Data Collection Flashcards
Random
each member has equal chance of selection
therefore sample should be representative of population
not bias
simple random sample
each sample has equal chance of selection
need sampling frame
each member allocated number then number chosen at random
e. g. random number generator
e. g. lottery sampling - names drawn out of hat
population
complete set of items of interest
Census
measures every member of population
Census pro’s
everyone represented
accurate
Census con’s
costly
time consuming
lots of data
can’t use if destroying objects
sample
subset of population measured used to make generalisations about population
sample pro’s
quicker
cheaper
less data
used if destroyed
sample con’s
you need a representation thats fair
bigger better than smaller sample
not greatly accurate
sampling units
individual members of a population
sampling frame
list of sampling units
Systematic sampling
elements chosen at regular intervals from list
e.g. size 20 from 100 (every 5th interval)
first person chosen at random
stratified sampling
population divided into mutually exclusive strata (e.g. males and females)
random sampling taken from each
No. in sampled in stratum = (no. in stratum/no. in population) x 100
simple random sampling pro’s
no bias
easy and cheap
each sampling unit has easy and known chance of selection
simple random sampling con’s
not suitable when sample/population is large
sampling frame needed