1. TYPES OF DATA Prof. Dr. Hamit ACEMOĞLU

2. The aim By the end of this lecture, students will be avare of types of data.

3. The goals -Distinguish between a sample and a population, -Distinguish between categorical and numerical data, -Describe different types of categorical and numerical data, -Explain the meaning of the terms; variable, percentage, ratio (quotient), rate, score, -Explain what is ment by censored data.

4. Obesity among students ● 2500 students of a school [population] students from randomly selected 250 students [sample], we decided to collect data on height and weight variables. By studying the height and weight of students we aim to analyze statistics of obesity cases.

5. Obesity among students * The body mass index (BMI) or Quetelet index is a value derived from the mass (weight) and height of an individual. The BMI is defined as the body mass divided by the square of the body height, and is universally expressed in units of kg/m 2, resulting from mass in kilograms and height in metres.massweightunitskilogramsmetres * BMI=mass/(height) 2, (kg/m 2 )

6. Data & statistics -Data: uninterpreted information. -Variable: a logical set of attributes. -Parameter: is used insted of variable when we collect information about that variable throguh entire population. -Population: is a complete set of items that share at least one property in common that is the subject of a statistical analysis. -Sample: a subset of a population. -Observation: is the active acquisition of information from a primary source.

7. Data & statistics ● Statistics is the study of the collection, analaysis, interpretation, presentation, and organization of data. -INPUT -ANALYSIS -OUTPUT ● Biostatistics is the application of statistics to a wide range of topics in biology.

8. Data & statistics ● Data: -Categorical (qualitative) data: -Numerical (quantitative) data:

9. Categorical (qualitative) data Nominal data: Ordinal data:

10. Categorical (qualitative) data Nominal data: 1=male 2=female Ordinal data: 1=thin 2=normal 3=overweight 4=fatty 5=obese

11. Categorical (qualitative) data Dichotomous (binary) data: may only exist at two levels of measurement. e.g: yes / no live / death

12. Numerical (quantitative) data Discrete data: Continuous data:

13. Types of variable

14. Distinguishing between data types To distinguish the kinds of variables when choosing the statistical methods to be used and when analyzed using SPSS will be significant. Categorical variables were the number and numerical variables are presented as percentages shown with criteria such as mean and standard deviation.

15. Distinguishing between data types Although the distinction between categorical and numerical data is usually more accurate, sometimes the number of categories may be more in ordinal variable. These cases may be treated as ordered categorical variables. e.g: Pain scale with seven categories.

16. Drived data Percentage: is a number or ratio expressed as a fraction of 100. (%) Ratio or quotients: is a relationship between two numbers indicating how many times the first number contains the second. (a/b) Proportion: the relationship of two variables whose ratio is constant. (a/a+b or b/a+b) Rate (Desease rate): in which the number of disease events occuring among individuals in a study is divided by the total number of years (time) of follow-up of all individuals in that study. Score: indicates how sensitively a liklyhood function.

17. Variables are distinguished by SPSS as nominal, ordinal and numerical. (Descrete and continuous numeric data are treated in the same way).

18. Types of variable

22. We need to collect numerical data as well as in case of numerical variables. If we want classification we can categories later. e.g: a survey questioning, the age variable should be in an open-ended way; Desired : "How old are you ? : ______ " Undesired : "How old are you?” a) less than 20 b) 20-40 c) 41-60 d) more than 60

23. Censored data ● Sometimes you do not have information about the exact status of our data. In this case we can talk about censored data. ● Having cencored data is undesirable in terms of statistics. We have to get the real value of the data if possible, or this situation should be noted.

24. Censored data 1-Cut-off value ●<x “low” ●>y “high” 2-left due to ● death ● imigration ● rejecting the study

25. SUMMARY Data (definitions) ? Statistics, biostatistics ? Variable calassifications ? Censored data ?

26. The End