3.3.2 Section B: Fieldwork

Question 1

Stages of geographical enquiry:

Answer 1

Stage 1 - Introduction and Planning
Stage 2 - Data Collection
Stage 3 - Data Presentation
Stage 4 - Data Analysis
Stage 5 - Reaching Conclusions
Stage 6 - Evaluation of Geographical Enquiry

Question 2

Hypothesis:

Answer 2

An idea to be tested, which can either be proved or rejected

Question 3

Primary data:

Answer 3

data you collected yourself for a specific purpose

Question 4

Secondary data:

Answer 4

data someone else collected for a different purpose

Question 5

Stratified sampling:

Answer 5

collecting data from different groups of a population to ensure fair representation, or deliberately introducing bias

Question 6

Advantages of stratified sampling:

Answer 6

• ensures representation of different populations
• can be flexible - ensures representation of different populations

Question 7

Disadvantages of stratified sampling:

Answer 7

hard to establish the proportions of sub-populations

Question 8

Systematic sampling:

Answer 8

collecting data at specific intervals

Question 9

Advantages of systematic sampling:

Answer 9

• straightforward
• ensures good coverage

Question 10

Disadvantages of systematic sampling:

Answer 10

may be time-consuming

Question 11

Random sampling:

Answer 11

collecting data at random

Question 12

Advantages of random sampling:

Answer 12

• useful with large samples
• avoids bias

Question 13

Disadvantages of random sampling:

Answer 13

avoids bias

Question 14

Quantitative data:

Answer 14

numerical data

Question 15

Qualitative data:

Answer 15

non-numerical, opinion based data

Question 16

What do some qualitative research methods use?

Answer 16

Some qualitative research methods sometimes use number scales so that responses can be put into rank order e.g. EQS

Question 17

Tally chart:

Answer 17

• Record your results is faster than writing out words or figures all the time
• If you record your findings in this, the data is already collected

Question 18

Divided bar chart:

Answer 18

• Useful way to present a whole set of data, which can be divided up into different parts
• More effective than a pie charts if you have a large number of sectors

Question 19

Pie chart:

Answer 19

Best to use when you are trying to compare parts of a whole

Question 20

Dispersion graph:

Answer 20

• A dispersion graph shows the range of a set of data
• It shows whether the data tends to group or disperse
• It can also be used to compare sets of data
• The values are plotted on the vertical axis
• There is also a short horizontal axis which can show the frequency (number of times) the variable occurs
• Measures of spread are easily calculates from this data presentation type

Question 21

Uses of dispersion graphs:

Answer 21

• Can see spread of data
• Easy to interpret
• Used with wide range of data

Question 22

How do you interpret dispersion graphs?

Answer 22

• can make comparisons between dispersion graphs
• show variety of statistical info about the data e.g. range, median, UQ & LG, IQR

Question 23

Radar graph:

Answer 23

• Are a way of comparing multiple quantitative variables
• This makes them useful for seeing which variables have similar values or if there are any outliers amongst each variable
• These charts are also useful for seeing which variables are scoring high or low within a dataset

Question 24

Proportional symbols:

Answer 24

• Maps scale the size of simple symbols (usually a circle or square) proportionally to the data value found at that location
• They are a simple concept to grasp: The larger the symbol, the “more” of something exists at a location

Question 25

Line graph:

Answer 25

- Used to determine relationships between the two different things - The x-axis is used to measure one event (or variable) and the y-axis is used to measure the other. Used to present continuous data

Question 26

Use of maps:

Answer 26

- used to show locations and maps - mini-graphs and charts can be located on maps - this makes it easier to compare patterns at specific locations - consider using isolines or chloropleth maps

Question 27

Use of GIS and photos:

Answer 27

- used to show historic maps o sites which have been lost to erosion - useful for aerial shots of rivers to show land use - helps to show how place have changed after being affected by storms

Question 28

Use of tables:

Answer 28

- can be used to present raw data that you and your group collected - useful o highlight patterns and trends - can be highlighted and annotated, and can help to identify anomalies (any data which looks unusual)

Question 29

Use of graphs and charts:

Answer 29

- wide range of graphs and charts available - can show data patterns clearly - easier to read than a table of data

Question 30

Why do we need to present data?

