detailed analysis, the old timey SAS Editor is my preference. If you happen to be like me, all you need to do to output your data set is click on it in the process flow and select EXPORT.
You want to export this file as a stand-alone data set, not as a step in a project. Just select the first option and you can save it like any file, select the folder you want, give it the name you want. No LIBNAME statement required.
And it’s a beautiful sunny day in Santa Monica, so that’s it on this project for today.
Since it’s the 4th of July, I figure no one is very work-focused today and it would be a good time for one of my occasional rants.
I read a book this week, The Savage Damsel and the Dwarf. It was a good story for a lots of reasons, a primary one being that it didn’t follow the usual narrative of beautiful damsel in distress rescued by charming prince.
In fact, the beautiful damsel is kind of a stupid jerk and it is her overlooked, smarter sister who heads out to find a knight to save the castle. Said knight isn’t the sharpest knife in the drawer, either. In fact, most of the knights in the story seem to have been bonked on the head a few times too many.
In the end, the beautiful damsel is rescued by the not-too-bright knight and they go off to King Arthur’s court. The sister ends up with another guy who pretty much sucks at being a knight, so he gives it up and they live happily ever after together as he takes over the family lands and becomes a highly successful farmer.
One reason I almost never watch TV or movies is that the story is so predictable. Love at first sight. Unappreciated younger brother becomes greatest knight ever through magic potion/ love of a good woman. Bad guy defeated by good guy. Lots of fighting scenes. Overlooked woman develops into a beauty and the guy finally notices her.
More people should write their own story. Truly , fighting occasionally , with intervening sitting around the castle drinking beer waiting for a fight does sound like an incredibly boring life. A lot of the stuff the Knights (and people now) fight over is stupid.
“You insulteth mine honor.”
Yeah, so we should hack each other to pieces with swords? Get over it.
The ‘savage damsel’ falls in love with a knight who falls in love with her beautiful sister. When she has the chance to make him love her forever, she starts thinking past the first minute she imagines him declaring his love for her and tries to see being middle-aged, sitting by the fire with Sir Dumb-As-A-Rock and concludes, “Oh, hell, naw.”
Sir Lancelot loses a joust and disappears. And he doesn’t come back.
I liked the book a lot because it didn’t follow the recipe for fantasy stories. I ordered it for my granddaughter because it has a great life lesson – write your own story.
My usual disclaimer when I write about a product: No one paid me diddly-squat to write this.
In the last post, I used SAS Enterprise Guide to filter out a couple of ‘bad’ records that came from test data, then I created a summary table of the number of questions answered and the percentage correct. Then, I calculated the mean percentage correct for the around 84%. That seemed a bit high to me.
Having (temporarily) answered the first question regarding the number of individual subjects and the average percent of correct answers from the 424 subjects, I turned to the next question:
Is there a correlation between percentage correct and the number of questions attempted? That is, do students who are getting the answers correct persist more often?
Since I had both variables, N and the mean correct (which, since this was score 0= correct, 1= incorrect gave me the percentage correct) from the summary tables I had created in the previous step, it was a simple procedure to compute the correlation.
I just went to the TASKS menu, selected MULTIVARIATE and then CORRELATIONS
Under ANALYSIS VARIABLES correct_ N for the ‘correct’ variable, which is a variable that holds whether the student answered correctly, 0(= no) or 1(=yes). Under CORRELATE WITH I dragged correct_mean, which has the percentage each student answered correctly.
Since it is just a bivariate correlation and the correlation of X with Y = the correlation of Y with X , it would make absolutely no difference if I switched the spots where I dragged the two variables.
I also note that the minimum number of answers attempted is 1. Now, I have done (and published) analyses of these data elsewhere, as this is an on-going project.
Other analyses from this same project can be found in:
Because of these analyses of ‘Fidelity of Implementation’, that is the degree to which a project is implemented as planned, I am pretty sure that these data include a large proportion of students who only had the opportunity to play the game once.
So … I decided to run a scatter plot and check my suspicion. This is pretty simple. I just go to the TASKS menu and select GRAPH then SCATTER PLOT.
I selected 2-D Scatter Plot
Then, I clicked on the DATA tab, dragged correct_Mean under Horizontal and Correct_N and vertical, then clicked RUN.
