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Analyses are based on 85 responses, similarly distributed across trials (table 1), with a modest amount of missing data.
Due to a modest amount of missing data for participant occupation (8%) we carried out multiple imputation of missing observations to assess whether the missing data biased our results.
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To address the modest amount of missing data, we used multiple imputation on the set of independent variables.
There was a small amount of missing data on place of residence.
Modest amounts of missing data would not enter regression models as discrete variables (yes or no) so they probably could not have confounded these analyses.
The amount of missing data for Beliefs and Dispositions, while not negligible, represents a reasonably small amount of missing data.
HRQOL research with repeated measures can be completed with a very small amount of missing data.
Because of the relatively small amount of missing data casewise exclusion of missing cases was performed.
We used missing indicator variables for the small amount of missing data.
Given the small amount of missing data, missing values were replaced with item means.
The small amount of missing data from items were imputed in the maximum likelihood estimation procedures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com