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Classification of Data

In addition to differentiation of data in above four categories, one can Classify The Data into two broad categories:
  • Qualitative data
  • Quantitative data
  1. Qualitative data
    1. Also known as discrete data. It is composed of only certain values and none in between like 3 or 4 but not anything in between.
    2. It is classified only by counting the variables (e.g. individuals) having the same characteristics e.g.
    3. For example: In qualitative data people with same characteristic are counted to form specific groups or classes e.g.
      Attacked, Died, Cured, Vaccinated, Males etc.
    4. The results obtained from qualitative data are expressed as any of these:
      1. Ratio
      2. Proportion
      3. Percentage
      4. Rate
    5. The statistical methods commonly employed in analysis of qualitative (discrete) data are:
      1. ​Standard error of proportion
      2. Chi-square test
  2. Quantitative data
    1. It is also known as continuous data.
    2. Discrete (qualitative) data can take only certain values, and none in between if the data can take any value; it is called continuous (quantitative) data. Most biomedical variables are continuous e.g.
      1. Weight
      2. Height
      3. Blomod pressure
      4. Age

Qualitative Data

Quantitative Data

Finite, discrete variable

Infinite, continuous variable

No Magnitude

Magnitude – scale

Rate, ratio, proportion

Mean, SD, correlation coefficient

Bar, pie, pictogram, spot map

Histogram; Frequency polygon; Line diagram, Scatter diagram

Answer to question is in Yes / No

Answer to question is an quantitative value with unit

  1. Methods of Data Presentation
    1. Tabular method: for scientific audience e.g in journals etc. Frequency table, association table, correlation table.
    2. Graphical method: for administrators & lay public: Bar diagram, histogram, frequency polygon.
    3. Pictorial method: for lay & illiterate public: pictogram.
  2. Summarizing Data:
    1. Histogram is a pictorial diagram of frequency distribution of grouped data of a continuous variable. (AI’09).
      • ​​To display a nominal scale data of discrete variable a bar diagram is used.
    2. Bar graphs are identical to a histogram except that each rectangle on the graph is clearly separated from the others by a space demonstrating that the data are separate categories. It could be:
      1. Simple
      2. Multiple/Compound
      3. Component/Proportional
    3. Line diagrams are used to show the trend of some event with the passage of time.
    4. Pie diagram: used for qualitative data, denotes proportion, degree of angle denotes the frequency and area of sector.
    5. A pictogram is a pictorial depiction of discrete data where symbols are used instead of a bar and the frequency of the event is shown by the number of times the symbols are repeated.
    6. Scatter diagram is drawn to show the relationship between two variables. Data set needs to be quantitative.
Extra Edge:  

For Calculating exponential growth: 
P (Final Value) = c (Initial value) [ 1 + r (Fractional increase) ] n  
e.g.: one bacteria divides every 30 min for 4 hrs. If the growth follows exponential pattern, bacterial population after 4 hours:
4 hrs = 180 min
30 min = (180 /30) = 6 times
n = 6
c = 1, initial population
r = 1, fractional increase
(1 + r)n = (1+1)6 = 26
P = 26 = 64

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