This produced the graph below.
Now, this graph isn’t fancy but it serves its purpose, which is to show me that there IS in fact a correlation of mean correct and the number of problems attempted. Look at that graph a minute and tell me that you don’t see a linear trend – but it is pulled off by the line of 1.0 at the far end.
This did NOT fit my preconceived notion, though, that the lack of correlation was due to the players who played once, and so there would be a bunch of people who had answered 1 or 2 questions and got 100% of them correct. Actually, those 100-percenters were all over the distribution in terms of number of problems attempted.
This reminds me of a great quote by Isaac Asimov,
The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ (I found it!) but ‘That’s funny …’
Well, we shall see, as our analysis continues …
You can also follow the link above to donate a copy of the game to a school or give as a gift.
The government is extremely fond of amassing great quantities of statistics. These are raised to the nth degree, the cube roots are extracted, and the results are arranged into elaborate and impressive displays. What must be kept ever in mind, however, is that in every case, the figures are first put down by a village watchman, and he puts down anything he damn well pleases.
Any time you do anything with any data your first step is to consider the wisdom of Sir Josiah Stamp and check the validity of your data. One quick first step is using the Summary Tables task from SAS Enterprise Guide. If you are not familiar with SAS Enterprise Guide, it is a menu driven application for using SAS for data analysis. You can open a program window and write code if you like, and I do that every now and then but that’s another post. In my experience, SAS Enterprise Guide works much better with smaller data sets – defined by me, as the blog owner, of less than 400,000 records or so. Your mileage may vary depending upon your system.
How to do it:
- Open SAS Enterprise Guide
- Open your data set – (FILE > OPEN > DATA)
- From the TASKS menu, select DESCRIBE and then SUMMARY TABLES. The window below will pop up
- Drag the variables to the roles you want for each. Since I have less than 450 usernames here, I just quickly want to see are there duplicates, errors (e.g. ‘gret bear’ is really the same kid as ‘grey bear’ , with a typo). I also want to find out the number of problems each student attempted and the percent correct. So, I drag ‘username’ under CLASSIFICATION VARIABLES and ‘correct’ under ANALYSIS variables. You can have more than one of each but it just so happens I only have one classification and one analysis variable I’m interested in right now.
5. Next click on the tab at left that says SUMMARY TABLES and drag your variables and statistics where you want them. I want ‘username’ as the row, so I drag it to the side, ‘correct’ as the column, N is already filled in as a statistic if you drag your classification variable to the table first. I also want the mean, so I drag that next to the N. Then, click RUN.
Wait a minute! Didn’t I say I wanted the percent correct for each student? Why would I select mean instead of percent?
Because the pctN will simply tell me what percent of the total N responses from this username make up. I don’t want that. Since the answers are score 0 = wrong, 1= right, the mean will tell me what percentage of the questions were answered correctly by each student. Hey, I know what I’m doing here.
6. Look at the data! In looking at the raw data, I see that there are two erroneous usernames that shouldn’t be there. These data have been cleaned pretty well already, so I don’t find much to fix.Now, I want to re-run the analysis deleting these two usernames.
7. At the top of your table, you’ll see an option that says “Modify Task”. Click that.
8. Under TASK FILTER pull down the first box to show the variable ‘username’. Pull down the second box to show the option NOT EQUAL TO and then click the three dots next to the third box. This will pull up a list of all of your values for usernames. You can select the one you want to exclude and click OK. Next to the three dots, pull down to select AND, then go through this to select the second username you want to delete. You can also just type in the values, but I tend to do it this way because I’m a bad typist with a bad short-term memory.
11. From the DESCRIBE menu again select SUMMARY STATISTICS
12. Drag ‘correct_mean’ under ANALYSIS VARIABLES and click RUN.
The resulting table gives me my answer – the mean is .838 with a standard deviation of .26 for N=424 subjects. So … the average subject answered 84% of the problems correctly. This, however, is just the first step. There are couple more interesting questions to be answered with this data set before moving on. Read the next step here.
It’s been a good week for the darling daughters.
The Spoiled One graduated summa cum laude, also president of the senior class, and is heading to the east coast to attend a small liberal arts college where she has an academic scholarship and a spot on the soccer team.
