SWP32RES RESEARCH FOR SOCIAL WORK PRACTICE B

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SWP32RES RESEARCH FOR SOCIAL WORK PRACTICE B

• LECTURE FIVE - Sampling

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The next key point to be addressed in
undertaking a piece of research is

• Who is that information to be collected from?
• What particular part of the general population is
  it going to come from?
• How is that particular part of the population to
  be selected and in what sort of numbers?
• These are the essential questions to be answered
  in sampling.

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• You need to make a decision about exactly which
  individuals, families, groups, organisations,
  communities or events you will examine.
• In other words, what sample will you choose?

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• Researchers tend to select a portion of that
  population for study, that is a sample, and
  either
• 1) study and describe that sample in some depth
  or
• 2) attempt to generalise from these findings from
  the sample to the population.

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• A well-chosen sample can result in useful
  findings, whereas a carelessly chosen or
  inappropriate sample can negate the value of
  findings in a research study that may otherwise
  be well implemented.

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• For sampling purposes, a population is the entire
  collection of people or elements that share some
  defined characteristics.
• For example, all social workers working in
  community health centres for less than two years
  or elderly parents (more than 65 years of age) of
  intellectually disabled adults.

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• A further sub-category of the population is the
  sampling frame which varies from the population,
  in that it is an actual listing of sampling
  elements or cases from which a sample is drawn.
• The sampling frame is an available list that the
  researcher hopes will be a reasonable
  approximation of the defined population.

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• A sample is a subset of individuals selected for
  study from persons or objects within a defined
  population.
• A case or element is the basic unit of analysis
  in a research study such as each actual person in
  a sample of individuals. Collectively, the
  persons selected for the study constitute the
  sample. Individually, each person is a case or
  element of the sample.

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• A representative sample is one that is very
  similar to the population from which it is drawn,
  which is usually ensured by random sampling
  whereby a sample is drawn from a population so
  that every member of the population has an equal
  chance of being selected in the sample.

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TYPES of SAMPLES

• There are two general types of samples
• 1) PROBABILITY and
• 2) NON-PROBABILITY.

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PROBABILITY SAMPLING

• 1) SIMPLE RANDOM SAMPLING
• 2) SYSTEMATIC RANDOM SAMPLING
• 3) STRATIFIED RANDOM SAMPLING
• PROPORTIONAL
• NON-PROPORTIONAL OR DISPROPORTIONATE

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• 4) MULTISTAGE CLUSTER SAMPLING

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NON PROBABILITY SAMPLING

• 1) ACCIDENTAL
• 2) QUOTA
• 3) PURPOSIVE
• 4) SNOWBALL

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• 5) THEORETICAL

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• Probability samples are those that use random
  sampling in at least one stage of the sampling
  process
• Random does not mean haphazard it means that
  every element in the population has the same
  known probability of being selected for inclusion
  in the sample.

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• Nonprobability samples are those that do not use
  random sampling and where some elements of the
  population have a greater or lesser chance of
  being selected.

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1. Simple random sampling is a sample
in which every element or unit of the target
population has an equal chance of being selected
in the sample. It basically involves randomly
selecting some predetermined number of persons
from the sampling frame.
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5 steps in selecting a simple random sample

• 1. Obtain a complete sampling frame
• 2. Give each case a unique number starting at
  one
• 3. Decide on the required sample size
• 4. Select that many numbers by whatever method
• 5. Select the cases that correspond to the
  randomly chosen numbers.

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• 2. Systematic random sampling is a simpler,
  less costly alternative to simple random
  sampling. It involves no random generation of
  numbers

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• It involves the researcher determining the number
  in the sampling frame and the sample size, using
  the sampling fraction method (k), where the
  formula is K N/n where N the population and n
  sample..
• For example, if the sampling frame was 200 and
  the sample size 50, the required sampling
  fraction 200 divided by 50 4. Therefore every
  fourth case in the sampling frame is selected.

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• 3. Stratified random sampling improves on a
  simple random sample and a systematic random
  sample by ensuring representation of
  particular subgroups in the population eg.
  male students undertaking a social work course.

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• The target population is divided into a number of
  strata and a sample drawn from each stratum.
  Existing knowledge of the population is used to
  increase the representativeness of the sample by
  dividing it into stratifying variables (a
  characteristic on which we want to ensure correct
  representation in the sample.)

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Stratified random sampling can be further divided
into

• a) proportional stratified sampling and
• b) non-proportional or disproportionate
  stratified sampling.

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• 4. Another form of probability sampling is
  multistage cluster sampling. This occurs when
  a comprehensive sampling frame does not
  exist and it is not possible to construct one.

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Cluster random sampling takes place when the
population is divided into clusters or units,
then successively smaller subunits. At each
level, units and subunits are randomly
selected. So cases or a random sample of all
elements within each of the selected clusters
is studied.
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• 2) Nonprobability sampling is done when random
  sampling would be inappropriate such as such as
  in an exploratory study when in-depth
  information is required or when a population
  has not fully described or identified or in
  qualitative studies.

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Nonprobability sampling is divided into

• 1) Accidental or incidental or convenience or
  availability sampling
• 2) Quota sampling
• 3) Purposive sampling or judgemental sampling

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4) Snowball sampling 5) Theoretical sampling
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How large should my sample be? (sample size)

• 2 rules
• 1) the greater the number the better for
  generalisation
• 2) how homogenous is your population (how
  alike). If less homogeneous, you will need
  more and a range of strata.

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Sample Size

• Depends on
• 1) resources available for getting the sample
• 2) the nature of the study and
• 3) the type of statistical analysis to be used
  in the study.

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SAMPLING - SOME COMMON TERMS

• POPULATION
• SAMPLING FRAME
• SAMPLE
• CASE OR ELEMENT

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• PROBABILITY SAMPLING
• RANDOM SAMPLING
• REPRESENTATIVENESS
• NON-PROBABILITY SAMPLE