The Workflow of Data Analysis Using Stata

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Author: J. Scott Long
Publisher: Stata Press
Copyright: 2009
ISBN-13: 978-1-59718-047-4
Pages: 379; paperback

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The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Aimed at anyone who analyzes data, this book presents an effective strategy for designing and doing data-analytic projects.


In this book, Long presents lessons gained from his experience with numerous academic publications, as a coauthor of the immensely popular Regression Models for Categorical Dependent Variables Using Stata, and as a coauthor of the SPOST routines, which are downloaded over 20,000 times a year.


A workflow of data analysis is a process for managing all aspects of data analysis. Planning, documenting, and organizing your work; cleaning the data; creating, renaming, and verifying variables; performing and presenting statistical analyses; producing replicable results; and archiving what you have done are all integral parts of your workflow.


Long shows how to design and implement efficient workflows for both one-person projects and team projects. Long guides you toward streamlining your workflow, because a good workflow is essential for replicating your work, and replication is essential for good science.


An efficient workflow reduces the time you spend doing data management and lets you produce datasets that are easier to analyze. When you methodically clean your data and carefully choose names and effective labels for your variables, the time you spend doing statistical and graphical analyses will be more productive and more enjoyable.


After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. Long presents conventions that greatly simplify data analysis—conventions for naming, labeling, documenting, and verifying variables. He also covers cleaning, analyzing, and protecting your data.


While describing effective workflows, Long also introduces the concepts of basic data management using Stata and writing Stata do-files. Using real-world examples, Stata commands, and Stata scripts, Long illustrates effective techniques for managing your data and analyses. If you analyze data, this book is recommended for you.


Comments from readers


You have written the book that I had planned to write someday. But I’m glad I didn’t—your book is much better. Congratulations, this was greatly needed.


Prof. Bill Gardner
The Ohio State University


I will post the announcement of Workflow on my door with the following note: “I’m glad to help anybody who followed at least 25% of the advice Long provides—and brings me their do-files!”


Prof. Alan C. Acock 
Oregon State University


I just wanted to send you a thank you for taking the time to write this book. I feel a little like an obsessed fan because I read it for several hours last night, bought 3 copies for my new research team and am presenting our new organization scheme tomorrow. It turns out that we have just finished a first flurry of data collection and hiring and I’ve been scratching my head about how to systematize some aspects. It is a perfect time to superimpose a structure. I’ve used aspects of your plan in my own work (hence my eagerness to adopt) but having this coherent volume is a wonderful and practical resource. I learned a lot from reading this. Thank you!


Elizabeth Gifford, Ph.D.
Research Scientist
Duke University


I just received a knock at my door with my new copy of The Workflow of Data Analysis Using Stata. I immediately ripped off the packaging and began perusing it. Just before the knock, I was attempting to write a program to get Stata to save the r(mean) and r(sd) for two variables following a summarize command to be saved for a ttesti command. After looking at your book for about two minutes, I stumbled upon pages 91–92, where it gave me all the information I need. … I have only had the book about 10 minutes and already it has made my life easier. Thanks much, and I am already looking forward to reading the rest of the book!


Claire M. Kamp Dush, Ph.D. 
The Ohio State University


I am a Spanish professor of public economics who is at present enjoying a study-research leave at Melbourne University (Australia). Because of that I have had the time to read your book from cover to cover. I just want to thank you for the incredible work you have done! A book such as this one is a must for anyone trying to make an academic career. Definitely, I will recommend it to my graduate students as soon as I go back to Spain. If I had the chance to reach this book twenty years ago I would have been much more efficient doing my work. Never is it too late! Thanks!


Prof. Jose Felix Sanz-Sanz
Dept. of Applied Economics 
Universidad Complutense de Madrid


J. Scott Long is Chancellor’s Professor of Sociology and Statistics and Associate Vice Provost for Research at Indiana University–Bloomington. He has contributed articles to many journals, including American Sociological Review, Social Forces, American Statistician, and Sociological Methods and Research. He was editor of Sociological Methods and Research from 1987 to 1994. Dr. Long has authored or edited seven previous books on statistics, including the highly acclaimed Regression Models for Categorical and Limited Dependent Variables. In 2001, he received the Paul Lazarsfeld Memorial Award for Distinguished Contributions to Sociological Methodology. Each summer at the University of Michigan, he teaches workshops at the Inter-University Consortium for Political and Social Research Summer Program in Quantitative Methods of Social Research.