The book co-authored by Darling Daughter One and Darling Daughter Three won International Sports Biography of the Year, and the two lovelies pictured above flew to London to receive the award.
The Perfect Jennifer has tenure now and is finishing out another year of being an outstanding teacher.
A couple of years ago, there was a book with the thesis that Chinese mothers are superior and all Americans are raising a bunch of lazy slackers. It irritated me and I wrote a blog with the title “Why American mothers are superior” because that seemed more professional than “Go Fuck Yourself” . And no, in all seriousness, I really don’t think that one race or country has better mothers, but I also think the idea that if we don’t regiment our children lock-step for 18 years straight into MIT we are a bunch of losers is irritating as fuck.
You might think this is my rubbing it in post to say, “How you like me now? My kids are doing awesome.”
You’d be wrong. To paraphrase Erma Bombeck yet again, no mother should ever be arrogant because she can’t be sure that at any moment the principal won’t call to tell her that one of her children rode a motorcycle through the gymnasium.
I wanted to talk about something different – definitions of success that Tiger Mom Lady probably would not understand at all.
A friend of mine has a son in his mid-twenties who lives at home. He earned a degree from a two-year college. He is not crushing it as a hedge fund manager, but rather, has a regular job with benefits. I’m sure Tiger Mom would be dismayed if he was her kid.
My friend was distraught over the situation at work. The company had been acquired and reorganized. Her new boss was a nightmare and she came home in tears more often than not. Despite over a decade of good performance, she was afraid she was going to be laid off and was becoming depressed and stressed. They couldn’t afford to make the payments on their house on one income, and they had already lost a home back in 2008 when the housing marketing imploded. They were the collateral damage of those hedge fund managers.
It was at this point that her son (remember him?) stepped up. He had been living at home to save money for a down payment on a house of his own. Since he is single, has no children and gets along well with his parents, it seemed like a good arrangement, and he was paying them rent, but a lot less than it would cost to go out and get his own apartment. Plus, there were those home-cooked meals. He said something like this,
Look, you took care of me for 26 years. I make enough money now to cover the mortgage. If you are that unhappy about your job, quit. Even if you don’t quit your job, at least quit worrying about being laid off. I’ll pick up any slack. Between Dad and me, we got you covered.
Look at this family – they all love each other, the mom, dad and son. They get along well enough that he feels comfortable living at home to save money. Her son is hard-working and appreciates the fact that his parents have done what they could to support him. He can take the perspective of another person, see the stress his mother is experiencing and offer to do what he can to alleviate it out of appreciation for what they have done for him.
In my view, my friend is a success as a mother and her son is a success as a human being.
Where we left off, I had created some parcels and was going to do a factor analysis later. Now, it’s later. If you’ll recall, I had not find any items that correlated significantly with the food item that also made sense conceptually. For example, it correlated highly with attending church services but that didn’t really have any theoretical basis. So, I left it as a single variable. Here is my first factor analysis.
proc factor data= parcels rotate= varimax scree ;
Var socialp1 – socialp3 languagep spiritualp spiritual2 culturep1 culturep2 food;
You can see from the scree plot here that there is one factor way at the top of the chart with the rest scattered at the bottom. Although the minimum eigen value of 1 criterion would have you retain two factors, I think that is too many, for both logical and statistical reasons. The eigenvalues of the first two factors, by the way, were 4.74 and 1.10 .
Even if you aren’t really into statistics or factor analysis, I hope that this pattern is pretty clear. You can see that every single thing except for the item related to food loads predominantly on the first factor.
These results are interesting in light of the discussion on small sample size. If you didn’t read it, the particular quote in there that is relevant here is
“If components possess four or more variables with loadings above .60, the pattern may be interpreted whatever the sample size used .”
Final Communality Estimates: Total = 5.845142
These communality estimates are also relevant but it is nearly 1 am and I have to be up at 6:30 for a conference call, so I’ll ramble on about this some more next time.
First of all, what are parcels? Not the little packages your grandma left on the table in the hall when she came back from shopping. Well, not only that.
In factor analysis, parcels are simply the sum of a small number of items. I prefer using parcels when possible because both basic psychometric theory and common sense tells me that a combination of items will have greater variance and, c.p., greater reliability than a single item.
