Explain Data Analysis

In this step, the data which is collected is arranged according to some pattern or a particular format and this analysation of the data is mainly done to provide the data with a meaning.

In the beginning the data is raw in nature but after it is arranged in a certain format or a meaningful order this raw data takes the form of the information. The most critical and essential supporting pillars of the research are the analysation and the interpretation of the data.

Both these aspects of the research methodology are very sensitive in nature and hence it is required that both these concepts are conducted by the researcher himself or under his very careful and planned supervision. With the help of the interpretation step one is able to achieve a conclusion from the set of the gathered data.

Analysis of the data can be best explained as computing some of the measures supported by the search for relationship patterns, existing among the group of the data.

Research depends a great deal on the collected data but it should be seen that this collected data is not just a collection of the data but should also provide good information to the researcher during the various research operations. Hence to make data good and meaningful in nature and working, data analysis plays a very vital and conclusive role. In this step data is made meaningful with the help of certain statistical tools which ultimately make data self explanatory in nature.

According to Willinson and Bhandarkar, analysis of data ‘involves a large number of operations that are very closely related to each other and these operations are carried out with the aim of summarizing the data that has been collected and then organizing this summarized data in a way that helps in getting the answers to the various questions or may suggest hypothesis.’

Purpose of Analysis of data
The purpose of the scientific analysis was first explained by Leon Festinger and Daniel Katz and according to both of them; the purpose of the analysis of the data can be explained as follows –

1. Should be very productive in nature, with high significance for some systematic theory.
2. Should be readily disposed to the quantitative treatment.

Procedure for the Analysis of the data

Data collected can be used in the best possible effective manner by performing the following activities –
1. Carefully reviewing all the data collection.
2. Analyzing the data then with the help of certain suitable techniques.
3. Results obtained from the analysation of the data should then be related to the study’s hypothesis.

Analysation Steps
The various steps of the analysation of the data were given by Herbert Hyman and can be summarized as follows –

1. Tabulation of the data after conceptualization, relating to every concept of the procedure is done which ultimately provides an explanation based on the quantitative basis.

2. Tabulation in the same way is carried out for every sub group, which gives quantitative description.

3. To get statistical descriptions consolidating data for different aspects is brought into use.

4. Examination of such data is then done, which helps in improving the evaluation of the findings.

5. Different qualitative and non statistical methods are brought into the use for obtaining quantitative description but only if it is needed.

Types of Analysis
1. Descriptive Analysis –
• Also referred to as the One Dimensional Analysis.
• Mainly involves the study of the distribution of one variable.
• Depicts the benchmark data.
• Helps in the measurement of the condition at a particular time.
• Acts as the prelude to the bi – variate and multivariate analysis.
• Such an analysis may be based on the one variable, two variables or more than two variables.
• Helps in getting the profiles of the various companies, persons, work groups etc.

2. Casual analysis –
• Also referred to as the Regression Analysis.
• Has their root in the study of how one or more variables affect the changes in the other variable.
• Explains the functional relationship between two or more variables.
• Helps in experimental research work.
• Explains the affect of one variable on the other.
• Involve the use of the statistical tools.

3. Co – Relative Analysis –
• Involves two or more variables.
• Helps in knowing correlation between these two or more variables.
• Offers better control and understanding of the relationships between the variables.

4. Inferential Analysis –
• Involves tests of significance for the testing of the hypothesis.
• Helps in the estimation of the population values.
• Helps in the determination of the validity data which can further lead to draw some conclusion.
• Takes an active part in the interpretation of the data.