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AQA GCSE Geography > 3.3.2 Section B: Fieldwork > Flashcards

3.3.2 Section B: Fieldwork Flashcards

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1
Q

Stages of geographical enquiry:

A

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

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2
Q

Hypothesis:

A

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

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3
Q

Primary data:

A

data you collected yourself for a specific purpose

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4
Q

Secondary data:

A

data someone else collected for a different purpose

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5
Q

Stratified sampling:

A

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

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6
Q

Advantages of stratified sampling:

A
  • ensures representation of different populations
  • can be flexible - ensures representation of different populations
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7
Q

Disadvantages of stratified sampling:

A

hard to establish the proportions of sub-populations

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8
Q

Systematic sampling:

A

collecting data at specific intervals

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9
Q

Advantages of systematic sampling:

A
  • straightforward
  • ensures good coverage
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10
Q

Disadvantages of systematic sampling:

A

may be time-consuming

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11
Q

Random sampling:

A

collecting data at random

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12
Q

Advantages of random sampling:

A
  • useful with large samples
  • avoids bias
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13
Q

Disadvantages of random sampling:

A

avoids bias

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14
Q

Quantitative data:

A

numerical data

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15
Q

Qualitative data:

A

non-numerical, opinion based data

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16
Q

What do some qualitative research methods use?

A

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

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17
Q

Tally chart:

A
  • 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
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18
Q

Divided bar chart:

A
  • 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
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19
Q

Pie chart:

A

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

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20
Q

Dispersion graph:

A
  • 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
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21
Q

Uses of dispersion graphs:

A
  • Can see spread of data
  • Easy to interpret
  • Used with wide range of data
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22
Q

How do you interpret dispersion graphs?

A
  • can make comparisons between dispersion graphs
  • show variety of statistical info about the data e.g. range, median, UQ & LG, IQR
23
Q

Radar graph:

A
  • 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
24
Q

Proportional symbols:

A
  • 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
25
Q

Line graph:

A
  • 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
26
Q

Use of maps:

A
  • 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
27
Q

Use of GIS and photos:

A
  • 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
28
Q

Use of tables:

A
  • 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)
29
Q

Use of graphs and charts:

A
  • wide range of graphs and charts available
  • can show data patterns clearly - easier to read than a table of data
30
Q

Why do we need to present data?

A

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

31
Q

How do you ensure your presentation techniques are appropriate?

A
  • 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
32
Q

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

A

Scale, neatly drawn and have title and axis labelled

33
Q

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

A

Scales, north arrow, keys, titles

34
Q

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

A

correct and working shown

35
Q

Central tendency:

A
  • a description of the ‘average’ within a dataset
  • there are three ways of measuring central tendency: mean, median and mode
36
Q

Measures of spread:

A
  • 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
37
Q

IQR:

A
  • 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
38
Q

UQ:

A
  • the upper quartile is the median of the upper half of the data
  • the3(n+1)4value
39
Q

LQ:

A
  • the lower quartile is the median of the lower half of the data
  • the(n+1)4value
40
Q

How do you analyse data?

A
  • 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
41
Q

Accuracy:

A
  • How close to the true value?
  • (Is it correct to the nearest mm?)
42
Q

Reliability:

A
  • The extent to which your investigation produced consistent results
  • (Are they repeatable?)
43
Q

Validity:

A

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

44
Q

How do you draw evidenced conclusions?

A
  • 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
45
Q

What does an evaluation involve?

A
  • 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?
46
Q

How do you evaluate conclusions?

A
  • 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?
47
Q

Sources of error:

A
  • sample size
  • frequency of sample
  • type of sampling
  • equipment used
  • time of survey
  • location of survey
  • quality of secondary data
48
Q

How is sample size a source of error?

A

smaller sample sizes usually means lower quality data

49
Q

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

A

fewer sites reduces frequency, which then reduces quality

50
Q

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

A

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

51
Q

How can the equipment used be a source of error?

A

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

52
Q

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

A

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

53
Q

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

A

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

54
Q

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

A

age and reliability of secondary data affect their over quality

AQA GCSE Geography (45 decks)

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