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::'''3. MLM can Handle Missing Data:''' Missing data is permitted in MLM without causing additional complications. With RM-ANOVA, subject’s data must be excluded if they are missing a single data point. Missing data and attempts to resolve missing data (i.e. using the subject’s mean for non-missing data) can raise additional problems in RM-ANOVA.
::'''4. MLM can also handle data in which there is variation in the exact timing of data collection''' (i.e. variable timing versus fixed timing). For example, data for a longitudinal study may attempt to collect measurements at age 6 months, 9 months, 12 months, and 15 months. However, participant availability, bank holidays, and other scheduling issues may result in variation regarding when data is collected. This variation may be addressed in MLM by adding “age” into the regression equation. There is also no need for equal intervals between measurement points in MLM.
::'''5. MLM is relatively easily extended to discrete data.''' <ref>{{cite book | last = Molenberghs | first = Geert | title = Models for discrete longitudinal data | publisher = Springer Science+Business Media, Inc | ___location = New York | year = 2005 | isbn = 978-0387251448 }}</ref>
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