Data Cleaning - Online Course
A 3-Day Livestream Seminar Taught by
Bianca Manago10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
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? We will also provide guidance for ensuring that, once you make a decision, your findings are reproducible.
Starting October 31, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
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 Professor Paul Allison’s Missing Data seminar.
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 Professor Paul Allison’s Missing Data seminar.
Computing
The empirical examples and exercises in this course will emphasize Stata, but there will be equivalent code and examples presented/available for R. To fully benefit from the course, you should have access to a 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 familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent online resources for building your R skills.
The empirical examples and exercises in this course will emphasize Stata, but there will be equivalent code and examples presented/available for R. To fully benefit from the course, you should have access to a 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 familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent online resources for building your R skills.
Who should register?
This course is for anyone who works with data and wants to improve their data management practices. It will be useful for graduate students, junior scholars, and also provide insights to seasoned researchers.
This course is for anyone who works with data and wants to improve their data management practices. It will be useful for graduate students, junior scholars, and also provide insights to seasoned researchers.
Seminar outline
Day 1: Getting started with data cleaning
- Introduction to data cleaning
- Types of data
- Steps of data cleaning
- Documentation
- Compiling data
- Downloading/collecting
- Merging
- Appending
- Reshaping
- Labeling and naming variables
Day 2: Examining data quality
- Examining data
- Detecting anomalous data
- Univariate statistics/graphs
- Bivariate statistics/graphs
- Detecting missing data
- Creating indicator variables
- Data cleaning dilemmas
- Altering variables
- Recoding variables
- Transforming variables
Day 3: Recoding & creating variables
- New variables
- Scale construction
- Substantive variable combination
- Re-configuring data
- Re-examining data
- Documentation and presentation
- Summary and conclusion
Day 1: Getting started with data cleaning
- Introduction to data cleaning
- Types of data
- Steps of data cleaning
- Documentation
- Compiling data
- Downloading/collecting
- Merging
- Appending
- Reshaping
- Labeling and naming variables
Day 2: Examining data quality
- Examining data
- Detecting anomalous data
- Univariate statistics/graphs
- Bivariate statistics/graphs
- Detecting missing data
- Creating indicator variables
- Detecting anomalous data
- Data cleaning dilemmas
- Altering variables
- Recoding variables
- Transforming variables
Day 3: Recoding & creating variables
- New variables
- Scale construction
- Substantive variable combination
- Re-configuring data
- Re-examining data
- Documentation and presentation
- Summary and conclusion
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.