 # Sampling Moazzam Ali.

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Sampling Moazzam Ali

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 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. Research design - sampling

Sampling Designs Basically two sampling strategies available:
Probability sampling Non-probability Sampling

Probability Sampling Random Stratified Random Systematic Cluster
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 Sampling
A 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 Typically used in qualitative research
When 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 Research design - sampling

Quota Sampling 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 Research design - sampling

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