Sampling: How to Select a Few to Represent the Many
Chapter 4
How and Why Do Samples Work?
Sample = a small collection of units taken from a larger collection.
Population = a larger collection of units from which a sample is taken.
Random sample = a sample drawn in which a a random process is used to select units from a population
These are best to get an accurate representation of the population
But are difficult to conduct.
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Convenience sampling (Accidental or Haphazard) = a non-random sample in which you use an nonsystematic selection method that often produces samples very unlike the population.
Quota sample = non-random sample in which you use any means to fill pre-set categories that are
characteristics of the population.
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Purposive (Judgmental) sampling = a non-random sample in which you use many diverse means to
select units that fit very specific characteristics.
Snowball (network) sampling = a non-random sample in which selection is based on connections in a
pre-existing network.
Coming to Conclusions about Large Populations
Universe = the broad group to whom you wish to generalize your theoretical results e.g. all people in
California.
Population = a collection of elements from which you draw a sample e.g. all adults in the LA metro area.
Coming to Conclusions about Large Populations
Sampling frame = a specific list of sampling elements in the target population e.g. telephone directory or
tax records.
Population parameter = any characteristic of the entire population that you estimate from a sample (as
opposed to the sample statistic).
Coming to Conclusions about Large Populations
Sampling ratio = the ratio of the sample size to the size of the target population.
Sampling Techniques
There are two basic techniques for sampling:
Probability which includes
Simple random
Stratified random
Cluster sampling
Non-probability where the probability of any member being chosen is unknown, which includes
Haphazard sampling – convenience
Purposive sampling – meets predetermined criterion
Quota sampling – reflecting the numerical composition of various subgroups in the population
Coming to Conclusions about Large Populations
Why Use a Random Sample?
Random samples are most likely to produce a sample that truly represents the population.
They are purely mathematical or mechanical.
Allow calculation of probability of outcomes with great precision.
sampling ratio = the ratio of the sample size to the size of the target population.
Sampling error = the degree to which a sample deviates from a population.
Coming to Conclusions about Large Populations
Types of Random Samples
Simple Random Samples = sample elements selected from the frame based on a mathematically random
selection procedure
most times, a proper random sample yields results that are close to the population parameter
Sampling distribution = A plot of many random samples, with a sample characteristic across the bottom
and the number of samples indicated along the side.
Coming to Conclusions about Large Populations
Types of Random Samples
Systematic Sampling = An approximation to random sampling in which you select one in a certain
number of sample elements, the number is from the sampling interval.
Sampling Interval = the size of the sample frame over the sample size, used in systematic sampling to
select units.
Coming to Conclusions about Large Populations
Types of Random Samples
Stratified Sampling = a type of random sampling in which a random sample is draw from multiple
sampling frames, each for a part of the population.
Coming to Conclusions about Large Populations
Coming to Conclusions about Large Populations
Types of Random Samples
Cluster (multi-stage) sampling = a multi-stage sampling method, in which clusters are randomly
sampled, then a random sample of elements is taken from sampled clusters.
Coming to Conclusions about Large Populations
Coming to Conclusions about Large Populations
Three Specialized Sampling Techniques
Random Digit Dialing = Computer based random sampling of telephone numbers.
Sampling Hidden Populations
Hidden Population = A group that is very difficult to locate and may not want to be found, and therefore,
are difficult to sample.
Inferences from A Sample to A Population
How to Reduce Sampling Errors
the larger the sample size, the smaller the sampling error.
the greater the homogeneity (or the less the diversity), the smaller its sampling error.
How Large Should My Sample Be?
the smaller the population, the bigger the sampling ratio must be for an accurate sample.
as populations increase to over 250,000, sample size no longer needs to increase.
Sample Size and Precision of Population Estimates (from Cozby (2009), Methods in Behavioral Research,
McGraw Hill)
Inferences from A Sample to A Population
How to Create a Zone of Confidence
Confidence interval = a zone, above and below the estimate from a sample, within which a population
parameter is likely to be.
Types of Variables (Cozby, 2007)
Person Variables – fairly stable characteristics of an individual
Situational Variables – environmental or situational conditions such as difficulty of a task, weather
conditions etc.
Response variables – individual responses such as state anxiety, reaction time
Types of Variables
Independent or Predictor Variables – what you hypothesize as the “cause”
Dependent or Outcome Variable – what you hypothesize as the “effect”
Mediating variable – a variable that mediates the relation between the cause and effect.
Possible relations between variables