Mediation Analysis in R - Method 2 (Boostrapping confidence Interval)

This video shows how you can do mediation analysis in R using bootstrapping confidence interval. I will show how to write a R function for mediation analysis from scratch. I will also explain the standard error (SE) and standard deviation (SD) in the context of bootstrapping. Finally, I will explain how to calculate bias in bootstrapping and how to calculate the confidence interval for indirect effect based on normal distribution assumption. Complete R code for mediation analysis: https://github.com/TidyPython/Mediati... R Markdown pdf File: https://github.com/TidyPython/Mediati... Slides used: https://github.com/TidyPython/Mediati... Method 1 video:    • Mediation analysis in R - Method 1   The hypothetical data used in the tutorial: https://github.com/TidyPython/Mediati... [Note: This data is generated via R programming (i.e., not real data). Please do not interpret the findings from a theoretical perspective. Please do not use it outside of this tutorial. ] 0:00 - Overview 1:16 - How to use bootstrapping in mediation analysis in R 3:20 - R code for mediation analysis from scratch (use boot() function in R) 5:11 - Confidence intervals (normal distribution and percentile) to test indirection effect 6:20 - Histogram for indirect effects in R 6:55 - Standard Error (SE) and Standard Deviation (SD) for indirect effect in bootstrapping 9:02 - Bias in bootstrapping 10:20 - Use bias in confidence interval based on normal distribution assumption Disclaimer: The accuracy and comprehensiveness of this tutorial are not assured. It is advisable to verify independently or seek advice from professionals. The author of this tutorial bears no responsibility or liability for its accuracy and completeness.