Data Cleaning – October 2024
Event Phone: 1-610-715-0115
Upcoming Dates
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31OctData Cleaning10:00 AM-3:30 PM
Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
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 Livestream Seminar Taught by Bianca Manago, Ph.D.
The management and cleaning of data is essential to the integrity of research findings. Unfortunately, there is little formal focus on how to approach data cleaning. For example, when someone says “you need to clean the data” what exactly does that mean? What steps are involved and in what order? How do we decide what needs to be done?
Data cleaning involves all the steps that occur between data collection and analysis (e.g., merging, appending, labeling, data analytics, cross-validation, constructing/re-constructing variables for analysis, identifying missing data). This seminar will cover all these topics. This seminar will also provide a more general framework for approaching data cleaning which is rooted in a desire to represent data both accurately and fully. This framework informs decisions about an ideal order in which data cleaning should be conducted. This framework also delves into some of the trickier issues. For example, when you come across anomalous, vague, or missing data – what kinds of things should you consider? I will also provide guidance for ensuring that, once you make a decision, your findings are reproducible.
After taking this seminar, you will have the skills to:
- Clean different types of data step-by-step.
- Compile data.
- Label and name variables.
- Examine data.
- Solve data cleaning dilemmas.
- Alter variables.
- Incorporate new variables.
- Re-configure data.
- Re-examine data.
- Identify missing data.*
- Document and present your data.
*While this seminar will help you to identify missing data, it does not spend considerable time on how to address missing data. If you’d like to learn more about methods for handling missing data, check out Dr. Paul Allison’s Missing Data seminar.
This course is for anyone who works with data. It will be useful for graduate students, junior scholars, and also provide insights to seasoned researchers.
Venue: Livestream Seminar