Just so you know that I learned my share of useless things in graduate school, c.p. is Latin for ceteris paribus which translates to “other things being equal”. The word “etcetera” meaning other things, has the same root.
Know you know. But I digress. Even more than usual. Back to parcels.
As parcels can be expected to have greater variance and greater reliability, harking back to our deep knowledge of both correlation and test theory we can assume that parcels would tend to have higher correlations than individual items. As factor loadings are simply correlations of a variable (be it item or parcel) with the factor, we would assume that – there’s that c.p. again – factor loadings of parcels would be higher.
Jeremy Anglim, in a post written several years ago, talks a bit about parceling and concludes that it is less of a problem in a case, like today, where one is trying to determine the number of factors. Actually, he was talking about confirmatory factor analysis but I just wanted you to see that I read other people’s blogs.
The very best article on parceling was called To Parcel or Not to Parcel and I don’t say that just because I took several statistics courses from one of the authors.
To recap this post and the last one:
I have a small sample size and due to the unique nature of a very small population it is not feasible to increase it by much.I need to reduce the number of items to an acceptable subject to variables ratio. The communality estimates are quite high (over .6) for the parcels. My primary interest is in the number of factors in the measure and finding an interpretable factor.
So… here we go. The person who provided me the data set went in and helpfully renamed the items that were supposed to measure socializing with people of the same culture ‘social1’, ‘social2’ etc, and renamed the items on language, spirituality, etc. similarly. I also had the original measure that gave me the actual text of each item.
Step 1: Correlation analysis
This was super-simple. All you need is a LIBNAME statement that references the location of your data and then:
PROC CORR DATA = mydataset ;
VAR firstvar — lastvar ;
In my case, it looked like this
PROC CORR DATA = in.culture ;
VAR social1 — art ;
The double dashes are interpreted as ‘all of the variables in the data set located from var1 to var2 ‘ . This saves you typing if you know all of your variables of interest are in sequence. I could have just used a single dash if they were named the same, like item1 – item17 , and then it would have used all of the variables named that regardless of their location in the data set. The problem I run into there is knowing what exactly item12 is supposed to measure. We could discuss this, but we won’t. Back to parcels.
Since you want to put together items that are both conceptually related and empirically – that is, the things you think should correlate do- you first want to look at the correlations.
Step 2: Create parcels
The items that were expected to assess similar factors tended to correlate from .42 to .67 with one another. I put these together in a ver simple data step.
data parcels ;
set out.factors ;
socialp1 = social1 + social5 ;
socialp2 = social4 + social3 ;
socialp3 = social2 + social6 + social7 ;
languagep = language2 + language1 ;
spiritualp = spiritual1 + spiritual4 ;
culturep1 = social2 + dance + total;
culturep2 = language3 + art ;
There was one item that asked how often the respondent ate food from the culture, and that didn’t seem to have a justifiable reason for putting with any other item in the measure.
Step 3: Conduct factor analysis
This was also super-simple to code. It is simply
proc factor data= parcels rotate= varimax scree ;
Var socialp1 – socialp3 languagep spiritualp spiritual2 culturep1 culturep2 ;
I actually did this twice, once with and once without the food item. Since it loaded by itself on a separate factor, I did not include it in the second analysis. Both factor analyses yielded two factors that every item but the food item loaded on. It was a very nice simple structure.
Since I have to get back to work at my day job making video games, though, that will have to wait until the next post, probably on Monday.
Someone handed me a data set on acculturation that they had collected from a small sample size of 25 people. There was a good reason that the sample was small – think African-American presidents of companies over $100 million in sales or Latina neurosurgeons. Anyway, small sample, can’t reasonably expect to get 500 or 1,000 people.
The first thing I thought about was whether there was a valid argument for a minimum sample size for factor analysis. I came across this very interesting post by Nathan Zhao where he reviews the research on both a minimum sample size and a minimum subjects to variables ratio.
Since I did the public service of reading it so you don’t have to, (though seriously, it was an easy read and interesting), I will summarize:
- There is no evidence for any absolute minimum number, be it 100, 500 or 1,000.
- The minimum sample size depends on the number of variables and the communality estimates for those variables
- “If components possess four or more variables with loadings above .60, the pattern may be interpreted whatever the sample size used .”
