Variables Sherine ShawkySherine 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

“A variable is any quantity that varies. Any attribute, phenomenon or event that can have different values” Definition Of Variable

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 Interdependent Correlation Interdependent

Role Of Variable Independent Dependent Independent Dependent Confounding Independent Dependent Effect modifier Association

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 NominalOrdinal

1- Nominal Variable Unordered qualitative categories Dichotomous (2 categories) Multichotomous (> 2 categories)

2- Ordinal Variable Ordered qualitative categories Scorebirth order Categorical social class Numerical discrete parity

Continuous Variable Numerical Discrete Continuous & Numerical Discrete Variables

Types of Variables - Quantitative - Dichotomous - Multichotomous - Score - Categorical - Numerical discrete How much? How many? Who, How, where, when, What,…etc.?

Age in years: Height in cm: Gender: 1) male, 2) female Data Collection Tool Social class: 1) low, 2) middle, 3) high.

Data Transformation Data Reduction Creation of composite variable

Data Reduction Example Data: Age from 47 individuals Arrange in ascending order: 20, 21, 22, 23, 23, 24, 25, 29,29, 30, 30, 34, 34, 34, 34, 34, 34, 35, 35, 36, 37, 39, 39, 40, 43, 43, 43, 46, 46, 47, 47, 48, 48, 48, 50, 52, 56, 56, 58, 59, 59, 60, 62, 64, 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, Ordinal: Twenties, Thirties, Forties, Fifties, Sixties Nominal: Young or Old

Creation Of Composite Variable Quantitative Qualitative Single variables Composite variable Quantitative Qualitative

Data Presentation TabularDiagrammatic

VariableTableChart Nominal - Frequency - Percentage - Pie - Column or Bar Ordinal - Frequency - Percentage - Cumulative frequency - Cumulative percentage - Pie - Column or Bar - Linear - Ogive Interval - Frequency - Percentage - Cumulative frequency - Cumulative percentage - Histogram - Frequency polygon - Ogive Continuous - Mean, SD - Mean, 95%CI - Scatter - Box plot Data Presentation

Frequency Table

Pie Chart

Column Chart Single CategoryAll categories %

Bar Chart Single CategoryAll categories %

Frequency and Cumulative Frequency Table

Linear Chart Ogive (Cumulative Percentage) 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 VariableTotalMeanSD95% CI Age (years) or

Scatter Diagram

Box-whisker plot 20 27N = SEX FemaleMale AGE in years

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.