Sampling

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Sampling
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Definition

• Sampling the process of selecting a number of
  individuals for a study in such a way that they
  represent the larger group from which they were
  selected.

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Example

• What is the opinion of 5,000 teachers on unions?
• Would take 1,250 hours to interview all of the
  teachers
• 125 hours to interview 10 or 500 teachers
• Possibly the same results in less time
• Note must acknowledge biases when selecting the
  group for study

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Definition of a Population

• Population the group of interest to the
  researcher
• the group to which she or he would like the
  results of the study to be generalized
• Generalizability the extent to which the results
  of one study can be applied to other populations
  or situations

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Examples of a Population

• All 10th graders in the US
• All elementary school gifted children in Utah
• All 1st grade physically disabled students in
  Utopia Country who have participated in preschool
  training

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Two Important Points About Populations

• Populations may be virtually any size and may
  cover almost any geographic area
• The entire group the researcher would really like
  to generalize to is rarely available

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Two Types of Populations

• Target population the population that the
  researcher would like to generalize
• available population the population that the
  researcher can realistically select from

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Example

• High school principals opinion on a six day
  school week
• Cant interview all of the nations principals
• Cant interview even a sample of the nations
  principals (still to big)
• Maybe, sample in your own state

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Describe the Characteristics of Your Sample

• Include in your description
• Number of participants
• Demographic of the sample

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Selecting a Random Sample
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Elements of a Good Sample

• Meaningfulness of the study
• Generalizability of the results

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3 Steps to Sampling

• Identify the population
• Determine the required sample size
• Select the sample

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Probability Sampling Techniques

• Simple Random Sampling
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling

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Simple Random Sampling
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Definition

• The process of selecting a sample in such a way
  that all individuals in the defined population
  have an equal and independent chance of being
  selected for the sample

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Advantages of Random Sampling

• Least amount of human interference in selecting
  the sample
• Therefore best way to obtain a representative
  sample

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Table of Random Number

• A list of randomly picked 5 digit numbers
• Found in Table A.1 in the Appendix

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Steps in Simple Random Sampling

• 1. Identify and define the population
• 2. Determine the desired sample size
• 3. List all members of the population
• 4. Assign all individuals on the list a
  consecutive number from zero to the required
  number (Each person must have the same number of
  digits)

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Steps in Simple Random Sampling (Continued)

• 5. Select an arbitrary number in the table of
  random numbers
• 6. For the selected number, look at only the max
  number of digits (800 people 3 digits)

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Steps in Simple Random Sampling (Continued)

• 7. If the number corresponds to the number
  assigned to any of the individuals in the
  population then that individual is in the sample
  (If 801 is drawn, number doesn't count)
• 8. Go to the next number in the column and repeat
  step 7 until the the desired number of
  individuals has been selected for the sample

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Disadvantage of Simple Random Sampling

• May not always be a completely even split of
  participants
• Click here to return to Probability Sampling
  Techniques

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Stratified Sampling
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Definition

• The process of selecting a sample in such a way
  that identified subgroups in the population are
  represented in the sample in the same proportion
  that they exist in the population

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Example

• Survey of a national election (before the
  election) to determine the most likely winner

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Equal Sized Samples

• Used to compare the performance of different
  subgroups
• Selection is from subgroups in the population
  rather than the population as a whole

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Example of Equal Sized Samples

• Study of two different teaching methods
• Must have equal numbers of low, average, and high
  ability students in each sample before you split
  the groups with the two different teaching
  methods
• Guarantees equal representation of different
  levels of the participants in the sample

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Steps in Equal-Sized Groups Stratified Sampling

• 1. Identify and define the population
• 2. Determine desired sample size
• 3. Identify the variables and subgroups for which
  you want to guarantee appropriate, equal
  representation

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Steps in Equal-Sized Groups Stratified Sampling
(Continued)

• 4. Classify all members of the population as
  members of subgroups
• 5. Randomly select an appropriate number of
  individuals from each of the subgroups

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ExampleProportional Stratified Sampling

• Equal representation of teachers for the question
  about unions (for more information please consult
  pages 128 and 129)
• Click here to return to Probability Sampling
  Techniques

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Cluster Sampling
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• Cluster sampling is when groups not individuals
  are randomly selected.
• Clusters can be communities, states, school
  districts, and so on.

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• The steps in cluster sampling are similar to
  those in random sampling, except the random
  selection of groups is involved, not individuals.

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• Both stratified and cluster sampling often use
  multi-stage sampling.

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Systematic Sampling
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• Systematic sampling is sampling in which
  individuals are taken from a list by taking every
  Kth name, where K equals the number of people on
  the list divided by the number of participants
  desired for the sample.

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Determining Sample Size

• Samples should be as large as possible in
  general, the larger the sample, the more
  representative it is likely to be, and the more
  generalizable the results of the study will be.

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• Minimum, acceptable sample sizes depend on the
  type of research, but there are no universally
  accepted minimum sample sizes.

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Avoiding Sampling Error and Bias

• Sampling error is beyond the control of the
  researcher and occurs as part of random selection
  procedures.

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• Sampling bias is systematic and is generally the
  fault of the researcher.
• Bias can result in research findings
• being invalid.
• A major source of bias is the use of nonrandom
  sampling techniques.

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Selecting a Nonrandom Sample

• Researchers cannot always select random samples
  and occasionally must rely on nonrandom selection
  procedures.

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• When nonrandom sampling techniques are used, it
  is not possible to specify what probability each
  member of a population has of being selected for
  the sample and it is often difficult to even
  describe the population from which a sample was
  drawn and to whom results can be generalized.

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• Three types of nonrandom sampling are convenience
  sampling, which involves using as the sample
  whoever happens to be available
• purposive sampling, which involves selecting a
  sample the researcher believes to be
  representative of a given population
• and quota sampling, which involves giving
  interviewers exact numbers, or quotas, of persons
  of varying characteristics who are to be
  interviewed.

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• Any sampling bias present in a study should be
  fully described in the final research report.

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Qualitative Sampling Definition and Purpose

• Qualitative research most often deals with small,
  purposive samples. The researcher's insights
  guide the selection of participants.

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• A variety of purposive sampling approaches are
  used in qualitative research, including intensity
  sampling, homogeneous sampling, criterion
  sampling, snowball sampling, and random
  purposive sampling.

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• The use of purposive sampling requires that the
  researcher describe in detail the methods used to
  select a sample.