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To account for cases in which data were missing, analysis was performed for each variable (e.g. BFMDRS-M) using only those cases for which values were available at all three time points.
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Near-miss analysis is an incident investigation technique that detects and subsequently analyses indicators of failures.
First, a missing data analysis was performed on the WHPCI items, after which five cases had to be excluded from further analysis because values were missing for ≥ 30% of the items.
Second, a missing value analysis was performed.
A missing values analysis was undertaken.
Initially, the items were checked for plausibility, and missing data analysis was performed.
A specific missing data analysis was performed to analyze data quality.
A missing values analysis was conducted in accord with best practice guidelines (Jeličić, Phelps, & Lerner, 2009; Schlomer, Bauman, & Card, 2010).
A missing value analysis was conducted for independent variables relating to condom use with steady and casual sex partners.
Out of 7,124 datasets in the German Health Survey- 1998, 228 (4.0%) MCS and PCS datasets were missing; therefore analysis was based on 6,836 consecutive patients.
Before analysis, quality was tested by verification of random samples, the items were checked for plausibility, and missing data analysis was performed.
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