zArchive ISS W1 Types of data

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21 Terms

1
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What are the two main types of data?

  • Quantitative (numerical)

  • Qualitative (categorical)

<ul><li><p>Quantitative (numerical) </p></li><li><p>Qualitative (categorical)</p></li></ul><p></p>
2
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What are the two types of quantitative/numerical data?

  • Continuous

  • Discrete

<ul><li><p>Continuous</p></li><li><p>Discrete</p></li></ul><p></p>
3
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What are the two types of qualitative/categorical data?

  • Multiple categories

  • Binary

<ul><li><p>Multiple categories</p></li><li><p>Binary</p></li></ul><p></p>
4
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What are the two types of ‘multiple categorical’ qualitative/categorical data?

  • Ordinal

  • Nominal

<ul><li><p>Ordinal</p></li><li><p>Nominal</p></li></ul><p></p>
5
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List two characteristics of quantitative/numerical variables.

  • Takes numerical values only

  • The values reflect the actual measurement (with units) of the subjects or objects we are measuring

6
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Describe two characteristics of qualitative/categorical variables.

  • Contains only categories which can usually be labelled by words/phrases

  • Each category represents a particular characteristic of interest within a group of subjects or objects which often used as labels.

7
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When are numeric values used for qualitative/categorical variables?

When coding in statistical packages at data entry stage.

8
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What type of variable is this example: “number of apples eaten last week ( 0 / 1 / 2 / 3 or more )

  • Categorical

  • >2 categories

  • Ordinal

9
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What must you report with continuous variables?

The unit of measurement

10
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When would a discrete variable become a qualitative/categorical (ordinal) variable? Give an example.

  • If multiple discrete categories were collapsed into a single category.

  • When options for 3 / 4 / 5 get lumped into a >=3 category.

11
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What is a difference between ordinal and nominal variables?

  • Ordinal can be ranked i.e. they have a meaningful order.

  • Nominal categories have no order.

12
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What is a common feature of ordinal, nominal and binary variables?

They all contain categories that are mutually exclusive.

13
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Can you take an average of nominal variable categories?

No - it’s meaningless and/or not possible.

14
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How are you transforming this data: a continuous scale is dissected into a smaller range of scale, i.e. age group: 0-10, 11-20, 21-30, ...etc

Continuous to ordinal transformation.

15
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How are you transforming this data: you cut a range of values into 2 parts, i.e. disease status from a bio marker: <7 = disease, ≥7 = non-disease

Continuous or discrete to binary transformation.

16
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How are you transforming this data: Log transform a continuous skewed variable, i.e. ln(age)

Continuous to a different form of continuous transformation.

17
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Give 2 examples of continuous variables.

  • Height (cm)

  • Weight (g/kg)

  • Circumference (mm)

18
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Give 2 examples of discrete variables.

  • No. of children in a family

  • No. of apples consumed in one week

19
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Give 2 examples of ordinal variables.

  • Disease stage (Mild / Moderate / Severe)

  • Likert scale (Strongly agree / Agree / Neutral / Disagree / Strongly disagree)

20
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Give 2 examples of nominal variables.

  • Ethnicity (White / Black / Asian / Chinese / Other)

  • Blood group (A / B / AB / O)

21
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Give 2 examples of binary variables.

  • Sex (male / female)

  • Status (Dead / Alive)

  • BMI (<30kg/m2 / ≥30kg/m2)

  • Obese (Yes / No)