List of tables
List of figures
List of examples
Preface (PDF)
A word about fonts, files, commands, and examples
1 Introduction
1.1 Replication: The guiding principle for workflow 
1.2 Steps in the workflow 
1.2.1 Cleaning data 
1.2.2 Running analysis 
1.2.3 Presenting results 
1.2.4 Protecting files
1.3 Tasks within each step 
1.3.1 Planning 
1.3.2 Organization 
1.3.3 Documentation 
1.3.4 Execution
1.4 Criteria for choosing a workflow 
1.4.1 Accuracy 
1.4.2 Efficiency 
1.4.3 Simplicity 
1.4.4 Standardization 
1.4.5 Automation 
1.4.6 Usability 
1.4.7 Scalability 
1.5 Changing your workflow 
1.6 How the book is organized
2 Planning, organizing, and documenting
2.1 The cycle of data analysis 
2.2 Planning 
2.3 Organization 
2.3.1 Principles for organization 
2.3.2 Organizing files and directories 
2.3.3 Creating your directory structure 
A directory structure for a small project 
A directory structure for a large, one-person project 
Directories for collaborative projects 
Special-purpose directories 
Remembering what directories contain 
Planning your directory structure 
Naming files 
Batch files 
2.3.4 Moving into a new directory structure (advanced topic) 
Example of moving into a new directory structure
2.4 Documentation 
2.4.1 What should you document? 
2.4.2 Levels of documentation 
2.4.3 Suggestions for writing documentation 
Evaluating your documentation 
2.4.4 The research log 
A sample page from a research log 
A template for research logs 
2.4.5 Codebooks 
A codebook based on the survey instrument 
2.4.6 Dataset documentation 
2.5 Conclusions 
3 Writing and debugging do-files
3.1 Three ways to execute commands 
3.1.1 The Command window 
3.1.2 Dialog boxes 
3.1.3 Do-files 
3.2 Writing effective do-files 
3.2.1 Making do-files robust 
Make do-files self-contained 
Use version control 
Exclude directory information 
Include seeds for random numbers 
3.2.2 Making do-files legible 
Use lots of comments 
Use alignment and indentation 
Use short lines 
Limit your abbreviations 
Be consistent 
3.2.3 Templates for do-files 
Commands that belong in every do-file 
A template for simple do-files 
A more complex do-file template 
3.3 Debugging do-files 
3.3.1 Simple errors and how to fix them 
Log file is open 
Log file already exists 
Incorrect command name 
Incorrect variable name 
Incorrect option 
Missing comma before options 
3.3.2 Steps for resolving errors 
Step 1: Update Stata and user-written programs 
Step 2: Start with a clean slate 
Step 3: Try other data 
Step 4: Assume everything could be wrong 
Step 5: Run the program in steps 
Step 6: Exclude parts of the do-file 
Step 7: Starting over 
Step 8: Sometimes it is not your mistake 
3.3.3 Example 1: Debugging a subtle syntax error 
3.3.4 Example 2: Debugging unanticipated results 
3.3.5 Advanced methods for debugging 
3.4 How to get help 
3.5 Conclusions 
4 Automating your work
4.1 Macros 
4.1.1 Local and global macros 
Local macros 
Global macros 
Using double quotes when defining macros 
Creating long strings 
4.1.2 Specifying groups of variables and nested models 
4.1.3 Setting options with locals 
4.2 Information returned by Stata commands 
Using returned results with local macros 
4.3 Loops: foreach and forvalues 
The foreach command 
The forvalues command 
4.3.1 Ways to use loops 
Loop example 1: Listing variable and value labels 
Loop example 2: Creating interaction variables 
Loop example 3: Fitting models with alternative measures of education
Loop example 4: Recoding multiple variables the same way 
Loop example 5: Creating a macro that holds accumulated information 
Loop example 6: Retrieving information returned by Stata 
4.3.2 Counters in loops 
Using loops to save results to a matrix 
4.3.3 Nested loops 
4.3.4 Debugging loops 
4.4 The include command 
4.4.1 Specifying the analysis sample with an include file 
4.4.2 Recoding data using include files 