- There should be at least three measured variables per factor and preferably more.
This makes a lot of sense if you think about factor loadings in terms of what they are, correlations of an item with a factor. With correlations, if you have a very large correlation in the population, you’re going to find statistical significance even with a small sample size. It may not be precisely as large as your population correlation, but it is still going to be significantly different than zero.
So … this data set of 25 respondents that I received originally had 17 items. That seemed clearly too many for me. I thought there were two factors, so I wanted to reduce the number of variables down to 8, if possible. I also suspected the communality estimates would be pretty high, just based on previous research with this measure.
Here is what I did next :
- Parallel analysis
- Factor Analysis
I can’t believe I haven’t written at all on parceling before and hardly any on the parallel analysis criterion, given the length of time I’ve been doing this blog. I will remedy that deficit this week. Not tonight, though. It’s past midnight, so that will have to wait until the next post.
In a very random life event, I was asked a lot of questions recently by people exploring making a movie about my life. This is not the interesting part, because in Hollywood people are always talking about making movies that come to nothing …
The interesting thing was how many times the answer to a question was,
Sister Marion, my sixth-grade math teacher.
I was not a very prepossessing child.
In fact, if there was such a word as anti-possessing (which there is not), that would have defined me well. I was short, overweight, often dressed in my brother’s too-big clothes because I was too lazy to look for my own uniform and didn’t care about my appearance. I was also the type of child who knew the definition of words like ‘prepossessing’ and mocked other children, and teachers, if they did not. It probably doesn’t surprise you to hear that I was not wildly popular.
My grades were not the best, partly because I often forgot my homework in the mad rush to get five kids out the door early enough that my mother could make it to work on time. Partly it was because I am EXTREMELY near-sighted, a fact no one discovered until the third or fourth grade (thank you, Lions Club vision screening!) and even after that I usually could not see the board because I could not manage to have a pair of glasses for more than a few weeks without losing them. Glasses were not cheap and my family didn’t have a lot of extra cash so it would usually be months between pairs.
Then, I got the chicken pox and was out of school for a week. Despite all of the bewailing about how stupid today’s children are compared to yesteryear, back then we learned fractions in sixth-grade, not fifth, and I had missed the entire week when these were introduced. A petty teacher (and the world has too damn many of those), might have been gratified by the fact that a pain-in-the-ass, know-it-all kid was finally going to be put in her place.
I’d like to think that Sister Marion realized that the only thing I felt I had going for me was being smart and that’s why I had to rub everyone’s face in it. Maybe she realized I needed a friend, and a new perspective.
Whatever it was, she paired me up with another child in the class, Diane, who wasn’t a star student overall, but was very good at math, and told her to explain to me what we had learned while I was out. Not only did I get caught up on fractions, but I learned not to underestimate people based on appearances or first impressions. Just because a person wasn’t a great reader didn’t mean she couldn’t be good at math. Diane and I actually had conversations, and she introduced me to another friend of hers, also named Diane. I called one of the Dianes on the phone – it was the first time I had ever had another kid at school to call – and I was 11.
Sister Marion was nice to me. If you think every teacher is nice to every child then perhaps you need to go back and read the beginning of this post. When I think back, I can only think of two teachers I had before I got expelled from the public school system who were consistently nice to me, Sister Marion and Mr. Cartwright, my 8th grade algebra teacher.
It’s probably no coincidence that I’m good at math and made a career of it.
It’s funny how often when they asked me questions, Sister Marion’s name came up.
Did you have a teacher who you particularly admired?
Was there a teacher who interested you in mathematics?
What made you decide that you wanted to teach?
Who were your role models in life?
I’m not saying that she was the only person who was a role model or who made a difference. However, she was exactly what we try to be at 7 Generation Games – a change in the trajectory that made me shift from doing all right in school with no effort to doing better and better with more effort. She was a person that made me think I could be more than ordinary.
Of course I make an effort to encourage the students who show exceptional effort and ability. Then, I remember Sister Marion and make an extra effort to also encourage students who are annoying, rude, don’t do their work.
When I think of Sister Marion, I am reminded yet again of the truth of that saying:
I touch the future. I teach.
Want to see what I did with math once I grew up?
