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.
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.
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
- How to get the sample demographics and descriptive statistics for your dependent and independent variable.
- 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 ;
WHERE race ne “” ;
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!).
Next, I want to look at the results of the TUKEY test.
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.
There is no difference in obesity among Caucasians, African-Americans and Latinos.
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 ;
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.
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.
When 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.
Click 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.
This gives me a chart, and it appears that whites have a lower body mass index than black or Latino respondents in this survey.
My 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:
- Do those differences persist when you control for age and gender?
- 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.
1. IDENTIFY THE VARIABLES YOU HAVE AVAILABLE
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.
2. GENERATE A HYPOTHESIS
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.
3. RUN DESCRIPTIVE STATISTICS
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 “” ;
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.
4. PUT TOGETHER YOUR FIRST TABLE
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.
For many students just learning statistics, the relationship of z-scores and probability is confusing.
Let’s try this concrete example. Here is a chart of the distribution of height in a sample of over 2,800 women.
Notice that the peak, the mode is around 62-63 inches.
You can see the frequency table here, as well as a larger picture of the histogram. You’ll notice the median is between 62 and 63 inches
The mean is 62.7 – between 62 and 63 inches.
Looks like a normal distribution in that mean = median = mode.
Let’s go back to that mean of 62.7 inches. The standard deviation in this population is 2.46. What would 2 standard deviations above the mean be? Let’s round our 2 x 2.46 = 4.92 up to 5.
The mean + 2 standard deviations = 62.7 + 5 = 67.7
So, this is a perfect demonstration of what we mean when we say that 97.5% of the people fall below 2 standard deviations above the mean.
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
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
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:
… 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,
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.
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.
I finally am getting around to something in SAS Studio that I think is really cool – the tasks.
Although they don’t look identical to SAS Enterprise Guide just because the screen layout is a little different, these are really, really similar to the tasks you would see in EG.
If you are using this with a real beginner class, you can start out with using the sashelp directory. Otherwise, the only programming they will need is to assign their directory at the beginning. Run that program and away you go .
Let’s take an example with SASHELP.
1. Log into your SAS on-demand account and select SAS Studio
2. Click on tasks in the left pane.
3. Select statistics
4. Select distribution analysis
Note that Studio will show you the type of variables required for your analysis
It will also tell you in the right window pane whether you are unable to run your analysis and, if so, what you are missing
To select a variable, just click the + sign and you’ll see a list of the variables in your data set
Select the desired variables for the roles and click your little running guy at the top. (This will be greyed out until you have all of the required fields filled in.)
Your results will appear in the right window pane.
If you want to see the code, click on the code button.
proc univariate data=SASHELP.BWEIGHT noprint ;
class married ;
histogram Weight ;
To keep your program, copy the code
- Click on folders
- Click on the far left button and select new program
paste in your code.
Save your program.
You could do a whole bunch of analyses this way and come up with a lot of results and a long SAS program without knowing anything about programming SAS. Some people would find that horrifying but I think it’s really cool.
A really interesting assignments for students would be to take a data set in the sashelp library, do as many tasks as they can and see what interesting results and conclusions they can find.
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
- The 25 functions I use most.
- Uses of PROC FORMAT .
- Multinomial logistic regression.
- The many facets of PROC FREQ.
- Factor analysis
- SAS for basic biostatistics
- Macro for data cleaning
- Model selection procedures
- Mixed models vs PROC GLM
- 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)
As you have no doubt brilliantly deduced from the title, this is installment number four in Mama AnnMaria’s Guide on How Not to Get Your Sorry Ass Fired.
Do the work you are assigned to do.
This may seem like anyone with a brain functioning above zombie level could figure this out. However, I have seen many people who do not appear to be zombies who can’t quite master this concept.
Variation 1: Doing work other than what you are assigned because you are more confident doing something else.
If you are supposed to be writing a new database program but instead you update the wiki because you don’t consider yourself an expert on SQL and PHP then you are on the road to getting your sorry ass fired. Unless you lied on your resume (in which case you deserve to be fired), I’m willing to bet that your boss knows you are not an expert and either:
- Assigned you something that requires below expert level of expertise, or
- Believes you can learn enough to accomplish the task.
In either case, do the work you are assigned to do.
Variation 2: Doing work you were assigned to do later instead of the work you were assigned to do now.
Again, this is often because you feel more confidence in your ability to plan the office Christmas party than updating the operating system. However, it is July and your company is still using Windows XP.
Get on it.
Variation 3: Finding an excuse why you cannot do the work.
This is the quickest path for getting your sorry ass fired. Let me explain how it looks to me:
I have given you work to do because I don’t have enough time to do it all myself. Instead of doing the work, you come back to me with more work, making it my problem instead of your problem. Do you really think I hired you because I was thinking to myself,
“Gee, I don’t have enough work to do. What I really need here is someone who will bring me more work.”
Some people might ask,
“But don’t you want me to tell you if there is a problem?”
Let me explain this to you in short words.
I want you to do the work I assigned you to do. That is why I assigned it to you instead of doing it myself.
This doesn’t mean that, for example, if you don’t know the password to log in to the server you shouldn’t ask someone. It does mean that most of the time, you should solve the problem yourself if possible.
The worst example of this I ever met was a woman who never could do any work because there was always some obstacle in her way. The final straw came the day she told her boss that she had not created and printed the company newsletter because they were out of paper.
Guess whose job it was to order the paper? When he pointed this out to her, she argued that her job description said to order office supplies but no one had specifically told her that the office was out of paper.
She got her sorry ass fired.