Statistics Globe

We are now in the second week of my course on Missing Data Imputation in R, and we have already covered several important topics.

Here is a selection of the most interesting points so far:

- MCAR vs MAR vs MNAR response mechanisms
- Little’s MCAR test and its assumptions
- Logistic regression for analyzing missingness
- Bias introduced by listwise deletion
- Simple imputation methods and their limitations

The visualization below shows a few of the topics we have already discussed in the course.

Next, we will move on to more advanced imputation methods that help preserve the true distribution of the data and maintain high data quality.

Interested in learning more about these topics? You can still join the course by enrolling here: statisticsglobe.com/online-course-missing-data-imp…

Talk to you soon.

Joachim

#missingdata #statistics #datascience #rstats

13 hours ago | [YT] | 11

Statistics Globe

Final reminder: the Statistics Globe online course, "Missing Data Imputation in R," starts today!

It would be great to see you in the course. If you’re interested, you can still register here: statisticsglobe.com/online-course-missing-data-imp…

#imputation #bias #rstats #statistics #datascience

1 week ago | [YT] | 17

Statistics Globe

The Statistics Globe online course, "Missing Data Imputation in R," starts tomorrow!

I’ve made two modules available for free as a preview if you’re still unsure about joining:

- Module 1) Course Structure & About the Instructor. Learn how the course is structured and what to expect from the sessions: statisticsglobe.com/online-course-mdir-prv-introdu…
- Module 4) Simple Missing Data Imputation Techniques. Understand basic imputation methods and why they might even harm your data quality: statisticsglobe.com/online-course-mdir-prv-simple-…

It would be great to have you in the course. So if you are interested, please register now: statisticsglobe.com/online-course-missing-data-imp…

Looking forward to seeing you there!

Joachim

#rstats #statistics #datascience #missingdata #imputation

1 week ago | [YT] | 18

Statistics Globe

Missing data is one of the most common challenges in data analysis, yet many practitioners still underestimate how much it can distort their results.

To help you avoid the most frequent mistakes, and to give you some insight into my upcoming online course on missing data imputation in R, I’ve put together a series of social media posts that explain key concepts related to missing data.

Below you can find important missing data topics along with posts that cover them in more detail.

1. Why imputation method selection matters: x.com/JoachimSchork/status/1989242911456674159

Not all imputation methods capture the true structure of the data. This post shows how deterministic and stochastic regression imputation fail to reflect nonlinear patterns, while predictive mean matching and random forest preserve the original relationships much better.

2. A closer look at predictive mean matching: www.facebook.com/groups/statisticsglobe/posts/1768…

Predictive mean matching selects donors from similar observed cases instead of inserting a direct model prediction. Because each imputed value comes from an actual data point, it maintains the distribution and nonlinear patterns more effectively than standard regression-based approaches.

3. Multiple imputation with the mice workflow: www.linkedin.com/feed/update/urn:li:activity:73965…

Multiple imputation creates multiple data sets to reflect uncertainty, leading to more reliable and unbiased analyses. The post explains the mice workflow in R: mice() to generate imputations, with() to analyze each data set, and pool() to combine the results.

Want to learn more about missing data imputation in R? Join my online course starting on December 1: statisticsglobe.com/online-course-missing-data-imp…

Talk to you soon.

Joachim

#missingdata #statistics #dataanalysis #datascience #rstats

2 weeks ago | [YT] | 21

Statistics Globe

Next Monday, the Statistics Globe online course on Missing Data Imputation in R will begin, and registration is going very well.

If you want to improve your missing data workflow and strengthen your R skills, make sure to sign up soon so you’re ready for the course starting on December 1.

By enrolling in the course, you’ll receive lifetime access to:

- 12 course modules with 7 hours of video content.
- A structured program covering theory and hands-on imputation in R.
- An exclusive comments section for questions, support, and networking.
- R scripts, exercises, and practical examples to strengthen your skills.

Check out the course: statisticsglobe.com/online-course-missing-data-imp…

I’m really looking forward to the start of the course.

Talk to you soon.

Joachim

#rstats #missingdata #datascience #statistics

2 weeks ago | [YT] | 22

Statistics Globe

Handling missing data is a critical aspect of data analysis, and two common approaches are often applied: listwise deletion and imputation. The method you choose can significantly impact the quality and reliability of your results, making it crucial to select wisely.

❌ Listwise deletion is a simple method but comes with notable disadvantages. By removing all rows with missing values, it reduces the size of your data set, often leading to distorted results. This approach risks bias, loss of statistical power, and skewed insights, especially when data is not missing completely at random (MCAR). Furthermore, it assumes that eliminating incomplete rows does not introduce additional bias, an assumption that rarely holds in real-world scenarios.

