I’m preparing a data set for analysis and since the data are scored by SAS I am double-checking to make sure that I coded it correctly. One check is to select out an item and compare the percentage who answered correctly with the mean score for that item. These should be equal since items are scored 0=wrong, 1=correct.

When I look at the output for my PROC MEANS it says that 31% of the respondents answered this item correctly, that is, mean = .310.

However, the correct answer is D and when I look at the results from my PROC FREQ it shows that 35% of the respondents gave ‘D’ as the correct answer.

What is going on here? Is my program to score the tests off somewhere? Will I need to score all of these tests by hand?

Real hand soaps

I am sure those of you who are SAS gurus thought of the answer already (and if you didn’t, you’re going to be slapping your head when you read the simple solution).

By default, PROC FREQ gives you the percentage of non-missing records. Since many students who did not know the answer to the question left it blank, they were (rightfully) given a zero when the test was automatically scored. To get your FREQ and MEANS results to match, use the MISSING option, as so

PROC FREQ DATA =in.score ;

You will find that 31% of the total (including those who skipped the question) got the answer right.

Sometimes it’s the simplest things that give you pause.

Do you have a bunch of sites bookmarked with articles you are going to go back and read later? It’s not just me, is it?

One of my (many) favorite things at SAS Global Forum this year was the app. It included a function for emailing links to papers you found interesting. Perhaps the theory is that you would email these links to your friends to rub it in that their employer did not like you well enough. I emailed links to myself to read when I had time. Finally catching up on coding, email and meetings, today, I had a bit of time.

I was reading a paper by Lisa Henley

A Genetic Algorithm for Data Reduction.

It’s a really cool and relatively new concept – from the 1970s – compared to 1900 for the Pearson chi-square, for example.

In brief, here is the idea. You have a large number of independent variables.  How do you select the best subset? One way to do it is to let the variables fight it out in a form of natural selection.

Let’s say you have 40 variables. Each “chromosome” will have 40 “alleles” that will randomly be coded as 0 or 1, either included in the equation or not.

You compute the equation with these variables included or not and assess each equation based on a criterion, say, Akaike Information Criterion or the Root Mean Square Error.

You can select the “winning” chromosome/ equation either head to head, whichever has the higher AIC/ RMSE , although there are other methods of determination, like giving those with the higher criterion a higher probability of staying.

You do this repeatedly until you have your winning equation. Okay, this is a bit of a simplification but you should get the general idea. I included the link above so you could check out the paper for yourself.

Then, while I was standing there reading the paper, the ever-brilliant David Pasta walked by and mentioned the name of another paper on use of Genetic Algorithm for Model Selection that was presented  at the Western Users of SAS Software conference a couple of years back.

I don’t have any immediate use for GA in the projects I’m working on at this moment. However, I can’t even begin to count the number of techniques I’ve learned over the years that I had no immediate use for and then two weeks later turned out to be exactly what I needed.

Even though I knew the Genetic Algorithm existed,  I wasn’t as familiar with its use in model selection.

You’ll never use what you don’t know – which is a really strong argument for learning as much as you can in your field, whatever it might be.

We’ve looked at data on Body Mass Index (BMI) by race. Now let’s take a look at our sample another way. Instead of using BMI as a variable, let’s use obesity as a dichotomous variable, defined as a BMI greater than 30. It just so happened (really) that this variable was already in the data set so I didn’t even need to create it.

The code is super-simple and shown below. The reserved SAS keywords are capitalized just to make it easier to spot what must remain the same.  Let’s look at this line by line

LIBNAME  mydata “/courses/some123/c_1234/” ACCESS=READONLY;
PROC FREQ DATA = mydata.coh602 ;
TABLES race*obese / CHISQ ;
WHERE race NE “” ;

LIBNAME  mydata “/courses/some123/c_1234/” ACCESS=READONLY;

Identifies the directory where the data for your course are stored. As a student, you only have read access.
PROC FREQ DATA = mydata.coh602 ;

Begins the frequency procedure, using the data set in the directory linked with mydata in the previous statement.

TABLES race*obese / CHISQ ;

Creates a cross-tabulation of race by obesity and the CHISQ following the option statistic produces the second table you see below of chi-square and other statistics that test the hypothesis of a relationship between two categorical variables.
WHERE race NE “” ;

Only selects those observations where we have a value for race (where race is not equal to missing)

Pretty obvious? Runs the program.

Cross-tabulation of race by obesity


Similar to our ANOVA results previously, we see that the obesity rates for black and Hispanic samples are similar at 35% and 38% while the proportion of the white population that is obese is 25%. These numbers are the percentage for each row. As is standard practice, a 0 for obesity means no, the respondent is not obese and a 1 means yes, the person is obese.

