Bias in Survey Sampling
In survey sampling, bias refers to the tendency
of a sample
to systematically over- or under-estimate a population
Bias Due to Unrepresentative Samples
is representative. This means that
each sample point represents the attributes of a known number of
Bias often occurs when the survey sample does not accurately represent the
The bias that results from an unrepresentative sample is called
selection bias. Some common examples of
selection bias are described below.
- Undercoverage. Undercoverage occurs when some
members of the population are inadequately represented in the sample.
A classic example of undercoverage is the Literary
Digest voter survey, which predicted that
Alfred Landon would beat Franklin Roosevelt in the 1936
presidential election. The survey sample suffered from
undercoverage of low-income voters, who tended to be
How did this happen? The survey relied on a
convenience sample, drawn from telephone directories and
car registration lists. In 1936, people who owned cars and
telephones tended to be more affluent. Undercoverage is often
a problem with convenience samples.
- Nonresponse bias. Sometimes, individuals
chosen for the sample are unwilling or unable to participate
in the survey. Nonresponse bias is the bias that results
when respondents differ in meaningful ways from nonrespondents.
The Literary Digest survey illustrates this problem.
Respondents tended to be Landon supporters; and nonrespondents,
Roosevelt supporters. Since only 25% of the sampled voters
actually completed the mail-in survey, survey results
overestimated voter support for Alfred Landon.
The Literary Digest experience illustrates a common
problem with mail surveys. Response rate is often low, making
mail surveys vulnerable to nonresponse bias.
- Voluntary response bias. Voluntary response
bias occurs when sample members are self-selected volunteers,
An example would be call-in radio shows that solicit
audience participation in surveys on controversial topics
(abortion, affirmative action, gun control, etc.). The
resulting sample tends to overrepresent individuals who
have strong opinions.
Random sampling is a procedure for sampling
from a population in which (a) the selection of a sample unit
is based on chance and (b) every element of the population has
a known, non-zero probability of being selected. Random
sampling helps produce representative samples by eliminating
voluntary response bias and guarding against undercoverage
bias. All probability sampling methods rely on
Bias Due to Measurement Error
A poor measurement process can also lead to bias. In survey
research, the measurement process includes the environment
in which the survey is conducted, the way that questions are
asked, and the state of the survey respondent.
Response bias refers to the bias that results
from problems in the measurement process. Some examples of response
bias are given below.
- Leading questions. The wording of the question
may be loaded in some way to unduly favor one response over
another. For example, a satisfaction survey
may ask the respondent to indicate where she is satisfied,
dissatisfied, or very dissatified. By giving the respondent
one response option to express satisfaction and two
response options to express dissatisfaction, this survey
question is biased toward getting a dissatisfied
- Social desirability. Most people like to
present themselves in a favorable light, so they will be
reluctant to admit to unsavory attitudes or illegal activities
in a survey, particularly if survey results are not
confidential. Instead, their responses may be biased toward
what they believe is socially desirable.
Sampling Error and Survey Bias
A survey produces a sample statistic, which is used to estimate
a population parameter. If you repeated a survey many times,
using different samples each time, you might get a different
sample statistic with each replication. And each of the
different sample statistics would be an estimate for the
same population parameter.
If the statistic is
unbiased, the average of all the statistics from all possible
samples will equal the true population parameter; even though
any individual statistic may differ from the population parameter.
The variability among statistics from different samples is called
Increasing the sample size tends to reduce the sampling error;
that is, it makes the sample statistic less variable. However,
increasing sample size does not affect survey bias. A large
sample size cannot correct for the methodological problems
(undercoverage, nonresponse bias, etc.) that produce survey bias.
The Literary Digest example discussed above
illustrates this point. The sample size was very large - over 2
million surveys were completed; but the large sample size could
not overcome problems with the sample - undercoverage and
Test Your Understanding
Which of the following statements are true?
I. Random sampling is a good way to reduce response bias.
II. To guard against bias from undercoverage, use a convenience sample.
III. Increasing the sample size tends to reduce survey bias.
IV. To guard against nonresponse bias, use a mail-in survey.
(A) I only
(B) II only
(C) III only
(D) IV only
(E) None of the above.
The correct answer is (E). None of the statements is true.
provides strong protection against bias from
voluntary response bias;
but it is not effective against
convenience sample does not protect against
undercoverage bias; in fact, it sometimes causes undercoverage
bias. Increasing sample size does not affect survey
bias. And finally,
using a mail-in survey does not prevent
nonresponse bias. In fact, mail-in surveys are quite
vulnerable to nonresponse bias.