Type of data

FETP India

Describing

Competency to be gained from this lecture

Identify the different types of data to use appropriate methods to describe

their distribution

Key issues

• Qualitative data• Quantitative data• Distribution

Data: A definition

• Set of related numbers• Raw material for statistics• Example:

Temperature of a patient over time Date of onset of patients

Data

Epidemiological process

• We want to describe a population• We collect data• We analyze data into information

“Data reduction”

• We interpret the information• We use the information for decision

making

Data

Types of data

• Qualitative data No magnitude / size Classified by counting the units that have

the same attribute Types:

• Binary • Nominal• Ordinal

• Quantitative data

Qualitative

Qualitative, binary data

• The variable can only take two values 1, 0 Yes, No

• Example: Sex

• Male, female

Female sex• Yes, No

Qualitative

REC SEX--- ---- 1 M 2 M 3 M 4 F 5 M 6 F 7 F 8 M 9 M 10 M 11 F 12 M 13 M 14 M 15 F 16 F 17 F 18 M 19 M 20 M 21 F 22 M 23 M 24 F 25 M 26 M 27 M 28 F 29 M 30 M

Sex Frequency Proportion

Female 10 33.3%

Male 20 66.7%

Total 30 100.0%

Frequency distribution for a qualitative binary variable

Qualitative

Using a pie chart to display qualitative binary variable

FemaleMale

Distribution of cases by sexQualitativ

e

Qualitative, nominal data

• The variable can take more than two values Any value

• The information fits into one of the categories

• The categories cannot be ranked• Example:

Nationality Language spoken Blood group

Qualitative

REC NATION--- ------- 1 JORDAN 2 YEMEN 3 IRAN 4 JORDAN 5 YEMEN 6 JORDAN 7 YEMEN 8 TCHAD 9 SUDAN 10 IRAN 11 YEMEN 12 IRAN 13 JORDAN 14 SUDAN 15 IRAN 16 SUDAN 17 JORDAN 18 SUDAN 19 IRAN 20 YEMEN 21 SUDAN 22 YEMEN 23 SUDAN 24 IRAN 25 YEMEN 26 YEMEN 27 YEMEN 28 SUDAN 29 YEMEN 30 SUDAN

Frequency distribution for a qualitative nominal variable

Country Frequency Proportion

Yemen 11 36.7%

Sudan 8 26.7%

Iran 6 20.0%

Jordan 5 16.6%

Total 30 100.0%

Using a horizontal bar chart to display qualitative nominal variable

0 5 10 15

Jordan

Iran

Sudan

Yemen

Frequency

Distribution of cases by nationalityQualitativ

e

Qualitative, ordinal data

• The variable can only take a number of value than can be ranked through some gradient

• Example: Severity

• Mild, moderate, severe

Vaccination status• Unvaccinated, partially vaccinated, fully

vaccinated

Qualitative

REC Status --- ------- 1 1 2 1 3 2 4 2 5 1 6 2 7 1 8 2 9 3 10 2 11 1 12 3 13 1 14 3 15 1 16 3 17 1 18 1 19 3 20 1 21 1 22 2 23 1 24 2 25 2 26 1 27 2 28 3 29 2 30 2

Clinical status: 1: Mild; 2 : Moderate; 3 : Severe

Frequency distribution for a qualitative ordinal variable

Severity Frequency Proportion

Mild 13 43.3%

Moderate 11 36.7%

Severe 6 20.0%

Total 30 100.0%

0

5

10

15

Mild Moderate Severe

Frequency

Using a vertical bar chart to display qualitative ordinal variable

Distribution of cases by severityQualitativ

e

Key issues

• Qualitative data• Quantitative data

We are not simply counting We are also measuring

• Discrete• Continuous

Quantitative

Quantitative, discrete data

• Values are distinct and separated• Normally, values have no decimals• Example:

Number of sexual partners Parity Number of persons who died from measles

Quantitative

REC CHILDREN--- ------- 1 1 2 2 3 5 4 6 5 3 6 4 7 1 8 1 9 2 10 3 11 1 12 2 13 7 14 3 15 4 16 2 17 1 18 1 19 1 20 1 21 2 22 3 23 1 24 4 25 2 26 1 27 6 28 4 29 3 30 1

Frequency distribution for a quantitative, discrete data

Children Frequency Proportion

1 11 36.7%

2 6 20.0%

3 5 16.7%

4 4 13.3%

5 1 3.3%

6 2 6.7%

7 1 3.3%

Total 30 100.0%

0

2

4

6

8

10

12

1 2 3 4 5 6 7

Number of children

Frequency

Distribution of households by number of children

Using a histogram to display a discrete quantitative variable

Quantitative

Quantitative, continuous data

• Continuous variable• Can assume continuous uninterrupted

range of values• Values may have decimals• Example:

Weight Height Hb level What about temperature?

