A typology of sampling methods.
The definition for MixedProbabilityNonprobability has been rephrased to make the term clearer to users.
DEFINITION REPHRASED: MixedProbabilityNonprobability
DDI 3.2
A brief textual description or classification of the type of sampling procedure used. Supports the use of an external controlled vocabulary.
Module Name
Element Name
datacollection
TypeOfSamplingProcedure
DDI 2.5
The type of sample and sample design used to select the survey respondents to represent the population. May include reference to the target sample size and the sampling fraction.
Element Number in DDI 2.1
Element/Attribute Name
2.3.1.4
sampProc
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SamplingProcedure
Sampling Procedure
1.1
urn:ddi-cv:SamplingProcedure
urn:ddi-cv:SamplingProcedure:1.1
http://www.ddialliance.org/Specification/DDI-CV/SamplingProcedure_1.1_Genericode1.0_DDI-CVProfile1.0.xml
http://www.ddialliance.org/Specification/DDI-CV/SamplingProcedure_1.1.html
http://www.ddialliance.org/Specification/DDI-CV/SamplingProcedure_1.1_InputSheet_Excel2003.xls
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DDI
Code
Value of the Code
Term
Descriptive Term of the Code
Definition
Definition of the Code
CodeKey
The unique identification of each item in a code list.
TotalUniverseCompleteEnumeration
Total universe/Complete enumeration
All units (individuals, households, organizations, etc.) of a target population are included in the data collection. For example, if the target population is defined as the members of a trade union, all union members are invited to participate in the study. Also called "census" if the entire population of a regional unit (e.g. a country) is selected.

Probability
Probability
All units (individuals, households, organizations, etc.) of a target population have a non-zero probability of being included in the sample and this probability can be accurately determined. Use this broader term if a more specific type of probability sampling is not known or is difficult to identify.

Probability.SimpleRandom
Probability: Simple random
All units of a target population have an equal probability of being included in the sample. Typically, the entire population is listed in a "sample frame", and units are then chosen from this frame using a random selection method.

Probability.SystematicRandom
Probability: Systematic random
A fixed selection interval is determined by dividing the population size by the desired sample size. A starting point is then randomly drawn from the sample frame, which normally covers the entire target population. From this starting point, units for the sample are chosen based on the selection interval. Also known as interval sampling. For example, a company survey seeks a sample of 1,000 employees out of 10,000 total. Beginning with a random starting number, every 10th name from the employee list of the company will be invited to participate in the study.

Probability.Stratified
Probability: Stratified
The target population is subdivided into separate and mutually exclusive segments (strata) that cover the entire population. Independent random samples are then drawn from each segment. For example, in a national public opinion survey the entire population is divided into two regional strata: East and West. After this, sampling units are drawn from within each region using simple or systematic random sampling. Use this broader term if the specific type of stratified sampling is not known or difficult to identify.

Probability.Stratified.Proportional
Probability: Stratified: Proportional
The target population is subdivided into separate and mutually exclusive segments (strata) that cover the entire population. In proportional stratified sampling the number of elements chosen from each stratum is proportional to the population size of the stratum when viewed against the entire population. For example, a country is divided into two regional strata that comprise 80 percent (West) and 20 percent (East) of the total population. For a sample of 1,000 people, 800 (i.e., 80 percent) would be drawn from the West and 200 (i.e., 20 percent) from the East to accurately represent their proportion in the total population.

Probability.Stratified.Disproportional
Probability: Stratified: Disproportional
The target population is subdivided into separate and mutually exclusive segments (strata) that cover the entire population. In disproportional sampling the number of units chosen from each stratum is not proportional to the population size of the stratum when viewed against the entire population. The number of sampled units from each stratum can be equal, optimal, or can reflect the purpose of the study, like oversampling of different subgroups of the population. For example, a country is divided into two regional strata that comprise 80 percent (West) and 20 precent (East) of the country's population. If equal representation of the two regions is needed in a study, half the sample may be drawn from the West and half from the East, so that each region is represented by 50 percent of the sample. If a more detailed analysis of the population from the East is needed, 40 percent of the units may be drawn from the West and 60 percent from the East, so that the East is over-represented.

