Types of variable & summarising data Flashcards

1
Q

What is a variable?

A

an aspect that can take different values for different participants

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2
Q

What are the two types of variables?

A

Categorical (dead, alive)

Quantitative (continuous) (numbers)

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3
Q

What is a binary variable?

A

a variable that only has two categories

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4
Q

What are the 3 types of categorical variables?

A

Binary

Nominal

Ordinal

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5
Q

What is a nominal variable?

A

unordered labelled categories (3 or more)

eg blood type: A, B, AB, O

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6
Q

What is a ordinal variable?

A

small set of ordered categories

eg none, mild, moderate, severe

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7
Q

What are quantitative variables?

A

values that have quantitative meaning

higher number = more concept eg age

have a large number of potential values eg age, weight

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8
Q

What 2 types of graphical summary can be used to analyse quantitative variables?

A

Histogram

Box and whisker plot

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9
Q

What are the 5 histogram shapes?

A

Symmetrical (normal)

Positive skew

Negative skew

Bimodal (2 populations on one graph)

Uniform

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10
Q

When should ‘mean’ & standard deviation be used?

A

to summarise symmetrical distributions

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11
Q

When should ‘median’ & inter quartile-range be used?

A

to summarise skewed/asymmetrical distributions

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12
Q

What is distribution of data?

A

different values that occur and the frequency they occur for a given variable

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13
Q

What are the two ways to summarise distributions?

A

Numbers (descriptive stats)

Graphs

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14
Q

What are the 2 ways to describe categorical data numerically

A

Frequency (actual number)

Relative frequency (proportion or percentage of people in a category)

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15
Q

How is categorically data described graphically

A

using a bar chart where height indicates the number of the category’s

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16
Q

What are the 3 aspects of quantitative data that must be summarised

A

Average (middle of distribution)

Variation (data spread)

Symmetry (is there symmetry either side of the average?)

17
Q

What are 4 ways variation can be quantified?

A

Standard deviation

Inter quartile range

95% range

range