by Christos Giannoulis, PhD
Attributes are often measured using multiple variables with different upper and lower limits. For example, we may have five measures of political orientation, each with a different range of values.
Each variable is measured in a different way. The measures have a different number of categories and the low and high scores on each measure are different.
Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago? 
Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?
Or panicked when someone running a big meta-analysis asks you to share your data?
If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.
We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses.
Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them.
This is important because it means you can use an unlikely tool to help you code: Microsoft Word.
I know what you’re thinking. Word? Really?
Yep, it’s true. Essentially it’s because Word has much better Search-and-Replace options than your stat software’s editor.
Here are a couple features of Word’s search-and-replace that I use to help me code faster:
There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.
Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?
In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:
Sometimes when you’re learning a new stat software package, the most frustrating part is not knowing how to do very basic things. This is especially frustrating if you already know how to do them in some other software.
Let’s look at some basic but very useful commands that are available in R.
We will use the following data set of tourists from different nations, their gender and numbers of children. Copy and paste the following array into R.
A <- structure(list(NATION = structure(c(3L, 3L, 3L, 1L, 3L, 2L, 3L,
1L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 2L), .Label = c("CHINA",
"GERMANY", "FRANCE"), class = "factor"), GENDER = structure(c(1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L
), .Label = c("F", "M"), class = "factor"), CHILDREN = c(1L,
3L, 2L, 2L, 3L, 1L, 0L, 1L, 0L, 1L, 2L, 2L, 1L, 1L, 1L, 0L, 2L
)), .Names = c("NATION", "GENDER", "CHILDREN"), row.names = 2:18, class = "data.frame")
Want to check that R read the variables correctly? We can look at the first 3 rows using the head() command, as follows:
head(A, 3) NATION GENDER CHILDREN 2 FRANCE F 1 3 FRANCE M 3 4 FRANCE M 2
Now we look at the last 4 rows using the tail() command:
tail(A, 4) NATION GENDER CHILDREN 15 FRANCE F 1 16 FRANCE M 1 17 FRANCE F 0 18 GERMANY F 2
Now we find the number of rows and number of columns using nrow() and ncol().
nrow(A) [1] 17 ncol(A) [1] 3
So we have 17 rows (cases) and three columns (variables). These functions look very basic, but they turn out to be very useful if you want to write R-based software to analyse data sets of different dimensions.
Now let’s attach A and check for the existence of particular data.
attach(A)
As you may know, attaching a data object makes it possible to refer to any variable by name, without having to specify the data object which contains that variable.
Does the USA appear in the NATION variable? We use the any() command and put USA inside quotation marks.
any(NATION == "USA") [1] FALSE
Clearly, we do not have any data pertaining to the USA.
What are the values of the variable NATION?
levels(NATION) [1] "CHINA" "GERMANY" "FRANCE"
How many non-missing observations do we have in the variable NATION?
length(NATION) [1] 17
OK, but how many different values of NATION do we have?
length(levels(NATION)) [1] 3
We have three different values.
Do we have tourists with more than three children? We use the any() command to find out.
any(CHILDREN > 3) [1] FALSE
None of the tourists in this data set have more than three children.
Do we have any missing data in this data set?
In R, missing data is indicated in the data set with NA.
any(is.na(A)) [1] FALSE
We have no missing data here.
Which observations involve FRANCE? We use the which() command to identify the relevant indices, counting column-wise.
which(A == "FRANCE") [1] 1 2 3 5 7 9 10 14 15 16
How many observations involve FRANCE? We wrap the above syntax inside the length() command to perform this calculation.
length(which(A == "FRANCE")) [1] 10
We have a total of ten such observations.
That wasn’t so hard! In our next post we will look at further analytic techniques in R.
About the Author: David Lillis has taught R to many researchers and statisticians. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. David holds a doctorate in applied statistics.
See our full R Tutorial Series and other blog posts regarding R programming.
In my last article, Hierarchical Regression in Stata: An Easy Method to Compare Model Results, I presented the following table which examined the impact several predictors have on one’ mental health.
At the bottom of the table is the number of observations (N) contained within each sample.
The sample sizes are quite large. Does it really matter that they are different? The answer is absolutely yes.
Fortunately in Stata it is not a difficult process to use the same sample for all four models shown above.
As I have mentioned previously, Stata stores results in temp files. You don’t have to do anything to cause Stata to store these results, but if you’d like to use them, you need to know what they’re called.
To see what is stored after an estimation command, use the following code:
ereturn list
After a summary command:
return list
One of the stored results after an estimation command is the function e(sample). e(sample) returns a one column matrix. If an observation is used in the estimation command it will have a value of 1 in this matrix. If it is not used it will have a value of 0.
Remember that the “stored” results are in temp files. They will disappear the next time you run another estimation command.
So how do I use the same sample for all my models? Follow these steps.
Using the regression example on mental health I determine which model has the fewest observations. In this case it was model four.
I rerun the model:
regress MCS weeks_unemployed i.marital_status kids_in_house religious_attend income
Next I use the generate command to create a new variable whose value is 1 if the observation was in the model and 0 if the observation was not. I will name the new variable “in_model_4”.
gen in_model_4 = e(sample)
Now I will re-run my four regressions and include only the observations that were used in model 4. I will store the models using different names so that I can compare them to the original models.
My commands to run the models are:
regress MCS weeks_unemployed i.marital_status if in_model_4==1
estimates store model_1a
regress MCS weeks_unemployed i.marital_status kids_in_house if in_model_4==1
estimates store model_2a
regress MCS weeks_unemployed i.marital_status kids_in_house religious_attend if in_model_4==1
estimates store model_3a
regress MCS weeks_unemployed i.marital_status kids_in_house religious_attend income if in_model_4==1
estimates store model_4a
Note: I could use the code if in_model_4 instead of if in_model_4==1. Stata interprets dummy variables as 0 = false, 1 = true.
Here are the results comparing the original models (eg. Model_1) versus the models using the same sample (eg. Model_1a):
Comparing the original models 3 and 4 one would have assumed that the predictor variable “Income level” significantly impacted the coefficient of “Frequent religious attendance”. Its coefficient changed from -58.48 in model 3 to 6.33 in model 4.
That would have been the wrong assumption. That change is coefficient was not so much about any effect of the variable itself, but about the way it causes the sample to change via listwise deletion. Using the same sample, the change in the coefficient between the two models is very small, moving from 4 to 6.
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.