3.3.2 Section B: Fieldwork Flashcards
Stages of geographical enquiry:
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
Hypothesis:
An idea to be tested, which can either be proved or rejected
Primary data:
data you collected yourself for a specific purpose
Secondary data:
data someone else collected for a different purpose
Stratified sampling:
collecting data from different groups of a population to ensure fair representation, or deliberately introducing bias
Advantages of stratified sampling:
- ensures representation of different populations
- can be flexible - ensures representation of different populations
Disadvantages of stratified sampling:
hard to establish the proportions of sub-populations
Systematic sampling:
collecting data at specific intervals
Advantages of systematic sampling:
- straightforward
- ensures good coverage
Disadvantages of systematic sampling:
may be time-consuming
Random sampling:
collecting data at random
Advantages of random sampling:
- useful with large samples
- avoids bias
Disadvantages of random sampling:
avoids bias
Quantitative data:
numerical data
Qualitative data:
non-numerical, opinion based data
What do some qualitative research methods use?
Some qualitative research methods sometimes use number scales so that responses can be put into rank order e.g. EQS
Tally chart:
- 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
Divided bar chart:
- 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
Pie chart:
Best to use when you are trying to compare parts of a whole
Dispersion graph:
- 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
Uses of dispersion graphs:
- Can see spread of data
- Easy to interpret
- Used with wide range of data
How do you interpret dispersion graphs?
- can make comparisons between dispersion graphs
- show variety of statistical info about the data e.g. range, median, UQ & LG, IQR
Radar graph:
- 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
Proportional symbols:
- 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
Line graph:
- 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
Use of maps:
- 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
Use of GIS and photos:
- 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
Use of tables:
- 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)
Use of graphs and charts:
- wide range of graphs and charts available
- can show data patterns clearly - easier to read than a table of data
Why do we need to present data?
To be able to visually see patterns and compare results in order to easily and quickly interpret the data and make conclusions
How do you ensure your presentation techniques are appropriate?
- 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
How do you ensure your presentation techniques are appropriate for graphs?
Scale, neatly drawn and have title and axis labelled
How do you ensure your presentation techniques are appropriate for maps?
Scales, north arrow, keys, titles
How do you ensure your presentation techniques are appropriate for stats?
correct and working shown
Central tendency:
- a description of the ‘average’ within a dataset
- there are three ways of measuring central tendency: mean, median and mode
Measures of spread:
- 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
IQR:
- 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
UQ:
- the upper quartile is the median of the upper half of the data
- the3(n+1)4value
LQ:
- the lower quartile is the median of the lower half of the data
- the(n+1)4value
How do you analyse data?
- 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
Accuracy:
- How close to the true value?
- (Is it correct to the nearest mm?)
Reliability:
- The extent to which your investigation produced consistent results
- (Are they repeatable?)
Validity:
How suitable was your method for answering the question it was intended to?
How do you draw evidenced conclusions?
- 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
What does an evaluation involve?
- 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?
How do you evaluate conclusions?
- 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?
Sources of error:
- sample size
- frequency of sample
- type of sampling
- equipment used
- time of survey
- location of survey
- quality of secondary data
How is sample size a source of error?
smaller sample sizes usually means lower quality data
How is the frequency of a sample a source of error?
fewer sites reduces frequency, which then reduces quality
How can the type of sampling be a source of error?
sampling approaches may creat ‘gaps’ and introduce bias in the results
How can the equipment used be a source of error?
the wrong/innacurate equipment can affect overall quality by producing incorrect results
How is can the time of the survey be a source of error?
different days or times of day might influence perceptions and pedestrian flows
How can the location of the survey be a source of error?
big variations in environmental quality can occur between places very close to each other
How can the quality of the secondary data be a source of error?
age and reliability of secondary data affect their over quality
