Technical Working Session on Profiling Equity Focused Information

Transcription

1 Technical Working Session on Profiling Equity Focused Information Using to create, knowledge and wisdom (with a particular focus on meta) June, 2015 UN ESCAP, Bangkok 24/06/ Aims 1. Outline the role of National Statistical Systems (NSS) in ensuring equity for children Review processes or models for using to create to support sound equity policy 3. Highlight the importance of meta in equity profiling 24/06/

2 Development and the revolution Data must be available, and must be turned into the that can be confidently used by people to understand and improve their lives and the world around them. The Secretary-General s Independent Expert Advisory Group on a Data Revolution for Sustainable Development (IEAG) 24/06/ The revolution - fundamental requirements Enhanced national and international statistical capacities, rigorous indicators, reliable and timely sets, new and non-traditional sources and broader and systematic disaggregation to reveal inequities Synthesis report of the Secretary-General on the post-2015 sustainable development agenda 24/06/

3 The role of the NSS - from providers to knowledge and creators Goal: Turn raw from survey and administrative sources into and knowledge in support of effective equity policy WISDOM KNOWLEDGE Applied Context Applied knowledge Organized to provide context INFORMATION Meaning Linked observations to provide meaning DATA Raw Unlinked, discrete observation 24/06/ The role of the NSS - from providers to knowledge and creators Goal: Turn raw from survey and administrative sources into and knowledge in support of effective equity policy WISDOM KNOWLEDGE Applied Context Poorer families prioritize boys education Girls primary school attendance is low INFORMATION Meaning Female, aged 5-10 years, not at school. DATA Raw F, Cat2, N 24/06/

4 Generating, knowledge and wisdom an iterative model 24/06/ Generating, knowledge and wisdom an iterative model a. Identifying users b. Engaging users c. Specifying user need 24/06/

5 Generating, knowledge and wisdom an iterative model a. Translating into discrete, measurable indicators b. Specifying the needed to support indicators 24/06/ Generating, knowledge and wisdom an iterative model a. Reviewing/adapting existing collections b. Designing additional new collections (if required) c. Developing an plan of those 24/06/

6 Generating, knowledge and widsom an iterative model a. Preparing for investigation b. Cleaning for c. Identifying characteristics of the (Exploratory ) d. Drawing conclusions about inequity 24/06/ Generating knowledge an iterative model a. Reported to the users in support their requirements b. Tailoring the format and content of the communication to users requirements 24/06/

7 Generating knowledge an iterative model The users may have feedback, which results in additional. As such, much of the analytical cycle is iterative 24/06/ Compiling meta are critical for efficient, effective and integrated equity Definition - Meta describe a set Benefits - The three main benefits of meta are: Improves the value/benefit of through re-use Support the design and development of new collections Enable the consistent use of standards and best practices 24/06/

8 Compiling meta are critical for efficient, effective and integrated equity Equity typically come from a variety of producers such as the Departments of Labour, Health and Education School attendance records MICs Integrated Labour Force Survey Joining or integrating these sources will depend on good meta CRVS systems 24/06/ Meta Questionnaire: Country perception of meta availability Data set: 18 responses (11 countries) 1. Overview, scope and coverage Sample Survey Design 3. Data Collection processing Access to meta from surveys and administrative sources 6. Benefits of meta Ref - International Household Survey Network - Quick Reference Guide For Data Archivists IHSN 24/06/

9 Accessibility of meta 24/06/2015 Meta Questionnaire: Country perception of meta availability Data set: 18 responses (11 countries) Overview, scope and Coverage (1) mean 3a b a b a b a b mean II. Sample Survey Design (2) mean III. Data Collection (3) 10a b mean IV. Data processing (4) a b a b mean V Access to meta from surveys and administrative sources (5) 15a b c mean VI. Benefits of meta (6) 16a b c mean mean /06/ Meta Questionnaire: Country perception of meta Often Quite often Sometimes Rarely Never Accessibility of meta by core components Overview, scope, coverage (1) Sample Survey Design (2) Data Data Collection (3) processing (4) Core components of meta Survey Administrative not specified Access to meta (5) Benefits of meta (6) Meta about sources of collected in administrative systems is generally less accessible than survey In particular Data processing where details of the variables held in the set and details about the accuracy and precision of the are the two least accessible items of meta Individual meta sets are generally accessible to all user groups less so for the media and academia But is this true of the breadth of meta held? 24/06/

10 Meta : Initial observations on raw meta files It appears as though meta are not commonly defined and produced within countries. Are there single unified frameworks being used in countries? The difference in practices appears to be much greater across countries It may not always be easy to find individual meta files. And all meta may not always be consolidated and available in one place Querying meta may be challenging due to the unstructured nature of some Some institutions may not want to share comprehensive meta 24/06/ Integrated some key recommendations and challenges Identifying a senior meta champion to overcome stovepipe mentality. Cultivate the independence of the National Statistical System. Develop policies to harmonize. Concepts, definitions, naming conventions and classifications all must agree from one source to another. Build meta base to support harmonization policies. Ref: The Struggle for Integration and Harmonization of Social Statistics in a Statistical Agency 24/06/

11 Practical session is the quality of healthcare improving? Questions. 1. How would you measure this? What are some of the key indicators? What are the best sources of for theses indicators? 3. Are the good enough quality or fit for purpose? Considerations. 1. How easy would it be to identify the sources relevant to this question? How easy would it be to assess the gaps and quality of available? a. Periodicity b. Coverage c. Levels of disaggregation d. Accuracy and precision 3. What are the strengths and weaknesses of your meta in answering these questions? 24/06/ ANNEX 24/06/

12 Data preparation compiling a set Goal Create a structured set from survey and administrative entries Steps: Transposing from survey questionnaires Stripping out unnecessary variable from admin Compiling meta Converting into a format required by analytical software 24/06/ Cleaning correcting errors in the set Goal Data cleaning, also called cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from. Examples: Remove duplicates Find and assess outliers Assess completeness of Note - When multiple sources need to be integrated, the need for cleaning increases significantly. Integrated or coherent equity profiles across a range of socio-economic issues will require common definitions and standards to be applied to issues such as [P6, 6 bullets, UNICEF Q&A]. 24/06/

13 Exploratory Describe key features or messages in the. Goal Understand the s underlying structure and variables, to develop intuition about the set, to consider how that set came into existence, and to decide how it can be investigated with more formal statistical methods. Steps: Exploration may result in additional cleaning or additional requests for, so these activities may be iterative in nature. Descriptive statistics such as the average or median may be generated to help understand the Data visualization may also be used to examine the in graphical format, to obtain additional insight regarding the messages within the 24/06/ Inference draw conclusions related to the specified need Goal Statistical inference is used to draw conclusions (inferences) about the population from which the were obtained. Examples: Estimate sampling error, e.g. standard error, confidence intervals of point observations. Test hypotheses such as children in rural and urban areas have the same access to primary education Calculate measures of interest such as the neonatal mortality rate Model the relationship between variables such as gender and geographic location on outcomes such as the probability of stunted growth. 24/06/