Sampling Concepts and Designs

Sampling

• Sampling: process of selecting a portion of a population to represent the whole
• Population: the complete group of individuals that share specific characteristics
• Sample: a subset of the population that is selected for study
• Target population: the entire population that the researcher is interested in
• Accessible population: the portion of the target population directly available to the researcher
• Eligibility criteria: characteristics used to define who is included in the study population
• Representative sample: sample closely mirroring the characteristics of the population
• Sampling bias: systematic over- or under-representation of a population segment
• Strata: mutually exclusive groups within a population based on a characteristic
• Elements: individual units within the sample and population

Sampling Designs

• Non-probability sampling: non-random methods
  • Convenience sampling: selecting the most readily available individuals
  • Quota sampling: dividing the population into strata and then taking a convenience sample from each stratum
  • Consecutive sampling: recruiting all eligible participants over a specific period or until a sample size is reached
  • Purposive sampling: researchers carefully select participants based on specific criteria
• Probability sampling: random selection methods
  • Simple random sampling: the most basic method, where each element has an equal chance of being selected
  • Stratified random sampling: the population is divided into strata, and then random samples are selected from each stratum
  • Systematic sampling: selecting every kth element from an ordered list

Sample Size

• Sample size: the number of participants included in a study
• Larger sample size: improves representativeness and reduces sampling error
• Power analysis: a statistical method used to determine the optimal sample size for a study

Critique of Sampling Plans

• Response rate: the proportion of people who participate in a study compared to the number sampled
• Nonresponse (response) bias: differences between those who participate and those who decline participation

Data Collection in Quantitative Research

• Data collection: the process of gathering measurable data
• Types of data:
  • Self-report: participants provide information about themselves
  • Observational: observing and recording behaviors or events
  • Biophysiologic measures: obtaining data directly from the body (in vivo) or through laboratory analysis (in vitro)

Self Reports

• Question forms:
  • Closed-ended: participants select from predefined response options
  • Open-ended: participants provide their own answers
• Instruments:
  • Interview schedule: questions asked face-to-face or by phone
  • Questionnaire: respondents complete the instrument themselves

Observational Method

• Methods of structured observation: category systems, checklists, rating scales
• Observational sampling: selecting specific times or intervals for observation

Biophysiologic Measures

• In vivo: measurements taken directly from a living organism
• In vitro: measurements taken from biomaterials extracted from a living organism and analyzed in a laboratory

Data Quality in Quantitative Research

• Measurement of variables: assigning numbers to represent the amount of a specific attribute present
• Obtained score: the actual data value for a participant
• True score: the ideal score that would be obtained with a flawless instrument
• Error: factors that distort measurement
• Factors that contribute to measurement error:
  • Situational contaminants: environmental factors that influence measurement
  • Transitory personal factors: temporary individual characteristics that affect measurement
  • Response-set biases: consistent patterns in responding that influence measurement
  • Administration variations: differences in how the instrument is administered

Key Criteria for Evaluating Quantitative Measures

• Reliability: consistency and accuracy of an instrument in measuring the target attribute

  • Test-retest reliability: stability of measurement over time
  • Inter-rater reliability: agreement between different observers
  • Internal consistency: the consistency of individual items within an instrument

• Validity: the extent to which an instrument measures what it is intended to measure

  • Face validity: judgment based on whether the instrument appears to measure the intended construct
  • Content validity: the extent to which the instrument includes a representative sample of items for the construct
  • Criterion validity: the relationship between the instrument and an external criterion
  • Construct validity: evidence that the instrument measures the theoretical construct it is designed to measure

This quiz covers essential concepts and designs related to sampling in research. You'll learn about population, sample, eligibility criteria, and various sampling methods, including non-probability sampling techniques. Test your understanding of how to properly select samples and avoid sampling bias.