// Numbas version: exam_results_page_options {"name": "Statistics: Match variable types", "extensions": [], "custom_part_types": [], "resources": [], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "question_groups": [{"pickingStrategy": "all-ordered", "questions": [{"name": "Statistics: Match variable types", "tags": [], "metadata": {"description": "

match different variables to either: 'nominal','ordinal','interval','ratio'

", "licence": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"}, "statement": "

Select the level of measurement for the variable:

{question} is a {ans} variable. The definitions of the types of variable are below:

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### Nominal

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This is something that fits into categories without an order. For example hair colour (blonde, brown, red, etc). There is no intrinsic order to these categories.

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If the variable only has two categories (for example an answer to a yes/no question) then it is said to be a binary variable.

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### Ordinal

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This is a categorical variable that has an order. For example, level of education (high school, college, university, …). We can say which order they go in but we don’t know how big the gaps between them are.

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### Interval

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This is something that has order and we know the step size between each measurement is the same. For example, IQ scores and temperature measured in centigrade. Interval scales do not have a meaningful zero point, for example an IQ of zero is impossible and isn’t the absence of heat.

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### Ratio

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This is like an interval variable but it also has a meaningful zero. Things like height and weight are ratio scales. Since ratio scales have a true zero point to measure things from statements like \$6cm\$ is twice \$3cm\$ make sense since these values are both measured relative to the same zero point.

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keys generates a list of the dicionary keys

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runs through each variable type and checks it against the answer, returns 1 if true and 0 if false

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{question}

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