Probability.Cluster
Probability: Cluster
The target population is divided into naturally occurring segments (clusters) and a probability sample of the clusters is selected. Data are then collected from all units within each selected cluster. Sampling is often clustered by geography, or time period. Use this broader term if a more specific type of cluster sampling is not known or is difficult to identify.

Probability.Cluster.SimpleRandom
Probability: Cluster: Simple random
The target population is divided into naturally occurring segments (clusters) and a simple random sample of the clusters is selected. Data are then collected from all units within each selected cluster. For example, for a sample of students in a city, a number of schools would be chosen using the random selection method, and then all of the students from every sampled school would be included.

Probability.Cluster.StratifiedRandom
Probability: Cluster: Stratified random
The target population is divided into naturally occurring segments (clusters); next, these are divided into mutually exclusive strata and a random sample of clusters is selected from each stratum. Data are then collected from all units within each selected cluster. For example, for a sample of students in a city, schools would be divided into two strata by school type (private vs. public); schools would be then randomly selected from each stratum, and all of the students from every sampled school would be included.

Probability.Multistage
Probability: Multistage
Sampling is carried out in stages using smaller and smaller units at each stage, and all stages involve a probability selection. The type of probability sampling procedure may be different at each stage. For example, for a sample of students in a city, schools are randomly selected in the first stage. A random sample of classes within each selected school is drawn in the second stage. Students are then randomly selected from each of these classes in the third stage.

Nonprobability
Non-probability
The selection of units (individuals, households, organizations, etc.) from the target population is not based on random selection. It is not possible to determine the probability of each element to be sampled. Use this broader term if the specific type of non-probability is not known, difficult to identify, or if multiple non-probability methods are being employed.

Nonprobability.Availability
Non-probability: Availability
The sample selection is based on the units' accessibility/relative ease of access. They may be easy to approach, or may themselves choose to participate in the study (self-selection). Researchers may have particular target groups in mind but they do not control the sample selection mechanism. For example, students leaving a particular building on campus may be approached, or individuals may volunteer to participate in response to invitations that do not target them specifically, but a larger group to which they may belong. Also called "convenience" or "opportunity" sampling.

Nonprobability.Purposive
Non-probability: Purposive
Sample units are specifically identified, selected and contacted for the information they can provide on the researched topic. Selection is based on different characteristics of the independent and/or dependent variables under study, and relies on the researchers' judgement. The study authors, or persons authorized by them have control over the sample selection mechanism and the universe is defined in terms of the selection criteria. Also called "judgement" sampling. For example, a medical researcher may intentionally select individuals who are similar in most respects, except on the outcome of the research topic, which can be a specific disease. Some types of purposive sampling are typical/deviant case, homogeneous/maximum variation, expert, or critical case sampling.

Nonprobability.Quota
Non-probability: Quota
The target population is subdivided into separate and mutually exclusive segments according to some predefined quotation criteria. The distribution of the quotation criteria (gender/age/ethnicity ratio, or other characteristics, like religion, education, etc.) is intended to reflect the real structure of the target population or the structure of the desired study population. Non-probability samples are then drawn from each segment until a specific number of units has been reached. For example, if the target population consists of 45 percent females and 55 percent males, a proportional quota sample will have the same gender percentages, while in a non-proportional quota sample the percentages will be different, based on some study-related consideration (for instance, the need to oversample for certain under-represented segments of the population).

Nonprobability.RespondentAssisted
Non-probability: Respondent-assisted
Sample units are identified from a target population with the assistance of units already selected (adapted from "Public Health Research Methods", ed. Greg Guest, Emily E. Namey, 2014). A typical case is snowball sampling, in which the researcher identifies a group of units that matches a particular criterion of eligibility. The latter are asked to recruit other members of the same population that fulfil the same criterion of eligibility (sampling of specific populations like migrants, etc.).

MixedProbabilityNonprobability
Mixed probability and non-probability
Sample design that combines probability and non-probability sampling within the same sampling process. Different types of sampling may be used at different stages of creating the sample. For example, for a sample of minority students in a city, schools are randomly selected in the first stage. Then, a quota sample of students is selected within each school in the second stage. If separate samples are drawn from the same target population using different sampling methods, the type of sampling procedure used for each sample should be classified separately.

Other
Other
Use if the sampling procedure is known, but not found in the list.