Answer 30

To be able to visually see patterns and compare results in order to easily and quickly interpret the data and make conclusions

Question 31

How do you ensure your presentation techniques are appropriate?

Answer 31

- Show your data clearly according to the type of data that it is e.g. Continuous and discrete data - Are sample sizes different? - If yes, what could you do to make them comparable - Showing information spatially - Locate on a map or aerial photo

Question 32

How do you ensure your presentation techniques are appropriate for graphs?

Answer 32

Scale, neatly drawn and have title and axis labelled

Question 33

How do you ensure your presentation techniques are appropriate for maps?

Answer 33

Scales, north arrow, keys, titles

Question 34

How do you ensure your presentation techniques are appropriate for stats?

Answer 34

correct and working shown

Question 35

Central tendency:

Answer 35

- a description of the ‘average’ within a dataset - there are three ways of measuring central tendency: mean, median and mode

Question 36

Measures of spread:

Answer 36

- Measures of Central Tendency are useful; in identifying ‘average’ values - However, they give no indication of how the values in a data set are spread around this average - Data sets may have the same mean but very different highest and lowest values

Question 37

IQR:

Answer 37

- The interquartile range is the difference between the lower quartile and the upper quartile - The interquartile range is another measure of spread, except that it has the added advantage of not being affected by large outlying values

Question 38

UQ:

Answer 38

- the upper quartile is the median of the upper half of the data - the 3(n+1)4 value

Question 39

LQ:

Answer 39

- the lower quartile is the median of the lower half of the data - the (n+1)4 value

Question 40

How do you analyse data?

Answer 40

- identify patterns and trends in data and describe them - make links between different sets of data e.g. how sediment size and roundness seem to change at the same time - identify anomalies - unusual data which do not fit the general pattern of results - explain reasons for patterns you are sure about - e.g. data that might show a process operating along a river, such as deposition - suggest possible reasons for patterns you are unsure about - e.g. why results suddenly change in a way that you can’t explain

Question 41

Accuracy:

Answer 41

- How close to the true value? - (Is it correct to the nearest mm?)

Question 42

Reliability:

Answer 42

- The extent to which your investigation produced consistent results - (Are they repeatable?)

Question 43

Validity:

Answer 43

How suitable was your method for answering the question it was intended to?

Question 44

How do you draw evidenced conclusions?

Answer 44

- Return to the stated hypotheses - Write a statement about what evidence supports how strongly the hypothesis is found to be true or false - Note which element of geographical theory is linked to the fieldwork - Any unusual results should be acknowledged and explained

Question 45

What does an evaluation involve?

Answer 45

- Identifying problems with data collection method & data collected - Suggesting other data that might be useful - Evaluating conclusions - Suggesting how to extend the scope of the study - The final evaluation should explain any problems encountered when collecting data - Was the right equipment used? - Is there other equipment available that might have made data collection more efficient or accurate? - Should more data have been collected / more sites visited? - Were the right sites visited? - Are there any other measurements that might have been useful?

Question 46

How do you evaluate conclusions?

Answer 46

- Were the conclusions a fitting reflection to the aims and hypotheses stated in the coursework? - Did the study help to answer questions on this? - Was this a good title/ aim in the first place? - Were the hypotheses specific enough to be able to be assessed easily? - Was the location for the study appropriate? - If you were to repeat this study again – how could you have improved the accuracy of the results?

Question 47

Sources of error:

Answer 47

- sample size - frequency of sample - type of sampling - equipment used - time of survey - location of survey - quality of secondary data

Question 48

How is sample size a source of error?

Answer 48

smaller sample sizes usually means lower quality data

Question 49

How is the frequency of a sample a source of error?

Answer 49

fewer sites reduces frequency, which then reduces quality

Question 50

How can the type of sampling be a source of error?

Answer 50

sampling approaches may creat ‘gaps’ and introduce bias in the results

Question 51

How can the equipment used be a source of error?

Answer 51

the wrong/innacurate equipment can affect overall quality by producing incorrect results

Question 52

How is can the time of the survey be a source of error?

Answer 52

different days or times of day might influence perceptions and pedestrian flows

Question 53

How can the location of the survey be a source of error?

Answer 53

big variations in environmental quality can occur between places very close to each other

Question 54

How can the quality of the secondary data be a source of error?

Answer 54

age and reliability of secondary data affect their over quality