Up Random Sampling with SPSS Adequacy of a Sample A Brief Quiz

Chapter 11




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
(also called “test statistics”) numbers
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)

         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: “a range of values of a sample statistic that is likely (at a given level of probability, called a confidence level) to contain a population parameter” (Vogt, 2005, p. 55).

standard deviation: though computed differently, a measure that attempts to summarize the average deviation of scores from the mean, by estimating such a value from the square root of the variance s2; symbolized for the sample standard deviation as s.

    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 nearly identical distributions
             --use of volunteers: samples of volunteers may differ greatly
                from other members of the population
III.  Forms of Sampling


     A.  Random Sampling

probability sampling: techniques that use randomization to identify samples.

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

a.  advantages: 1. sampling error can be computed; 2. simple randomization can be used by researchers
as a means to control for individual differences
among participants in the sample; 3. the representativeness of the sample is virtually ensured since random sampling means that any differences between population and sample characteristics are at random.

b.   disadvantages: 1. simple random sampling is difficult to use in many field settings because a list of all the events in the population may not exist; 2. this sampling method may be time-consuming since the steps involved in identifying and choosing
events can be somewhat complicated.

simple random sampling: a method by which researchers select participants or events such that each event in the population has an equal chance of selection

2.    Stratified Random Sampling  

a.   advantage: useful where dividing the sample according to some stratification variable is an important part of the study

b.   disadvantages: in addition to the disadvantages of
any random sampling, this method has the weaknesses that (1) this sampling method involves increased expense for the researcher, (2) the
weights that should be associated with each stratification variable may not always be known.

stratified random sampling: A method by which researchers select participants or events to represent known proportions of characteristics in the population. After population characteristics are identified (such as the number of mean and women in the population), a random sample of a given size is drawn from each population stratification variable consistent with the population proportions.

3.    Cluster Sampling

a.   advantages: useful when geographically based
samples have been involved.

b.  disadvantages: in addition to the disadvantages of

      any random sampling, this method has the weakness
that it requires larger samples than simple random sampling.

B. Nonrandom Sampling

cluster sampling: a method of sampling “in which elements are selected in two or more stages, with the first of naturally occurring clusters and the last stage being the random selection of elements within clusters “ (Schutt, 2006, pp. 1-5).




          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

--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


1. tends to show great biases;
2. no sampling error computation is possible;
3. severely limits conclusions that may be drawn by


IV.  Dealing With Sampling Problems
       A. Participant 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