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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).
According to a 2015 report by the UN’s International Telecommunication Union, 2/3rd of global internet users come from the developing world. In some countries, like India, mobile phone use is as high as 75%. These trends are generating large amounts of data, which present a new opportunity for tech-savvy development practitioners. Governments and NGOs can utilize the data to make well-informed decisions and provide more effective services at a cheaper price. The question however, is how to do this.
With the steady rise of the number of impact evaluations (IEs) per year, it should come as no surprise that not every single IE will show a positive impact. The authors of “no impact” evaluations will understandably be worried that their work will not be academically published nor be used for public policy. There is, however, still value in such information. Evidence that a particular program does not work paves the way for alternative interventions to happen. Licona (2017) provides several examples where null results in Mexican education programs encouraged the tweaking of aspects such as selection criteria, consolidation of redundant programs, and budget optimization.
Managing multiple editors in STATA: For this week’s blog post, [...]
We work with clients who are running poverty interventions in [...]