NetCourse® 151: Introduction to Stata Programming
- Content:
-
An introduction to Stata programming dealing with what most statistical
software users mean by programming, namely, the careful performance of
reproducible analyses.
Through a combination of lectures, example applications, and carefully
chosen problems, the course addresses the full range of methods and
techniques necessary to be most productive in the Stata environment.
- Course lecturer:
- Bill Gould, president of StataCorp and head of development
- Course leaders:
- Kerry Kammire, technical services analyst at StataCorp
- Jared Stewart, technical services representative at StataCorp
- Course length:
- 6 weeks (4 lectures)
- Dates:
- July 5–August 16, 2013 (details)
- Prerequisites:
- Stata 12, installed and working
- Basic knowledge of using Stata interactively
- Internet web browser, installed and working
(course is platform independent)
- Price:
- $125.00
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Course content
Lecture 1: Organization of analysis
- Welcome
- Entering and executing a program
- Mechanical method 1: The do-file
Aside: Stata's built-in Do-file Editor
- Mechanical method 2: The interactive program command
- Mechanical method 3: program in a do-file
- Mechanical method 4: Combination do-files
- Mechanical method 5: Ado-files
Aside: Using Stata and an editor or word processor
- Organizing do-files
- An individual do-file
- A do-file to perform verification
- Infiling data
Aside: Working with datasets that are too large
- Reproducibility
- Indexing
- assert as an alternative to branching
- Consuming calculated results
Lecture 2: Macros, arguments, and looping
- Introduction
- Macros
- How macros might be used
- Macro names
- The related-persons example
- Another example (plant data)
- Potential problem—variable scope
- More on arguments
- Branching and looping
- Physical program style
- foreach
- Looping across observations
- if
There is a one-week break between Lectures 2 and 3 in this course because we
have found that the extra time is necessary for discussion.
Lecture 3: Examples and applications
- Introduction
- Data management example
- Handling time and date variables
- Checking assumptions
- Returned values and saving results
- What can be returned in r()?
- Referring to returned results in other programs
- Referring to returned results in the program that sets them
- Bootstrapped standard errors
- Aside: reading a trace
- A warning on bootstrapping
- Speeding up bootstrapping
- Bootstrapping, how to
- Monte Carlo simulations
- postfile and post
- Using quietly
- Speeding up simulations
Lecture 4: Ado-files
- Introduction
- A first real ado-file
- discard
- More improvements to doanl
- capture
- The exit command
- Making doanl a general tool
- Writing a help file for doanl
- Do-files, programs, and ado-files: when to use which
- Temporary variables
- Temporarily destroying data
- Temporary files
- An analysis-specific ado-file
- General-purpose (GP) ado-files
- A GP ado-file
- Fine tuning display output
- Stata syntax
- syntax
- varlist macro
- syntax’s other specifiers
- Whether to use syntax
- A note on quotes
- Version control
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