# SADC Course in Statistics Overview of Sampling Methods I (Session 03)

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SADC Course in Statistics Overview of Sampling Methods I (Session 03)

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 describe what is meant by a simple random sample and a stratified random sample discuss the benefits and limitations of simple random sampling take a simple random sample using a table of random numbers explain how strata may be chosen, why this is a useful consideration, and how a stratified sample may be taken

To put your footer here go to View > Header and Footer 3 Underlying mathematics is mostly about estimation of one numerical quantity – a population characteristic. Sample size formulae generally relate to this objective but are applicable only to very simple scenarios Some ideas are more broadly useful than that… In this session, we will discuss just two approaches to sampling… Introduction to statistical sampling

To put your footer here go to View > Header and Footer 4 Simple random sampling Simplest form of sampling procedure Procedure aims to give each member in the population an equal chance of entering the sample Rarely done in real situations which are usually multi-stage. But some element of randomness is important at some stage. Often, final stage units are selected using simple random sampling

To put your footer here go to View > Header and Footer 5 How do we take a simple random sample? The procedure is to: Allocate a number to each member in the sampling frame consisting of all eligible population members Pick numbers at random from this list, discarding any that occur twice Sample the required number of members without replacement

To put your footer here go to View > Header and Footer 6 Using a random number table The handout accompanying this session explains the process involved in using a random number table. The process involved will be discussed in class with an example of selecting 6 units from a sampling frame of 743 members. You will then be asked, in discussion with your neighbour, to select a sample of 7 members assuming you have a sampling frame with 490 members.

To put your footer here go to View > Header and Footer 7 Benefits of simple random sampling At any sample size, sampling objectively should avoid biases of subjective methods (…but at smaller sample sizes there is always a chance of a disconcerting sample coming up) Provides estimate of accuracy – e.g. the standard deviation of an estimate Claim to represent the whole population holds if sample is of adequate size.

To put your footer here go to View > Header and Footer 8 Some challenges to SRS Often there is no adequate sampling frame Generally have non-homogeneous population Geographical spread means travel costs may be excessive Not useful if information is needed at various levels, e.g. district based estimates as well as estimates for the country as a whole

To put your footer here go to View > Header and Footer 9 Generalities about stratification If sections of the population are known to be internally relatively homogeneous with respect to key feature observed – these are good strata Sampling separately within each stratum gives relatively accurate information for less effort …

To put your footer here go to View > Header and Footer 10 Two-stratum island Separate small samples from wet- & dry- zones more effective than an uncontrolled mixture sample:- Wet Dry

To put your footer here go to View > Header and Footer 11 Stratified sampling Segment entire population into subsets = strata. These should not overlap. Note: may be unclear which stratification is best. Sampling within strata is generally done at random. Effective if members of most strata are similar to each other in studied characteristics : even a small sample yields good understanding. Post-stratification can also be useful

To put your footer here go to View > Header and Footer 12 Ineffective stratification e.g. if all villages contain a mixture of farmers, traders, artisans, and livelihoods differ mainly by occupation, then occupation is an effective stratification variable, while village is not. The village becomes a miniature of the population itself if %s in different occupations are about the same in all villages

To put your footer here go to View > Header and Footer 13 How to select a random sample Discussion and demonstration using Excel

To put your footer here go to View > Header and Footer 14 Some practical work follows …