Chapter 10 Flashcards
(34 cards)
The aggregate of cases in which a researcher is interested
Population
Selection of a portion of the population (a sample) to represent the entire population
Sampling
Subpopulations of a population (e.g., male/female)
Strata
The entire population of interest
Target Population
The portion of the target population that is accessible to the researcher, from which a sample is drawn.
Accessible population
A sample whose key characteristics closely approximate those of the target population—a sampling goal in quantitative research.
Representative sample
HOw is representative sampling achieved?
Probability sampling
Homogeneous populations
Larger samples
The systematic over- or under-representation of segments of the population on key variables when the sample is not representative.
Sampling bias
Differences between sample values and population values.
Sampling error
Involves random selection of elements: each element has an equal, independent chance of being selected
Probability sampling
Does not involve selection of elements at random
Non-probability sample
Examples of non-probability sampling
Convenience sampling
Snowball (network) sampling
Quota sampling
Purposive sampling
Use of the most conveniently available people
Most widely used approach by quantitative researchers
Most vulnerable to sampling biases
Convenience Sampling
Referrals from other people already in a sample
Used to identify people with distinctive characteristics
Used by both quantitative and qualitative researchers; more common in qualitative.
Snowball Sampling
Convenience sampling within specified strata of the population.
Enhances representativeness of sample.
Infrequently used, despite being a fairly easy method of enhancing representativeness.
Quota 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
Consecutive 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.
Purposive
Types of Probability Sampling
Simple random sampling
Stratified random sampling
Cluster (multistage) sampling
Systematic 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
Simple 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
Stratified Random 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
Cluster Sampling
Sample Size Qualifications
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 for studies seeking causal inference.
Examples of Records, Documents, and Available Data
Hospital records (e.g., nurses’ shift reports)
School records (e.g., student absenteeism)
Corporate records (e.g., health insurance choices)
Letters, diaries, minutes of meetings, etc.
Photographs
Major Types of Data Collection Methods
Self-report
Observation
Biophysiologic measures
