Sampling Techniques

sampling

act of studying or examining only a segment of the population to represent a whole

true

T/F: sampling lets you focus on more complex data

target reference population

type of population

-overall group of people for generalization of findings

-universe of interest

accessible population

type of population

-portion of target population that has a chance of being selected or included based on locale

sampling population

type of population

-population from which sample will be actually taken

sampling frame

source/list used to select participants

-most important determining factor in the selection of a sampling design to used for a given study

sampling bias

form or error you get from sampling

true

T/F: rare condition requires more sample size

false

T/F: same variability requires more sample size

true

T/F: higher sample size leads to more accurate data

systematic sampling

type of probability sampling

-has sampling interval

-list arranged alphabetically in some order

stratified sampling

type of probability sampling

-forming groups or stratas then kukuha ng sample

cluster sampling

type of probability sampling

-dividing the population into groups and then selecting groups

convenience sampling

type of non-probability sampling

-mc

-subjects chosen based on availability

-one form is consecutive sampling

quota sampling

type of non-probability sampling

-each stratum is represented in the same proportion in the population

-discontinuation of subject allocation once quota is reached

purposive sampling

type of non-probability sampling

-handpicking the subjects based on a criteria to meet the needs

snowball sampling

type of non-probability sampling

-used when sampling frame is not available

-chain-referral

-for sensitive topics