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Bias 
A tendency to underestimate or overestimate a population value of interest.
C.V.
See Coefficient of variation of sample estimates.
Census
An enumeration of the total population of interest. Since no
sample is selected from the population, there is no sampling
error. However, nonsampling errors are still possible in a census.
Coefficient of variation of sample estimates (C.V.)
The ratio of the standard error for an estimate to the
mean value of the estimate. This is used to measure the imprecision in survey
estimates introduced by sampling. A coefficient of variation of 1 percent would
indicate that an estimate could vary slightly due to sampling error, while a coefficient
of variation of 50 percent means that the estimate is very imprecise. The most
common way to improve the coefficient of variation requires increases in sample
size that are typically expensive to accomplish.
Cooperation rate
The percentage of in-scope individuals (or organizations)
who complete a survey after being contacted. The denominator for the cooperation
rate excludes individuals (or organizations) whom one has tried unsuccessfully
to contact. Thus, the cooperation rate for a survey will be higher than its
response rate unless all selected individuals (or organizations)
are contacted.
Coverage
The extent of correspondence between the target population and
the sampling frame. Ideally, all members of the target population
are included in the sampling frame. However, this is infrequently the case for
major surveys. Coverage is rarely estimable in precise terms; however, survey
designers are usually aware of the likely reasons for undercoverage and can often
estimate the extent of the problem. In addition to the problem of undercoverage
(missing population members), sampling frames can suffer from overcoverage, i.e.,
the inclusion of units that do not belong on the sampling frame and/or the listing
of a given unit more than once. These problems are usually correctable. Duplicate
listings are either deleted prior to sample selection or
are corrected for by appropriate statistical adjustments. Listings that are not
in-scope according to the survey definition are typically
deleted during data collection or analysis and corresponding statistical adjustments
are made to estimate the likely extent of out-of-scope
cases among the survey nonrespondents.
Estimation procedures
Procedures followed in making population estimates from the survey responses.
Imputation
The process by which one estimates missing values for items that a survey respondent
failed to provide.
In-scope
Sampling units that are part of the population of interest.
Item nonresponse
The failure of a respondent to answer a particular item
on the survey. When item nonresponse is high and respondents and nonrespondents
differ substantially, item nonresponse can be a serious threat to the accuracy
of the estimates. Imputation techniques can be used to reduce
the impact of this problem, but the extent to which they are effective is difficult
to determine.
Measurement error
The extent to which there are discrepancies between survey results and the true
value of what the survey researcher is attempting to measure. There are several
possible sources of error here. Respondents may report
inaccurate information because they do not have the required information, due
to carelessness, or because they do not understand the question asked. Alternately,
respondents may provide accurate information, but errors are introduced in the
data processing stage due to keypunching, coding, or programming errors. Since
it is often not possible to determine the "true value" of what one is trying
to measure, precise estimates of measurement error are usually not possible.
However, techniques exist for obtaining some information about the likely extent
of measurement error. For example, information reported by individuals may be
compared with appropriate institutional records on the individual.
Microdata
Nonaggregated data about the units sampled. For surveys of individuals, microdata
contain records for each individual interviewed; for surveys of organizations,
the microdata contain records for each organization.
Multimodal survey
A survey in which more than one data collection mode was used, e.g., a mix of
mail and phone data collection. This approach is often used in large surveys
because mail data collection is cheaper than phone but response
rates are typically too low to meet desired levels. Mail nonrespondents
are surveyed by phone. The major problem with this approach is that the mode
of data collection may produce different answers. This can potentially lead
to incorrect inferences about the associations among variables.
Out-of-scope
Sampling units that are not part of the population of interest.
For example, in the National Survey of Recent
College Graduates, only individuals who received a bachelor's or master's
degree within a specified time frame are of interest. If an educational institution
provided the name of an individual who failed to graduate, the individual would
be considered out-of-scope for the survey. Information
on this individual would not be included in the final estimates from the survey.
Population
The individuals or organizations of interest in a given survey. In sample
surveys one makes inferences about the population from the sample selected.
Probability proportional to size (pps)
A sampling technique in which the probability of a unit's being selected is
based on a measure of size. For example, if the measure of size is expenditures,
organizations with high expenditures are selected with higher probability than
organizations with low expenditures.
Respondent
The individual or organization providing the information requested in the survey.
The type of respondent influences what type of information can be obtained,
e.g., individuals completing a degree may provide different information about
the degree than a representative of the academic institution granting the degree
would provide.
Response rate
Indicates the percentage of sample members who provided
information in response to being surveyed. Care in interpreting response rates
is necessary, because there is not one single uniformly accepted measure of
response rate. One common measure, used extensively in demographic surveys,
is the percentage of in-scope sample members who responded
to the survey. In surveys that focus on estimating expenditures, the response
rate is often calculated as the percentage of the total expenditures represented
by responding sample members. This measure is often referred to as a weighted
response rate (though weighting may also be used to adjust for different probabilities
of sample selection).
Sample
The individuals or organizations selected to represent the population.
Sample design
The procedures used in selecting the sample. These procedures
can be as simple as randomly selecting a certain percentage of the cases.
However, more complex designs are frequently used in order to obtain reliable
information about a particular group(s) of interest and/or to minimize the
cost of obtaining the information desired.
Sample frame
Those individuals or organizations from which one selects the actual sample
for the survey. Ideally, the sample frame is the same as the target population.
In reality, however, there are often differences.
Scope of survey
The population to which the researcher plans to generalize
his or her results. The scope of the survey may be limited by both theoretical
and practical considerations. For example, while it may be of theoretical interest
to obtain information on the characteristics of institutionalized individuals,
practical difficulties often lead researchers to declare such individuals out-of-scope
for a survey. Out-of-scope cases may be eliminated at
the time of sample frame construction or during data collection
or data processing.
Standard error
This is a commonly used measure of how precisely one can estimate a population
value from a given sample. For large sample surveys, a reasonable
interpretation of the standard error is that approximately 68 percent of the time
the sample estimate will be within one standard error of the population value.
For example, if one estimates that the mean income for individuals within a specified
group is $30,000 with a standard error of $5,000, one would be right 68 percent
of the time in assuming that the true (or population) mean income for the group
is between $25,000 and $35,000.
Subsample
A sample selected from a sample frame
that is itself a sample of a larger population. Often the original
sample is used to identify individuals or organizations of interest or is used
to sort units into groups to be sampled at different rates.
Stratification
A sampling technique in which sampling is done separately for separate parts
of the population. Stratification is often used to ensure
that one has an adequate number of sampling units with relatively rare characteristics
(e.g., stratification may be done on race/ethnic status if one wishes to make
comparisons among racial/ethnic groups).
Target population
Those individuals or organizations about whom one wishes to make inferences
on the basis of the survey results.
Two-stage sample
A sample selected in two steps. In one common type of
two-stage sample, the first stage consists of a sample of organizations of
interest and the second stage consists of individuals within organizations.
Unit nonresponse
The failure of an individual or organization to respond to the survey. When
unit nonresponse is high and respondents and nonrespondents
differ substantially, unit nonresponse can be a serious threat to the accuracy
of a survey. There are statistical techniques that can be used to reduce the
impact of this problem, but all rest on assumptions about the characteristics
of missing units that are difficult to evaluate without expensive additional
data collection.
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