Types of Samples: Probability Sampling (Representative Samples)

Types of samples
The best sampling is probability sampling, because it increases the likelihood of obtaining
samples that are representative of the population.
Probability sampling (Representative samples)
Probability samples are selected in such a way as to be representative of the population.

They provide the most valid or credible results because they reflect the characteristics of
the population from which they are selected (e.g., residents of a particular community,
students at an elementary school, etc.). There are two types of probability samples: random
and stratified.
Random sample
The term random has a very precise meaning. Each individual in the
population of interest has an equal likelihood of selection. This is a
very strict meaning -- you can't just collect responses on the street and
have a random sample.
The assumption of an equal chance of selection means that sources such as a telephone
book or voter registration lists are not adequate for providing a random sample of a
community. In both these cases there will be a number of residents whose names are not
listed. Telephone surveys get around this problem by random-digit dialing -- but that
assumes that everyone in the population has a telephone. The key to random selection is
that there is no bias involved in the selection of the sample. Any variation between the
sample characteristics and the population characteristics is only a matter of chance.
Stratified sample
A stratified sample is a mini-reproduction of the population. Before

sampling, the population is divided into characteristics of importance
for the research. For example, by gender, social class, education level,
religion, etc. Then the population is randomly sampled within each
category or stratum. If 38% of the population is college-educated,
then 38% of the sample is randomly selected from the college-
educated population.
Stratified samples are as good as or better than random samples, but they require a fairly
detailed advance knowledge of the population characteristics, and therefore are more
difficult to construct.
How to Construct a probability (representative) sample
Nonprobability samples (Non-representative samples)
As they are not truly representative, non-probability samples are less desirable than
probability samples. However, a researcher may not be able to obtain a random or stratified
sample, or it may be too expensive. A researcher may not care about generalizing to a
larger population. The validity of non-probability samples can be increased by trying to
approximate random selection, and by eliminating as many sources of bias as possible.
Quota sample
The defining characteristic of a quota sample is that the researcher

deliberately sets the proportions of levels or strata within the sample.
This is generally done to insure the inclusion of a particular segment
of the population. The proportions may or may not differ dramatically
from the actual proportion in the population. The researcher sets Two of each species
a quota, independent of population characteristics.
Example: A researcher is interested in the attitudes of members of different religions

towards the death penalty. In Iowa a random sample might miss Muslims (because there
are not many in that state). To be sure of their inclusion, a researcher could set a quota of
3% Muslim for the sample. However, the sample will no longer be representative of the
actual proportions in the population. This may limit generalizing to the state population. But
the quota will guarantee that the views of Muslims are represented in the survey.
Purposive sample
A purposive sample is a non-representative subset of some larger

population, and is constructed to serve a very specific need or purpose.
A researcher may have a specific group in mind, such as high level
business executives. It may not be possible to specify the population --
they would not all be known, and access will be difficult. The researcher
will attempt to zero in on the target group, interviewing whomever is
available.
A subset of a purposive sample is a snowball sample -- so named because one picks up the
sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by
asking a participant to suggest someone else who might be willing or appropriate for the study.
Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc.
Convenience sample
A convenience sample is a matter of taking what you can get. It is

an accidental sample. Although selection may be unguided, it probably is not
random, using the correct definition of everyone in the population having an
equal chance of being selected. Volunteers would constitute a convenience
sample.
Non-probability samples are limited with regard to generalization. Because they do not truly
represent a population, we cannot make valid inferences about the larger group from which
they are drawn. Validity can be increased by approximating random selection as much as
possible, and making every attempt to avoid introducing bias into sample selection.
Sampling: Sample size
In a very general sense, the larger the sample, the better -- because larger samples tend to
be more similar to the population from which they are drawn. However, if the population of
interest is small, then the sample can be relatively small. Large samples require more time
for data collection and analysis, and are therefore more costly than smaller ones.
If a treatment is known to have a fairly strong effect, it may show up in an experiment

involving a small sample. On the other hand, a small sample for a survey may miss
individuals holding a minority point of view. For surveys one has to consider refusal and
spoilage rates (incomplete responses, illegible answers, nonsensical replies). In such cases
the researcher should aim for a larger sample in order to cover the losses.
Increasing the number of variables and/or their

levels requires more participants. For example,
comparing attitudes of 20 lower division and 20 20 Lower 20 Upper
Level
upper division college students toward college division division
athletics may be a reasonable number. If the
Greek 10 Yes 10 No 10 Yes 10 No
samples of 20 each are broken down into
fraternity/sorority vs. non-greek students, the 5 5 5 5 5 5 5 5
Gender
number in each category declines. A gender division M F M F M F M F
leads to only 5 persons per cell -- probably too small
for drawing any conclusions.
Appropriate sample size depends on
1. population size
2. available resources (time, money)
3. strength of effect being measured
4. refusal and spoilage rates
5. number of analyses to be performed
Experience Sampling Method (ESM)

Experience sampling is a systematic way of having participants provide samples of their
ongoing behavior.
Three methods
Participants' reports are dependent (contingent) upon either a signal, pre-established

intervals, or the ocurrence of some event.
Signal contingent
The participant is signaled with a beeper, cell phone call, or programmed watch at random
times within a fixed time period (e.g., between 8 AM and 9 PM). At the signal, the
participant records the behavior of interest (e.g., activity, location, mood, thoughts).
Example: The focus of this study(1) was home activity and school performance of Latino
children. Researchers randomly selected eight 10 and 11-year olds from an ongoing
longitudinal study. The children were paged randomly within a time block of 4 PM-6 PM on
Monday to Saturday for 12 weeks. About 3 calls a day were made. The children received
$.50 for each returned call. The fieldworker (on the phone) asked "What were you doing
when the pager went off?" along with a few follow-up questions. The accumulated responses
were subsequently coded into more general activity categories.
Interval contingent
Participant is assigned pre-set intervals for reporting events. For example, before going to
bed at night, the participant fills out a checklist of the day's activities.
Event contingent
The event is determined by the research project, for example, migraine headache, or
phoning a friend. The participant makes a record whenever the key event occurs. The
recording of the event depends upon (is contingent) on its occurrence.
Advantages and disadvantages
These reports are personal and subjective. They can be quantified by coding and
counting.
The data are similar to those obtained by diaries. An advantage is that the signal
makes then less dependent upon memory.
The technique is less intrusive than direct observation.
The data tend to be a little thin -- don't have the richness of ethnography. It is only
a small sample of behavior, and little is known about the setting or context

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Types of samples

Probability sampling (Representative samples)

Probability samples are selected in such a way as to be representative of the population.

A stratified sample is a mini-reproduction of the population. Before

How to Construct a probability (representative) sample

Nonprobability samples (Non-representative samples)

The defining characteristic of a quota sample is that the researcher

Example: A researcher is interested in the attitudes of members of different religions

A purposive sample is a non-representative subset of some larger

A convenience sample is a matter of taking what you can get. It is

If a treatment is known to have a fairly strong effect, it may show up in an experiment

Increasing the number of variables and/or their

Appropriate sample size depends on

Experience Sampling Method (ESM)

Participants' reports are dependent (contingent) upon either a signal, pre-established

Advantages and disadvantages

The technique is less intrusive than direct observation.

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