Introduction

This blog is a free Stata tutorial. I have been using Stata for the last two years now for different applied work in economics and other fields of the social sciences. If you are in your undergraduate or graduate studies or if you are working for some agency that performs social research, you will probably need to use Stata in the context of your project. Stata has an extensive manual which is very accessible, in my opinion, but in order to know how to use it, one needs to already know the commands' names.

However, if you are new to Stata, and you have a project to do, there is a sequence of actions you probably need to do. This tutorial is constructed to follow this sequence: data assembly and construction of additional variables. Then I deliberately skip talking about commands that perform statistical analyses and leave it to your statistics or econometrics courses. But the second part of the tutorial (steps #5-#8) are dedicated to automating those commands and the creation of tables which will report the results. In addition, there are best practices of how to write code that will be easy to follow and change if needed.

I am assuming the reader has basic knowledge of Econometrics (regressions etc.) and I will not get into issues of how to specify an appropriate model. I will concentrate, though, on the practical steps one needs to do before and after the regressions, and how to organize the code so as to minimize mistakes.

The tutorial is divided to steps. You might not need to go through all the steps, so feel free to move on if you see the step is irrelevant for you. You can also navigate through the tutorial with the labels on the left bar. They consist of keywords (like an index) and steps numbers (like a table of contents).

The steps are as follows (keep in mind that the tutorial is still under construction):

• Step #1: Getting the Data - if your Data isn't in .dta format - Excel as an example
• Step #2: Combine Multiple Datasets into One - for datasets already in .dta format
• Step #3: Simple Data Manipulation - generate variables, change values and drop variables or observations
• Step #4: Thank God for the egen Command - a very powerful command that extends the possibilities of data manipulation.
• Step #5: Keeping commands' calculations - How to tell your program to use the output from reg, sum, etc.
• Step #6: Automation - macros, loops, and other sorts of fun

• Step #7: Exporting Results to a Spreadsheet - Excel as an example

• Step #8: Program Definition - if you start to see the same code in many .do files, maybe you should read this step.

I did not find time to fill in steps #7 and #8 here. For step #7, and other good things, I prepared some slides for a short Stata sequence I gave at the department. You can find them here.

Good luck!

Introduction

This blog is a free Stata tutorial. I have been using Stata for the last two years now for different applied work in economics and other fields of the social sciences. If you are in your undergraduate or graduate studies or if you are working for some agency that performs social research, you will probably need to use Stata in the context of your project. Stata has an extensive manual which is very accessible, in my opinion, but in order to know how to use it, one needs to already know the commands' names.

However, if you are new to Stata, and you have a project to do, there is a sequence of actions you probably need to do. This tutorial is constructed to follow this sequence: data assembly and construction of additional variables. Then I deliberately skip talking about commands that perform statistical analyses and leave it to your statistics or econometrics courses. But the second part of the tutorial (steps #5-#8) are dedicated to automating those commands and the creation of tables which will report the results. In addition, there are best practices of how to write code that will be easy to follow and change if needed.

I am assuming the reader has basic knowledge of Econometrics (regressions etc.) and I will not get into issues of how to specify an appropriate model. I will concentrate, though, on the practical steps one needs to do before and after the regressions, and how to organize the code so as to minimize mistakes.

The tutorial is divided to steps. You might not need to go through all the steps, so feel free to move on if you see the step is irrelevant for you. You can also navigate through the tutorial with the labels on the left bar. They consist of keywords (like an index) and steps numbers (like a table of contents).

The steps are as follows (keep in mind that the tutorial is still under construction):

• Step #1: Getting the Data - if your Data isn't in .dta format - Excel as an example
• Step #2: Combine Multiple Datasets into One - for datasets already in .dta format
• Step #3: Simple Data Manipulation - generate variables, change values and drop variables or observations
• Step #4: Thank God for the egen Command - a very powerful command that extends the possibilities of data manipulation.
• Step #5: Keeping commands' calculations - How to tell your program to use the output from reg, sum, etc.
• Step #6: Automation - macros, loops, and other sorts of fun

• Step #7: Exporting Results to a Spreadsheet - Excel as an example

• Step #8: Program Definition - if you start to see the same code in many .do files, maybe you should read this step.

I did not find time to fill in steps #7 and #8 here. For step #7, and other good things, I prepared some slides for a short Stata sequence I gave at the department. You can find them here.

Good luck!