Skip to main content


Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Table 1 Description of the measurement domains, properties, aspects, and statistics and methods

From: Quality appraisal of generic self-reported instruments measuring health-related productivity changes: a systematic review

Domains Properties Aspects Statistics/Methods
  Internal consistency   Cronbach’s alpha or Kuder-Richardson formula (KR-20) to determine relevance
Factor analysis or principal component analysis to determine whether items form one or more than one scale
Reliability   Intraclass correlation coefficient (ICC) or Cohen’s kappa
Measurement error   Standard error of measurement (SEM)
Smallest detectable change (SDC)
Change beyond measurement error
Limits of agreement (LoA)
Minimal important change to determine the adequacy of measurement error
  Content validity Face validity Assessment of relevance of all items for the construct, aim and target group
Assessment of important missing items
Construct validity   
  Structural validity Factor analysis to confirm the number of subscales present
Hypotheses testing Assessment of a priori hypotheses, clearly indicating both direction and magnitude of the correlation or difference
Cross-cultural validity Assessment of adequate reflection of the performance of the items of the original instrument
Criterion validity   Correlation
Area under the receiver operator characteristics curve (AUC)
Sensitivity and specificity
    Assessment of a priori hypotheses focussing on the change score of an instrument in the hypotheses
Area under the receiver operator characteristic curve (AUC)