MOAZZAM ALI

Sampling

Sampling

• How we select from an infinite number of observations we could possibly make

• Why do we sample?– Size of the population– Cost of obtaining elements– Convenience and accessibility of elements

• Sampling is the process of obtaining information from a subset (sample) of a larger group (population)

• The results for the sample are then used to make estimates of the larger group

• From Chapter 7 in Babbie & Mouton (2001)

Characteristics of a Good Sample

Good sampling design should:

• Relate to the objectives of the investigation • Be practical and achievable; • Be cost –  effective in terms of equipment and

labour; • Provide estimates of population parameters that

are truly representative and unbiased. Ideally, representative samples should be:

• Taken at random so that every member of the population of data has an equal chance of selection;

• Large enough to give sufficient precision; • Unbiased by the sampling procedure or

equipment.

Sampling Terminology

Element

• The unit about which information is collected• Typically the elements are people• But look at the section on “unit of analysis” again:

any of them could be elements (schools, universities, corporations, etc.)

Sampling Terminology

Population

• All the potential study elements, as defined• Careful specification of the population

Sample Population

• Almost impossible to guarantee that every element meeting your definition of “the population” has a chance to be selected into the sample.

• Thus the “study population” will be somewhat smaller than “the population”

Sampling Terminology

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Sampling Unit

• Typically the sampling units are the same as the elements and probably the units of analysis

• (We are not going to look into more complex sampling units)Sampling Frame

• The actual list of sampling units (or elements). • e.g. if you want to study “Students at the University of

Cape Town”, there is a list of such sampling units (but there are a number of definition issues to be resolved here) Sample

• A subset of a population selected to estimate the behaviour or characteristics of the population.

Sampling Designs

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Basically two sampling strategies available:

Probability samplingNon-probability Sampling

Probability Sampling

Each member of the population has a certain probability to be selected into the sample

Types of Probability Sampling

Random Stratified Random Systematic Cluster

Random Sampling

Population members are selected directly from the sampling frame

Equal probability of selection for every member (sample size/population size)

400/10,000 = .04 Use random number table or random number

generator

Systematic Sampling

Order all units in the sampling frame based on some variable and number them from 1 to N

Choose a random starting place from 1 to N and then sample every k units after that

Stratified Sampling

The chosen sample contains a number of distinct categories which are organized into segments, or strata– equalizing "important" variables

• year in school, geographic area, product use, etc.

Steps:– Population is divided into mutually exclusive and

exhaustive strata based on an appropriate population characteristic. (e.g. race, age, gender etc.)

– Simple random samples are then drawn from each stratum.

Stratified Sampling

Stratified Sampling

The sample size is usually proportional to the relative size of the strata.

Ensures that particular groups (e.g. males and females) within a population are adequately represented in the sample

Has a smaller sampling error than simple random sample since a source of variation is eliminated

Cluster Sampling

The Population is divided into mutually exclusive and exhaustive subgroups, or clusters, usually based on geography or time period

Each cluster should be representative of the population i.e. be heterogeneous.

Means between clusters should be the same (homogeneous)

Then a sample of the clusters is selected. then some randomly chosen units in the selected

clusters are studied.

Cluster Sampling

divide population into clusters (usually along geographic boundaries)

randomly sample clusters

measure units within sampled clusters

Non-probability Sampling

Members selected not according to logic of probability (or mathematical rules), but by other means (e.g. convenience, or access)

Types of Non-Probability Sampling

convenience sampling judgement sampling snowball sampling quota sampling

Convenience Sampling

Convenience SamplingA researcher's convenience forms the basis for selecting a sample. people in my classes Mall intercepts People with some specific characteristic (e.g. bald)

Purposive Sampling

Select the sample on the basis of knowledge of the population: your own knowledge, or use expert judges to identify candidates to select

Typically used for very rare populations, such as deviant cases.

Snowball Sampling

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Typically used in qualitative researchWhen members of a population are difficult to

locate, for covert sub-populations, non-cooperative groups

Recruit one respondent, who identifies others, who identify others,….

Primarily used for exploratory purposes

Quota Sampling

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• A stratified convenience sampling strategy• Begins with a table that describes the

characteristics of the target population– e.g. the composition of postgraduate students at UCT in

terms of faculty, race, and gender• Then select on a convenience basis, postgraduate

students in the same proportions regarding faculty, race, and gender than in the population

• Of course, the quota frame (the proportions in the table) must be accurate

• And biases may be introduced when selecting elements to study

Thank You