Classification of Data | Statistics for SSC CGL PDF Download

 Data is not always neatly organized. An analyst or investigator must arrange the gathered data properly for better analysis and achieving desired outcomes. One crucial method for organizing such data is data classification. Through this approach, raw data is transformed into various statistical series to yield more valuable and insightful results.

Classification of Data

Objectives of Classification of Data

• Brief and Simple: Raw data collected needs to be organized into different groups for better understanding. Classifying data helps present information clearly and concisely, making analysis easier.
• Utility: Analysts gather information from various sources and categorize it to highlight similarities, improving its usefulness for investigation.
• Distinctiveness: Sorting data into categories makes differences more obvious, aiding in drawing distinct conclusions from heterogeneous data.
• Comparability: Data classification enables comparison between datasets, simplifying the process of estimating and analyzing information.
• Scientific Arrangement: Organizing data based on common attributes facilitates a logical and reliable arrangement, enhancing the data's credibility.
• Attractive and Effective: Well-classified data not only enhances effectiveness but also makes information visually appealing and easy to comprehend at a glance.

Characteristics of a Good Classification 

• Comprehensiveness: To achieve better outcomes, it's crucial to organize the gathered raw data in a thorough manner. This involves placing each piece of data into a specific class, category, or group, ensuring nothing is overlooked.
• Clarity: The individual collecting and assessing data should categorize the raw information into distinct groups with clarity. Ensuring clear allocation of items into different categories prevents any confusion for the reader or investigator.
• Homogeneity: For effective investigation results, it's important to group the collected raw data into homogeneous categories. Classifying data based on similar attributes facilitates easier and more accurate data analysis.
• Suitability: For data classification to be useful, it should align with the investigation's objectives. For instance, when determining a country's literacy rate, classifying data based on income and expenditure is irrelevant. The data should be categorized as educated and uneducated.
• Stability: Consistency in how data is classified is essential. This means the criteria for data classification should remain constant across investigations. Each type of investigation should use the same classification criteria.
• Elastic: Data classification should be flexible. This flexibility allows for adjustments to the classification if the purpose or focus of the investigation changes. Investigators should be able to modify the classification as needed.

Bases of Classification of Data

Classification of data is the process of arranging data in an orderly manner. Now let us learn the types of data classification in statistics. The four important bases of classification are discussed below:

Geographical Classification of data

• The grouping of information based on where it comes from is known as geographical data classification.
• Simply put, geographical data classification involves organizing information by geographical location or area.
• For example, if we classify data on the production of sugarcane, pulses, or cotton based on four main regions in India, it falls under geographical data classification.

It is also recognized as ‘spatial classification of data’.

Chronological Classification of data

Qualitative Classification of data

• Classification of information based on qualities and features is called qualitative data classification.
• In this type of grouping, data is categorized by specific attributes like gender, honesty, hair color, literacy, intelligence, religion, etc.
• When using qualitative data classification in statistics, the attribute being studied is not measurable; it can only be identified as present or absent.
• Qualitative data is usually split into two main categories: 
  • Data is organized based on characteristics.
  • Examples of attributes include gender, honesty, hair color, literacy, intelligence, and religion.
  • Attributes cannot be measured directly.

Qualitative data has two primary types.

• Simple classification of data
• Manifold classification of data

Simple Classification of data

• Objective: In this kind of sorting of information, we split data into two exact groups. For instance, if we are looking at students based on one feature, like their level of education, we can divide them into two categories: educated and uneducated. Similarly, they can also be categorized into basic education or higher education.
• Method: This method of sorting where we create two distinct groups is known as simple or twofold or dichotomous data classification. Here, we form two groups, one with the specified feature and the other without it.

Manifold Classification of data

• Apart from creating only two groups, if we further split the information based on additional characteristics within those groups, it becomes known as manifold classification. This means that after sorting data into two groups based on one characteristic, each of those groups is then divided into two more based on another characteristic. This can result in multiple levels of data classification with more than just two categories.
• For instance, we can classify the student population into males and females. Then, these groups can be further divided into educated and uneducated individuals, which can then be categorized into basic education and higher education levels.

Quantitative Classification of data

• Quantitative classification of data deals with numbers that can be measured or calculated, unlike qualitative classification. It allows data to be sorted into numerical categories.
• A quantitative variable gives information on a numerical scale and can be measured or operated on. Examples include weight, temperature, volume, height, income, student grades, and numerical values.
• In statistics, data is categorized based on different ranges of values, showing how things change over time or across various areas. Quantitative classification is also known as classification by variables. There are two types of quantitative data: discrete and continuous.
• Discrete quantitative data includes variables that can be counted and have a specific, finite value.
• Continuous quantitative data encompasses values that can be measured and can vary across a range.

Discrete quantitative data example: 

• The number of students in a class.
• The total data stored on a USB drive.

Continuous quantitative data example: 

• The temperature each day in summer.
• The weight of each passenger on a train.

Important Points on Classification of Data

• Classification of Data: Data is sorted into types, and we study them one by one. Understanding classification in statistics is crucial.
• Variable: Variable means something that changes. It can differ from person to person, time to time, or place to place. There are two main types: discrete and continuous variables.
• Frequency and Frequency Distribution: Frequency shows how often each variable appears. Frequency distribution involves analyzing data based on measurable variables like rates, weight, or payments.
• Table: A structured way of organizing statistical data with columns and rows. Rows are horizontal, while columns are vertical.
• Types of Data Classification:
  • One-way Classification: Sorting data based on a single characteristic.
  • Two-way Classification: Sorting data based on two characteristics simultaneously.
  • Multi-way Classification: Sorting data based on more than two characteristics at once.
• Levels of Measurement: There are four levels of statistical data measurement: nominal, ordinal, interval, and ratio, ranging from the least to the most precise.