Making Sense of Data for Effective Policy Analysis

1. Making Sense of Data for Policy Analysis Professor Daniel Tevera Department of Geography, Environmental Studies and Tourism University of the Western Cape ISIbalo Conference 2016 St. George Hotel, Pretoria

2. Overview • Research should generate credible data • Data process • Why making sense of data can be difficult • Cases: 1-3 • Using graphics

3. Research should generate credible data • Research should aim to collect credible data that is valid and reliable. • Gaining access is vital for effective data collection. The researcher should consider how he/she will gain: - access to people - access to datasets - access to documents

4. https://www.google.com/fusiontables/DataSource?docid=1mUVUyAhPJBHKq_Qe1isxE7_XbjSjjGDCR7htM58https://www.google.com/fusiontables/DataSource?docid=1mUVUyAhPJBHKq_Qe1isxE7_XbjSjjGDCR7htM58

5. Data analysis Data analysis (whether qualitative or quantitative data) consists of: • Description and summary of the data • Identification of relationships between variables • Comparison of variables • Identification of the differences between variables • Forecasting outcomes

6. What is the difference between analysis and interpretation? • Analysis: is about describing data with tables, graphs, or narrative; transforming data into information • Interpretation: is about adding meaning to information by making connections and comparisons and by exploring causes and consequences

7. Why making sense of data can be difficult Reasons why: • Irregular data process • Ambiguities around variables, terminology and unit of analysis • Research design problems • Data organization/presentation problems • Data analysis and interpretation problems • The gap between producers and users of research is wide

8. Irregular data process Characterised by: • Vague research question • Poor quality data (e.g.. not cleaned) • Data organization/presentation problems • Data is analysed but is not interpreted

9. Ambiguities around research variables, terminology and unit of analysis Research variables • Confusion around dependent (response) and independent (predictor or explanatory) variables . • Independent variable is the predictor or explanatory variable • Dependent variable is the responsive variable. Terminology • Often household and family are used interchangeably Unit of analysis (level at which data will be collected) • Individuals or household?

10. Research design and data collection problems • Not knowing when the research design should be longitudinal, cross-sectional (survey), experimental (a typical scientific experiment), after-only design (e.g. Studies of floods), before-after design. • The sampling procedure is not explained or the standard procedures are not followed. (e.g. Is it going to be probability or non-probability sampling? Random or snowball sampling?). Terms are not explained. • Sample size is not given. • Research ethics procedures are not followed.

11. Data misuse arising from presentation errors • Misleading averages • Meaningless percentages • Overgeneralisation • Data dredging/selective presentation of data • Inappropriate comparisons • Misleading charts

12. Data misuse arising from inappropriate analysis • The purpose of analysing data is to obtain usable and useful information. • However, at times wrong conclusions are drawn from inconclusive thereby resulting in: • Sample errors • Presentation errors • Extrapolating the trend

13. Academic research and policy-makers • Research evidence is appealing to policy-makers if it: • addresses questions of interest to policy-makers • Is accessible • provides solutions to problems • is valid, reliable and scientifically rigorous

14. Thank you