SADC Course in Statistics Hierarchies of Units and non-traditional sampling approaches (Session 18)

To put your footer here go to View > Header and Footer 2 Learning Objectives By the end of this session, you will be able to explain the importance of recognising hierarchies of units and the need to consider sampling effort at the different stages understand the need for in-depth studies with a few units for qualitative enquiries appreciate a wide range of non-traditional sampling approaches that may be used in the overall sampling scheme

To put your footer here go to View > Header and Footer 3 Hierarchies of units Information exists at various levels & these need to be acknowledged, analysed and used in the sampling scheme, e.g. farming household, village, parish, sub-county, county, district, Uganda. For example, for research in one district, –Sub-county = primary unit –Parish = secondary unit –farming household = ultimate sampling unit. Often referred to as multi-stage sampling, but this suggests successive time points – we prefer hierarchical

To put your footer here go to View > Header and Footer 4 Disposition of resources Consider for example Mukono district in Uganda It has 17 sub-counties How should sub-counties be sampled? In each selected sub-county should sampling be –2 parishes x 20 h/holds in each? –8 parishes x 5 h/holds each? Can results be generalised to the district with the scheme adopted? Use 8, to predict and confirm makes more sense!

To put your footer here go to View > Header and Footer 5 Sampling up hierarchies Top level units well known, characterised in many ways. Total number of units is likely to be small. Random sampling often meaningless, as likely to be unrepresentative in face of large amounts of information about units (&/or politically unacceptable) Units at top level are often chosen purposively using knowledge about these units (see slide 11 for an example)

To put your footer here go to View > Header and Footer 6 Sampling down hierarchies Ultimate units need to be anonymous to research users who need assurance of purposeful, well-defined, objective selection. Must avoid selections based on vaguely- defined variables that make respondent selections different in each location. There is a need to incorporate an element of random sampling at some stage, at least at the final level of sampling.

To put your footer here go to View > Header and Footer 7 Objectives in hierarchies Need to think about sample size and information use (e.g. reporting and uptake) at each level of hierarchy. Need to ensure sample selection is related closely to research objectives and in turn to the recommendation domain for which results are intended.

To put your footer here go to View > Header and Footer 8 Numericalness of objectives Main objective in statistical sampling texts is accurate numerical estimation: not usually the aim in many studies having qualitative aims! In on-farm studies, do not expect new technologies to apply equally well to all farmers Which technologies are most effective for which types of farmers is likely to be an important objective. Estimation of numerical quantities less relevant here, so use common-sense based approaches

To put your footer here go to View > Header and Footer 9 Comparison as objective Rather than quantitative estimation [or a qualitative description], we often want to compare groups, with each other – implies different priorities as to representativeness … Population 80:20 split implies estimation best based on 80:20 sample split BUT comparison best based on 50:50 sample split so both things compared are equally well understood.

To put your footer here go to View > Header and Footer 10 Many comparisons Example : some areas have good, some bad transport; some have good environmental conditions, some have bad conditions; some have, some have not started a District Poverty Initiative Programme. Does study cover all combinations??

To put your footer here go to View > Header and Footer 11 Two of everything! Good environmental conditions Bad environmental conditions Trans- port DPIP +DPIP -DPIP +DPIP - Good ---Include --- Poor Include--- Include

To put your footer here go to View > Header and Footer 12 Combining or splitting studies Combine a short questionnaire-style study with more intensive qualitative and/or experimental follow-up Sampling in the 1 st phase may help determine samples for choice of 2 nd phase sites – Table top sampling plan – as shown in next slide

To put your footer here go to View > Header and Footer 13 People sampled within each of a number of communities Subsample for deeper research

To put your footer here go to View > Header and Footer 14 Succession of studies If we can have a succession of studies linked by sampling scheme, read-through may be useful, if respondent burden permits The next slide gives a graphical illustration

To put your footer here go to View > Header and Footer 15 Vertical links = in-depth studies Horizontal at top = main survey study Horizontals below = sectoral studies

To put your footer here go to View > Header and Footer 16 Segmenting a study Core items done with all respondents, others modularised and done with separate sub- samples – reduces respondent burden Or studies separately undertaken but linked by common core items

To put your footer here go to View > Header and Footer 17 Main and sub-theme coverage of segmented study Core Questions Module 1 Module 2 Module 3 All sections of respondent sample

To put your footer here go to View > Header and Footer 18 Targeting special groups e.g. how do we sample the poor? Screening methods OK for target groups common in population Snowball sampling explores rarer, mutually- aware, subgroups Adaptive sampling - adjust effort where more are found – theory not well developed, currently mainly suits immobile populations.

To put your footer here go to View > Header and Footer 19 Replicated sampling Entire structure of study repeated in miniature, n times In quantitative studies, can incorporate any number of complications (same ones in each replicate) then treat overall summaries from the replications like a random sample Analogy could work for qualitative research which seeks reproducibility / generalisability

To put your footer here go to View > Header and Footer 20 References SSC (2000) Some Basic Ideas of Sampling. Statistical Guidelines Series supporting DFID Natural Resources Projects, Statistical Services Centre, The University of Reading, UK. Available at Wilson, I.M. (2005) Some Practical Sampling Procedures for Development Research, pp in Holland, J.D. and Campbell, J. (editors) (2005) Methods in Development Research ; Combining Qualitative and Quantitative Approaches, ITDG Publishing. ISBN Wilson, I.M. and E.F. Allan (2004) Sampling Concepts In: Stern, R.D., Coe, R., Allan, E. and Dale, I.C.(ed.s) Statistical Good Practice for Natural Resources Research. CAB International, Wallingford, U.K. pp.65 – 86

To put your footer here go to View > Header and Footer 21 Practical work follows …