Enriching Human Development Analysis: Use of Qualitative Data

Presentation Transcript

By Richard Ssewakiryanga

Team Leader

Uganda Participatory Poverty Assessment Process

Ministry of Finance, Planning and Economic Development

P.O.Box 8147, Kampala, Uganda

Tel: 256 41 236205

Email: Richard.Ssewakiryanga@finance.go.ug

Outline

What is the role of qualitative data?
What approaches are used in qual. data?
Comparison of qual. and quant approaches
Possible ways of Combining approaches
Uganda Lessons on Qualitative data and policy change

HDRO/RBA Regional Technical Workshop on Measuring Human Development

Nairobi – September - 2007

Qualitative data are not exclusive domain of qualitative research
Qualitative therefore refers to that which may not be rendered into numerical form
Quantitative researchers accept that the goal of science is to discover the truths that exist in the world and use scientific method build a more complete understanding of reality

Qualitative researchers recognize that relevant reality as far as human experience is concerned is that which takes place in subjective experience, in a social context, and in historical time
Thus, qualitative research is concerned about uncovering knowledge about how people think and feel about the circumstances in which they find themselves WITHOUT making judgments about whether those thoughts and feelings are valid or statistically representative
In policy work - Participatory Poverty Assessment (PPAs) have been very instrumental in getting Govts to appreciate the role of qualitative data

PPA Approaches

Bottom-up consultations beginning at the local/community level
Reliance on participatory methodologies from the PRA research tradition
Emphasis on deepening understanding of poverty and policy
Bridging gaps between state and non-state actors

Comparison of qualitative and quantitative approaches

Qualitative/PPA

Based on local people’s perceptions and experiences

Subjective (based on people’s views about intangible aspects such as well being/ill being

Quantitative/Survey

Based on household income using consumption expenditure as a proxy

Objective (based on actual counts of a phenomena)

Types of data

Qualitative/PPA

Purposive – selects sites to capture regional variations

Representative - aims at producing data that captures diversity in poverty trends within the sampled population

Quantitative/Survey

Random – sites selected on the basis of probability

Representative - aims at producing ‘generalisable’ poverty trends in the entire population

Types of sampling

Qualitative/PPA

Contextual methods – attempts to capture poverty trends within socio-economic and cultural contexts

Time span/intervals – determined by participant using time trend analysis tools

Quantitative/Survey

Non-contextual – designed to collect poverty trends that are untainted by contextual peculiarities

Time span/intervals – determined by researcher and respondents only required to recollect income and consumption data as determined structured in the q’naire

Types of methods

Qualitative/PPA

Complex criteria is used to determine poverty status

The intensity and extent of shocks is used to determine mobility in well being

Quantitative/Survey

Increase in household consumption means reduction in poverty

Proportion of households below the poverty line is used to determine increases or reduction on poverty

Nature of results

Qualitative/PPA

Strategic responses and people’s expectations

Can only be interpreted in context (too many variables have to be considered)

Quantitative/Survey

Perverse consumption (e.g alcohol consumption, fuel-wood)

Income considered a proxy for poverty (only one variable is used)

Possible Limitations

Relationship Btn the 2 Data Sets

Findings are not directly comparable – there is no contradiction btn the two data sets

Different dimensions of poverty are used in trend analysis (income vs holistic)
Different levels are considered in poverty analysis (individual/household vs community)
Different assumptions are used in measuring poverty (researcher's definitions vs people’s)
Different methodologies (population samples, reference points, time intervals)

Possible Ways of Combining

Each data set has limitations but combining is possible in 3 major ways:

Integrating methodologies – (use qual or quant to design quant or qual q’naire, survey respondents, sample strata e.t.c)
Merging findings (analyzing info from qual or quant to derive one set of recommendations)
Enriching qual or quant results (by explaining, confirming verifying and ‘refuting’)

Possibilities in the context of HDRs

Ensure systematic use of qual data thru joint data collection, analysis and reporting in all HDRs

Don’t use qual data only when you have bad or no statistics!

Support professional collection of qual data
Invest in combined methodology initiatives/networks at the global, regional and national levels
Create ‘qual-data literacy’ in policy circles as part of HDR advocacy work