Data Collection

Question 1

Random

Answer 1

each member has equal chance of selection

therefore sample should be representative of population

not bias

Question 2

simple random sample

Answer 2

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

Question 3

population

Answer 3

complete set of items of interest

Question 4

Census

Answer 4

measures every member of population

Question 5

Census pro’s

Answer 5

everyone represented

accurate

Question 6

Census con’s

Answer 6

costly

time consuming

lots of data

can’t use if destroying objects

Question 7

sample

Answer 7

subset of population measured used to make generalisations about population

Question 8

sample pro’s

Answer 8

quicker

cheaper

less data

used if destroyed

Question 9

sample con’s

Answer 9

you need a representation thats fair

bigger better than smaller sample

not greatly accurate

Question 10

sampling units

Answer 10

individual members of a population

Question 11

sampling frame

Answer 11

list of sampling units

Question 12

Systematic sampling

Answer 12

elements chosen at regular intervals from list

e.g. size 20 from 100 (every 5th interval)

first person chosen at random

Question 13

stratified sampling

Answer 13

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

Question 14

simple random sampling pro’s

Answer 14

no bias

easy and cheap

each sampling unit has easy and known chance of selection

Question 15

simple random sampling con’s

Answer 15

not suitable when sample/population is large

sampling frame needed

Question 16

systematic sampling pro’s

Answer 16

simple and quick

suitable for large samples and populations

Question 17

systematic sampling con’s

Answer 17

sampling frame needed

can be bias if sampling frame isn’t random

Question 18

stratified sampling pro’s

Answer 18

accurately reflects population structure

proportional representation of groups within population

Question 19

stratified sampling con’s

Answer 19

population must be clearly divided into distinct strata

selection within stratum suffered similar disadvantages as simple random sampling

Question 20

quota sampling (non-random)

Answer 20

interviewer/researcher selects sample that reflects characteristics of whole population

Question 21

quota sampling steps

Answer 21

population divided into groups according to characteristic

size of group determines proportion of sample that should have that characteristic

interviewer assesses then allocates people into appropriate quota

if person refuses or group is full they are ignored.

Question 22

Opportunity/convenience sampling

Answer 22

take sample from those available and fit the criteria pop what you’re looking for

e.g. !st 20 people outside a radio shop with records

Question 23

quota sampling pro’s

Answer 23

allows small sample to reflect population

no sampling frame

quick, easy and cheap

allows comparison between groups in a population

Question 24

quota sampling con’s

Answer 24

non-random bit might introduce bias

costly and maybe inaccurate dividing people into groups

increasing size, increases groups which adds to time taken and expense

non-responses aren’t recorded

Question 25

opportunity sampling pro’s

Answer 25

easy to do

cheap

Question 26

opportunity sampling con’s

Answer 26

unlikely to provide a representative sample

dependent on individual researched

Question 27

quantitive variables/data

Answer 27

deal with numbers

e.g. shoe size

Question 28

qualitative variables/data

Answer 28

deal non-numerically

e.g. hair colour

Question 29

continuous variable

Answer 29

can take any value in given range

e.g. time : 2, 2.02, 2.001 secs etc.

Question 30

discrete variable

Answer 30

only takes specific values in given range

e.g. girls in my bedroom ( not 2.5 but 10)