I’ve picked up the book Wonder Women, several times, read a few dozen pages and put it down again. Half-way through with it, I’m still not sure what I think of it.

I make an effort to read books from people who have a very different perspective from me. Most recently, I finished “Tell My Sons”, by a career army officer who has been diagnosed with terminal cancer. Highly recommended. It was interesting to see how strongly he felt about the army, how much it was part of his identity. His take on many things, from his view of his father to marriage were different from mine. I understand how people can argue a lot and still be in love, and I appreciated his openness about that. The Invisible Developer and I never fight, just because he’s the calmest person in the world. I see people out on the lake ice fishing all of the time, and see kids building snow forts in the winter. I hate cold weather and the I.D. and The Spoiled One both are far more into watching Game of Thrones than building any castles.

The jarring note I keep running into with Wonder Women, is where she talks about women of her generation. We’re five years apart and almost everything she has said doesn’t apply to me. I never had Barbies – we didn’t have money for things that weren’t necessities, which certainly included Barbies. Despite her saying that every little girl, even the one she adopted from Russia, wanted Barbies, I’m pretty certain I never did. (Barbie gets a lot of press in this book.) I never recall anyone saying or even implying that boys didn’t like smart girls. Probably some didn’t but they were the really, really stupid boys I wasn’t interested in anyway.

She talks about how impossible it is to be happy, successful, division 1 soccer champion and get excellent grades, be a mother and an executive. I won the world judo championships, finished college at 19, earned a PhD, founded a few companies, raised three kids (working on the fourth). I’ m pretty happy. Some of it has been hard, sometimes damn hard. As I always tell the kids I coach about winning, “If it was easy, everyone would do it.”

Perhaps there are two big differences between Spar (the author of Wonder Women and president of Barnard College) and me.

  1. I expected life to be hard. There are some things, like your husband dying, that it is impossible to be prepared for, but if you expect to have to work to exhaustion some times, for life to be unfair, for some men to be jerks, for the opportunities and expectations for women and men to be far from equal – it is perhaps easier to deal with when reality is that way.
  2. No one ever expected me to be perfect. In fact, I think people were more likely to expect me to be in prison.

One of the things that bothers me about Spar’s book is that so far she hasn’t mentioned that schools like hers are part of the problems she perceives. Some how or other, Washington University in St. Louis accepted me as a 16-year-old freshman based on nothing more than kick-ass SAT scores. I got an F in sophomore English because I thought the book of Chinese poetry I was assigned to read for a book report was stupid and I turned in a report on Anna Karenina instead. Then there was that episode in ninth-grade where the vice-principal was going to give me swats (yes corporal punishment was allowed then) and I told him that if he hit me, I would hit him back. I got expelled.

My ‘permanent record’ was far from perfect.

I wonder how many schools now would give someone like me a chance. What Wash U perceived (I guess) is that I had potential. When you are 16, you aren’t supposed to have achievements, for heaven sakes, you’re a kid. The Spoiled One plays soccer. Someone asked me what her goals were, to get an NCAA Division 1 scholarship or what. I said, “I think she just likes to play soccer.”

On a PRESCHOOL application, one of the questions was “What are her extracurricular activities?” Darling daughter number one was tempted to put “playing with my little ponies.” Instead, she decided to apply to a different preschool.

My point – no one is a success at five or sixteen. Maybe if schools like Barnard (and Harvard, where she taught previously) realized that, Spar wouldn’t have needed to write a book.

If you want to be a programmer, entrepreneur or a statistician, the best advice that I can give is,

“Don’t believe other people are smarter than you.”

Sometimes that is hard advice to take. I read an interesting blog post by Ali Berlinksi, “I miss being stereotyped”, about being Asian-American and moving to an area in Spain where people had met so few Asians that they had no stereotypes. She said she missed the advantage of having people just assume she was studious, intelligent and good at math.

Of course, as she notes, most stereotypes are not nearly so benign. Many groups – Native Americans, women, Hispanics – are assumed not to be as good in math, or programming or really not the start-up type. Not many people say it that bluntly any more.