The CHISQ option produces the table below. The first three statistics are all tests of statistical significance of the relationship between the two variables. Table with chi-square statistics

You can see from this that there is a statistically significant relationship between race and obesity. Another way to phrase this might be that the distribution of obesity is not the same across races.

The next three statistics give you the size of the relationship. A value of 1.0 denotes perfect agreement (be suspicious if you find that, it’s more often you coded something wrong than that everyone of one race is different from everyone of another race). A value of 0 indicates no relationship whatsoever between the two variables. Phi and Cramer’s V range from -1 to +1 , while the contingency coefficient ranges from 0 to 1. The latter seems more reasonable to me since what does a “negative” relationship between two categorical variables really mean? Nothing.

From this you can conclude that the relationship between obesity and race is not zero and that it is a fairly small relationship.

Next, I’d like to look at the odds ratios and also include some multivariate analyses. However, I’m still sick and some idiot hit my brand new car on the freeway yesterday and sped off, so I am both sick and annoyed.  So … I’m going back to bed and discussion of the next analyses will have to wait until tomorrow.

So far, we have looked at

  1. How to get the sample demographics and descriptive statistics for your dependent and independent variable.
  2. Computing descriptive statistics by category 

Now it’s time to dive into step 3, computing inferential statistics.

The code is quite simple. We need a LIBNAME statement. It will look something like this. The exact path to the data, which is between the quotation marks, will be different for every course. You get that path from your professor.

LIBNAME mydata “/courses/ab1234/c_0001/” access=readonly;

DATA example ;
SET mydata.coh602;
WHERE race ne “” ;
run ;

I’m creating a data set named example. The DATA statement does that.

It is being created as a subset from the coh602 dataset stored in the library referenced by mydata. The SET statement does that.

I’m only including those records where they have a non-missing value for race. The WHERE statement does that.

If you already did that earlier in your program, you don’t need to do it again. However, remember, example is a temporary data set (you can tell because it doesn’t have a two level name like mydata.example ) . It resides in working memory. Think of it as if you were working on a document and didn’t save it. If you closed that application, your document would be gone.  Okay, so much for the data set. Now we are on to ….. ta da da

Inferential Statistics Using SAS

Let’s start with Analysis of Variance.  We’re going to do PROC GLM. GLM stands for General Linear Model. There is a PROC ANOVA also and it works pretty much the same.

PROC GLM DATA = example ;

CLASS race ;

MODEL bmi_p = race ;

MEANS race / TUKEY ;

The CLASS statement is used to identify any categorical variables. Since with Analysis of Variance you are comparing the means of multiple groups, you need at least one CLASS statement with at least one variable that has multiple groups – in this case, race.

MODEL dependent = independent ;

Our model is of bmi_p  – that is body mass index, being dependent on race. Your dependent variable MUST be a numeric variable.

The model statement above will result in a test of significance of difference among means and produce an F-statistic.

What does an F-test test?

It tests the null hypothesis that there is NO difference among the means of the groups, in this case, among the three groups – White, Black and Hispanic . If the null hypothesis is accepted, then all the group means are the same and you can stop.

However, if the null hypothesis is rejected, you certainly also want to know which groups are different from which other groups. After that significant F-test, you need a post hoc test (Latin for “after that”. Never say all those years of Catholic school were wasted).

There are a lot to choose from but for this I used TUKEY. The last statement requests the post hoc test.

Let’s take a look at our results.

I have an F-value of 300.10 with a probability < .0001 .

Assuming my alpha level was .o5 (or .01, or .001, or .ooo1) , this is statistically significant and I would reject my null hypothesis. The differences between means are probably not zero, based on my F-test, but are they anything substantial?

If I look at the R-square, and I should, it tells me that this model explains 1.55% of the variance in BMI – which is not a lot. The mean BMI for the whole sample is 27.56.

You can see complete results here. Also, that link will probably work better with screen readers, if you’re visually impaired (Yeah, Tina, I put this here for you!).

ANOVA table


Next, I want to look at the results of the TUKEY test.

table of post hoc comparisons


We can see that there was about a 2-point difference between Blacks and Whites, with the mean for Blacks 2 points higher. There was also about a 2-point difference between Whites and Hispanics. The difference in mean BMI between White and Black samples and White and Hispanic samples was statistically significant. The difference between Hispanic and Black sample means was near zero with the mean BMI for Blacks 0.06 points higher than for Hispanics.

This difference is not significant.

So …. we have looked at the difference in Body Mass Index, but is that the best indicator of obesity? According to the World Health Organization, who you’d think would know, obesity is defined as a BMI of greater than 30.

