Defining the Population
All samples must be drawn from some larger population, and that requires a prior definition of the population. In practical terms, every research project has to consider which kinds of data sources will be eligible for the study, regardless of whether those data sources consist of people to be interviewed, sites to be observed, or texts and other media to be examined.
Stating the eligibility criteria that determine whether a given data source is included in the total population is technically known as defining a sampling frame. For example, a study examining “inner-city schools” must begin by defining which schools belong in that population, and this outlines a sampling frame that determines whether any given school is eligible for inclusion in the study.
In qualitative research, issues related to defining
the overall populations are generally treated as part of purposive sampling, which inherently requires an explicit definition of the kinds of data sources that are of interest.
In essence, determining which data
sources met the goal of purposive sampling for a qualitative study is equivalent to defining a set of eligibility requirements for the population (i.e., creating a sampling frame). Hence, the concept of purposive sampling falls within the broad process of defining the population of potential data sources.
Qualitative researchers have also developed a number of more specific techniques for defining eligible populations through purposive sampling. Among these are theoretical sampling, which selects cases according to their ability to advance research goals such as theory development; maximum variation sampling, which examines a wide range of different cases within the population of interest; and stratified sampling, which divides the overall sample into specified subsets for comparative purposes. Each of these techniques
amounts to a strategy for implementing the
goal of purposive sampling, in order to meet a specific set of research purposes. Taken together, these strategies facilitate the in-depth interpretation of a systematically selected set of data sources, which is one of the hallmarks of qualitative research.
David L. Morgan
See also Convenience Sample; Nonprobability Sampling; Population; Probability Sampling; Purposive Sampling; Quota Sampling; Random Sampling; Sample; Sample Size; Sampling Frame; Snowball Sampling; Stratified Sampling; Theoretical Sampling
Further Readings
Kalton, G. (1983). Quantitative applications in the social sciences: Vol. 35. Introduction to survey sampling. Thousand Oaks, CA: Sage.
Mason, J. (2002). Qualitative researching (2nd ed.).
Thousand Oaks, CA: Sage. Patton, M. Q. (2001). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.
Stating the eligibility criteria that determine whether a given data source is included in the total population is technically known as defining a sampling frame. For example, a study examining “inner-city schools” must begin by defining which schools belong in that population, and this outlines a sampling frame that determines whether any given school is eligible for inclusion in the study.
In qualitative research, issues related to defining
the overall populations are generally treated as part of purposive sampling, which inherently requires an explicit definition of the kinds of data sources that are of interest.
In essence, determining which data
sources met the goal of purposive sampling for a qualitative study is equivalent to defining a set of eligibility requirements for the population (i.e., creating a sampling frame). Hence, the concept of purposive sampling falls within the broad process of defining the population of potential data sources.
Qualitative researchers have also developed a number of more specific techniques for defining eligible populations through purposive sampling. Among these are theoretical sampling, which selects cases according to their ability to advance research goals such as theory development; maximum variation sampling, which examines a wide range of different cases within the population of interest; and stratified sampling, which divides the overall sample into specified subsets for comparative purposes. Each of these techniques
amounts to a strategy for implementing the
goal of purposive sampling, in order to meet a specific set of research purposes. Taken together, these strategies facilitate the in-depth interpretation of a systematically selected set of data sources, which is one of the hallmarks of qualitative research.
David L. Morgan
See also Convenience Sample; Nonprobability Sampling; Population; Probability Sampling; Purposive Sampling; Quota Sampling; Random Sampling; Sample; Sample Size; Sampling Frame; Snowball Sampling; Stratified Sampling; Theoretical Sampling
Further Readings
Kalton, G. (1983). Quantitative applications in the social sciences: Vol. 35. Introduction to survey sampling. Thousand Oaks, CA: Sage.
Mason, J. (2002). Qualitative researching (2nd ed.).
Thousand Oaks, CA: Sage. Patton, M. Q. (2001). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.
