Such processes of these data sets, the possible statistics give EU Member States and candidate biases and limitations in the interpretation and countries the means to gauge their success application of the available data and statistics in achieving gender equality against the dual to comparisons across countries and over time. [...] In other words, quality is the extent to which statistics are fit for the purpose of This document aims to encourage more measuring the concept of interest and conveying data producers and providers to collect and the result to the users. [...] be assessed on entirely absolute grounds, but Nonetheless, all three of the dimensions above should rather be evaluated with respect to the are important, as the institutional and process target audience (that is, the users of the data) aspects of data quality are considered when and the use to which the data are expected to selecting new data providers for the Database. [...] whether the outputs meet the current and potential needs of users; With the adoption of the European Statistics Code of Practice in 2005 and its revisions in 2011 • the accuracy and reliability dimension, which and 2017, Eurostat and the statistical authori- shows whether estimates and computations ties of the EU Member States have committed are consistently close to their exact or true themselves. [...] involved in the development and implementa- tion of statistical strategies and work plans, so Considerable progress has been achieved to that indicators and data are gender-sensitive date throughout the EU in the regular produc- and the priorities and needs of both women tion and dissemination of data disaggregated and men are taken into account within the sta- by sex, even when gender is not the.
Authors
Related Organizations
- Pages
- 98
- Published in
- Lithuania
Table of Contents
- Abbreviations 7
- Country Codes 7
- Frequently used abbreviation 7
- Introduction 8
- 1. Getting our terms right 10
- 1.1. Data and statistics 10
- 1.2. Data sets and indicators 10
- 1.3. Metadata 11
- 2. The general framework 12
- 2.1. Detailed presentation 12
- 2.2. Executive summary 18
- 3. Gender statistics and gender-sensitive indicators 20
- 3.1. Definitions and scope 20
- 3.1.1. Gender and sex: conceptual definitions 20
- 3.1.2. Gender statistics 22
- 3.1.3. Gender indicators 23
- 3.2. Uses and users of gender statistics 24
- 3.3. Quality implications 24
- 4. Guidelines for gender-sensitive data production and quality assessment 26
- 4.1. Motivation 26
- 4.2. Sex and gender identity: measurement 27
- 4.2.1. Introduction 27
- 4.2.2. Reasons for using (biological) sex in gender statistics 27
- 4.2.3. International practice 28
- 4.2.4. Summary and discussion 32
- 4.3. Multiple and intersecting inequalities 34
- 4.4. Data collection modes and stages 36
- 4.5. Selection of research topics and identification of data required 37
- 4.6. Analysis, modification and extension of definitions, concepts and research methods 38
- 4.7. Administrative register design 39
- 4.8. Survey design 40
- 4.8.1. Sample design (definition of the sampling unit and the sampling frame; choice of sampling method and sample size) 40
- 4.8.2. Choice of interview mode (online completion, one-on-one interview, one-on-one interview with self-completion blocks, etc.) 41
- 4.8.3. Questionnaire development and testing 41
- 4.8.4. Interview protocol development and interviewer training 42
- 4.9. Data processing (coding, validation and cleaning, weighting, imputation) 42
- 4.9.1. General considerations 42
- 4.9.2. Intersectional analysis and breakdown variables 43
- 4.10. Presentation of data and metadata 45
- 4.10.1. Macrodata (statistics) 45
- 4.10.2. Microdata 46
- 4.10.3. Metadata 46
- 4.10.4. Data analysis and preparation of tables and graphs 46
- 4.10.5. Presenting data on intersecting inequalities 49
- 4.11. Executive summary 50
- 5. Evaluation and selection of sources for EIGE’s Gender Statistics Database 51
- 5.1. Evaluation and selection of data providers 51
- 5.2. Evaluation and selection of statistical outputs 51
- 5.2.1. General principles 51
- 5.2.2. Minimum standard 52
- 5.3. Presentation of quality-compromised data 52
- 5.4. Current data sources 55
- 5.4.1. External 55
- 5.4.2. Internal 55
- 5.5. Executive summary 57
- 6. Technical guidelines for contributing to EIGE’s Gender Statistics Database 58
- 6.1. Background: structure of the Database 58
- 6.1.1. Principles and building blocks 58
- 6.1.2. Technical implementation 62
- 6.2. Preparation of data for the Database: instructions and examples 63
- 6.2.1. Technical formats 63
- 6.2.2. Steps to be followed 63
- 6.2.3. Examples of data set structures 64
- 6.2.4. Preparation of metadata in ESMS format 66
- 7. Definitions of quality dimensions in Eurostat’s Concepts and Definitions Database 67
- 8. Euro-SDMX Metadata Structure 2.0 73
- 9. Technical appendix: sample size considerations 83
- 9.1. Introduction and problem statement 83
- 9.2. Implications for data publication 85
- Annex 1 88
- Bibliography 90