The next step we might want to take is examine our null hypothesis using categorical variable, obese or not obese. That, is our next analysis and next post.

In the last post, I posed the following null hypothesis as an example:

There is no difference in obesity among Caucasians, African-Americans and Latinos.

You can see the results from the statistical analyses here.

Since my question only pertains to those three groups, let’s begin by creating a data set with just those subjects.

libname mydata “/courses/ab1234/c_0001/” access=readonly;

Data example ;
set mydata.coh602;
where race ne “” ;

Don’t forget to run the program!

Now, let’s do something new and use something relatively new, the tasks in SAS Studio. On the left screen, click on TASKS, then on STATISTICS, then click DATA EXPLORATION.


tasks in left pane

Once you click on DATA EXPLORATION, in the right window pane you’ll see several boxes, but the first thing you need to do is select the correct data set. To do that, click on the thing that looks like a sort of spreadsheet.

tasks2When you do that, you’ll see the list of libraries available to you. You need to scroll all the way down to the WORK library. This is where temporary data sets that you create are stored. Click on the WORK library to see the list of data sets in it.

Dataset example in work librarySelect the EXAMPLE data set and click on OK. Now that you have your data set, it is time to select your variables.

Window with task rolesClick on the +  next to the variables and you’ll get a list of variables from which you can select.  Scroll down and select the variable you want. First, as shown above, I selected RACE for the classification variable.

Selecting variables

This gives me a chart, and it appears that whites have a lower body mass index than black or Latino respondents in this survey.

bar chart of BMIMy next analysis is to do the summary statistics. I simply click on SUMMARY STATISTICS under the statistics tab (it’s right under data exploration) and select the same two variables. You can click here to see the results. Mean BMI for both the black and Hispanic samples was 29, while for whites it was 27. Standard deviations for the three groups ranged from 5.7 to 6.9 which was actually less than I expected.

So, there are differences in body mass index by race/ ethnicity, but that leaves a few questions left:

  1. Do those differences persist when you control for age and gender?
  2. While there are differences in body mass index, that doesn’t necessarily mean more people are obese. Maybe there are more underweight white people. Hey, it’s possible.

Well, now you have a chart and a table to add to the table you created in the first analyses. In the next post, we can move on to those other questions.


I get asked this question fairly often so I thought I would do a few posts on it. The most common problem is that a student who is new to statistics has no idea where to even start.

These examples use SAS but you could use any package you like.

My recommendation to students beginning to learn statistics is to start with some type of publicly available data set, getting some experience with real data.


The first thing to do is examine the contents of the dataset. Look at the variables you have available. With SAS, you would do this with PROC CONTENTS.

Your program at this point is super simple

LIBNAME mydata “path to where your data are” ;

PROC CONTENTS DATA = mydata.datasetname ;

Normally, you would come up with a hypothesis first and then collect the data. The advantage of working with public use data sets is you don’t have to go to the time and expense of interviewing 40,000 people. The disadvantage is that you are limited to the variables collected.


Looking at the California Health Interview Survey data, I came up with the following null hypothesis:

There is no difference in obesity among Caucasians, African-Americans and Latinos.


You need descriptive statistics for three reasons. First, if you don’t have enough variance on the variables of interest, you can’t test your null hypothesis. If everyone is white or no one is obese, you don’t have the right dataset for your study. Second, you are going to need to include a table of sample statistics in your paper. This should include standard demographic variables – age, sex, education, income and race are the main ones. Last, and not necessarily least, descriptive statistics will give you some insight into how your data are coded and distributed.

proc freq data = mydata.coh602 ;
tables race obese srsex aheduc ;
where race ne “” ;

proc means data= mydata.coh602 ;
var ak22_p srage_p ;

where race ne “” ;
run ;

You can see the results from the code above here.

Notice something about the code above – the WHERE statement. My hypothesis only mentioned three groups – Caucasians, African-Americans and Latinos. Those were the only three groups that had a value for the race variable. (This example uses a modified subset of the CHIS , if you are really into that sort of thing and want to know.) Since that is the population I will be analyzing, I do not want to include people who don’t fall into one of those three groups in my computation of the frequency distributions and means.


Using the results from your first analysis, you are all set to write up your sample section, like this


The sample consisted of 38,081 adults who were part of the 2009 California Health Interview Survey. Sample demographics are shown in Table 1.

<Then you have a Table 1>

Variable …………N….     %


  • Black 2,181 5.7
  • Hispanic ,4926 13.0
  • White 30,974 81.3


  • Male 15,751 41.4
  • Female 22,330 58.6

Variable ……N ….. Mean… SD

Age…………38,081 55.4 18.0

Income  37,686 $69,888  $63,586


I’ll try to write more soon, but for now The Invisible Developer is pointing out that it is past 1 a.m. and I should get off my computer.


