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It is still an open question whether to consider the ICF qualifiers "8 – not specified" and "9 – not applicable" missing values represents the best strategy to cope with these response alternatives.
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Vaseghi [6] formulate the general form of polynomial interpolator and statistical interpolators applicable for missing data imputation purpose.
"?": Character not applicable or missing.
*NA, not applicable; ?, data missing from farm records.
We treated data coded as blank but applicable as missing at random.
*: only present in matrix of Table 3. "?": Character not applicable or missing.
Where applicable, the missing indicator method was used [ 18], so that patients with a missing baseline value could be incorporated.
However, since the questionnaire was intended to apply to all cancer patients, rates of not applicable or missing data higher than 10% were accepted.
The second model of scoring (model 2) considered the response 'not applicable' as missing data, thereby classifying an individual as unconcerned.
They are applicable under missing completely at random (MCAR) as well as missing at random (MAR) [ 14], while simple repeated univariate analyses for each time point using test procedures such as the t-test, ANOVA, or the Wilcoxon rank sum test rely on the more restrictive assumption of MCAR.
Higher levels of reliability (alpha ≥ 0.9) are required of measures that are being used to compare an individual's scores across time [ 19] and for such purposes the full scale score would be needed with no more than one applicable item missing (excluding N/A items).
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