Workflow of Data Analysis Using Stata – October 2022

Event Phone: 1-610-715-0115

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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

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 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.

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
  • Documentation
  • Dual workflow (separating data management and analyses)
  • Writing robust script files
  • Using log files
  • Variable naming
  • Value labeling
  • Reproducibility and replication
  • Examining data

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