Data Analysis: Editing, Coding, Tabular Representation of Data
Data Analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. In today’s business, data analysis is playing a role in making decisions more scientific and helping the business achieve effective operation.
EDITING is the process of checking and adjusting responses in the completed questionnaires for omissions, legibility, and consistency and readying them for coding and storage.
Purpose of Editing
Purpose of Editing For consistency between and among responses. For completeness in responses– to reduce effects of item non-response. To better utilize questions answered out of order. To facilitate the coding process.
Basic Principles of Editing
- Checking of the no. of Schedules / Questionnaire)
- Completeness (Completed in filling of questions)
- To avoid Inconstancies in answers.
- To Maintain Degree of Uniformity.
- To Eliminate Irrelevant Responses.
Types of Editing
- Field Editing
Preliminary editing by a field supervisor on the same day as the interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent.
- Office Editing
Editing performed by a central office staff; often done more rigorously than field editing.
The process of identifying and classifying each answer with a numerical score or other character symbol. The numerical score or symbol is called a code, and serves as a rule for interpreting, classifying, and recording data. Identifying responses with codes is necessary if data is to be processed by computer.
Coded data is often stored electronically in the form of a data matrix – a rectangular arrangement of the data into rows (representing cases) and columns (representing variables) The data matrix is organized into fields, records, and files:
Field: A collection of characters that represents a single type of data.
Record: A collection of related fields, i.e., fields related to the same case (or respondent).
File: A collection of related records, i.e. records related to the same sample.
Tabular Representation of Data
Presentation of data is of utter importance nowadays. After all everything that’s pleasing to our eyes never fails to grab our attention. Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted.
A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.
Components of Data Tables
- Table Number: Each table should have a specific table number for ease of access and locating. This number can be readily mentioned anywhere which serves as a reference and leads us directly to the data mentioned in that particular table.
- Title: A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study and the nature of classification of data.
- Headnotes: A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.
- Stubs: These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row.
- Caption: A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column.
- Body or field: The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’.
- Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if required.
- Source: When using data obtained from a secondary source, this source has to be mentioned below the footnote.
Construction of Data Tables
There are many ways for construction of a good table. However, some basic ideas are:
- The title should be in accordance with the objective of study: The title of a table should provide a quick insight into the table.
- Comparison: If there might arise a need to compare any two rows or columns then these might be kept close to each other.
- Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table.
- Headings: Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’.
- Footnote: A footnote should be given only if needed.
- Size of columns: Size of columns must be uniform and symmetrical.
- Use of abbreviations: Headings and sub-headings should be free of abbreviations.
- Units: There should be a clear specification of units above the columns.
The Advantages of Tabular Representation
- Ease of representation: A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation.
- Ease of analysis: Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion etc.
- Helps in comparison: In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value.
- Economical: Construction of a data table is fairly easy and presents the data in a manner which is really easy on the eyes of a reader. Moreover, it saves time as well as space.