How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.

Area of Science:

• Psychological research methodology
• Statistical modeling
• Data analysis

Background:

• Standard linear regression is common in psychology but struggles with incomplete data and many predictors.
• Regularization methods like LASSO enhance interpretability and prediction but complicate missing data handling.
• Traditional methods for missing data with LASSO (listwise deletion, ad hoc imputation) cause bias and reduce predictive power.

Purpose of the Study:

• To present and illustrate three methods for fitting LASSO regression with multiple imputation for missing data.
• To provide practical guidance for implementing these approaches in psychological research.
• To discuss the implications of each method and suggest future research directions.

Main Methods:

• Utilizing multiple imputation techniques to address missing data.
• Applying LASSO (Least Absolute Shrinkage and Selection Operator) regression for variable selection.
• Demonstrating implementation with a practical applied example in psychological research.

Main Results:

• The tutorial outlines three distinct approaches for integrating multiple imputation with LASSO.
• Practical implementation guidance is provided for researchers.
• The study highlights the potential pitfalls of standard missing data treatments when combined with regularization.

Conclusions:

• Combining multiple imputation with LASSO offers a robust solution for psychological studies with missing data.
• Careful consideration of different integration approaches is necessary for optimal results.
• Further research is needed to establish definitive best practices for this methodology.