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 
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 

B. Sample Size 

2. Sampling Guidelines 

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 s^{2}; symbolized for the sample standard deviation as s. 
C. Statistical Effects of Small Samples 

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 
1. Simple Random Sampling
a. advantages: 1.
sampling error can be computed; 2. simple randomization can be used by
researchers
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 timeconsuming since the steps involved in identifying and
choosing 
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 
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 b. disadvantages: in addition to the disadvantages of
any random
sampling, this method has the weakness 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. 15).

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: limitations:
1. tends to show great biases; 

IV. Dealing
With Sampling Problems 


informed consent: the requirement that individuals be permitted to withdraw from an experiment or study 
B.
Looking for Evidence of Randomization in Research Articles 
