Workflow of Data Analysis: A Short Course
A 4-Day Livestream Seminar Taught by Bianca Manago, Ph.D.
Data management and analysis are difficult. Without a structured and systematic way to approach these processes, they are even harder. Additionally, scientific advancements and research progress depend on replication and reproducibility. Since there are dozens of decisions that go into data management, if they are not conducted in a way that facilitates sharing, they preclude replication.
This seminar is designed to teach researchers how to prepare and analyze data 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 July 9, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. 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 and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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 Stata and 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, etc. 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.)
- Organizing files using a standardized directory structure
- Preserving data and findings
- Effectively documenting findings, data sources, cleaning methods
- Separating data management and analyses using dual workflow
- Writing robust script files
- Naming variables
- Labeling variables and values
- Creating research that is both reproducible and replicable
- Examining data quality
Stata users are encouraged to use Stata version 18, but earlier versions should also work for most exercises.
R users should also download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here before the seminar begins.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
Who Should Register?
This course is for anyone who wants to improve the efficiency and accuracy of their data management, analysis, and presentation. You should have experience with data analysis, as well as familiarity with Stata or R.
PART 1: INTRODUCTION TO WORKFLOW
- What is “workflow”?
- Why care about WF?
- WF and replication
- Steps in and principles of WF
PART 2: PLAN, ORGANIZE, DOCUMENT, AND PRESERVE
- 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
- Organizing files and folders
- Preserving data and preventing loss
PART 3: SCRIPT FILES IN R
- Strengths and weaknesses of R for workflow
- Dual workflow
- Robust script files
- Legible script files
- Automation in script files
PART 4: CLEANING, LABELING, & MISSING DATA
- Naming and labeling variables
- Missing data
- Merging data
- Verifying data
PART 5: ANALYZING & PRESENTING FINDINGS
- Principles of data analysis
- Documenting provenance
- The posting principle
- Presenting findings
PART 6: COLLABORATION
- Key factors in collaboration
- Introducing workflow with co-authors
- Coordinating workflow with multiple authors
Reviews of Workflow of Data Analysis
“This should be required learning for all researchers. The course covers the principles/rationale for having good workflow as well as concrete steps you can do right away to improve your own workflow. I was able to immediately implement steps that I know will make me a better researcher. I also have the resources I need to tackle larger changes to my workflow in the coming weeks. It’s been 5 years since I completed my PhD and I wish I had taken this course in my first year. I left this course feeling excited and empowered.”
Marta Mulawa, Duke University
“This was one of the most practical and useful Statistical Horizon workshops I’ve participated in to date! Bianca’s teaching style was great – she was knowledgeable and provided a nice blend of content coverage as well as applied exercises.”
Sage Hawn, Boston University/VA Boston Healthcare System
“I loved the big-picture approach! The way the instructor merged together all aspects of workflow from beginning to end so that everyone would find something to help them improve. I appreciated the practical advice to start with a few changes and then add in more as time goes on. Very inspiring!”
Diane Holmberg, Department of Psychology, Acadia University
“Great class! Bianca Manago is a superb teacher; positive, enthusiastic, extremely knowledgeable, clear, and responsive and helpful to students. I gained extremely valuable insights, principles, tips, and practical strategies that will improve the quality, reproducibility, and replicability of all my research.”
Ken Coburn, Health Quality Partners (HQP)
“Bianca was a great, knowledgeable entertaining teacher. She really made what could have been very dry material seem exciting. The knowledge I gained in this course will revolutionize the way I approach my research projects.”
Laura Prichett, Johns Hopkins
“Bianca’s depth of material and her engaging delivery was wonderful. The course was very practical in perspective. I have attended other R courses that did not increase my working knowledge – Bianca’s workshop has already. I also liked the opportunities for useful student interaction, which I associate with Bianca’s teaching style.”
Rod Ling, University of Newcastle
Tuesday, July 9 –
Friday, July 12, 2024
Schedule: All sessions are held live via Zoom. All times are ET (New York time).
10:30am-12:30pm (convert to your local time)
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.