4.4.3 Caution when using include files 
4.5 Ado-files 
4.5.1 A simple program to change directories 
4.5.2 Loading and deleting ado-files 
4.5.3 Listing variable names and labels 
4.5.4 A general program to change your working directory 
4.5.5 Words of caution 
4.6 Help files 
4.6.1 nmlabel.hlp 
4.6.2 help me 
4.7 Conclusions 
5 Names, notes, and labels
5.1 Posting files 
5.2 The dual workflow of data management and statistical analysis 
5.3 Names, notes, and labels 
5.4 Naming do-files 
5.4.1 Naming do-files to re-create datasets 
5.4.2 Naming do-files to reproduce statistical analysis 
5.4.3 Using master do-files 
Master log files 
5.4.4 A template for naming do-files 
Using subdirectories for complex analysis 
5.5 Naming and internally documenting datasets 
Never name it final! 
5.5.1 One time only and temporary datasets 
5.5.2 Datasets for larger projects 
5.5.3 Labels and notes for datasets 
5.5.4 The datasignature command 
A workflow using the datasignature command 
Changes datasignature does not detect 
5.6 Naming variables 
5.6.1 The fundamental principle for creating and naming variables 
5.6.2 Systems for naming variables 
Sequential naming systems 
Source naming systems 
Mnemonic naming systems 
5.6.3 Planning names 
5.6.4 Principles for selecting names 
Anticipate looking for variables 
Use simple, unambiguous names 
Try names before you decide 
5.7 Labeling variables 
5.7.1 Listing variable labels and other information 
Changing the order of variables in your dataset 
5.7.2 Syntax for label variable 
5.7.3 Principles for variable labels 
Beware of truncation 
Test labels before you post the file 
5.7.4 Temporarily changing variable labels 
5.7.5 Creating variable labels that include the variable name 
5.8 Adding notes to variables 
5.8.1 Commands for working with notes 
Listing notes 
Removing notes 
Searching notes 
5.8.2 Using macros and loops with notes 
5.9 Value labels 
5.9.1 Creating value labels is a two-step process 
Step 1: Defining labels 
Step 2: Assigning labels 
Why a two-step system? 
Removing labels 
5.9.2 Principles for constructing value labels 
1) Keep labels short 
2) Include the category number 
3) Avoid special characters 
4) Keeping track of where labels are used 
5.9.3 Cleaning value labels 
5.9.4 Consistent value labels for missing values 
5.9.5 Using loops when assigning value labels 
5.10 Using multiple languages 
5.10.1 Using label language for different written languages 
5.10.2 Using label language for short and long labels 
5.11 A workflow for names and labels 
Step 1: Plan the changes 
Step 2: Archive, clone, and rename 
Step 3: Revise variable labels 
Step 4: Revise value labels 
Step 5: Verify the changes 
5.11.1 Step 1: Check the source data 
Step 1a: List the current names and labels 
Step 1b: Try the current names and labels 
5.11.2 Step 2: Create clones and rename variables 
Step 2a: Create clones 
Step 2b: Create rename commands 
Step 2c: Rename variables 
5.11.3 Step 3: Revise variable labels 
Step 3a: Create variable-label commands 
Step 3b: Revise variable labels 
5.11.4 Step 4: Revise value labels 
Step 4a: List the current labels 
Step 4b: Create label define commands to edit 
Step 4c: Revise labels and add them to dataset 
5.11.5 Step 5: Check the new names and labels 
5.12 Conclusions 
6 Cleaning your data
6.1 Importing data 
6.1.1 Data formats 
ASCII data formats 
Binary-data formats 
6.1.2 Ways to import data 
Stata commands to import data 
Using other statistical packages to export data 
Using a data conversion program 
6.1.3 Verifying data conversion 
Converting the ISSP 2002 data from Russia 
6.2 Verifying variables 
6.2.1 Values review 
Values review of data about the scientific career 
Values review of data on family values 
6.2.2 Substantive review 
What does time to degree measure? 
Examining high-frequency values 
Links among variables 
Changes in survey questions 