Last week, I happened to be in a seventh-grade math class at a predominantly Hispanic school, I asked,

“How many of you would like to be a programmer or design computer games?”

One girl’s hand shot up while the rest of the students looked at me (and her), as if it was a crazy question. I persisted,

“Why not? Seriously, why not? “

This wasn’t a remedial class. The math the class was doing when I walked in was closer to eighth-grade level than seventh, and remember, the school year just started. It’s often more a lack of encouragement rather than being actively discouraged.

My friend, Hayward Nishioka, is a phenomenal judo instructor and competitor, author of several books. We were having lunch this week and he said to me,

“You know, you need to give young people permission. You say to the student, you know, YOU could earn your black belt, YOU could become an instructor, too. You have that ability. Then they go ahead and do it, because you have given them that permission.”

I’m not sure that is true of everyone. Some people telling them they can’t it just makes them more determined. But, he is correct about a lot of people. He’s also correct that it is harder to keep going when you don’t have a lot of confidence you will succeed.

Programming is one of those things that takes a lot of perseverance – why do you think they call it hacking? It’s easy to get discouraged when your first attempt doesn’t run – and believe me, once you get out of CS 101 and get into real problems, your first attempt almost NEVER runs. Sometimes your second, third and eleventh don’t either. It happens to everyone. It’s normal.

What I’m afraid I see in too many classrooms, though, is that students have not been encouraged to believe they will succeed in the end or that that math and programming are things they should expect to be able to figure out. So, when they have that fifth failure, they just assume they aren’t smart enough.

Here’s another piece of good advice. Check out github.com – a place where you can find a generous number of code examples (and I feel terrible guilt that I have not contributed – although it is written on my whiteboard as one of the ‘must get around to’ items). When you are first learning a language, it’s great to see finished examples of  ‘the big picture’. Reading books on a language is great, but no substitute for actual working on a project. For me, starting with something like programming a tip calculator is as boring as watching paint dry. I’d rather jump in there and do something like a game. With github, you can read through examples and see where what you are learning is being applied.

Not everything on github runs or works as desired. People put up projects for review, projects that are in progress. As you gain more experience, you might want to download a project that is similar to what you want to do and just modify it. You’ll certainly see code that you would have written differently. You’ll see code where it is obvious that the person who put it up actually just downloaded it from somewhere and modified it, because there are modules, functions, that don’t really do anything — they’re left over from whatever the original program was. That’s your first insight into no, not everyone is smarter than you.

The nice thing about github is you can kind of lurk anonymously and look over other people’s shoulders and see that  no one else is perfect either.

As you gain even more experience, you’ll eventually start downloading code that you think, “Hey, I could do this part better. …”

Someone told me, no one has math anxiety, they have dumb anxiety – they are afraid that other people will think they’re dumb. This is another thing that github may help you with. I’ve never once looked at anything, and thought, “That person is really dumb.”  More likely, I’d think, they must be new to programming.

On occasion, I’ve downloaded a program from someone who had a reputation as being really smart, and found ways to improve it, for my purposes anyway. Did I think, “Wow, I must be smarter than that person”?

Not even once. What I actually think is, “This saved me a couple of days work and I really feel good that I can improve on something someone this smart wrote.”

So, my two points, before I toddle off to bed with a glass of Chardonnay:

1. Math, statistics, programming – you can learn it. Just start and keep going.

2. Github is awesome.

 

Years ago, I was teaching a course on psychological assessment in a program on Addiction Counseling. The counseling professors had a term for people who moved to another city or state in an attempt to solve their substance abuse problems, “going geographic”.

The problem with that strategy, they said, is that wherever you go, there you are. You take your problems with you.

I was thinking of that this morning as I drove through downtown LA on my way to a board meeting. I’ve lived a lot of places in my life – four countries, six states, eleven cities. Yet, every time I fly into LAX and see our giant glow sticks , or drive through the city and see the downtown skyline, I think how much I love LA.

20130922-004700.jpg

Yes, traffic is a mess, the cost of living is through the roof and we have our share of problems, but there is no place like LA.

