<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Quality has become the key concept in official statistics. There is a general consensus that we have to consider several components when assessing the quality of statistical information. Relevance, accuracy, timeliness, punctuality, comparability, coherence, accessibility and clarity are the dimensions most frequently mentioned. In this article we use regression analysis to evaluate the contribution of these different dimensions when assessing the overall quality of statistical products. We do this using the information collected in the structured consultation with users and experts from both inside and outside the Spanish Central Administration carried out by the Working Group of the Spanish High Council on Statistics, responsible for the preliminary draft of the proposals and recommendations of this council for the Spanish Multiannual Statistical Programme 2013-2016. We find that the abovementioned dimensions have different weights in the overall assessment of perceived quality (with accuracy and reliability having the highest weight, and relevance having the lowest) and that the structure differs between both types of users.</p></abstract>

Quality has become the key concept in official statistics. There is a general consensus that we have to consider several components when assessing the quality of statistical information. Relevance, accuracy, timeliness, punctuality, comparability, coherence, accessibility and clarity are the dimensions most frequently mentioned. In this article we use regression analysis to evaluate the contribution of these different dimensions when assessing the overall quality of statistical products. We do this using the information collected in the structured consultation with users and experts from both inside and outside the Spanish Central Administration carried out by the Working Group of the Spanish High Council on Statistics, responsible for the preliminary draft of the proposals and recommendations of this council for the Spanish Multiannual Statistical Programme 2013-2016. We find that the abovementioned dimensions have different weights in the overall assessment of perceived quality (with accuracy and reliability having the highest weight, and relevance having the lowest) and that the structure differs between both types of users.

Are All Quality Dimensions of Equal Importance when Measuring the Perceived Quality of Official Statistics? Evidence from Spain

Institut d’Estadı´stica de Catalunya, Via Laietana 58, 08003 Barcelona, Spain

Departament d’Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain

Department d’Economia i d’Histo`ria Econo`mica, Universitat Auto`noma de Barcelona, Edifici B, Campus Bellaterra, 08193 Bellaterra, Barcelona, Spain

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Quality has become the key concept in official statistics. There is a general consensus that we have to consider several components when assessing the quality...

Are All Quality Dimensions of Equal Importance when Measuring the Perceived Quality of Official Statistics? Evidence from Spain

Published Online: Sep 02, 2014

Page range: 547 - 562

Received: Oct 01, 2012

Accepted: Mar 01, 2014

DOI: https://doi.org/10.2478/jos-2014-0034

Keywords
Quality, dimensions, consultation, weight, regression

© by Alex Costa

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Are All Quality Dimensions of Equal Importance when Measuring the Perceived Quality of Official Statistics? Evidence from Spain

Published Online: Sep 02, 2014

Page range: 547 - 562

Received: Oct 01, 2012

Accepted: Mar 01, 2014

DOI: https://doi.org/10.2478/jos-2014-0034

KeywordsQuality, dimensions, consultation, weight, regression

© by Alex Costa

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Quality, dimensions, consultation, weight, regression