1. Sampling
A sample is a small number of
individuals representing a larger
group.
2. Samples and Populations
A sample in a research study is a
relatively small number of individuals
about whom information is obtained. The
larger group to whom the information is
then generalized is the population.
3. Why use samples?
Although the best data comes from
studying an entire population, samples are
used because they are smaller and less
unwieldy. It can be too time consuming
and expensive to study an entire
population.
4. Defining the population
Whether a researcher is drawing a sample
or is studying an entire population, the
population needs to be defined. This
helps focus the research.
5. Target vs. accessible populations
The target population is the population a
researcher would like to generalize to.
Often this isn’t possible, so the accessible
population is used. For example, a
researcher might want to target all male
elementary teachers in the United States,
but actually collects data from the male
elementary teachers in Hawaii.
6. Random vs. nonrandom sampling
Random sampling is completely based on
chance. For example, one might identify
all members of a population, (n=250) write
their names on separate pieces of paper,
and then draw 25 names out of a hat to
determine who is actually to be included in
the study.
7. Nonrandom sampling
In a nonrandom sample, members are
selected on the basis of a particular set of
characteristics, rather than a random
chance of being included.
8. Simple random sample
In a simple random sample, each and
every member of a population has an
equal and independent chance of being
selected.
9. Table of random numbers
A table of random numbers is used to
identify the people to be included in a
sample. These are usually found in
statistics books, or can be generated by
some calculators and computers.
10. Stratified random sample
In stratified random sampling, subgroups
within a target population are identified to
be included in proportion to the numbers
in which they exist in the population. For
example, a researcher studying
aggressive behavior in dog breeds found
in Hawaii would want to include a sample
of registered breeds in the proportion they
are found in the state.
11. Cluster sampling
In situations where simple random
sampling isn’t possible, as is often the
case in schools, groups or clusters are
identified for inclusion in research. For
example, a researcher might choose to
study all of the students in some specific
classes.
12. Two stage random sampling
This technique combines random
sampling with cluster sampling. It allows a
bigger group to be targeted for
generalization.
13. Systematic sampling with a random
start
In this procedure, a random number is
generated to identify the first member
selected for a sample, and then every nth
member of the population is selected for
inclusion. For example, the first member
selected in a population of 500 might be #
412, and then every 7th person is chosen:
419, 426, 433, 440, 447, and so on.
When you pass 500, you loop back to the
beginning.
14. Sampling ratio
This is the proportion of individuals
selected for a study. For example, you
might select to study ten percent of the
population. The ratio is defined as the
sample size divided by the population
size.
15. Convenience sample
When it isn’t possible to draw a random or
systematic nonrandom sample, a
researcher might choose to study the
individuals who are available. This is
known as a convenience sample.
16. Purposive sampling
A purposive sample is one identified on
the basis of specific characteristics
identified by the researcher. For example,
if a researcher wanted to study all of the
foreign-born teachers in a school district,
he or she would try to identify all of those
individuals and include only them.
17. External validity
Since the entire point of sampling is to
generalize the results to a larger
population, researchers need to be sure
their work actually does represent the
population. The extent to which
information can be generalized to a larger
population is known as external validity.
18. Representative samples
A representative sample provides the
most accurate portrayal of the population
being studied.
19. Replication studies
A replication study follows the format of a
previous study, but uses a new group of
subjects or a new set of conditions or
both.
20. Ecological generalizability
This term refers to the degree to which a
study can be generalized to a different set
of conditions. For example, researchers
studying rural schools might have difficulty
generalizing their results to urban schools.
21. Data
Data is a plural word that refers to the
kinds of information researchers collect.
Data should be followed by a plural verb,
such as “Data are” or “Data were”.
22. Instrumentation
The process of preparing to collect data is
called instrumentation. It involves the
selection of the method by which data will
be collected, as well as the procedures
and conditions for collecting them.
23. Validity
This term refers to the defensibility of the
inferences a researcher can make from a
study using an instrument.
24. Reliability
Reliability refers to consistency of results.
If a study is repeated, will it yield similar
findings? A good example of reliability
might be having three different people
grading students’ essays. Will all three of
them agree on what constitutes an A, B,
C, etc? Or will their scoring vary widely?
If there is a large variety, the grades would
not be reliable.
25. Objectivity
This characteristic refers to the absence of
subjective bias on the part of the
researcher. For example, political analyst
with a particular ideological bent might
conduct a poll differently from one who
has no affiliation.
26. Different types of instruments
Researcher instruments are used by the
researcher to collect data; a tally sheet or rubric
are examples.
Subject instruments are completed by the
subject. A survey questionnaire is an example.
Informant instruments are completed by
knowledgeable participants providing
information in addition to that collected by
researchers and given by subjects.
27. Selecting instruments
Instruments may be selected in one of two
ways. Either a researcher locates one
that has been developed by another
person, or he/she designs a new one.
The advantage of selecting existing ones
is that they have often been field tested for
reliability and validity.
28. Collecting data
Data may be collected in a variety of
ways. Respondents might give written
responses, or they might perform a task.
Doing a miscue analysis on a student is
an example of a performance analysis.
29. Rating scales
The difference between observation and rating
is that when a researcher rates a subject, he or
she is making a judgment of some type. On the
other hand, when a researcher makes an
observation, he or she is merely recording
behavior and not judging it. For example, a
rating might be that a girl made 3 baskets in 20
attempts, thus scored 2 on a scale of poor to
good on free throws, while an observation would
just note the number of baskets/attempts.
32. Scores
Raw scores are the initial scores obtained
on a test. The number right out of a total
number of questions is an example.
Derived scores have been scaled to show
their relative position with respect to other
raw scores.
34. Norm-referenced vs. Criterion
referenced
A norm-referenced test is developed to
provide scores that replicate a normal
curve among the population tested. Thus,
among a population taking the test, half of
the people should score above average
and half below.
A criterion referenced test is based on a
goal and an identified percentage is
targeted to reach that goal.
35. Measurement scales
A nominal scale, the simplest scale, identifies
groups by a number, e.g. “1” for male and “2” for
female.
An ordinal scale provides an rating from most to
least. A Likert scale is an ordinal scale.
An interval scale is an ordinal scale that has the
addition of equal distances between the points.
IQ is measured using an interval scale.
A ratio scale is an interval with a true zero and is
rarely used in educational measurement.
