chapter 5 and 6 Flashcards
define population in a research design
large group of interest to a researcher
sample
small set of individuals from a population who participate in a study.
Intended to represent population
target population
group defined by the researcher’s specific interests. Individuals usually share one characteristic
accessible population
portion of the target population consisting of individuals who are accessible to be recruited as participants in the study
representativeness of a sample
extent to which the characteristics of the sample accurately reflect those of the pop
representative sample
sample with the same characteristics as the pop
biased sample
sample with diff characteristics from those of the pop
selection bias or sampling bias
occurs when participants or subjects are selected in a manner that increases the probability of obtaining a biased sample
2 basic categories of sampling methods:
probability and non-probability
probability sampling
the odds of selecting a particular individual are known and can be calculated
3 conditions for probability sampling:
- exact size of pop is known and possible to list all individuals
- each individual in the pop must have a specified probability of selection
- when a group of individuals are assigned the same probability, the selection process must be unbiased. Must be a RANDOM PROCESS
nonprobability sampling
odds of selecting a particular individual are not known. Researcher does not know population size and cannot list the members of a pop
which runs a higher risk of producing a biased sample, probability or nonprobability sampling?
nonprobability
Simple random sampling
each individual in a pop has an equal chance of being selected.
Each selection is independent of the others.
2 principa methods of random sampling
- sampling with replacement
2. sampling without replacement
problem with simple random sampling
there is a chance of selecting a very distorted sample
Systematic sampling
Prob sampling.
selecting every nth person. The “nth” is calculated by dividing population size by the desired sample size.
principle of independence is violated, but ensures high degree of representativeness
Statified random sampling
Prob sampling
select equal-sized random samples from several SUBGROUPS in a population and combine them into one overall sample.
This ensures that individuals from each subgroup will be represented in the study
Cluster sampling
prob sampling.
randomly selecting pre-existing naturally formed groups.
e.g., researcher wants to obtain a large sample of third-grade students from the city school system. Instead of selecting 300 students one at a time, the researcher can randomly select 10 classrooms of 30 students.
advantage of cluster sampling
- relatively quick and easy
- measurement of individuals can be done in groups, which can facilitate research project.
proportionate stratified sampling
like stratified sampling, but instead of selecting an equal sized sample from each subgroup, you select the proportions seen in the pop. e.g., if a class has 75 females and 25 males, you select more females than males in the two subgroups