Chapter 6 - Populations, samples, and sample size. After reading this chapter, you should be able to: Describe the distinctions among population, sampling frame, and sample; identify the population and sampling frame to select an appropriate sample; argue for how results from a sample are generalizable to its population. | Populations, samples, and sample size Chapter 6 In creating a sample -- Researchers make choices: Who to collect data from? What to collect data about? How much data to collect? Population → sample Population all units (people or things) possessing the attributes and characteristics of interest Sampling frame subset of units that have a chance to become part of the sample Sample subset of a population Researchers study the sample to make generalizations back to the population Identifying the population Choose the dimensions or characteristics meaningful to the hypothesis or research question Must be at least one common characteristic among all members of a population Must develop procedure to ensure representative sampling Addressing generalizability Extent to which conclusions developed from data collected from sample can be extended to its population Sample is representative to the degree that all units had same chance for being selected Representative sampling eliminates selection | Populations, samples, and sample size Chapter 6 In creating a sample -- Researchers make choices: Who to collect data from? What to collect data about? How much data to collect? Population → sample Population all units (people or things) possessing the attributes and characteristics of interest Sampling frame subset of units that have a chance to become part of the sample Sample subset of a population Researchers study the sample to make generalizations back to the population Identifying the population Choose the dimensions or characteristics meaningful to the hypothesis or research question Must be at least one common characteristic among all members of a population Must develop procedure to ensure representative sampling Addressing generalizability Extent to which conclusions developed from data collected from sample can be extended to its population Sample is representative to the degree that all units had same chance for being selected Representative sampling eliminates selection bias Representativeness can only be assured through random sampling Probability sampling The probability of any unit being included in the sample is known and equal When probability for selection is equal, selection is random Also known as random sampling Sampling error will always occur Types of probability sampling Simple random sampling Simplest and quickest Systematic sampling If used on a randomly ordered frame, results in truly random sample Stratified random sampling Random sampling within all subgroups Cluster sampling Random sampling within known clusters Nonprobability sampling Does not rely on random selection Weakens sample-to-population representativeness Used when other techniques will not result in an adequate or appropriate sample Used when researchers desire participants with special experiences or abilities Nonprobability sampling techniques Convenience sample Volunteer sample Inclusion/exclusion sample Snowball or network sample Purposive sample Quota sample Sample