I love it here so much because this is the place where I finally got my shit together. When I was young, well, things were not so good. I lived in Halifax for a while, with relatives, and I was still the original pain in the ass, but things got better. I lived in Tokyo for a year, made it through my junior year of college, and got my act together somewhat more. I lived in San Diego for a while, got a start as an engineer, divorced my first husband, had a baby (not in that order), won a world championships.

Skipping ahead … for the past 17 years, I’ve lived in the same house, raised four children, made wonderful friends, established two companies, been married to the same husband. Over the years, I’ve become more mature, things that used to bother me no longer do. To a much larger extent than when I was younger, I have figured life out.

Which is why, I realized this morning, I love LA so much. Because, wherever you go, there you are.

On October 3rd, Turtle Mountain Band of Chippewa is hosting the first ever (but to be annual) Tribal Disabilities Conference. The theme this year is “The World of Disabilities” and I am super-excited because I am going to be giving the keynote address along with my long-time friend and business partner, Dr. Erich Longie.

Agenda for conference

 

Click here for  a pdf file that is accessible to individuals with visual impairments.

There’s no real point to this post except that I’m happy to be invited to speak at this historic event, and I’m also excited to see my friend and other long-time colleague, Willie Davis, again. There are rumblings and rumors that some other old friends of mine may be in the vicinity as well. I’ve read that as people get older, marry, raise children, their friendships are less important to them. In my case, it’s the opposite. I’ve been through a lot of twists and turns in my life, and the older I get, the more I value the people who have been with me a long part of the path.

If you are in North Dakota, South Dakota, Minnesota, Manitoba or Saskatchewan, you’re not a very far drive away and it probably won’t be snowing yet. Come on down, stay at the Sky Dancer and come hear some really good speakers.

I’ll be honest, I bought Debora Spar’s book, Wonder Women, in part because of a New York Times review that quoted her as saying,

I was walking in National Airport, I had a very big interview. It was actually with the C.I.A. I was feeling very professional, and I was wearing my graduation suit, sensible shoes and I had this briefcase. There was a man walking towards me in the airport, a good-looking older man. And as he passed me, he said, “Wow, what a pair of tits.” I said, That’s it, they’re going off.

I picked up the book in Barnes and Noble expecting to hate it, thinking to myself,

“Right, just what the world needs, another wealthy, white woman executive who has had every possible break in her life, who represents maybe 1% of women telling women what they need to do.”

In the first few chapters, so far, the book is okay. I give her credit that she tries to be aware of her privilege, saying honestly  that she has never been poor and doesn’t really know what it’s like to not be able to feed your children, that she has been married for twenty-five years to a supportive spouse and never had to cope with being a single parent, compromising her career for her spouse and all those other problems of the 99%.

Still, she does over-generalize, saying that by the time she was in college Title IX had passed, women had the vote, all these battles for rights had been fought and won and that “Women of my generation ….” did not consider themselves feminists because they saw feminists as angry people who hated men and never shaved their legs. I looked it up, Spar is five years younger than me. She graduated from college six years after I did and received her Ph.D. the same year (I worked as an engineer for five years between my MBA and my doctoral program). I would think we are of the same generation and I never thought any of those things. I saw feminists as people who made it possible for me to compete in sports in college and in the world championships, to work as an engineer and to get an MBA. I consider myself a feminist because I believe that women should have equal pay for the same work, equal opportunities to attend school or to play sports. I shave my legs when I think about it and I just had my sixteenth anniversary for which The Invisible Developer surprised me with a new wedding ring with rubies and diamonds. Not necessary at all but appreciated.

Coffee

I was talking to my sister about this, who also considers herself a feminist, and she said,

“Wow! If I was walking through the airport and a man said, ‘Nice tits!’ I think I would accidentally spill my coffee on him. And if I wasn’t carrying a cup of coffee, I’d go and buy one to spill on him. Having my breasts surgically removed would be the last thing that would occur to me.”

