Types of Data Dr.Lely Lubna Alaydrus Community Medicine Department Aimst University

References : Statistics at Square One, 9 th Edition,TDV Swinscow,revised by MJ Campbell, University of Southampton,BMJ Group, Epidemiology, Biostatistics, and Preventive Medicine, 3 rd Edition, FJ James et al., Saunders, Philadelphia, Biostatistika, 1 st Edition, Budiarto E, EGC, Jakarta, Medical Statistics, 4 th Edition, Campbell MJ et al., Wiley, UK, 2007.

Data Definition : “ Factual information, especially information organized for analysis or used to reason or make decisions.”

Variable A measure of single characteristic that can vary is called : variable. The value of a variable for an element is called an observation or measurement.

Type of Data 1. Qualitative Data 2. Quantitative Data

Qualitative Data Described generally in words rather than number. = Categorical data Examples : * gender ( female, male ) ; * religion (islam, buddha, hindu, etc) ; * ethnicity (malay, chinese, indian, etc)

Quantitative Data Described generally in numbers. = numerical data Examples : age ( 20 yrs old ) height ( 168 cm ) Weight ( 55 kg ) blood pressure ( 120/80 mmHg )

Categorical data 1. Nominal data (unordered categories) 2. Ordinal data (ordered categories)

Nominal Data The name 'Nominal' comes from the Latin nomen, meaning “name”. Have no measurement scale Examples : sex (female, male) blood group ( 0, A, B, AB ) If there are only 2 categories, it is called: Binary data ( ex : good,bad ; female,male )

Ordinal data Many types of medical data can be characterized in terms of three or more qualitative values. Examples : Feeling of satisfaction : “very satisfied- fairly satisfied-not satisfied” Feeling of pain : “severe-moderate-mild-absent Systolic murmur : “ 1+, 2+, 3+….6+

Ordinal data : These data are not measured on a measurement scale. They form an ordinal (ordered, ranked) variable. The utility of such scales to quantify subjective assessment. Because they contain more information than nominal variable, ordinal variables enable more informative conclusions to be drawn.

Quantitative Data Described generally in numbers. 2 types of quantitative data : continuous and discrete. Examples : age ( 20 yrs old ) height ( 168 cm ) weight ( 55 kg ) blood pressure ( 120/80 mmHg )

Continuous Data Data from measurement. Any value can be put within a given range. Can be with decimal number. Examples : height ( cm ), weight (58.5 kg), serum glucose levels blood pressure

Discrete Data Data from calculation. Will be positive number, no decimal. Each data can be seperated clearly. Examples : Number of children in family ( 5 children ) Number of attacks of asthma per week ( 2 times a week )

Discrete data -- Gaps between possible values Continuous data -- Theoretically, no gaps between possible values

Data Qualitative Data Quantitative Data Nominal Ordinal Continuous Discrete Type of data :

Transform the data How : ? ContinuousNominal Ordinal

Examples : Blood pressure : 165/100 ; 170/85 ; 175/90 ; 180/95 mmHg 80/60 ; 90/75 ; 95/70 mm Hg 110/70 ; 120/80 ; 130/85 mmHg 165/100 ; 170/85 ; 175/90 ; 180/95 mmHg 80/60 ; 90/75 ; 95/70 mm Hg 110/70 ; 120/80 ; 130/85 mmHg Continuous data

Transform the data : Hypertensive : 165/100 ; 170/85 ; 175/90 ; 180/95 mmHg Normotensive : 110/70 ; 120/80 ; 130/85 mmHg Hypotensive : 80/60 ; 90/75 ; 95/70 mm Hg Ordinal

Transform the data : Ordinal Nominal How : ?

Transform the data : Hypertensive Normotensive Hypotensive Normal BP Abnormal BP Nominal Data

Exercises 1 : Continuous Nominal 1.How to transform the data ? 2.Give an example.

Exercises 2 : Nominal Continuous 1.Can be data transformed ? 2.If yes, give an example.

Data Reduction e.g. for Age Continuous:20, 21, 22,…………… , 30-39, , Ordinal:Twenties, Thirties, Forties, Fifties, Sixties Nominal:Young, Old

Exercises 3 : Ordinal Nominal 1.Can be data transformed ? 2.If yes, give an example.

Data collection : 1. Primary data Data is collected directly by the researcher. (ex : survey ) 2. Secondary data Data is collected by other people. ( ex : medical record in hospital ) 3. Tertiary data Data from text books,encyclopedias, etc.

Variable A measure of single characteristic that can vary is called : variable. The value of a variable for an element is called an observation or measurement.

Level of data measurement It is important to understand the different levels of measurement, As levels of measurement play a part in determining the arithmetic and the statistical operations that are carried out on the data.

Four level of data measurement : 1. Nominal 2. Ordinal 3. Interval 4. Ratio

Nominal Have no measurement scale. Differentiate data qualitatively. Example : sex (female = 1, male = 2), the number here only a code in processing data. Easy to answered and processed. But doesn’t give deep information. The calculation is in proportion or percentage.

Ordinal The data is not measured on an exact measurement scale, but more information is contained in them than nominal level. Contain more information than nominal level. No absolute zero point. The difference between any two types of rankings is not the same along the scale.

Example : Education of A is higher than B. (Intelligence of A is 2 times more then B ? ) Student score in class : First rank – second rank – third rank Same differences?

Example: Stadge of Cancer : stadge I, II, III, IV

Interval The range can be determined with number. Example : temperature patient A = 38.6º C patient B = 39.6º C From this measurement, we can say that the temperature patient A is different than patient B, the temperature of patient B is higher 1º C from patient A.

Interval : The distances between each interval on the scale are equivalent along the scale from low interval to high interval.

Ratio Has absolute zero point. Example : age patient A = 15 yrs old age patient B = 30 yrs old We can say that : the age of patient A is different then patient B the age of patient B is higher for 15 yrs old then patient A. the age of patient B is 2 times higher then patient A.

Exercises : Which level of measurement these data : 1. Country of origin = ? 2. Ethnic group = ? 3. Eye colour = ? 4. Marital status = ? 5. Education level = ? 6. Number of pregnancies = ? 7. Body mass index = ? 8. HIV status +/- = ?

Types of Data Interval Small Medium Large Ordinal Height Ratio Females Nominal

Classifying Data 4 Levels of Measurement Nominal OrdinalRatio Interval Names, labels, or categories only Data cannot be ranked at all Religious Preference Gender EX: Religious Preference, Gender Similar to Ordinal, but we can find meaningful differences between data No “zero” or starting point Some ranking, but differences aremeaningless. Same as Interval, but Ratio has a true zero or starting point. Temp. (F & C), EX: Temp. (F & C), EX: Good/Bad, Letter Grades EX: Lengths, Distance traveled

Data Type and Statistical Test Variable 1Variable 2SummaryStat Test Nominal Ordinal Counts Proportion Chi Sqr Proportions Nominal Ordinal RatioMeansT-test Z-test Ratio Scatter PlotCorelation

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