Selecting
Subjects
If
we want to know if Billy performed better than
Sally, or if boys scored higher than girls in our
class, or even if Asian children receive higher
grades in our school than Caucasian children, the
selection of subjects is rather simple.
When we are testing the entire population of
possible subjects, we are adequately assured that no
subject bias has occurred.
A
population refers to the entire pool of possible
subjects. In
a classroom or other setting where the entire
population is relatively small, testing all subjects
may be simple.
However, if we are attempting to understand
or gain knowledge related to a large population,
such as all third grade children, all depressed
adults, or all retail employees, gathering and
testing everyone would be relatively impossible.
In this situation, we would need to gather a
sample of the population, test this sample, and then
make inferences aimed at the entire population of
which they represent.
When
determining which potential subjects from a large
population to include in our study there are several
approaches to choose from.
Each of these sampling techniques have its
own strengths and, of course, its own weaknesses.
The idea behind adequate sampling, however,
remains the same: to gather a sample of subjects
that is representative of the greater population.
The ideal research sample is therefore often
referred to as a representative sample.
Simple
Random Sample.
To assure that the sample of subjects
taken from a known population truly represents the
population, we could test every subject in the
population and choose only those who fall around the
mean of the entire population.
This technique is usually pointless because
doing so means we could just as easily have tested
the entire population on our independent and
dependent variables.
Therefore in order to make sure all possible
subjects have an equal opportunity to be chosen,
simple random sampling is most often the selection
method used.
To
choose a random group of 10 students from a class
of 30, for example, we could put everyones name in
a hat and use the first ten names drawn as our sample. In this method, subjects are chosen just as B6 is chosen
in a game of BINGO.
This technique can work well with a small population
but can be time consuming and archaic when the population
size is large.
To choose 30 students from a class of 250 students
would be easier utilizing technology and what is referred
to as a random number table.
A
random number table is a computer generated list of
numbers placed in random order. Each of the 250 students would be randomly assigned a number
between one and 250.
Then the groups would be formed once again
using a random number generator.
Figures 1.1 and 1.2 provide examples of how
subject selection and subject assignment to groups
might be determined based on this method.
Figure
1.1:
Random Number Table
Figure
1.2:
Random Assignment of Subjects to Groups
Systematic
Sample.
When
a population is very large, assigning a number to
each potential subject could also be tiresome and
time consuming.
A systematic sample is a random sample
compiled in a systematic manner.
If you had a list of all licensed teachers,
for example, and wanted to mail a survey to 200 of
them, systematic sampling might be the sampling
method of choice.
For this example, a page and a teacher number
on that page are determined at random.
This would represent the first subject and
the starting point for choosing the remaining
subjects.
A
random number would be generated, for example 150.
Then every 150^{th} teacher would become
a subject until you have selected enough for your
study. If
you complete the list before selecting enough subjects,
you would continue back at the beginning of the list.
Once the subjects are selected, the technique
of random assignment can be used to assign subjects
to particular groups.
Stratified
Random Sample.
The use of a stratified sample refers to the
breaking down of the population into specific
subsets before choosing which ones will take part in
the study. For
example, if you are studying all third grade
students in your state, you may want to make sure
that every county in your state is represented in
your study. If
you used a simple random sampling technique, you
could conceivably end up with many subjects from one
county and no subjects from other counties.
A stratified sample allows you to choose your
subject pool randomly from a predetermined set of
subsets. In
this example, we may want to choose 10 subjects at
random from each county within the state.
Other
subsets can also be used, such as age, race, or
socioeconomic background.
If you wanted to make sure that there were an
equal number of males and females, you could use sex
as your subset and then randomly choose the same
number of subjects from each subset.
This type of sampling is useful when the
population has some known differences that could
result in different outcomes. For instance, if you already know that 80% of the students
are male, you may want to select 40 male students
and 10 female students so that your sample
represents the breakdown of sex within the
population.
Cluster
Sample. Cluster
sampling could be considered a more specific type of
stratified sample.
When this technique is used, potential
subsets of subjects are first randomly eliminated
and then the remaining subsets are used to randomly
select the sample of subjects to be used in the
study. For example, if you are measuring the effect of prior work
experience on college grades in a particular state,
you may first make a list of all colleges in the
state. Then
you would randomly select a number of colleges to
either include or eliminate in the selection
process. Once
you have a subset of colleges, you could use the
same technique to randomly include or eliminate the
specific classes.
From the remaining classes, you would then
randomly select a group of students with work
experience and a group of students with no work
experience to be placed in your two groups.
Nonprobability
Sample. Nonprobability
refers to a group of subjects chosen based on their
availability rather then their degree of
representativeness of the population.
Surveys are often done in this manner.
Imagine going to the local mall to gather
information about the buying habits of mall
shoppers. Your
subject pool does not represent all mall shoppers
but rather those mall shoppers who happen to walk by
your location on that day.
The same would hold true for a survey over
the phone or via mail.
Those who respond to your questions or return
the mailed survey do not necessarily represent the
population at large.
Instead, they represent the population who
was home and was willing to respond to your
questions or those who took the time to complete and
return the survey.
While
at first glance this method seems unprofessional, it
allows for the gathering of information in a short
amount of time.
It is not considered standardized research
and would be scrutinized if submitted to a
professional journal, but it does have its place.
If youve ever visited a website and seen a
survey, you might have felt compelled to click on
the results link.
When watching a news program you may have not
changed channels because you are waiting for the
results of a survey that will be reported at the end
of the program.
We are highly interested in these
informal polls and using a nonprobability
sample is a quick way to gather large amounts of
information in a relatively short amount of time.
