How do mixed models deal with missing data?
The mixed model will retain the 70 people who have data for only one time point. It will use the 48 people with pretest-only data along with the 90 people with full data to estimate the pretest mean. If the data are missing at random, this will give you unbiased estimates of each of these means.
Can LMER handle missing data?
LMER in R will remove missing observations, i.e. a complete case analysis is performed. As with complete case analysis in a likelihood based procedure, depending on the nature of missingness, the resulting analysis is not biased, but is slightly inefficient.
What is a mixed effect regression?
We focus here on mixed-model (or mixed-effects) regression analysis,21 which means that the model posited to describe the data contains both fixed effects and random effects. Fixed effects are those aspects of the model that (are assumed to) describe systematic features in the data.
Can a linear mixed model be used for missing data?
That means keeping only the 90 people with complete data. This causes problems with both power and bias, but bias is the bigger issue. Another alternative is to use a Linear Mixed Model, which will use the full data set. This is an advantage, but it’s not as big of an advantage in this design as in other studies.
How does repeated measures ANOVA deal with missing data?
Another 48 completed only the pretest and 22 completed only the post-test. Repeated Measures ANOVA will deal with the missing data through listwise deletion. That means keeping only the 90 people with complete data.
How are missing data related to sample size?
The missing data was pretty random–some participants missed time 1, others, time 4, etc. Only 6 people out of 150 had full data. Listwise deletion created a nightmare, leaving only 6 people in the data set. Each person contributed data to 4 means, so each mean had a pretty reasonable sample size.