You want to not be taken seriously in America as a woman? Try coming from a poor family, being African-American, Native American or Hispanic. Unfortunately, there is no surgical cure for that, although some people “pass as white” if they are light enough, change their names, cut their hair or whatever it is they need to do to look professional enough.

Dr. Spar’s choices have paid off for her. Although we earned our doctorates the same year, she is president of Barnard College and I’m not. Sometimes I buy a book because I want to try  to understand a mindset that is completely foreign to me – and hers is one of those books. Everything from the choices she made as an adult to the toys she played with as a child are hard for me to understand. I didn’t have Barbie dolls as a child because we couldn’t afford it. My daughters had few Barbie dolls because I told them, “Barbie is stupid.” Darling daughter number one had a few because her paternal grandmother bought them. The Perfect Jennifer and Darling Daughter Number Three had a few, which, after  watching The Addams Family, they primarily used for cutting their heads off. The Spoiled One never had any Barbies because her sisters were in high school and college by the time she was old enough to want one and they told her, “Barbie is stupid.”

My point, and, by now you may have despaired of me having one, is that for me reading Spar’s book is as foreign an account as of a woman living in America who chooses to wear a burka, not work and not drive a car. Yes, I acknowledge that choice is your right, but it leaves me shaking my head and wondering, “Why would you choose that?”

Perhaps your husband owns all the oil fields in Saudi Arabia, is richer than God and you could afford to buy The Grove. Or maybe you’ll get to teach at Harvard and then become president of Barnard College. So, by some accounts, you’re very successful, and I hope you’re happy with yourself, both Mrs. Saudi Arabia Oil (hypothetical) and Dr. Debora Spar (actual), and I say that most sincerely.

In the same New York Times article, Spar comments that teenage girls are exhausted by trying to be “perfect”, take all of the AP classes, get high SAT scores – without acknowledging the hypocrisy that her college (and its six sister clones) , require exactly that for admission.

Maybe I was home sick with the measles and missed school the week that they indoctrinated us all that we had to fit in some mold or we would never be successful in life.

If so, thank God for the measles.

 

 

 

Box and whisker plots can give you an understanding of your data at a glance – IF you know what you’re looking at.

The BOX extends from the 25th percentile to the 75th percentile. That line in the middle is the median, also known as the 50th percentile. The diamond inside the box is the mean. The whiskers, those two lines at either end, extend from the box as far as the minimum and maximum values, up to 1.5 times the inter-quartile range. The inter-quartile range is the distance from the 25th percentile to the 50th. In other words, each whisker MAY extend up to 1.5 times the length of the box. (Different software packages use different values for the whiskers. This is what SAS does.) If there are any outliers beyond 1.5 times the inter-quartile range, they’ll be shown as asterisks after the end of the whisker. In the t-test output, SAS also shades an area for the 95% confidence interval.

The example below is part of the output from a t-test task in SAS Enterprise Guide. It is from the control group in our pilot study of Spirit Lake: The Game. The value plotted is the difference between post-test and pretest. So …. you can see that the mean difference between pre- and post-test for the control group was close to zero. The median was a little bit above zero. There are no really extreme outliers, and the distribution is a little skewed to the left, with the mean to the left of the median. The most extreme difference for the control group was an increase from pretest to post-test of 11 points. We can also see that zero falls squarely in the middle of our 95% confidence interval, so we can accept the null hypothesis that no significant increase in performance on the math test occurred for the control group. This isn’t really unexpected – you wouldn’t really anticipate large improvements in mathematics performance over only eight weeks.

box and whisker plot for control group

 

Let’s take a look at another box and whisker plot, this time for our experimental group in the same study.

 

We can see right away that the whole distribution has shifted to the right, and this time it is skewed to box and whisker plot experimental groupthe right. The median looks to be at about four points higher on the post-test and the mean is above that. The 25th percentile is at zero, in other words, 75% of the students showed some improvement from pretest to post-test. The 75th percentile is a nine-point improvement for the experimental group, versus three or four points for the control group.  It can also be seen that zero is not within the 95% confidence interval, not even particularly close, so we reject the null hypothesis that there was no improvement for the experimental group.

two plots side by sideIf we line the plots underneath each other, with zero at the same point, it is particularly easy to see that the improvement in scores from pretest to post-test for the group who played the game was noticeably higher than for the control group.