6.2.3 Missing-data review 
Comparisons and missing values 
Creating indicators of whether cases are missing 
Using extended missing values 
Verifying and expanding missing-data codes 
Using include files 
6.2.4 Internal consistency review 
Consistency in data on the scientific career 
6.2.5 Principles for fixing data inconsistencies 
6.3 Creating variables for analysis 
6.3.1 Principles for creating new variables 
New variables get new names 
Verify that new variables are correct 
Document new variables 
Keep the source variables 
6.3.2 Core commands for creating variables 
The generate command 
The clonevar command 
The replace command 
6.3.3 Creating variables with missing values 
6.3.4 Additional commands for creating variables 
The recode command 
The egen command 
The tabulate, generate() command 
6.3.5 Labeling variables created by Stata 
6.3.6 Verifying that variables are correct 
Checking the code 
Listing variables 
Plotting continuous variables 
Tabulating variables 
Constructing variables multiple ways 
6.4 Saving datasets 
6.4.1 Selecting observations 
Deleting cases versus creating selection variables 
6.4.2 Dropping variables 
Selecting variables for the ISSP 2002 Russian data 
6.4.3 Ordering variables 
6.4.4 Internal documentation 
6.4.5 Compressing variables 
6.4.6 Running diagnostics 
The codebook, problem command 
Checking for unique ID variables 
6.4.7 Adding a data signature 
6.4.8 Saving the file 
6.4.9 After a file is saved 
6.5 Extended example of preparing data for analysis 
Creating control variables 
Creating binary indicators of positive attitudes 
Creating four-category scales of positive attitudes 
6.6 Merging files 
6.6.1 Match-merging 
Sorting the ID variable 
6.6.2 One-to-one merging 
Combining unrelated datasets 
6.6.3 Forgetting to match-merge 
6.7 Conclusions 
7 Analyzing data and presenting results
7.1 Planning and organizing statistical analysis 
7.1.1 Planning in the large 
7.1.2 Planning in the middle 
7.1.3 Planning in the small 
7.2 Organizing do-files 
7.2.1 Using master do-files 
7.2.2 What belongs in your do-file? 
7.3 Documentation for statistical analysis 
7.3.1 The research log and comments in do-files 
7.3.2 Documenting the provenance of results 
Captions on graphs 
7.4 Analyzing data using automation 
7.4.1 Locals to define sets of variables 
7.4.2 Loops for repeated analyses 
Computing t tests using loops 
Loops for alternative model specifications 
7.4.3 Matrices to collect and print results 
Collecting results of t tests 
Saving results from nested regressions 
Saving results from different transformations of articles 
7.4.4 Creating a graph from a matrix 
7.4.5 Include files to load data and select your sample 
7.5 Baseline statistics 
7.6 Replication 
7.6.1 Lost or forgotten files 
7.6.2 Software and version control 
7.6.3 Unknown seed for random numbers 
Bootstrap standard errors 
Letting Stata set the seed 
Training and confirmation samples 
7.6.4 Using a global that is not in your do-file 
7.7 Presenting results 
7.7.1 Creating tables 
Using spreadsheets 
Regression tables with esttab 
7.7.2 Creating graphs 
Colors, black, and white 
Font size 
7.7.3 Tips for papers and presentations 
7.8 A project checklist 
7.9 Conclusions 
8 Protecting your files
8.1 Levels of protection and types of files 
8.2 Causes of data loss and issues in recovering a file 
8.3 Murphy’s law and rules for copying files 
8.4 A workflow for file protection 
Part 1: Mirroring active storage 
Part 2: Offline backups 
8.5 Archival preservation 
8.6 Conclusions 
9 Conclusions
A How Stata works
A.1 How Stata works 
Stata directories 
The working directory 
A.2 Working on a network 
A.3 Customizing Stata 
A.3.1 Fonts and window locations 
A.3.2 Commands to change preferences 
Options that can be set permanently 
Options that need to be set each session 
Function keys 
A.4 Additional resources