Variables Sherine Shawky MD Dr PH Assistant Professor
Variables Sherine Shawky, MD, Dr. PH Assistant Professor Department of Community Medicine & Primary Health Care College of Medicine King Abdulaziz University
Learning Objectives • Understand the concept of variable • Distinguish the types of variables • Recognize data processing methods
Performance Objectives • Select the variables relevant to study • Perform appropriate data transformation • Present data appropriately
Definition Of Variable “A variable is any quantity that varies. Any attribute, phenomenon or event that can have different values”
Information Supplied By Variables Indices of Person Indices of Place Indices of Time
Specification of Variable Clear precise standard definition Method of measurement Scale of measurement
Role Of Variable Correlation Interdependent
Role Of Variable Association Independent Dependent Independent Confounding Dependent Independent Effect modifier Dependent
Types of Variables Quantitative (continuous) Qualitative (Discrete)
I- Quantitative Variables • Data in numerical quantities that can assume all possible values • Data on which mathematical operations are possible • Example: age, weight, temperature, haemoglobin level, RBCs count
II- Qualitative Variables Qualitative variables are those having exact values that can fall into number of separate categories with no possible intermediate levels Nominal Ordinal
1 - Nominal Variable Unordered qualitative categories Dichotomous (2 categories) Multichotomous (> 2 categories)
2 - Ordinal Variable Ordered qualitative categories Score birth order Categorical social class Numerical discrete parity
Continuous & Numerical Discrete Variables Continuous Variable 3 - 2 - 1 - 0 1 2 3 Numerical Discrete 0 1 2 3
Types of Variables -Quantitative -Dichotomous -Multichotomous -Score -Categorical - Numerical discrete How much? Who, How, where, when, What, …etc. ? How many?
Data Collection Tool Age in years: Gender: 1) male, 2) female Social class: 1) low, 2) middle, 3) high Height in cm: .
Data Transformation Data Reduction Creation of composite variable
Data Reduction Example • Data: Age from 47 individuals • Arrange in ascending order: 20, 21, 22, 23, 24, 25, 29, 30, 34, 34, 34, 35, 36, 37, 39, 40, 43, 43, 46, 47, 48, 48, 50, 52, 56, 58, 59, 60, 62, 64, 67, 69
Data Reduction Example (cont. ) • Calculate the range: 69 -20= 49 • No. of intervals= 5 • Width of class= 49/5 = 9. 8 10 • Class intervals= 20 -29, 30 -39, 40 -49, 50 -59, 60 -69
Data Reduction Continuous: 20, 21, 22……. 69 Interval: 20 -29, 30 -39, 40 -49, 50 -59, 60 -69 Ordinal: Twenties, Thirties, Forties, Fifties, Sixties Nominal: Young or Old
Creation Of Composite Variable Single variables Quantitative Composite variable Quantitative Qualitative
Data Presentation Tabular Diagrammatic
Data Presentation Variable Nominal Ordinal Interval Continuous Table Frequency Percentage Cumulative frequency - Cumulative percentage - Frequency - Percentage - Cumulative frequency - Cumulative percentage - Mean, SD - Mean, 95 %CI - Chart - Pie Column or Bar Linear Ogive Histogram Frequency polygon - Ogive - - Scatter Box plot
Frequency Table
Pie Chart
Column Chart All categories Single Category %
Bar Chart All categories Single Category %
Frequency and Cumulative Frequency Table
Linear Chart Percentage Ogive (Cumulative Percentage) Stages of Breast Cancer
Frequency and Cumulative Frequency Table for Variable of Interval
Horizontal axis For Variable of Interval
% Histogram %
Frequency Polygon %
Tabular Presentation of Quantitative Data or Variable Total Mean SD Age (years) 47 95% CI 42. 1 13. 5 38. 2 -46. 0
Scatter Diagram
AGE in years Box-whisker plot 80 70 60 50 40 30 20 10 Male Female 20 N = 27 SEX
Conclusion The variable is the basic unit required to perform a research. The researcher has to select the list of variables relevant to the study objectives, specify every piece of information and assign its role. The type of variable should be set in order to allow for proper data collection, transformation and presentation.
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