So, there you have it, a couple of brief looks at the data improves your understanding of the results.

What is item difficulty analysis and how is it helpful?

Item difficulty analysis is simply examining what percentage of students answered each item correctly. Item difficulty analysis is one basic way to establish test validity. One would expect that items at the second-grade level would have the lowest level of difficulty, being answered by the largest percentage of our students, and at the other end, the items at the fifth-grade level would have the highest difficulty , and be answered correctly by the fewest students. Since the items are scored  0 = wrong, 1 = right, we can use the means to see what percentage of students answered correctly. A summary table can give you a nicely formatted table for a report but here we’re just exploring our data, so using the univariate statistics you already have as a result of a CHARACTERIZE DATA task in SAS Enterprise Guide is easier.

1. Click on the univariate statistics data set produced by the CHARACTERIZE DATA TASK to select it,

List data selected from menu

2. From the top menu, select TASKS > DESCRIBE > LIST

3. From the variables to assign pane, select the ones you want in your report, in this case Variable, N, NMISS, Mean, Min and Max.

List Data menu - data tab4.  Select the records you want in your report. (If you want all of them, you can skip this step.) Now this part is a bit confusing because there is a variable named “variable”. Your univariate statistics data set has a column named ‘variable” and in it is the name of each variable for which you will be listing the N, NMISS, mean, etc. I only want the scored variables in my analysis, where they were scored 0 for incorrect and 1 for correct.
Click on EDIT from the button you can’t see in the screen shot above because I cut it off, but there really is an edit button, I promise. From the first drop down menu, select Variable, from the next select Not In A List, then click on the three dots to bring up a new window. In that window, click on the bottom left where it says Add Values. I selected q1 – q24,gender, missdata,age,pretotal, posttotal and usernum to drop. Click OK.

Drop items from analysis5. Format the columns on your report. This part is also optional but I personally find it easier to scan through reports without six decimal places in every mean. So, I change the format by right-clicking on  Mean and selecting Properties. I click the CHANGE button next to format.

Drop down menu produced by right-clicking on mean

Then I click on Numeric for the format category, and scroll down to w.d. Under attributes, I put 8 for width and 2 for the number of decimal places. Then click OK.

6. Next, just to make the report even easier to read, I click Options and un-check the box next to Row Numbers

Click RUN to run the task

You don’t need to always export your output files to use them in some other program.  I needed an xls file for an example, so at this point, I selected all of these data from the output open in Enterprise Guide and copied, and then pasted them into an OpenOffice Calc file (Excel would work just as well).

I sorted them in descending order and here is a partial picture of the result. I also changed the name from “variable” to “item” to make it less confusing.

table of items sorted by item difficulty

It’s clear that the post-test and pre-test do not have the same number of people, so I need to be cautious of comparing them directly. However, within test comparisons are fine. The test items are in order of grade level, beginning with second-grade level through fifth-grade. The first few items should be answered correctly by the most people. We can see that is true both for the post-test and pre-test, although it’s not perfect. Three items at the second-grade level were answered by over 80% of the students who took the post-test. We can also see that, generally, a higher percentage of students answered the post-test questions correctly than the pretest, as we would hope.

If you could scroll down to the bottom, you’d find that items 5 and 6 have some of the lowest percentage correct of any item, so I make another note to examine those items in more detail.

 

I was going to write about item analysis today but all of the tweets and posts on sexism in the technology field distracted me. So, here are my comments on that and then I’m going back to work.

