Classification of Data – Meaning, Definition, Characteristics, Bases, and Types
Meaning and Definition of Classification of Data
Classification of data refers to the systematic arrangement of raw data into homogeneous groups or categories based on shared characteristics or common properties. The purpose of classification is to simplify large volumes of data, make comparisons easier, and prepare the data for analysis and interpretation.
According to Secrist, "Classification is the process of arranging data into sequences and groups according to their common characteristics." It is an essential step in the data preparation phase that transforms unorganized raw facts into meaningful groups for effective interpretation.
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Characteristics of Classification
1. Systematic Arrangement: Classification organizes scattered data into logical and scientific order.
2. Homogeneity: Items within a single class share similar traits or characteristics.
3. Mutual Exclusiveness: Each data item fits into only one class under a single basis of classification.
4. Exhaustiveness: All collected data must be placed into one of the defined categories.
5. Clarity and Simplicity: Well-structured classification promotes understanding and reduces confusion.
6. Flexibility: It should be adaptable for further subdivision or new data categories.
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Basis of Classification of Data
Classification can be done based on various principles or criteria, depending on the nature of the data and the objective of the study. The main bases include:
1. Chronological Basis: Classification based on time (e.g., years, months, hours).
2. Geographical Basis: Based on location such as states, regions, cities, or countries.
3. Qualitative Basis: Based on attributes or qualities such as gender, religion, caste, or education.
4. Quantitative Basis: Based on measurable quantities like age, income, height, weight, or price.
Each of these bases serves a different purpose and is chosen based on the research objective and data structure.
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Types of Classification of Data
1. Chronological Classification
In chronological classification, data is arranged in order of time. This is especially used in time-series analysis, where data shows changes over a period.
Example: Population growth from 2000 to 2020.
Purpose: To observe trends, changes, or patterns over time.
2. Geographical Classification
This type of classification groups data based on geographical location such as countries, states, districts, or cities.
Example: Literacy rates by state in India.
Purpose: To study regional variations and make location-based comparisons.
3. Qualitative Classification
In qualitative classification, data is grouped based on non-measurable attributes. These characteristics are descriptive rather than numerical.
Example: Classification by gender (male/female), religion (Hindu, Muslim, Christian), or education level (illiterate, graduate, postgraduate).
There are two types under this:
Simple Classification: Based on one attribute (e.g., gender).
Manifold Classification: Based on more than one attribute (e.g., gender and marital status).
4. Quantitative Classification
Quantitative classification involves grouping data according to numerical values or magnitudes. It is used when variables are measurable.
Example: Grouping people into income brackets: ₹0–₹10,000, ₹10,001–₹20,000, etc.
This type often uses:
Exclusive Method: The upper limit of a class is excluded (e.g., 10–20 means 10 ≤ x < 20).
Inclusive Method: Both class limits are included (e.g., 10–20 means 10 ≤ x ≤ 20).
Quantitative classification is commonly used in statistics for constructing frequency tables and histograms.
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Conclusion
Classification is an essential step in organizing data for research. It helps reduce complexity, highlights relationships among data elements, and enables effective analysis. By categorizing data based on time, location, qualities, or quantities, researchers can extract meaningful insights and present findings clearly. A well-defined classification system ensures that the data is logically arranged, easy to interpret, and aligned with the goals of the research.
