Workflow of Data Analysis
A 3-Day Remote Seminar Taught by
Bianca Manago, Ph.D.
Statistical analyses are only as good as the data that go into them. This is why the majority of time spent on any data analysis project should be spent, not on conducting the analyses (i.e., actually running the model), but instead on the steps needed to prepare the data for analysis. There are dozens of decisions that go into data management. If not properly documented or considered, those decisions can produce erroneous results or preclude replication.
This seminar is designed to teach researchers how to prepare data for analysis in a way that is both accurate and replicable. By following these principles, your data analytic projects will be both well-planned and executed. The scope of the seminar ranges from such broad topics as developing research plans to the detailed minutia of planning variable names.
Starting September 10, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on one’s own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
MORE DETAILS ABOUT THE COURSE CONTENT
This seminar is for researchers who are trying to establish or improve their workflow. I do not expect participants to be expert programmers; this seminar should be accessible to very novice R users, while still being useful to more advanced users. Lessons from this seminar balance ease of use with proper functioning, introducing researchers to useful tools, e.g., dual-pane browsers, macro programs, plain text editors, R Studio, and GitHub. For those who are already familiar with these tools, this seminar will teach you how to optimize them. Lessons from this seminar should make conducting research less painful, more efficient, more accurate, and reproducible.
This is a hands-on seminar with ample opportunities to plan and practice your workflow.
Some highlights include:
- Planning (analyses, sensitivity analyses, variable construction, etc.)
- Directory structure
- Data preservation
- Dual workflow (separating data management and analyses)
- Writing robust script files
- Using log files
- Variable naming
- Value labeling
- Reproducibility and replication
- Examining data
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, participants will receive an email with the meeting code and password you must use to join.
The empirical examples and exercises in this course will emphasize R, but there will be equivalent code and examples presented/available for Stata. To fully benefit from the course, you should use your own computer loaded with R or Stata. Whichever package you choose, you should already have a working understanding of the software and be able to complete basic functions in the software.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should Register?
This course is for anyone who wants to improve the efficiency and accuracy of their data analysis and presentation.
PART 1: INTRODUCTION TO WORKFLOW
1. What is “workflow”?
2. Why care about WF?
3. WF and replication
4. Steps in and principles of WF
PART 2: PLAN, ORGANIZE, DOCUMENT, AND PRESERVE
1. Planning research projects in the:
a. Large (overall questions, project checklist, and timeline)
b. Middle (data cleaning, analyses, tables, and figures)
c. Small (naming variables, naming files, value labels, and order of
2. Organizing files and folders
4. Preserving data and preventing loss
PART 3: SCRIPT FILES IN R
1. Strengths and weaknesses of R for workflow
1. Dual workflow
2. Robust script files
3. Legible script files
4. Automation in script files
PART 4: CLEANING, LABELING, & MISSING DATA
1. Naming and labeling variables
2. Missing data
3. Merging data
4. Verifying data
PART 5: ANALYZING & PRESENTING FINDINGS
1. Principles of data analysis
2. Documenting provenance
3. The posting principle
4. Presenting findings
PART 6: COLLABORATION
1. Key factors in collaboration
2. Introducing workflow with co-authors
3. Coordinating workflow with multiple authors