✔️ Imputation offers a more effective alternative by filling in missing values while preserving as much data as possible. This approach retains the structure of the data and leads to more reliable outcomes, avoiding the loss of valuable information caused by listwise deletion.

The visualization below highlights the differences between these methods. The left plot demonstrates how listwise deletion disrupts the true data distribution, particularly when missingness follows a Missing at Random (MAR) mechanism, resulting in bias and inconsistencies. The right plot shows how imputation closely aligns with the true values, maintaining the structure and integrity of the data.

To illustrate this concept further, I’ve created a video that demonstrates the differences between listwise deletion and missing data imputation using the R programming language.

You can watch the video here: https://www.youtube.com/watch?v=v9rzH...

Interested in exploring this topic deeper? Join my course on Missing Data Imputation in R, starting December 1.

More info about the course: statisticsglobe.com/online-course-missing-data-imp…

Talk to you soon.

Joachim

#statistics #datascience #rstats #mice #missingdata

2 weeks ago | [YT] | 15

Statistics Globe

My upcoming online course on Missing Data Imputation in R will be the most comprehensive course I have ever created!

Here is what you can expect:

- about 7 hours of video material
- more than 80 exercises with solutions
- reproducible R scripts for all lectures
- detailed discussions in the comments
- several bonus sections
- extensive further resources

To keep everything manageable, the course is organized into 6 course weeks and includes a two-week break over Christmas and New Year. You can use this break to finish any material you did not complete during the first three weeks or prepare for the second half. We will continue in January with the final three weeks.

You will also receive lifetime access to all materials, so you can follow the course entirely at your own pace. It’s no problem if the schedule does not fit your plans as you can revisit the content at any time in the future.

After completing the course, you will be well prepared to handle missing data imputation on an expert level, and your general skills in statistics and R programming will also improve significantly.

The visualization below shows some of the graphs and topics we will address in the course.

Here you can learn more about the course and its structure: statisticsglobe.com/online-course-missing-data-imp…

If you have any questions, feel free to reach out.

Talk to you soon.

Joachim

#rstats #statistics #datascience #dataanalysis #dataquality #bias

2 weeks ago | [YT] | 13

Statistics Globe

Friendly reminder that the Early Bird promotion for the Missing Data Imputation in R course ends today!

If you enroll by the end of the day, you will receive free access to another course of your choice. The full list is available here: statisticsglobe.com/courses

More details about the Missing Data course: statisticsglobe.com/online-course-missing-data-imp…

Looking forward to getting started!

Talk to you soon.

Joachim

#missingdata #statistics #datascience #dataanalysis #rstats

3 weeks ago | [YT] | 15

Statistics Globe

Just a quick reminder that the early bird promotion for my upcoming online course Missing Data Imputation in R ends tomorrow.

If you register by November 19, you can choose one additional Statistics Globe course for free. Many participants have already taken advantage of this, so I want to make sure you do not miss the chance.

More info and registration: statisticsglobe.com/online-course-missing-data-imp…

If you join by tomorrow, you can pick one free course from the following list:

- Introduction to R Programming for Absolute Beginners: statisticsglobe.com/online-course-r-introduction
- Data Manipulation in R Using dplyr & the tidyverse: statisticsglobe.com/online-course-data-manipulatio…
- Data Visualization in R Using ggplot2 & Friends: statisticsglobe.com/online-course-data-visualizati…
- Statistical Methods in R: statisticsglobe.com/online-course-statistical-meth…
- Principal Component Analysis (PCA): From Theory to Application in R: statisticsglobe.com/online-course-pca-theory-appli…

If you have any questions, just reply to this email.

Thank you and talk to you soon.

Joachim

#missingdata #imputation #bias #datascience #rstats #statistics

3 weeks ago | [YT] | 12

Statistics Globe

As a teaser for the upcoming Statistics Globe online course, "Missing Data Imputation in R", I’ve started sharing a series of social media posts that highlight key concepts on handling and imputing missing values.

Check out some of these posts below:

- Variability Problems in Imputed Values (432 Likes): www.linkedin.com/posts/joachim-schork_datasciencec…
- Imputation Methods Compared (376 Likes): x.com/JoachimSchork/status/1989242911456674159
- Random Forest Imputation Explained (523 Likes): www.facebook.com/groups/statisticsglobe/posts/1769…

Stay updated by connecting with me on social media: LinkedIn (www.linkedin.com/in/joachim-schork/), X (x.com/JoachimSchork), Facebook (www.facebook.com/groups/statisticsglobe).

If you’re interested in learning more, you can find the full course description here: statisticsglobe.com/online-course-missing-data-imp…

I hope to see you in the course!

Joachim

#rstats #missingdata #imputation #statistics #datascience

3 weeks ago | [YT] | 15