Since I already called my mom on Mother’s Day, I thought that I’d talk about another woman who was important in my life, a mentor, who I probably haven’t talked to in 20 years. (I know, I’m such an ungrateful bitch. )
Dr. Jane Mercer was not even in the same department as me. My dissertation was an analysis of the psychometric properties of Wechsler Intelligence Scale for Children – Revised , Mexicano, and she was a sociologist renowned for her expertise on the impact of social and cultural factors on intelligence test scores.
Shortly after I finished the first draft of my dissertation, my advisor received some distressing news (no, it wasn’t that he was my advisor, he already knew that). He and his wife had begun dating as very young teenagers. Other than his military service during World War II, they had been together ever since. When she was diagnosed with cancer, he walked into the dean’s office and just said, simply,
… And went on sabbatical with about a four-minute notice.
Everyone completely understood. His colleagues took over committee responsibilities. As his doctoral student that was furthest along, I taught his courses, like inferential statistics.
I was his only doctoral student writing a dissertation, and someone needed to step in to supervise my research. That was Dr. Jane Mercer.
Not only did she read every draft of my dissertation, recommend articles I read and journals to submit publications, introduce me to people at conferences (not a gesture to be underestimated when one is looking for a position) but, more importantly, she provided advice on life.
Here are a few of the things I learned from Dr. Mercer just by observing her.
1. NO MATTER HOW FAR YOU HAVE GONE DOWN THE WRONG ROAD, TURN BACK! Taped over her desk, Dr. Mercer had a piece of paper with this proverb typed on it. No matter how far you’ve gone down the wrong road, turn back. We’re told in America that quitters never win, bloom where you’re planted, you can’t fight city hall, you’re never going to win against big corporations. Making a change in anything from your employer to your gym to the crowd you hang around with can be treated as an act of disloyalty. People stay in situations long, long after they should have left because they are ‘committed’, ‘invested’, ‘cannot leave now’. The unwillingness to turn back after going a long way down the wrong road is the second biggest barrier most people’s happiness. The biggest is fear, which leads me to …
2. Have the courage to speak the truth as you see it. Being the most brilliant researcher in the world does no good to anyone if you are afraid to publish and publicize unpopular results. In the 1970s, many people thought intelligence tests were the answer to psychology’s long history of physics envy. At last, we were a real science with actual numbers, not this whacko dream interpretation stuff but measurement – hey, IQ even has a math word – quotient, in the name. Not to mention, companies like The Psychological Corporation and Educational Testing Service were big business (still are). Jane Mercer sincerely believed intelligence tests systematically underestimated the intelligence of low-income, minority children. In the case of Diana vs the State Board of Education, a lawsuit was filed on behalf a few Mexican-American children, including a little girl who spoke Spanish as her first language, was tested in English and determined to be mentally retarded. All of the big names (and big money) lined up on the side of the State Board of Education and Jane spoke up for the side of Diana. This may not seem like much now, but back then she had to stand up to a LOT of opposition, it was not happy times. She did it anyway.
3. Yes, you CAN have a job and a family. Men do it all the time. Jane was older than me and of that generation that was told women could either have a career or children but not both. By the time I met her, her four sons were all adults. She and her husband got along fine and seemed to agree that since they were both parents of these children they could both engage in parenting them. We couch things in daunting terms “Can women have it all?” Of course no one has it ALL. I’m finishing this blog post in the Denver airport. That empty spot you see at the end of jetway is where the plane I am taking back to Los Angeles should be.
I would like to have a non-eventful flight out of Denver airport, just once. You see, none of us can have it ALL but no one asks men whether they think they can manage a career and children.
4. Being the first or only woman in an area doesn’t mean you have to go along with that happy-to-be-here crap. Yes, she was a tenured professor at the University of California, which had damn few of them, but that didn’t mean she had to accommodate in any way because of her gender. Don’t take on female doctoral students because you don’t want to be type-cast as ‘only a good advisor for women’? Screw that! If they needed an advisor and she could help, she was on board. Don’t speak out about intelligence testing because people will think you are shrill or too emotional, not a real academic? Screw that twice! As you can see, I have taken that lesson deeply to heart but with less of her limits on profanity.
Woo-hoo – plane boarding now – only 90 minutes late – gotta go. Happy Mother’s Day.