Your English writing platform
Discover LudwigSuggestions(2)
Exact(2)
In the case of missing item data (only scales), up to 30% were replaced using the expectation-maximization algorithm (EM).
Scale constructions were performed under specific rules for missing item data: in summating to a total for each case, scores had to be valid on at least half of the items, if the number of items was even, and on at least half of the items plus one half, if the number of items was uneven.
Similar(58)
In all models, individuals with partially missing item level data were included, since estimation of missing data patterns is possible under both estimators (traditional ML and WLSMV).
In all models individuals with partially missing item level data were included, since estimation of missing data patterns is possible under traditional ML and WLSMV.
The missing item rate referred to the number of missing items of data among received forms at the related visit.
Missing item-level data are commonly reported.
Missing item-level data were imputed using multiple imputation.
Missing item-level data were imputed as the mean of at least 50% of the subscale items.
The percent of missing item-level data was low – 32 of the 36 items showed less than 1% missing (Table 1).
Multiple imputation was used to account for missing item-level data from the health survey and estimates were then weighted to take into account selective non-response to the health survey [ 21].
More specifically, we conclude that the resulting estimates are closer to those that would be obtained with a full enumeration of the sample with no missing item-level data.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com