1. I think sexism is cowardice and bullying.

Having coached and competed in combat sports for 43 years, I can tell you that pretty much all bullies are cowards because by definition, it means picking on someone weaker, less powerful than you. (There are some bullies who are just hateful to everyone but they are the minority.) In my life, I’ve been exposed to very few of the type of explicit sexist comments and attacks I see mentioned. One reason, I am sure, is that anyone who has met me for more than five minutes realizes that if they grabbed me, I’d be very likely to break their nose. Yes, you may be bigger than me, but I cheat. It wouldn’t be beyond me to pick up the glass on the conference table and smash it in your face. I don’t say that in a threatening way but as a fact. There are reasons I prefer not  to divulge behind it. The one time a man did come up behind me and run his hand up my leg, I turned around, picked him up and threw him into a wall. It wasn’t a feminist statement, it was a reflex. If you were a man and another man came up behind you, slid his hands between your legs and grabbed your crotch, what would your reflex be? It depends where you grew up and how. In some neighborhoods, you might turn around and punch him in the face. In others, you’d turn around and yell at him, maybe shove him, ask him what the hell he thought he was doing.

Now I’m old and people aren’t as inclined to grab me, which is fine by me.

I’m not particularly nice and never have been. A few years ago, I was at a judo practice, with a visiting team of Japanese male high school students and a group of male and female players from California. After practice, it was announced that the group was going to Hooters. The girls made excuses why they had other things to do after practice. I went up to the father who had announced this – a very nice man, who I like and saw regularly since his son and my daughter trained together, and said,

“Are you fucking kidding me? We go on all of the time about how there aren’t enough girls in judo and now you’re taking the kids to Hooters?”

Let me end this by saying, we ended up going to a different place and the girls went also. I read a letter someone wrote to her daughter’s teacher in a computer programming class, and I had the same reaction. I can guarantee you, and my daughters will back me up, that the very first time an incident of sexism happened to one of my children, I would be in that classroom and ask the teacher, in front of the students, if necessary,

“Are you fucking kidding me?”

I cannot say that it would never happen again, nor even never happen again to my daughters. Many times, growing up, they were mortified by my unwillingness to “go along”. As adults, and even teenagers, though, they have learned to stand up for themselves, all four of them. I can guarantee you that teacher would have taken a hell of a lot closer look at what was going on in his class, for all he might have gone to the teachers’ lounge and ranted about what a bitch I am. So, yes, that is one of my answers to everything – be a complete bitch when it’s called for. If I had been at Tech Crunch, I would have stood up and shouted,

“Are you fucking kidding me?”

2. Success is the best revenge

While I haven’t experienced a lot of explicit sexism and discrimination,  I think there has been plenty that was implicit, from my graduate school days when the professor would forget and address our classes (95% male) as “Gentlemen” to not getting as many job offers as my classmates, nor at as high a salary. The latter is a combination of things, though. I don’t come from a wealthy family, had no connections, had no idea how to dress or talk to make the executive types feel at ease (I definitely did not “fit in”), and, as we have already established, I’m not that nice of a person. More implicit discrimination has been the lack of encouragement and subtle discouragement of taking advanced math classes, programming classes. I had a friend when I was young who was Asian-American male, and he used to laugh about how our bosses and colleagues automatically assumed that he knew the answers to programming questions, that he knew a particular language. Being a good guy, he was also embarrassed by it. The truth is, he knew some languages better than I did and vice versa. Since our offices were side by side and we had lunch together a lot with groups of co-workers (he was dating my friend), it was easy for both of us to see the great discrepancy in the assumptions made about us, how often people asked me, but not him, “But did you actually ever have a course in multivariate statistics?”  Answer: Yes. Several, in fact, addressing different types.

It is irritating to know that many people perceive me as as inferior and continually having to prove myself, to continually get asked about my degrees, credentials, “but have you ever …” , as they go through an increasingly long list of attempts to disqualify me. Yes, I had  that class. Yes, I have written software for large organizations. Yes, some of it is for end users. Yes, I have a Ph.D. Yes, I have published in academic journals. No, I don’t have a degree in computer science — when they finally get to one, there is an almost perceptible sigh of relief, as my questioner condescendingly explains, “Well, you see, the reason that you  did not get selected for X is that you don’t have a degree in computer science … ”  Never mind that half of the men accepted didn’t either.

