Measures that may be used to data linkage quality include accuracy, sensitivity, specificity, precision and the false-positive rate. These measures are based on basic data that are simply defined from the results of data linkage (Figure 5). Figure 5. Classification of matches and links Not of all these measures are easily calculated, because their calculation depends on knowing the number of true non-matches or true negatives (TN). These are often unknowable or difficult to calculate. The most widely used quality measures are: sensitivity or the true positive rate: This is the proportion of matches that are correctly classified as matches. It may be defined as the proportion of all records in a file with a match in another file that is correctly accepted as links (true links) This measure is calculated as: TP/(TP+FN). precision or the positive predictor value is the proportion of all classified links that are true links or true positive. This measure is calculated as: TP/(TP+FP). Methodological issues When we are linking data from different sources like sample surveys, combined data and administrative data we can meet such problems as data missing, data overlapping, “unlinked data” etc. Errors might be detected in statistical units and target population when linking other data to this information. And if influential these errors need to be corrected in the S-DWH. There are three kind of missing data type: structural, temporal and errors. Temporal missing data data that is not available at this time, but it will be provided and incorporated in a short – period of time. Errors are incorrect values. A frequently expected error will be the NACE – code (activity). One of the problems is conflict between sources. Data conflict it is when two or more sources contain data for the same variable (with different values). In many cases, when two (or more) reliable sources conflict, one (or more) of those sources can be demonstrated to be unreliable. The main goal is to define data source priority for each indicator and rules determining the quality of priority data source. It is needed to be defined what data source for what indicator more reliable and determined by different criteria. In some other cases it could be needed additional analysis, and could be used more sophisticated methods, or even manual techniques. In Lithuania we use data source priority for each indicator in SBR and when priority sources are determined they are not changed. To determine priority source need to be defined priority
Measures that may be used to data linkage quality include accuracy, sensitivity, specificity, precision and the false-positive rate. These measures are based on basic data that are simply defined from the results of data linkage (Figure 5). Figure 5. Classification of matches and links Not of all these measures are easily calculated, because their calculation depends on knowing the number of true non-matches or true negatives (TN). These are often unknowable or difficult to calculate. The most widely used quality measures are: sensitivity or the true positive rate: This is the proportion of matches that are correctly classified as matches. It may be defined as the proportion of all records in a file with a match in another file that is correctly accepted as links (true links) This measure is calculated as: TP/(TP+FN). precision or the positive predictor value is the proportion of all classified links that are true links or true positive. This measure is calculated as: TP/(TP+FP). Methodological issues When we are linking data from different sources like sample surveys, combined data and administrative data we can meet such problems as data missing, data overlapping, “unlinked data” etc. Errors might be detected in statistical units and target population when linking other data to this information. And if influential these errors need to be corrected in the S-DWH. There are three kind of missing data type: structural, temporal and errors. Temporal missing data – data that is not available at this time, but it will be provided and incorporated in a short period of time. Errors – are incorrect values. A frequently expected error will be the NACE code (activity). One of the problems is conflict between sources. Data conflict it is when two or more sources contain data for the same variable (with different values). In many cases, when two (or more) reliable sources conflict, one (or more) of those sources can be demonstrated to be unreliable. The main goal is to define data source priority for each indicator and rules determining the quality of priority data source. It is needed to be defined what data source for what indicator more reliable and determined by different criteria. In some other cases it could be needed additional analysis, and could be used more sophisticated methods, or even manual techniques. In Lithuania we use data source priority for each indicator in SBR and when priority sources are determined they are not changed. To determine priority source need to be defined priority
DWH-SGA2-WP2%20-%202%20.4.1%20Data%20linking%20aspect%20of%20combining%20data%20in%20the%20S-DWH%20context_v1.0%20.docx