OCR A-Level Geography Skills and Fieldwork overview quiz quiz

Shaded choropleth rate map

Proportional-symbol map

Isoline contour map

Flow-line movement map

It cannot show any spatial pattern at all and so is completely useless for any distribution data

It can only ever be used to show the movement of people or goods between places

It implies uniform values within each area and is sensitive to the class boundaries chosen

It always exaggerates the importance of physically small areas on the map

About 550 (the arithmetic mean)

Exactly 500

About 3160

About 316

When the data are skewed or contain outliers that distort the mean

When the data are perfectly symmetrical and free of any extreme outliers

Never, because the arithmetic mean is always the best measure to use

Only when working with unordered categorical or nominal data

The middle value of a data set once it has been ranked in order

The average distance of values from the mean (the spread)

The single most frequently occurring value in the data set

The running total of all the values added together

A strong positive relationship, where both ranked variables rise together

No relationship at all between the two ranked variables

A strong negative relationship (one variable rises as the other falls)

A calculation error, because a value of minus 0.9 is impossible

By simply checking whether the calculated coefficient comes out positive or negative in sign

By the number of decimal places the calculated coefficient is quoted to

It is always automatically significant as long as it is above zero

By comparing it with a critical value at a chosen confidence level for the sample size

Because a third factor may drive both variables, or the link may be coincidental

Because correlation coefficients are always the result of measurement errors in the data

Because correlation techniques only ever work for data shown on choropleth maps

Because genuine causation always requires a strong negative coefficient value

Random sampling

Stratified sampling

Systematic sampling

No sampling

It is not important once the data have actually been collected and written up in the report

Because thinking about reliability is just a convenient way to increase the overall word count of the final report

Because unreliable data can produce misleading conclusions, so evaluation bounds the confidence of the conclusion

Because a careful reliability check fully replaces the need to go out and collect any primary data at all

Around 1000 words, worth just 5 percent

Around 8000 words, worth a full 50 percent

Around 500 words, worth only 2 percent

Around 3000 to 4000 words, 20 percent

A focused, answerable, theory-grounded question, sound method and a critical evaluation

Using as much secondary data from published sources as it is realistically possible to gather

Carefully avoiding the use of any statistical techniques anywhere in the report

Writing as close to the maximum permitted number of words as possible