R Tutorial - #6 - Model comparison (Hierarchical linear regression)
Channel: Statistics for Doctors
Date: 2020-12-16

Sections:
0:00 Introduction
1:10 Import the data set (The Child Aggression Study) and create a data frame
1:36 What is "model comparison"?
2:25 Defining the linear models to compare
4:03 Difference between the "reduced" vs. "full" models
4:46 Intercept-only model
6:08 The simple linear regression
6:22 Comparing the intercept-only model vs. simple linear regression
8:00 How to interpret the anova() output in model comparison
10:22 Comparing the other linear models
14:51 Final interpretation of the hierarchical linear regression

I begin by explaining what "model comparison" (aka hierarchical linear regression) is and then show you how to define the different linear models you want to compare. I then review what a "reduced" vs. "full" model is and use the intercept-only model and a simple linear regression as an example. I then compare these two models and go through the anova() output in detail. The rest of the tutorial involves comparing the other models we defined to find the linear model which best fits the data with the fewest number of predictors.

To freely download the R code and data sets used in this tutorial series, visit http://github.com/Statistics4Doctors. This tutorial analyzes a data set modified from data built by Dr. Andy Field (https://www.youtube.com/user/ProfAndyField) for his textbooks on statistics: Discovering Statistics using R (https://tinyurl.com/y6r6wsot) and SPSS (https://edge.sagepub.com/field5e).