Biostatistics Basics Lecture 02 (1).pptx

Biostatistics Lectures made easy

1. Biostatistics Lecture 02

2. VARIABLE A variable is a characteristic of an individual which takes different values at different situations. Height and weight of patients Stage of a disease Number of visits to a hospital 2

3. DATA The characteristics and properties, we wish to observe by members of a group which differ from one another. 3

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5. TYPES OF DATA Qualitative/Categorical Data Quantitative/Numerical Data Discrete Continuous 5

6. Data 2. Quantitative/Numerical 1. Qualitative/categorical b. Ordinal a. Nominal Continuous Discrete c. Interval d. Ratio 6

7. Levels of measurement Nominal Ordinal Interval Ratio 7

8. 1. Qualitative Data NOMINAL In nominal scale, the variables are divided into named categories. These categories however, cannot be ordered one above another (as they are not greater or less than each other). Examples: Sex/ Gender: male, female Blood groups: A, B, AB, O Note: When only two possible categories exist, the variable is sometimes called dichotomous, binary, or binomial. 8

9. ORDINAL In ordinal scale, the variables are also divided into a number of categories, but they can be ordered one above another, from lowest to highest or vice versa. Examples: ORDINAL DATA CATEGORIES Pain level mild, moderate, severe , very severe Opinion on a statement: fully agree, agree, disagree, totally disagree Note: The scale of measurement for most ordinal variables is called a Likert scale. 9

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11. 2. Quantitative Data Discrete variables Variable assumes whole values without decimals. Examples: How many times a person has been admitted to hospital; The number of decayed, missing, or filled teeth; The number of children. Despite what the demographers tell us, it’s impossible to have 2.13 children—kids come in discrete quantities. 11

12. Discrete data have values that can assume only whole numbers . 12

13. Continuous variables Continuous data may take any value, within a defined range. Blood Pressure Age Weight Length 13

14. Continuous data may take any value, within a defined range. 14

15. Discrete data -- Gaps between possible values Continuous data -- Theoretically , no gaps between possible values Number of Children Hb

16. INTERVAL In interval scale, the variables have constant, equal distances between values, but the zero point is arbitrary. Examples: Intelligence (IQ test score of 100, 110, 120, etc.) pH level 16

17. RATIO In ratio scale, the variables have equal intervals between values, the zero point is meaningful, and the numerical relationships between numbers is also meaningful. Examples: Weight (50 kilos, 100 kilos, 150 kilos, etc.) Pulse rate Respiratory rate 17

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19. Activity Identify the following as nominal, ordinal, discrete and continuous Smoking Status (Smoker or non-smoker) Nominal Data Number of cars in a car park Discrete Data Satisfaction level Ordinal Data Number of children in a family Discrete Data 19

20. Marital status Nominal Data Weight Continuous Data Blood glucose level Continuous Data Type of exercise (low, moderate, vigorous ) Ordinal Data Time taken to complete a task Continuous Data 20