Using aov_car to Handle Missing Data in Mixed-Design ANOVA Analysis: A Modified Approach

Understanding the Problem: Removing Missing Data from ANOVA Analysis Using aov_car

ANOVA (Analysis of Variance) is a statistical technique used to compare means among three or more samples. In this blog post, we will discuss how to perform an ANOVA analysis using the aov_car function in R, and address a common issue related to missing data in the context of mixed-design ANOVA.

Introduction to Mixed-Design ANOVA

Mixed-design ANOVA is a type of ANOVA that accounts for both within-subjects (repeated measures) and between-subjects variation. In this blog post, we will focus on using the aov_car function from the car package in R to perform mixed-design ANOVA.

The Problem: Missing Data

The question presents a scenario where three participants are missing all of ‘C’ (pre and post) data, but their ‘A’ and ‘B’ data is still available. The goal is to run an ANOVA analysis using aov_car without completely removing the data from these three participants.

Current Approach

The current code snippet attempts to perform the ANOVA analysis using aov_car. However, this approach results in a warning message indicating that missing values are present for certain IDs (P20, R21, and R22). The warning suggests removing those cases from the analysis.

library(tidyverse)
library(car)
library(afex)
library(emmeans)

my_anova <-aov_car(data ~ Group*Session*Condition 
                     + Error(PID/Session*Condition), na.rm = TRUE,  
                     data=my_data)

Alternative Approach

To address the issue of missing data, we can use the aov_ez function from the afex package instead. The aov_ez function allows us to specify which variables are within-subjects (repeated measures) and between-subjects.

library(afex)

my_anova2 <- aov_ez("PID", "data", 
                     my_data, 
                     within = c("Session", "Condition"), 
                     between = "Group", na.rm=TRUE)

However, this approach still doesn’t address the issue of missing data. We need to modify our strategy to account for the missing ‘C’ data.

Modified Approach

To remove missing data from specific variables while keeping other variables intact, we can use the aov_car function with the na.action="omit" argument.

library(car)

my_anova3 <- aov_car(data ~ Group*Session*Condition 
                     + Error(PID/Session*Condition), na.rm = TRUE,  
                     data=my_data, na.action="omit")

In this approach, we specify the na.action argument as "omit", which removes any observations that have missing values for specific variables.

Conclusion

By using the modified approach with the aov_car function and the na.action="omit" argument, we can perform an ANOVA analysis while removing only the missing data from specific variables. This ensures that the remaining data is used in the analysis, allowing us to draw more accurate conclusions about the effects of interest.

Additional Context: Handling Missing Data

When working with missing data, it’s essential to understand the implications and potential biases associated with different approaches. In this case, using aov_car with the na.action="omit" argument helps avoid the issues that arise from removing entire observations due to missing values.

However, there are other ways to handle missing data in ANOVA analysis, such as:

Using multiple imputation techniques to estimate missing values
Removing only specific variables with missing values while keeping others intact (as demonstrated above)
Considering alternative statistical methods that can accommodate missing data more effectively

Ultimately, the choice of approach depends on the nature and extent of the missing data, as well as the research question being addressed.

Additional Context: Choosing the Right ANOVA Approach

When deciding between aov_car and other ANOVA approaches (such as aov_ez), consider the following factors:

Mixed-design requirements: If your analysis involves both within-subjects (repeated measures) and between-subjects variation, use aov_car.
Complexity of design: For more complex designs with multiple factors and interactions, use aov_car.
Data availability: Consider the amount and quality of data available for each variable when choosing an approach.

By carefully selecting the right ANOVA approach and handling missing data effectively, researchers can ensure accurate and reliable results in their statistical analyses.

Last modified on 2024-07-07