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Adequacy of a Sample
Random Sampling with SPSS


                                       Chapter 11

                                        Sampling

 

Outline

 

Concepts

 

I.  The Role of Sampling in
    Quantitative Research
     A.  Relating Sampling to Other
           Concepts

sampling: selecting events from a (sample, population, statistic, parameter) population
population: the universe of events from which the sample is drawn
data: the actual individual events in the sample
statistic: a number computed from a sample (the term sometimes is used to refer to the study of quantitative information)
parameter: a number computed from a population

    B.  Sampling Begins by
          Defining the Population
    C. Careful Sampling Is a Key to
         Eliminate Bias
bias: in sampling, bias means a tendency for the sample to err so that it fails to represent the population
II.  Essentials of Sampling
    A. Representative Sampling: The
         Goal of Effective Sampling

representative sample: one that accurately reflects characteristics of the population from which it was drawn
    B. Sample Size
         1. The Notion of Sampling
              Error

sampling error: the degree to which a sample differs from population characteristics on some measure (in general, as the sample size gets to be a larger and larger proportion of the population, the amount of sampling error is reduced)
    Independent Research Boards:  groups that review research with attention to the manner of selecting subjects, protection of subjects' rights, steps to secure informed consent, protection of subject confidentiality, steps for protecting subjects from any study risks
         2. Sampling Guidelines
             --in pilot studies, small
               samples may be used;
               in speech and hearing
               science, samples of eight
               to fifteen subjects may be
               used in experiments in
               which physiological
               reactions are involved; for
               studies attempting to
               validate new measurement
               instruments, researchers
               may be told to sample quite
               large, at least 200 events;
               to use some statistical
               tools, such as multiple
               correlation or factor
               analysis, researchers
               need a minimum number of
               events for every variable
               included in the study
         3. Guidelines Based on
              Sampling Error
confidence intervals: the probability that sample statistics "capture" population parameters, within certain margins for error
    C.  Statistical Effects of Small
          Samples
          --when small samples are
             used, only very big effects
             stand out
          --statisticians have observed
             that samples of thirty or more
             events tend to produce
             identical distributions
    --use of volunteers:  samples of
       volunteers may differ greatly
       from other members of the
       population
III.  Forms of Sampling
     A.  Random Sampling random sampling: securing data such that each event in the population has an
equal chance of being selected
--systematic or periodic
   sampling: selecting
   respondents according to
   a predetermined schedule
   other than a random
   sequence
          1.   Simple Random Sampling simple random sampling: selection of data such that each event in the population has an equal chance of selection
          2.   Stratified Random
                Sampling   
stratified random sampling: samples are defined on the basis of known proportions within the population and random sampling is completed within each group
          3.   Cluster Sampling cluster sampling: sampling in which groups or areas (clusters) are randomly selected and from which an actual sample is drawn
    B. Nonrandom Sampling
         --advantages of
            nonrandom
            sampling:
            1. often allows the researcher
                 to get samples that
                 otherwise would be
                  unavailable
             2. nonrandom sampling
                 often invited by field and
                 quasi-experimental
                 research
          --limitations: 1. tends to show
             great biases; 2. no sampling
             error computation is possible;
             3. severely limits conclusions
             that may be drawn by
             researchers

          1.   Accidental or Convenience
                Sampling
accidental or convenience sampling: selection of events that are most readily available
          2.   Quota Sampling quota sampling: samples are defined on the basis of the known proportions within the population and nonrandom sampling is completed within each group
          3.   Purposive or Known Group
                Sampling
purposive or known group sampling: selection of events from groups that are known to possess a particular characteristic under investigation
          4.   Snowball
                Sampling
snowball sampling: selection of events on the basis of referrals from initial informants
IV.  Dealing With Sampling Problems
       A. Subject Refusal to Participate
            --ways respondents may
               refuse to participate:
               1.   they can decline initially
               to accept a questionnaire,
               answer any questions, or
               mail in a survey;
               2.   they can provide
               incomplete responses









informed consent: the requirement that individuals be permitted to withdraw from an experiment or study
      B.  Looking for Evidence of
           Randomization in Research
           Articles
           --we expect that research
             articles either go into detail
             explaining how they handled
             randomization, or we expect
             that some reference will be
             made to a table of random
             numbers