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Another limitation of the data was inconsistency in outcomes that is, for a given risk factor we would have expected to see an increase in all types of severe outcomes.
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The data was cleaned for inconsistencies and missing values.
The data was then cleaned and inconsistencies checked against the original forms.
Despite these inconsistencies, the data was still compared to determine whether there was a significant change in where jail suicides occurred based on facility size.
Prior to analysis, the data were checked for inconsistencies and cross-reference made to the original questionnaires to inform corrections.
After completion of data collection, the data were reviewed for inconsistencies and where necessary, corrections were made after contacting the respondents before data entry and analysis were done.
The data were dual entered and inconsistencies corrected.
First, the data were extracted and checked for inconsistencies, and then were tabulated and made ready for statistical processing using the programming language Perl (https://www.perl.org/) and the statistical computing environment R (https://www.r-project.org/).org/
First the data were checked for completeness and inconsistencies.
The data were checked for completeness and inconsistencies, edited, coded and entered into SPSS for window version 16.0 (SPSS Inc. version 16.1, Chicago, Illinois).
Therefore a careful exploration of heterogeneity between trials and inconsistency of the data is mandatory.
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CEO of Professional Science Editing for Scientists @ prosciediting.com