It is irritating and it is not fair. My solution is just to go back to work and succeed in spite of it. I don’t go to events sponsored by organizations like TechCrunch because it’s pretty clear they aren’t interested in me, so I put my efforts in directions like the Small Business Innovation Research awards, or Kickstarter, where the focus is more on the product than the producer.

Now that I’ve vented, I’m going back to work.

Sometimes the simplest things can make life easier. When I start exploring a new data set, the first thing I do is the Characterize Data task. With even modest-sized datasets this produces a lot of output. For example, the data from Spirit Lake: The Game, with about 80 variables and 88 subjects produced 93 pages. Clearly, I’m going to mostly scan through this looking for outliers, problems or anything interesting that strikes me before moving on to more focused analysis.

What would strike me as interesting?

Histogram of pretest scoresThis, for instance, shows the pretest scores for the whole group. You can se that the mode is 10 and the distribution is somewhat heavier to the right of that mean. This is interesting because the first 18 questions on the test are fourth-grade math or lower and more than a third of the students are in the fifth-grade. I’d have expected higher scores. At this point, I want to make a note to myself to take a look at the frequency distributions of grade by pre-test score and also the percentage correct by grade.

Moving on, I see that the post-test score graph seems to have shifted a bit to the right, as one would hope, if the game is successful in raising students’ math scores.

post test scoresYou can see that the mode is now 12. However, if you look closely, you can also see that t he Y axis is not the same on both graphs. It would be nice to take a look at these with the same axis. It would also be nice to take a look at these graphs separately by group, experimental and control. Of course, I want to do a dependent t-test, also a repeated measures ANOVA, with grade and group as the independent variables.

Now, I will certainly remember the repeated measures ANOVA and the t-test, but what about that other stuff? Not crucial, but, like I said, interesting, and might be nice in a presentation for a non-technical audience.

So … right in your SAS Enterprise Guide project there is an often over-looked option … go to FILE > NEW > NOTE

1NoteMenuand it will bring up a sticky note where you can write whatever it is you want to remember. Just close the note and it stays in your project as a small icon. Now,  when you come back to it – because you really are busy and got distracted by 47 other things you had to do – you can pick up right where you left off. As you are reviewing your project wondering if there is any analysis you wanted to do that you forgot, you can take two minutes to click through your notes.

I knew this feature was there for years and never used it, and now, because we have so many different tasks happening at once with our new start-up and I’m continuously being interrupted, it is a godsend.

 

 

 

 

I’m doing a Hands-On Workshop in Las Vegas at the Western Users of SAS Software conference in November. When they asked me for some topics, I thought about factor analysis or categorical data analysis, but I’m already doing a couple of other talks on those procedures.

I really felt like one of the most overlooked parts of data analysis was exploring your data and getting cozy with it.

Maria and Eva cuddled up

So, I’m talking about exploratory data analysis, with a focus on the basics. There is a lot of talk about BIG data, but many of us spend our days with not-very-big-at-all data where a few errant records here and there can throw off your results.

Also, I don’t think SAS Enterprise Guide was really designed for ginormous data sets.

My workshop uses data from the pilot study for 7 Generation Games. Before you can go out and collect enormous amounts of data, you need to pilot your measures, your methods – and at this stage it really is important to understand your data.

One quick tip  —

If you do the Characterize Data task (which you always should), it produces a data set of frequency distributions and another of univariate statistics. Notice in the second of the three pop-up windows, that’s the default. People often ignore those data sets created, which is too bad,

Characterize Data window with defaults

You can use the univariate statistics data set to create tables like the one below, selecting which descriptive statistics and variables you want.

Report of descriptive statistics

 

Of much more use than simple reports like this, you can sort your univariate statistics data set to get things like items in order of difficulty.  As always, I have way more material than I can fit in the time allotted, but one part I will definitely keep in is the usefulness of these data sets for getting a second quick look at your data. (The first is the Characterize Data task. Didn’t you just read that I said you should always do it? Sheesh!)

If you’re interested, you can come to the presentation, “Telling stories with your data – graphs, tables and basic, basic statistics with SAS Enterprise Guide” in Las Vegas in November. Or you can check back on this blog as I ramble on about it while writing my paper.

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