Eduqas A-Level Geography Fieldwork and the Independent Investigation (Component 4, the NEA) overview quiz quiz

A single fieldwork data-collection method, such as taking repeated measurements along one transect line in the study area

Only the written-up conclusion that is reached at the very end of the entire investigation

The structured sequence from question and hypotheses to data, presentation, analysis, conclusions and evaluation

A particular statistical significance test applied to a finished correlation coefficient

1,000 words, 5% of the A level

10,000 words, 50% of the A level

There is no word limit and it is not assessed

3,000 to 4,000 words, 20% of the A level

A minimum of four days across the course, covering both physical and human geography

No fieldwork at all is required, because the investigation can rely on secondary data alone

A single day of fieldwork carried out at any point during the two-year A-level course

A full twenty days of fieldwork, spread evenly across both years of the course

It is as broad and sweeping as possible, so that it can cover many different topics at once

It is focused, geographical, answerable and linked to specification theory

It is worded so that it requires no primary data collection of any kind in the field

It deliberately avoids setting out any hypotheses to be tested by the investigation

Choosing sampling points purely at random across the whole area using random numbers

Dividing the population into subgroups first and then sampling each one in proportion to its size

Taking points at a fixed interval (every nth point), giving even coverage and suiting transects

Sampling only the locations that happen to be easiest and most convenient to reach

They remove the need to analyse or apply any statistical tests to the collected data afterwards

They guarantee in advance that the chosen hypothesis will turn out to be correct

They make any further primary fieldwork in the study area completely unnecessary

They reduce bias and make data representative and reliable, supporting defensible conclusions

r_s = 1 - (6 sum of d squared) / (n(n squared minus 1))

r_s = 1 + (6 sum of d squared) / (n(n squared minus 1)) for ranked field data

r_s = (6 sum of d squared) divided by (n(n squared minus 1)) without the leading one

r_s = (sum of the rank differences d) divided by the number of paired sites n

To remove the need to write any conclusion at all, because the test result settles the whole enquiry by itself

To judge whether the relationship is likely real rather than due to chance, by comparing it to critical values

To artificially increase the size of the coefficient so the correlation looks much stronger

Because a correlation coefficient on its own is always wrong and cannot be trusted at all

A scatter graph

A storm hydrograph

A choropleth map

A kite diagram

Analysis simply restates the raw data values that are already shown on the graph in words

Description is the part of the write-up that requires statistics, whereas analysis needs no statistics or supporting theory at all to earn marks

There is no real difference, since description and analysis are essentially the same thing

Analysis interprets patterns, trends and anomalies with reference to theory and statistics, rather than just describing the graph

In the critical evaluation of reliability, validity and limitations

In simply describing the data-collection method in as much detail as possible

In choosing the broadest possible question so that it covers many topics at once

In deliberately avoiding any statistical analysis of the collected fieldwork data

Only the strengths of the investigation, leaving out any weaknesses or sources of error

Reliability, validity, sample size and bias, measurement error, conditions on the day, and possible improvements

Only the visual quality of the graphs and maps used to present the collected results

Nothing much at all, provided that the final results happen to turn out and support the original stated hypothesis