1. 4.2.3 Data Quality, Composite Indicators and Aggregation 1 DATA QUALITY, COMPOSITE INDICATORS AND AGGREGATION UPA Package 4, Module 2

2. 4.2.3 Data Quality, Composite Indicators and Aggregation 2 Data Quality, Composite Indicators and Aggregation Data errors Less is more; benefit-cost of data Data cleaning Composite indicators Introduction exercise 4.2.3 Exploring Data sets Introduction exercise 4.2.4 Aggregation

3. 4.2.3 Data Quality, Composite Indicators and Aggregation 3 Data Errors Biased data Outliers, error or extreme value Sample too small Too much precision or regularity (too good to be true) Missing values Inconsistencies

4. 4.2.3 Data Quality, Composite Indicators and Aggregation 4 Less is more; Benefit and Cost of Data Quality (full coverage and maintenance) Quantity (many variables but missing values and outdated) Physical Characteristics of a building Ownership Characteristics of a building

5. 4.2.3 Data Quality, Composite Indicators and Aggregation 5 Benefit and Cost of Data Data Benefit and Costs Strategy and clear objectives of developing databases Data (and functionalities) requirement study Data benefit, the value of information and quantification –costs reduction, effectiveness/priorities of (public) resource allocation –transparency, awareness, involvement Data costs high (acquisition, editing, conversion, updates, maintenance)

6. 4.2.3 Data Quality, Composite Indicators and Aggregation 6 Benefit and Cost of Data Primary and secondary data, data sharing Primary, ad-hoc, single use of data, (too) expensive Secondary matching with requirement for poverty studies Combination of existing data and samples Data collection embedded into institutional settings, from data projects to data processes

7. 4.2.3 Data Quality, Composite Indicators and Aggregation 7 Composite Indicators Poverty without reliable income data Slums Composite Indicator Human Development Indicator, Poverty Index Proxy indicators (consumption / income)

8. 4.2.3 Data Quality, Composite Indicators and Aggregation 8 Composite Indicators

9. 4.2.3 Data Quality, Composite Indicators and Aggregation 9 Aggregation Aggregate cases into a single summary case Break variable defines a group and create one case e.g. neighborhood Aggregate functions Summary, fractions

10. 4.2.3 Data Quality, Composite Indicators and Aggregation 10 Small Area Statistics Limited (existing) data, limited funds for data collection Sample survey and auxiliary data sets (+ analytical skills) = small area statistics Developing a model to identify the relationship between the survey and the auxiliary data more reliable estimates can be made and the possibilities to extrapolate to areas not covered by a household survey

11. 4.2.3 Data Quality, Composite Indicators and Aggregation 11 Introduction Exercise 4.2.3 Exploring Datasets Classifying interval data (number of foreigners, income, family size) into meaningful groups (e.g. low income, medium income, high income). Create cross tables and analyze relationships between these ordinal data sets.

12. 4.2.3 Data Quality, Composite Indicators and Aggregation 12 Introduction Exercise 4.3.2 Count incomeclTotal LowMediumHighVery High houseclLow3750250112 Medium511771820410 High118616 Total892282156538 Symmetric Measures.309.0447.517.000 c.288.0426.956.000 c 538 Pearson's RInterval by Interval Spearman CorrelationOrdinal by Ordinal N of Valid Cases Value Asymp. Std. Error a Approx. T b Approx. Sig. Not assuming the null hypothesis.a. Using the asymptotic standard error assuming the null hypothesis.b. Based on normal approximation.c. Cross table (mean income x mean house value) Municipalities in the Netherlands

13. 4.2.3 Data Quality, Composite Indicators and Aggregation 13 Introduction Exercise 4.2.3 Aggregation Central Bureau of Statistics of The Netherlands three main spatial units: Municipality (n=538) Districts (n=2382) Neighbourhoods (n=10737) Aggregation, summarizing data, why and what Spatially homogenous versus heterogeneous variables Which statistics to use (mean or other statistical figures) Simple and weighted aggregates

14. 4.2.3 Data Quality, Composite Indicators and Aggregation 14 Introduction Exercise 4.3.2

15. 4.2.3 Data Quality, Composite Indicators and Aggregation 15 14 4.2.3 Data Quality, Composite Indicators and Aggregation Introduction Exercise 4.3.2

16. 4.2.3 Data Quality, Composite Indicators and Aggregation 16 Introduction Exercise 4.2.3

17. 4.2.3 Data Quality, Composite Indicators and Aggregation 17 Introduction Exercise 4.2.3