UPDATE: Click here for step 2

First off, the good news. You can find all of the papers from SAS Global Forum 2015 online.  This is good news if you are anything like me (and you should be, because, let’s face it, I’m awesome) because even if you went to Dallas there were no doubt several papers you wanted to attend scheduled at the same time.

I liked everything I attended but there were two that stood out as really interesting. The first one was …

Taking the Path More Travelled – SAS Visual Analytics and Path Analysis
Falko Schulz
You can download it here

My idea of path analysis is a series of regression coefficients where you calculate direct and indirect effects. That is not the path analysis discussed in this paper.

He literally means what path did the customer (critter, whatever) take ?

For example, your path in using this website could be you went to the home page then blog page home then the previous entry on the blog.

While websites are an obvious use for this type of path analysis, there could be many others – customer experience in a call center, where people go in a huge department store, migration of humans or animals, path to achieving a job at a start-up.

Drop-off is often of interest in a path analysis – did they fall out of the path before the endpoint you wanted, e.g., sale, employment, customer support problem solved?

You can also look at weight in a path, not only whether they buy a widget but how much money did they spend?

Visual analytics allows for path segmentation. You can combine items or exclude items.
In the example, Schulz discussed using path analysis to see how effective your different online marketing methods are. Since many people will come from typing your name into a search engine, you may want to delete those paths and only include ads from Google adwords, blogher, your corporate website and other paid marketing efforts.

You can click on events and select Exclude to filter out all paths beginning with those events that are not of interest to you.

Sankey diagrams are available in visual analytics. Although these have their origin in uses like energy flow, they are now being applied all over the place.

Here is a sample from Schulz’s paper

sample sankey diagram

Sample Sankey Diagram

A Sankey diagram, FYI, shows the direction and quantity of the flow along a path. There is a blog devoted to Sankey diagrams here.

(This wasn’t mentioned in the paper, I just found that interesting. I’m sure there’s a blog out there devoted to Gantt charts that I could find if I looked, which I didn’t.)

Once the path analysis roles of interest are defined:

  • Event
  • Sequence
  • ID

… one of the first things to do would be drop the number of paths displayed. Just imagine the mess you would be looking at if you tried to visually display all of the paths someone took in navigating a website with even a few hundred pages.

You can edit the minimum path frequency, e.g., only show a path if at least 250 people took it.

This is just a brief, brief taste of what you can do with path analysis using SAS Visual Analytics and the coolness of SAS Global Forum. There was a lot, lot more and I’ll try to post about the second paper I really liked this week,

tractor in dirt field

but for now perhaps I should quit looking out the window and pay attention in this training session I’m sitting in at Fort Berthold (don’t tell Bruce I wrote my blog during it).

If you missed out on SAS Global Forum, you don’t need to wait until next year for your fix of networking, instruction (and possibly drinking). You can go to the Western Users of SAS Software conference in San Diego in September.

Great! You are using SAS Studio. It’s free. Even greater. You cleaned your data, created subscales. You have this perfect dataset and now, you want to save that dataset to your desktop and maybe do some more work with it, or just open up and admire it – who am I to judge?

Follow these three easy steps:

1. Right-click on the data set.

export option  from  libraries

2. Select Export and export it to one of your folders as, say, a .csv file.

3. Go to that file, select it, and click download.

download option from folder


Are you kidding me?

If you are a programmer, analyst, statistician, professor or student who uses SAS this is an opportunity to get to know your people and to get known.

I’m in Dallas for the SAS Global Forum, which I try to attend whenever I can. Yes, I could watch videos on the Internet, read books, read web pages, but I often don’t because I have a to-do list a mile long.

By presenting at the conference, I have to review what I am doing in teaching with SAS Studio and why.

SHAMELESS PLUG: My session on Preparing Students for the Real World with SAS Studio is a good one for both anyone who teaches with SAS and for anyone who is new to the SAS world and wants a good introductory session.

Since I am at the conference, I have a little bit of downtime to look into SAS resources. My new favorite is SAS communities. It’s a combination forum and free library. I must have looked into it at some point, because I had an account, but it seems to be more active now. I even submitted an article and poked around in the forum.

Then, of course, there are all of the sessions that I will attend, conversations  I will have with people, books I will hear about and buy, to read on the plane ride home.

It’s a week of learning.

But , but, you stutter like a motor boat, it’s expensive and far away. I can’t afford it. Besides, I would feel uncomfortable presenting at the same conference with all of those people who wrote the books on SAS (literally).

