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I'm really excited to be a part of the development community that uses WeWork for office space in the US. Next week, WeWork K Street (DC) and Cooper/Smith are hosting a panel discussion on data collection, analysis, and use in international development. The (large) panel includes staff from 8 different wework-based firms all working in data for development. Come check out the panel, ask some hard questions, and enjoy the free drinks.
This week’s blog will feature a set of Stata tricks we used to addresses a particular issue that we encountered in our dataset. Many of the variables were in string form and were not useable for Stata analysis. Furthermore, the values of the variables were not in the correct order for our purposes. A couple of commands came in handy here. Loops are useful for many different repetitive commands. They allowed us to quickly recode the values of a set of variables that have similar categorical values and also enabled us to destring sets of variables, setting them to numeric values. These numeric values were in turn reordered to fit a desired pattern. Finally, the labels for the numeric values were recoded to appear as the original text instead of just “1, 2, 3, etc”.
Building scalable and sustainable food systems presents many challenges along farming value chains – not least of which is the point where our small holder famers interact with agricultural inputs. For an isolated farmer with limited education, making wise choices about farming can be challenging, meaning that many farmers fall short of the mark when it comes to running successful commercial enterprises.
The coefplot command allows you to plot results from estimation commands. It is a user package, so you will have to find and install it by inputting into the Stata command line: findit coefplot
Many users of Stata looking to visualize statistics opt to output results into MS Excel via commands like -tabout-, -putexcel-, and -outreg-. While Excel offers an intuitive and comprehensive way to create summary tables, it lacks the professionalism of tables commonly found in published journal articles. These tables can be created using Latex, and fortunately, many Stata packages have Latex functionality. This article is a tutorial on how to use Stata's -tabout- command to create publishable and client-ready tables in Latex. It assumes the reader has a good grasp of Stata and -tabout-, but no knowledge of Latex. The tutorial is not intended to teach general Latex, but only enough Latex to take advantage of Stata's output commands.
When companies need to know what their consumer base is thinking, surveying is often the only scalable way to find out this information. Nevertheless, surveys take up a lot of time and can be incredibly boring. As the respondents’ patience gets zapped by the umpteenth question and their willpower dies, they employ coping strategies known as “satisficing”— a fancy way of saying that respondents just try to meet the lowest threshold of acceptability for an answer, rather than making the time to give the best response. This can be seen when questionnaires come back with all answers being “5/5”, “extremely happy”, or other arbitrary patterns that call to question their authenticity and potentially hurt data quality.
While using hashtags is simpler than generating the interaction term as a new variable, there is a necessary rule to remember: use the variable prefixes. In Stata, -i.[variable]- indicates that the variable is categorical, and -c.[variable]- indicates a continuous variable. Because the hashtag code assumes the variables in the interaction term are categorical, it is necessary to define numerical variables as numerical with the -c.- prefix. Wage = β0 + β1Education + β2Minority + β3Education*Minority + ε
On a recent project, the client wanted an idea of the skew of each of a large number of variables. The data originated from a satisfaction survey (1=very dissatisfied; 5=very satisfied). On our Excel presentation sheet, we were to choose from the following options to describe the population’s view regarding each variable: right-skewed (generally very dissatisfied), left-skewed (generally very satisfied), U-shaped (most were either very dissatisfied or very satisfied, with few being neutral), or normal-shaped (most were neutral, with few being either very dissatisfied or very satisfied).