Three-level multiple imputation: a fully conditional specification approach

Currently, there is a clear gap in the missing data literature for three-level models.

To date, the literature has only focused on the theoretical and algorithmic work

required to implement three-level imputation using the joint model (JM) method of

imputation, leaving relatively no work done on fully conditional specication (FCS)

method. Moreover, the literature lacks any methodological evaluation of three-level

imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in

order to implement FCS in the context of a three-level model and (2) to evaluate

both imputation methods. The simulation investigated a random intercept model

under both 20% and 40% missing data rates. The ndings of this thesis suggest

that the estimates for both JM and FCS were largely unbiased, gave good coverage,

and produced similar results. The sole exception for both methods was the slope for

the level-3 variable, which was modestly biased. The bias exhibited by the methods

could be due to the small number of clusters used. This nding suggests that future

research ought to investigate and establish clear recommendations for the number of

clusters required by these imputation methods. To conclude, this thesis serves as a

preliminary start in tackling a much larger issue and gap in the current missing data

literature.