Research Design - Sampling

7/31/2019 Research Design - Sampling

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Research design - sampling 1

Research design sampling

Chapter 7 in Babbie & Mouton (2001)

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

Why do we sample? Size of the populationCost of obtaining elementsConvenience and accessibility of elements

(in his study on suicide, Durkheim had relatives who heldhigh governmental positions, who were able to provideaccess to French statistical records on suicides)


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How do we decide what to observe?

This decision should be a matter of deliberate choice rather than chance.Representativeness a small sample of individuals from a population must contain essentially the same variations that exist in the population BUT: limited to those characteristics that are relevant to the substantive interests of the study, not ALL aggregate characteristics


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Key concepts

Element The unit about which information is collectedTypically the elements are peopleBut look at the section on unit of analysis again: any of them could be elements(schools, universities, corporations, etc.)


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Population

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


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Study 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


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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)


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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)


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Sample

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

O/head p. 169

Importance of sampling properly A sample exists to represent its parent population

We must know what the actual parent population is, otherwise we draw false conclusions

e.g. if we sample only women, we cannot safely make claimsabout men


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Deciding who to choose

Basically two sampling strategies available:

Probability sampling each member of thepopulation has a certain probability to beselected into the sampleNon-probability sampling members selected

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


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Non-probability sampling

Sometimes it is not possible to get the kind of information about populations required for probability sampling When the sampling frame is not known Complicates and limits statistical analyses

Often well-suited for qualitative research,where distribution of characteristics is not important


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Convenience sample

Rely on available respondents Most convenient method

Risky; exercise caution


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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


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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


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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 termsof 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


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Probability sampling

Bias introduced in non-probability samplingThe sample then not representative of the populationProbability sampling

Typically produce more representative samples Allow us to estimate the accuracy orrepresentativeness of the sampleRandom sampling is the key each element has an

equal chance of being selectedRandom sampling offers access to probability theory,through which we can estimate how representativeour sample is


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Probability sampling theory

Sampling distribution possible samples to be drawn

Sampling error The degree to which the sample characteristicsapproximate the characteristics of the populationThe smaller the sample, the greater the samplingerrorThe larger the sample, the smaller the sampling error- but only for probability sampling plans


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Estimating the true mean

Note how the notion of a sampling distribution relies on the Central Tendency Theorem, that you will recognize from the statistics part of the course

Standard error and standard deviation

InferencesConfidence levels


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Populations and sampling frames

In countries like South Africa, less-than-perfect conditions regarding sampling frames Either the information is not available, or if it is, it is subject to error (parts of it are missing) Remember: we use sampling frames as reflecting the population; ideally, the population also is the time frame but almost never the case So problems when there is a disparity between a population and the sampling frame we use


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Example

A good example is using telephone directories as sampling frames Or the difficulties with finding a complete list of all primary health care facilities in the country

And think about the risks in using municipal service records as a sampling frame for the residents of Cape Town


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Three kinds of probability samplingstrategies

1 Simple random sampling Establish a sampling frame (a list, e.g. of allthe companys customers, or all UCTstudents)

Assign a single number to each element in thelist

Use random numbers to select the elements


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2 Systematic sampling

Usually more efficient than SRS Establish a sampling frame Select the first element at random

Then select every nth

element in the list, until you have the required number of respondents e.g. with a population of 300, if we want a sample of 10, choose every 30 th element

Keep an eye out for peculiar arrangements in thesampling frame


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3 Stratified sampling

Sampling error reduced by 1. using a large sample2. a homogeneous population

Stratified sampling based on 2.


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Stratified sampling

Modifies random sampling and systematic sampling, to obtain a greater degree of representativeness Organize the population into homogeneous subsets, then sample randomly within each one e.g. for university students, stratify according to seniority and gender Stratification ensures equal proportions of people having the relevant characteristics are selected into your sample Depends on what variables are available to stratify on

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Research Design - Sampling