The expensive part I get. The not feeling like you could present at the same conference part is just silly, so I’m going to pretend you didn’t say that.

If travel and cost is an issue, present at your local conference. The call for papers for the Western Users of SAS Software (WUSS) is open. Do it now!

It is painless. You submit a 300-word abstract. You can submit a working draft of the paper at the same time. That’s not mandatory but it improves your chances.

There is even a mentoring program where old people (like me), will help you revise your program and get ready to present.

Writing and presenting the paper will force you to think about what you are doing and why. You will likely make some contacts of people who will be potential employers, collaborators or drinking buddies.

What are you waiting for? A personal invitation?

Fine! Here you go.



Need a topic? Here are 10 I would like to see

  1. The 25 functions I use most.
  2. Uses of PROC FORMAT .
  3. Multinomial logistic regression.
  4. The many facets of PROC FREQ.
  5. Factor analysis
  6. SAS for basic biostatistics
  7. Macro for data cleaning
  8. Model selection procedures
  9. Mixed models vs PROC GLM
  10. SAS Graphs without SAS/Graph (because SAS/Graph appears to be written in Klingon)

My point is that if I sat here and thought of 10 off the top of my head after two glasses of Chardonnay and half a glass of the champagne someone who will remain nameless bought at Costco and brought here from a state in the WUSS region, then I’ll bet you could come up with something really awesome stone-cold sober and given more than 60 seconds.

Let’s recap what we have learned here, shall we?

  • Join SAS communities,
  • Attend conferences, whether national or global,
  • Don’t be a wallflower – present!
  • Texas steak and wine is a good combination (not particularly related to SAS but true nonetheless)

waiter carrying champagne

view over the top of my ipad

It has been pretty well established that I am the worst soccer mom in the history of soccer moms. Most of the games I miss because I am somewhere else. My children have told me that my autobiography should be entitled, “I was out of town at the time” because most of the stories of their childhood begin this way.

Having come back in town shortly before the game this weekend, I was unaware that it was a two-day tournament 2 1/2 hours from home and that we were supposed to have reserved the hotel weeks ago. Hot tip: If you get your reservation last minute and have the choice of a close hotel or a nice hotel, get the nice one.

I fulfilled my obligation. I showed up. During the time The Spoiled One played, I watched. During half time and the breaks between the games I was able to write a couple of blog posts and test out SAS Studio.

If you look at the picture above you might see that I was working in a field surrounded by mountains. Not the best situation for Internet access, which I had via the hotspot on my iPhone.

SAS program screen

I was able to log on to SAS Studio with no problem. When I logged in on my iPad I had the screen shown above where I could just start typing my program in the code window.

To see folders, libraries, etc. tap the BROWSE link in the top left corner, as shown

list of folders and libraries

You can tap any of the categories to bring down the list of folders, libraries, etc. You can tap on a file to open it.

The one problem I did have, and depending on your situation, it may be a severe one, was that I could not get any of the libraries to open. I wanted to open the sashelp library and see if I could run some tasks using an open data set. This did not work. It is very possibly related to poor Internet due to laying in a soccer field ringed by mountains. I tried it last year in a movie theater and I was able to access the libraries. In this case, as you might guess from the top photo, the Internet was barely accessible.

Next, I tried simulating a homework problem a student might have, just typing in some data and running the program.

running a program

I have a bluetooth keyboard I use with my iPad and it all worked fine. I typed in data, tapped on the little running guy and my program ran fine. You can see the results below.

results of proc means

To save it, I held down the home button and the power button simultaneously, just like any time you take a screenshot on an iPad. Then, I emailed that screenshot to myself, so here you have results.

My point is that a student could do their homework using SAS Studio in the middle of a soccer field on an iPad, as long as it did not require external files, which most of the homework I assign does not. They could then email the results to their professor, still from the (dis)comfort of the field.

This is useful to know for three reasons:

  1. I travel frequently to areas where there is very limited bandwidth,
  2. Many of the students in my online courses live in areas with limited bandwidth,
  3. The Spoiled One’s team won their bracket in the State Cup, so it turns out that means they have more soccer games next weekend as they advanced in the tournament. This is not at the same field surrounded by mountains. It’s at a different field at the edge of the desert. Sigh.

Take-away points:

Your students should be able to use SAS Studio almost anywhere, even if all they have is an iPad.

This is doubly true if you don’t assign homework that requires accessing external datasets.

I’ll be able to review homework assignments for the course I am teaching next during the soccer tournament this weekend. (I really AM the worst soccer mom in the history of ever.)

girl playing soccer


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