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INTRODUCTION

Among national statistical agencies, quality is generally accepted as "fitness for purpose". Fitness for purpose implies an assessment of an output, with specific reference to its intended objectives or aims. Quality is therefore a multidimensional concept which does not only include the accuracy of statistics, but also stretches to include other aspects such as relevance and interpretability.

Over the last decade, considerable work has been undertaken in statistical and economic agencies to define and measure quality. The ABS DQF is based on the Statistics Canada Quality Assurance Framework (2002) and the European Statistics Code of Practice (2005). The ABS DQF is comprised of seven dimensions of quality, reflecting a broad and inclusive approach to quality definition and assessment. The seven dimensions of quality are
Institutional Environment, Relevance, Timeliness, Accuracy, Coherence, Interpretability and Accessibility. All seven dimensions should be included for the purpose of quality assessment and reporting. However, the seven dimensions are not necessarily equally weighted, as the importance of each dimension may vary depending on the data source and context.

The ABS DQF has been designed to be used in evaluating the quality of statistical collections and products (e.g., survey data, statistical tables), including administrative data. Depending on the nature of the collection or product being assessed, some dimensions will be more appropriate or important than others. For example, traditional measures of statistical accuracy for sample-based collections, such as sampling error and non-response error, may not apply to datasets which are by-products of administrative collections. For administrative data, other factors such as timeliness or relevance, for example, may be more important. We recommend that judgment is used in making assessments of quality, and that the quality dimensions are evaluated appropriately for the particular context.

This paper describes the ABS DQF, to enable its use in activities including the following:

• defining the quality of a data item or collection of data items (preparing a quality statement);
• assessing data in the context of a data need; and
• identifying data gaps and areas of future improvement.