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Sampling Plans. Basic Sampling Concepts. Population The aggregate of cases in which a researcher is interested Sampling Selection of a portion of the population (a sample ) to represent the entire population Eligibility criteria The characteristics that define the population

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## Sampling Plans

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**Basic Sampling Concepts**• Population • The aggregate of cases in which a researcher is interested • Sampling • Selection of a portion of the population (a sample) to represent the entire population • Eligibility criteria • The characteristics that define the population • Inclusion criteria • Exclusion criteria**Basic Sampling Concepts (cont.)**• Strata • Subpopulations of a population (e.g., male/female) • Target population • The entire population of interest • Accessible population • The portion of the target population that is accessible to the researcher, from which a sample is drawn**Sampling Goal in Quantitative Research**• Representative sample • A sample whose key characteristics closely approximate those of the population—a sampling goal in quantitative research • More easily achieved with: • Probability sampling • Homogeneous populations • Larger samples**Sampling Problems in Quantitative Research**• Sampling bias • The systematic over- or under-representation of segments of the population on key variables when the sample is not representative • Sampling error • Differences between sample values and population values**Types of Sampling Designs**• Probability sampling • Involves random selection of elements: each element has an equal, independent chance of being selected • Nonprobability sampling • Does not involve selection of elements at random**Question**Is the following statement True or False? • The difference between sample values and population values is referred to as the sampling bias.**Answer**• False • The sampling bias is the systematic over- or under-representation of segments of the population on key variables when the sample is not representative. Sampling error is the difference between sample values and population values.**Types of Nonprobability Sampling—Quantitative Research**• Conveniencesampling • Snowball (network) sampling • Quota sampling • Purposive sampling**Convenience Sampling**• Use of the most conveniently available people • Most widely used approach by quantitative researchers • Most vulnerable to sampling biases**Snowball Sampling**• Referrals from other people already in a sample • Used to identify people with distinctive characteristics • Used by both quantitative and qualitative researchers**Quota Sampling**• Convenience sampling within specified strata of the population • Enhances representativeness of sample • Infrequently used, despite being a fairly easy method of enhancing representativeness**Question**Which type of sampling is most vulnerable to bias? • Convenience sampling • Snowball sampling • Quota sampling • Purposive sampling**Answer**a. Convenience sampling • Although it is the most widely use approach for quantitative researchers, convenience sampling is the most vulnerable to sampling biases. Snowball, quota, and purposive sampling are less subject to bias.**Consecutive Sampling**• Involves taking all of the people from an accessible population who meet the eligibility criteria over a specific time interval, or for a specified sample size • A strong nonprobability approach for “rolling enrollment” type accessible populations • Risk of bias low unless there are seasonal or temporal fluctuations**Purposive (Judgmental) Sampling**• Sample members are hand-picked by researcher to achieve certain goals • Used more often by qualitative than quantitative researchers • Can be used in quantitative studies to select experts or to achieve other goals**Types of Probability Sampling**• Simple random sampling • Stratified random sampling • Cluster (multistage) sampling • Systematic sampling**Simple Random Sampling**• Uses a sampling frame– a list of all population elements • Involves random selection of elements from the sampling frame • Not to be confused with random assignment to groups in experiments • Cumbersome; not used in large, national surveys**Stratified Random Sampling**• Population is first divided into strata, then random selection is done from the stratified sampling frames • Enhances representativeness • Can sample proportionately or disproportionately from the strata**Cluster (Multistage) Sampling**• Successive random sampling of units from larger to smaller units (e.g., states, then zip codes, then households) • Widely used in national surveys • Larger sampling error than in simple random sampling, but more efficient**Question**Is the following statement True or False? • Stratified random sampling is associated with a larger sampling error but it is more efficient.**Answer**• False • Stratified random sampling enhances representativeness; cluster sampling is associated with a larger sampling error but is considered more efficient.**Sample Size**• The number of study participants in the final sample • Sample size adequacy is a key determinant of sample quality in quantitative research. • Sample size needs can and should be estimated through power analysis.**Sampling in Qualitative Research**• Selection of sample members guided by desire for information-rich sources • “Representativeness” not a key issue • Random selection not considered productive**Methods of Sampling in Qualitative Research**• Convenience (volunteer) sampling • Snowball sampling • Purposive sampling • Theoretical sampling**Types of Purposive Sampling in Qualitative Research**(Examples) • Maximum variation sampling • Extreme/deviant case sampling • Typical case sampling • Criterion sampling • Sampling confirming and disconfirming cases**Theoretical Sampling**• Preferred sampling method in grounded theory research • Involves selecting sample members who best facilitate and contribute to development of the emerging theory**Question**Is the following statement True or False? • Sampling in qualitative research is guided more by the desire for rich sources of information than by the need for random selection.**Answer**• True • Selection of sample members for qualitative research is guided by the desire for information-rich sources. The representativeness of the sample is not a key aspect and random selection is not considered productive.**Sample Size in Qualitative Research**• No explicit, formal criteria • Sample size determined by informational needs • Decisions to stop sampling guided by data saturation • Data quality can affect sample size.**Sampling in the Main Qualitative Traditions**Ethnography • Mingling with many members of the culture—a “big net” approach • Informal conversations with 25 to 50 informants • Multiple interviews with smaller number of key informants**Sampling in Phenomenology**• Relies on very small samples (often 10 or fewer) • Participants must have experienced phenomenon of interest**Sampling in Grounded Theory**• Typically involves samples of 20 to 40 people • Selection of participants who can best contribute to emerging theory (usually theoretical sampling)

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