Data Collection Flashcards

1
Q

Random

A

each member has equal chance of selection

therefore sample should be representative of population

not bias

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2
Q

simple random sample

A

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

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3
Q

population

A

complete set of items of interest

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4
Q

Census

A

measures every member of population

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5
Q

Census pro’s

A

everyone represented

accurate

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6
Q

Census con’s

A

costly

time consuming

lots of data

can’t use if destroying objects

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7
Q

sample

A

subset of population measured used to make generalisations about population

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8
Q

sample pro’s

A

quicker

cheaper

less data

used if destroyed

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9
Q

sample con’s

A

you need a representation thats fair

bigger better than smaller sample

not greatly accurate

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10
Q

sampling units

A

individual members of a population

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11
Q

sampling frame

A

list of sampling units

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12
Q

Systematic sampling

A

elements chosen at regular intervals from list

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

first person chosen at random

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13
Q

stratified sampling

A

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

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14
Q

simple random sampling pro’s

A

no bias

easy and cheap

each sampling unit has easy and known chance of selection

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15
Q

simple random sampling con’s

A

not suitable when sample/population is large

sampling frame needed

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16
Q

systematic sampling pro’s

A

simple and quick

suitable for large samples and populations

17
Q

systematic sampling con’s

A

sampling frame needed

can be bias if sampling frame isn’t random

18
Q

stratified sampling pro’s

A

accurately reflects population structure

proportional representation of groups within population

19
Q

stratified sampling con’s

A

population must be clearly divided into distinct strata

selection within stratum suffered similar disadvantages as simple random sampling

20
Q

quota sampling (non-random)

A

interviewer/researcher selects sample that reflects characteristics of whole population

21
Q

quota sampling steps

A

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.

22
Q

Opportunity/convenience sampling

A

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

23
Q

quota sampling pro’s

A

allows small sample to reflect population

no sampling frame

quick, easy and cheap

allows comparison between groups in a population

24
Q

quota sampling con’s

A

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

25
Q

opportunity sampling pro’s

A

easy to do

cheap

26
Q

opportunity sampling con’s

A

unlikely to provide a representative sample

dependent on individual researched

27
Q

quantitive variables/data

A

deal with numbers

e.g. shoe size

28
Q

qualitative variables/data

A

deal non-numerically

e.g. hair colour

29
Q

continuous variable

A

can take any value in given range

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

30
Q

discrete variable

A

only takes specific values in given range

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