StatLab Articles

In this article we demonstrate how to create new variables, recode existing variables, and label variables and values of variables. We work with the census.dta data that is included with Stata to provide examples.

generate: create variables

Here we use the generate command to create a new variable representing the population younger than 18 years old. We do so by summing up the two existing variables: poplt5 (population < 5 years old) and pop5_17 (population of 5 to 17 years old).

In Stata, the first step of analyzing a dataset is opening the data in Stata so that it knows which file you are working with. Yes, you can simply double click on a Stata data file that ends in .dta to open it, but we prefer to write syntax so we can easily reproduce the same work or use the scripts again when working on similar tasks. In this post, we introduce methods of reading in, using, and saving Stata and other formats of data files.

Data.gov catalogs US government data and makes them available on the web; you can find data on a variety of topics such as agriculture, business, climate, education, energy, finance, public safety, and many more. It is a good starting point for finding data if you don’t already know which particular data source to begin your search with; however, it can still be time consuming when it comes to actually downloading the raw data you need. Fortunately, Data.gov also includes APIs from across the government, which can help with obtaining raw datasets.

I'm still looking at the rhetoric from the presidential debates, this time focusing on the first general election debate between Hillary Clinton and Donald Trump.

Data sets provided by the US Census Bureau, such as the Decennial Census and American Community Survey (ACS), are widely used by researchers, among others. You can certainly find and download census data from the Census Bureau website, from the licensed data source Social Explorer, or from other free sources such as IPUMS-USA and then load the data into a statistical package or other software to analyze or present the data.

I'm teaching a Text as Data short course (using R) right now, and as a card-carrying political scientist, I couldn't resist using the ongoing campaign as an example (this was, in part, a way of handling my own anxiety about last Monday's debate---this is what I was doing while watching). So here goes...

Take a look at the following correlation matrix for Olympic decathlon data calculated from 280 scores from 1960 through 2004 (Johnson & Wichern, 2007, p. 499):

Loglinear models model cell counts in contingency tables. They're a little different from other modeling methods in that they don't distinguish between response and explanatory variables. All variables in a loglinear model are essentially "responses."

To learn more about loglinear models, we'll explore the following data from Agresti (1996, Table 6.3). It summarizes responses from a survey that asked high school seniors in a particular city whether they had ever used alcohol, cigarettes, or marijuana.

Plotting with color in R is kind of like painting a room in your house: You have to pick some colors. R has some default colors ready to go, but it's only natural to want to play around and try some different combinations. In this article, we'll look at some ways you can define new color palettes for plotting in R.

To begin, let's use the palette() function to see what colors are currently available:

Hurdle Models are a class of models for count data that help handle excess zeros and overdispersion. To motivate their use, let's look at some data in R. The following data come with the AER package. It is a sample of 4,406 individuals, aged 66 and over, who were covered by Medicare in 1988. One of the variables the data provide is number of physician office visits.