Quantitative

REC WEIGHT --- ------ 1 10.5 2 23.7 3 21.8 4 33.1 5 38.0 6 34.5 7 38.5 8 38.4 9 30.1 10 34.7 11 37.9 12 38.0 13 39.2 14 30.1 15 43.2 16 45.7 17 40.4 18 56.4 19 55.1 20 55.4 21 66.7 22 82.9 23 109.7 24 120.2 25 10.4 26 10.8 27 25.5 28 20.2 29 27.3 30 38.7

Weight Tally mark Frequency

10-19 III 3

20-29 IIIII 5

30-39 IIIII IIIII II 12

40-49 III 3

50-59 III 3

60-69 I 1

70-79 - 0

80-89 I 1

90-99 - 0

100-109 I 1

110-119 I 1

Frequency distribution for a continuous quantitative variable:

The tally mark

REC WEIGHT --- ------ 1 10.5 2 23.7 3 21.8 4 33.1 5 38.0 6 34.5 7 38.5 8 38.4 9 30.1 10 34.7 11 37.9 12 38.0 13 39.2 14 30.1 15 43.2 16 45.7 17 40.4 18 56.4 19 55.1 20 55.4 21 66.7 22 82.9 23 109.7 24 120.2 25 10.4 26 10.8 27 25.5 28 20.2 29 27.3 30 38.7

Weight Frequency Proportion

10-19 3 10.0%

20-29 5 16.7%

30-39 12 40.0%

40-49 3 10.0%

50-59 3 10.0%

60-69 1 3.3%

70-79 0 0.0%

80-89 1 3.3%

90-99 0 0.0%

100-109 1 3.3%

110-119 1 3.3%

Total 30 100.0%

Frequency distribution for a continuous quantitative variable,

after aggregation

Using a histogram to display a frequency distribution for a

continuous quantitative variable, after aggregation

0

2

4

6

8

10

12

14

0-9  10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99 100-9 110-9

Weight categories

Frequency

Distribution of cases by weightQuantitativ

e

87.0 84.0 51.1 64.9 71.5

88.8 62.7 14.2 87.0 44.7

48.9 27.8 88.3 39.9 11.1

64.0 31.4 32.6 73.4 34.8

89.7 56.1 37.9 67.5 38.3

32.6 33.1 52.0 62.9 39.5

44.6 56.6 82.1 70.3 83.6

34.3 78.7 52.1 63.1 82.4

50.2 43.0 16.6 78.2 72.7

11.1 49.7 32.6 49.4 79.1

18.9 64.7 37.1 74.2 88.9

59.7 82.5 69.3 81.5 72.3

61.9 34.9 48.1 18.7 54.9

46.4 58.9 39.4 66.9 47.9

40.9 74.9 31.1 55.8 57.6

37.6 23.3 44.4 21.8 81.6

21.6 75.7 35.9 33.9 24.6

77.2 30.0 48.1 18.7 67.6

52.3 24.3 48.9 76.3 43.2

17.3 43.9 76.2 45.0 55.7

Series of 100 values of a quantiative variable

Quantitative

ValuesFrequency0-9 010-19 820-29 730-39 1840-49 1650-59 1360-69 1170-79 1480-89 1390-99 0Total 100

0

5

10

15

20

0-9 10-19  20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

Frequency

Tabular and graphic representation of a distribution

Distribution

0

5

10

15

20

0-9 10-19  20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

Position

Dispersion

Describing a distribution

Summary Qualitative

Binary Nominal Ordinal

Sex Nationality Status  M Yemen MildM Jordan ModerateF Yemen SevereM Jordan MildF Sudan ModerateF Yemen MildM Sudan ModerateM Iran SevereF Jordan SevereM Iran MildF Yemen ModerateF Sudan ModerateM Iran MildM Yemen SevereM Jordan SevereF Jordan ModerateM Iran MildF Sudan MildM Yemen Mild

Quantitative

Discrete Continuous

Children Weight 1 56.41 47.82 59.93 13.11 25.71 23.02 30.03 13.72 15.42 52.51 26.61 38.21 59.02 57.92 19.63 31.72 15.13 33.91 45.6

Data type in computer software

Type of data Type of variable in Epi-Info software

Qualitative •Binary •Yes / No

•Nominal •Integer (Code/numbers)

•Ordinal •Integer (Code/numbers)

Quantitative •Discrete •Integer

•Continuous •Decimal

Avoid free field variables difficult to analyze

Exercise

• Consider the class• Describe the frequency distribution of

the following variable: Sex State of origin Involvement in surveillance to date (None,

partial, full time) Completed numbers of years in service Height in cm

Take home messages

• Qualitative data can be binary, nominal or ordinal

• Quantitative data can be discrete or continuous

• Distribution can be described with a table or a graph