1 Collecting Data

Question 1

raw

Answer 1

data before it is sorted
eg data from a survey

Question 2

quantitative

Answer 2

numerical
eg height

Question 3

qualitative

Answer 3

non numerical
eg colour

Question 4

continuous

Answer 4

can take any value on a scale
eg weight, length

Question 5

discrete

Answer 5

can only take particular values on a scale
eg shoe size, no. siblings

Question 6

categorical

Answer 6

can be sorted into non overlapping/ranked categories
eg gender

Question 7

ordinal

Answer 7

can be written in order / be given a numerical ranking scale
eg test scores

Question 8

bivariate

Answer 8

involves pairs of related date
eg working hours and pay

Question 9

multivariate

Answer 9

3+ sets of data
eg plants: colour, leaf size and height

Question 10

do intervals (usually) need to be equal widths

Answer 10

no

Question 11

primary

Answer 11

collected by/for the user

Question 12

secondary

Answer 12

collected by/for someone other than the current user
eg websites, newspapers, research articles, databases, census returns

Question 13

primary vs secondary (adv and disadv)

Answer 13

Question 14

census

Answer 14

survey/investagation with data from EVERY MEMBER of a population

Question 15

sampling units

Answer 15

people or items that are to be sampled

Question 16

sampling frame

Answer 16

a list of all the sampling units

Question 17

eg of population, SU and SF
(number of hours spent on hw is more in Y7 and Y9

Answer 17

P: all Y7 and Y9 students
SU: students in Y7&9
SF: list of Y7&9 students

Question 18

petersen capture recapture formula

Answer 18

m/n = M/N
no. marked in recapture/number in recapture = original number marked/total population

Question 19

assumptions made in capture recapture method

Answer 19

• P(caught) is same for all individuals
• marks are not lost and always recognisable
• sample size is large enough to be representative of population
• population has not changed (no members have entered or left, no births or deaths between release and recapture)
• marked individuals have mixed with rest of population between release and recapture

Question 20

random sample

Answer 20

every sampling unit (member of population) has an equal chance of being included

Question 21

pros and cons of random sample

Answer 21

P: more likely to be representative of population if sample size is large
- choice of members of sample is unbiased

D: need a full list of population
- need a large sample size

Question 22

problems of random sample

Answer 22

random numbers may be out of range
random numbers may be repeated

Question 23

how to generate random sample

Answer 23

1) RNG (eg calc) / names from a hat / random number table
2) ignore numbers out of range and duplicates
3) do this X times

Question 24

judgement sampling

Answer 24

using your judgement to choose a sample which is representative of the population

Question 25

opportunity sampling

Answer 25

using the people or items available at the time

Question 26

cluster sampling

Answer 26

use natural groups which occur in data list of clusters = sampling frame and some clusters are randomly selected to make up the sample (eg geographical areas)

Question 27

systematic sampling

Answer 27

choose a start point in the sampling frame at random and choose items at regular intervals

Question 28

quota sampling

Answer 28

group the population by chosen characteristics and take a quota from each group (eg age/gender)

Question 29

how to decide if a sampling method is suitable

Answer 29

- will it be biased - will SS be sensible - how quick and easy is method - how expensive

Question 30

stratified sample

Answer 30

contains members of each stratum in proportion to the size of that stratum. sample from each stratum is selected randomly

Question 31

describe how to do a stratified sample

Answer 31

1) calculations THEN -order each group into order - assign each a random no. - choose the relevant no. of people to survey

Question 32

data collection sheet

Answer 32

table/tally chart for recording your results

Question 33

direct observation

Answer 33

recording behaviour patterns systematically as you observe them

Question 34

independent variable

Answer 34

explanatory variable what you control (but change)

Question 35

dependent variable

Answer 35

response variable affected based on your changes to the explanatory variable

Question 36

extraneous variable

Answer 36

variable that you are not interested in but that could affect your results

Question 37

laboratory experiment

Answer 37

Question 38

field experiment

Answer 38

Question 39

natural experiment

Answer 39

Question 40

LabE pros and cons

Answer 40

Question 41

FieldE pros and cons

Answer 41

Question 42

NatE pros and cons

Answer 42

Question 43

simulation

Answer 43

can be used to model random real life evens to predict what could actually happen. easier and cheaper than collecting and analysing real data

Question 44

an experiment is valid/reliable if…

Answer 44

when replicating an experiment gives very similar data

Question 45

questionnaire

Answer 45

set of questions designed to obtain data

Question 46

open vs closed question

Answer 46

open: no suggested answers closed: answers to choose from

Question 47

con of open questions

Answer 47

every respondent gives a different answer so is hard to summarise and analyse the answers

Question 48

problem with opinion scales

Answer 48

most will answer somewhere near the middle unlikely to indicate a strong opinion - do not want to seem extreme

Question 49

what to do in questionnaires

Answer 49

- short questions, simple language - no biased/leading questions - intervals that don’t overlap - options cover all possibilities (0/never/don’t know) - include time frame - avoid questions respondents are unlikely to answer honestly

Question 50

interview pros and cons

Answer 50

Question 51

anonymous questionnaire pros and cons

Answer 51

Question 52

pilot survey

Answer 52

conducted on a small sample to test the design and methods of the survey checks: - respondents understand questions - closed questions include all likely answer options - questionnaire collects the information needed

Question 53

random response method

Answer 53

Question 54

how to answer estimate question about RRM

Answer 54

Question 55

outliers/anomalous data

Answer 55

values that do not fit the pattern of the data can be ignored if it is due to a measuring/recording error

Question 56

cleaning data

Answer 56

- identifying and correcting/removing inaccurate/extreme values - removing units or other symbols form data - deciding what do to with missing values

Question 57

control groups? and where are they often used

Answer 57

Question 58

matched pair test

Answer 58

where two group of people are used to test theffects of a particular factor each individual in a group is paired with an individual in the second group with similar characteristics barring the factors which is to be studied

Question 59

pros and cons of matched pairs

Answer 59

P: can control for different factors C: may have to test a large group at first to find enough matched pairs for a good test

Question 60

who is often used in MPTs

Answer 60

identical twins- easier to see different results disadvantage: limited supply of willing twins

Question 61

hypothesis

Answer 61

an idea that can be tested by collecting and analysing data

Question 62

designing investigations- what do you need to consider?

Answer 62

Question 63

difference between field and natural experiments

Answer 63

a field experiment the researcher manipulates the independent variable (IV), while in a natural experiment the researcher does not