What is Data Handling ?

Oct 20, 2021, 16:45 IST

About What is Data Handling ?

In Statistics, “Data Handling” is a crucial concept that ensures the integrity of the research data, because it addresses some important concerns like security, confidentiality, and therefore the preservation of the research data. In every field, we've information within the sort of a numerical figure. Every figure of this type is understood as an observation. Generally, the gathering of all the observation is named data. To handle the info, Statisticians use different data management methods. During this article, allow us to discuss what data handling is, and therefore the various methods to handle the data.

Table of Content for Data Handling

i. Defination of Data Handling

ii. Types of Data Handling

- Qualitative Data

- Quantitative Data

iii. How to Represent Data

iv. Representation of Data Using Bar Graph

What is Data Handling?

Data handling means collecting the set of knowledge and presenting during a different form. Data may be a collection of numerical figures that represents a specific quite information. the gathering of observations which are gathered initially is named the data. Data are often in any form. it's going to be words, numbers, measurements, descriptions or observations. Data handling is that the process of securing the research data is gathered, archived or disposed of during a protected and safe way during and after the completion of the analysis process.

Type of Data Handling

Data handling method can be performed based on the types of data. The data is classified into two types, such as:

i. Qualitative Data

ii. Quantitative Data

Qualitative data gives descriptive information of something whereas quantitative data gives numerical information about something. Here, the quantitative data is further divided into two. They are discrete data and continuous data. The discrete data can take only certain values such as whole numbers. The continuous data can take a value within the provided range.

Qualitative data is defined because the data that approximates and characterizes. Qualitative data are often observed and recorded. This data type is non-numerical in nature. This sort of knowledge is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods. Qualitative data in statistics is additionally referred to as categorical data – data which will be arranged categorically supported the attributes and properties of a thing or a phenomenon. Qualitative. For example, consider a student reading a paragraph from a book during one among the category sessions. An educator who is taking note of the reading gives feedback on how the kid read that paragraph. If the teacher gives feedback supported fluency, intonation, throw of words, clarity in pronunciation without giving a grade to the kid, this is often considered as an example of qualitative data.

How to Represent Data?

The data can be usually represented in the following ways. mention Below:

i. Bar Graph

ii. Line Graphs

iii. Pictographs

iv. Histograms

v. Stem and Leaf Plot

vi. Dot Plots

vii. Frequency Distribution

viii. Cumulative Tables and Graphs

 

Representation of Data Using Bar Graph

Data are often represented in various forms through numbers, pictures, tables, graphics, etc. the foremost common sort of graphical representation of knowledge is thru bar graphs. A bar chart or bar graph portrays a visible interpretation of knowledge with the assistance of vertical or horizontal rectangular bars of equal width which are uniformly spaced with reference to one another, where the lengths of the bars are proportional to the info to be represented. allow us to consider the subsequent example to know bar chart more closely:

In a school of 400 students, the percentage of attendance of students is shown below by the following table.

Attendance (in percentage)       Number of Students

60        105

70        199

80        23

90        73

Total    400

Each bar in the above example is of uniform width and the data which varies is represented on one of the axes. Another axis represents the measure of the variable data through the height of the bars. The heights or the lengths of the bars denote the value of the variable. These graphs are also used to compare certain quantities.

In this example, the attendance of the students is represented by the X-axis and the number of students on the Y-axis. The bars are of uniform width and the length of the bar is equal to the number of students. By observing the bar graph it can be concluded that the number of students with 60% attendance is 105, the number of students with 70% attendance is 199, the number of students with 80% attendance is 29 and the number of students with 90% attendance is 73. Thus, close observation of the bar chart makes the data representation simple and easy and therefore bar graph makes data organized, its analysis and interpretation simple.