1. Type of data FETP India Describing

2. Competency to be gained from this lecture Identify the different types of data to use appropriate methods to describe their distribution

3. Key issues Qualitative data Quantitative data Distribution

4. Data: A definition Set of related numbers Raw material for statistics Example:  Temperature of a patient over time  Date of onset of patients Data

5. 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

6. 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

7. Qualitative, binary data The variable can only take two values  1, 0  Yes, No Example:  Sex Male, female  Female sex Yes, No Qualitative

8. 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 SexFrequencyProportion Female1033.3% Male2066.7% Total30100.0% Frequency distribution for a qualitative binary variable Qualitative

9. Using a pie chart to display qualitative binary variable Distribution of cases by sex Qualitative

10. 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

11. 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 CountryFrequencyProportion Yemen1136.7% Sudan826.7% Iran620.0% Jordan516.6% Total30100.0%

12. Using a horizontal bar chart to display qualitative nominal variable Distribution of cases by nationality Qualitative

13. 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

14. 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 SeverityFrequencyProportion Mild1343.3% Moderate1136.7% Severe620.0% Total30100.0%

15. Using a vertical bar chart to display qualitative ordinal variable Distribution of cases by severity Qualitative

16. Key issues Qualitative data Quantitative data  We are not simply counting  We are also measuring Discrete Continuous Quantitative

17. 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

18. 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 ChildrenFrequencyProportion 1 1136.7% 2 620.0% 3 516.7% 4 413.3% 5 13.3% 6 26.7% 7 13.3% Total30100.0%

19. Distribution of households by number of children Using a histogram to display a discrete quantitative variable Quantitative

20. 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

21. 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 WeightTally markFrequency 10-19III3 20-29IIIII5 30-39IIIII IIIII II12 40-49III3 50-59III3 60-69I1 70-79-0 80-89I1 90-99-0 100-109I1 110-119I1 Frequency distribution for a continuous quantitative variable: The tally mark

22. 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 WeightFrequencyProportion 10-19310.0% 20-29516.7% 30-391240.0% 40-49310.0% 50-59310.0% 60-6913.3% 70-7900.0% 80-8913.3% 90-9900.0% 100-10913.3% 110-11913.3% Total30100.0% Frequency distribution for a continuous quantitative variable, after aggregation

23. Using a histogram to display a frequency distribution for a continuous quantitative variable, after aggregation Distribution of cases by weight Quantitative

24. 87.084.051.164.971.5 88.862.714.287.044.7 48.927.888.339.911.1 64.031.432.673.434.8 89.756.137.967.538.3 32.633.152.062.939.5 44.656.682.170.383.6 34.378.752.163.182.4 50.243.016.678.272.7 11.149.732.649.479.1 18.964.737.174.288.9 59.782.569.381.572.3 61.934.948.118.754.9 46.458.939.466.947.9 40.974.931.155.857.6 37.623.344.421.881.6 21.675.735.933.924.6 77.230.048.118.767.6 52.324.348.976.343.2 17.343.976.245.055.7 Series of 100 values of a quantiative variable Quantitative

25. ValuesFrequency 0-90 10-198 20-297 30-3918 40-4916 50-5913 60-6911 70-7914 80-8913 90-990 Total100 Tabular and graphic representation of a distribution Distribution

26. Position Dispersion Describing a distribution

27. Summary Qualitative Binary NominalOrdinal SexNationalityStatus MYemenMild MJordanModerate FYemenSevere MJordanMild FSudanModerate FYemenMild MSudanModerate MIranSevere FJordanSevere M IranMild FYemenModerate FSudanModerate M IranMild MYemenSevere MJordanSevere FJordanModerate M IranMild FSudanMild MYemenMild Quantitative Discrete Continuous ChildrenWeight 156.4 147.8 259.9 313.1 125.7 123.0 230.0 313.7 215.4 252.5 126.6 138.2 159.0 257.9 219.6 331.7 215.1 333.9 145.6

28. Data type in computer software Type of dataType 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

29. 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

30. 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