IREF419432false2026-05-13T03:12:21ZSDMX Cross Domain Concept SchemeThe Cross Domain Concept Scheme (starting from v2.0) is created from the set of concepts in the SDMX Glossary, which is an SDMX guideline containing concepts and related definitions that are useful for building and understanding data and metadata exchange arrangements based on SDMX. The Glossary provides definition of terms found in the SDMX Information Model, Data Structure Definitions (DSDs) and Metadata Structure Definitions (MSDs) at the time of the present release. It is recommended as a single entry point to a common SDMX terminology to be used in order to facilitate communication and understanding of the standard.In short, the overall message of the glossary is the following: if a term is used, then its precise meaning should correspond to the SDMX Glossary definition, and any reference to a particular phenomenon described in the SDMX Glossary should use the appropriate term.Version 1.0 of the SDMX Glossary, which replaces the Metadata Common Vocabulary (MCV) published in 2009, was finalised in February 2016. The Cross Domain Concept Scheme v1.0 was created from the MCV concepts.For any question, comment or correction, feel free to contact the SDMX Statistical Working Group (SWG) at the following address: swg@sdmx.org.CONTEXTNon-sampling errors may arise from many different sources such as defects in the sampling frame, faulty demarcation of sample units, defects in the selection of sample units, mistakes in the collection of data due to personal variations, misunderstanding, bias, negligence or dishonesty on the part of the investigator or of the interviewer, mistakes at the stage of the processing of the data, etc. Non-sampling errors may be categorised as:- Coverage errors (or frame errors) due to divergences between the target population and the frame population; - Measurement errors occurring during data collection; - Nonresponse errors caused by no data collected for a population unit or for some survey variables; - Processing errors due to errors introduced during data entry, data editing, sometimes coding and imputation; - Model assumption errors.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy; Accuracy - overall; Coverage error; Measurement error; Over-coverage rate; Sampling errorSOURCESDMX (2016) (https://sdmx.org/)Non-sampling errorError in sample estimates which cannot be attributed to sampling fluctuations.