Before doing any calculation on the raw data, it is important to first consider understanding the nature of the raw data. Understanding the nature of the raw data will help in organizing data into relevant groups and categories that will enhance easier and efficient data manipulation.
Also, before performing any calculations on data, it is important to study raw data to identify the various data attributes. Data attributes can be defined as the characteristic of a block of data. Understanding data attributes will enhance easier representation of data by grouping similar data together (McClave, Benson & Sincich, 2011).
This will involve an analysis of various data correlation patterns and grouping the raw data into blocks that have similar characteristics and correlations. Raw data have various attributes that need to be analyzed before performing any calculations. For example, raw data contains a lot of errors, it is in different formats, it is not validated, and it is unformatted, unconfirmed, and un-coded. In order to obtain reliable and concrete results, it is important to observe and analyze raw data before doing any calculation (McClave, Benson & Sincich, 2011).
Effective grouping of data will reduce the level of biasness in the raw data, and make the process of data analysis more effective and efficient. This will eventually ensure that the results obtained are reliable and effective, with minimal errors.
Data grouping is the process of organizing data into groups known as classes. In grouping data, it is important to classify the data and organize it into groups with same class intervals. Organizing data into class intervals is important because it will assist in the process of drawing frequency diagrams and histograms (McClave, Benson & Sincich, 2011).
Frequency can be defined as the number of times a data value occurs.
For example, if 20 people have the age of 60, then, the 60 years age is said to have a frequency of 20. Frequency tables have various advantages. They include but are not limited to the following advantages; simple to interpret, helps represent data effectively, and represent large data size. The above advantages enhance easier calculation of various measures of central tendency such as mean, mode, and median. On the other hand, frequency tables have some disadvantages associated with their use. They include the following disadvantages (McClave, Benson & Sincich, 2011).
- Large tables require more class intervals which makes it difficult to analyze large volumes data.
- Inadequate information. Frequency tables contain inadequate data that can be used to measure various parameters like standard deviation, measure of association, and the measure of dispersion.
In conclusion, the process of data grouping requires a deeper understanding of the various data grouping techniques. Also, the process requires a lot of care, and attention in order to avoid errors. Errors made at the initial stage of data grouping will eventually affect the integrity of the result obtained, hence; much detail should be given to the process of data grouping.
McClave, J. T., Benson, P. G & Sincich, T. (2011). Statistics for business and Economics. Boston, MA: Pearson-Prentice Hall.