Elaborate the Steps Involved in the Data Analysis Process.

Data analysis is involved in multi-process and functions. Under this article, you get the full details of the Data Analysis Process step by step. Let’s take a brief look at the data analysis process. Below are the key elements of the data analysis process.

• Editing.
• Coding.
• Data Conversion.
• Data Analysis.
• Interpretation.

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Editing:

Editing is the process of checking to detect and correct errors and omissions. Data editing is a requisite before the analysis of data is carried out. Editing ensures that the data is complete in all respect for subjecting them to further analysis. While editing the researcher must ensure that following requirements are fulfilled:

Legibility of entries:

The researcher should ensure that data are legible in order to be used. If the entries are not legible or there is a doubt about the meaning of entry, it should not be used.

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Completeness of entries:

The researcher should ensure whether there is an answer to all the questions. If there were any omission, the researcher sometimes would be able to deduce the correct answer from other related data on the same instrument. If this is possible, the data set has to be rewritten on the basis of the new information.

For example, the approximate family income can be inferred from other answers to probes such as occupation of family members, sources of income, approximate spending and saving and borrowing habits of family members etc.

If the information is vital and has been found to be incomplete, then the researcher can take the step of contacting the respondent personally again to solicit the requisite data again. If none of these steps could be resorted to the marking of the data as “missing” must be resorted to.

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Accuracy:

Apart from checking for omissions, the accuracy of each recorded answer should be checked. A random check process can be applied to trace the errors at this step.

Consistency in response can also be checked at this step. The cross verification to a few related responses would help in checking for consistency in responses.

The reliability of the data set would heavily depend on this step of error correction. While clear inconsistencies should be rectified in the data sets, fake responses should be dropped from the data sets.

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Consistency of entries:

Entries must be consistent. If one entry is inconsistent with another, there arises a question which of two is correct. Such discrepancies should be cleared up by questioning of the interviewer if possible. If they cannot be resolved, both the entries should be discarded.

Coding:

The editing data are then subject to codification and classification. Coding process assigns numerals or other symbols to the several responses of the data set. It is, therefore, a prequisite to prepare a coding scheme for the data set. The recording of the data is done on the basis of this coding scheme.

The responses collected in a data sheet varies, sometimes the responses could be the choice among a multiple response, sometimes the response could be in terms of values and sometimes the response could be alphanumeric.

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At the recording stage itself, if some codification were done to the response collected, it would be useful in the data analysis. When codification is done, it is imperative to keep a log of the codes allotted to the observations.

This code sheet will help in the identification of variables/observations and the basis for such codification. Coding may be numeric or alphabetic. But numeric coding is preferred to alphabetic.

Data Conversion:

Data conversion is the process of transforming data from a research project to computer. Dates back, key punch machines have been utilized to put the.data on computer cards.

As computers have become more sophisticated, data entry tends to be either instantaneous, or in computer-assisted telephone interviewing, or converted to magnetic media, such As disk or tape, for storage.

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Data Analysis:

Now the data analysis is made. Analysis means a critical examination of the assembled and grouped data for studying the characteristics of the object.

Data analysis summarizes the large mass of data into understandable and meaningful form. The reduction of data facilitates further analysis. Analysis may be broadly classified into following two:

• Descriptive. analysis.
• Inferential analysis.

Descriptive Analysis:

Descriptive analysis describes the nature of an object or phenomenon under study. Descriptive analysis provides us with profiles of organizations, work groups, persons and other subjects on. any of a multitude of characteristics such as size, efficiency, compositions, preferences etc. Descriptive analysis may be of following types:

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Uni-variate Analysis:

Uni-variate analysis describes data on one variable. This includes measures of central tendency and measures of dispersion.

Bivariate Analysis:

Bivariate analysis describes data on two variables. Once the data relating to single variables are summarized and their pattern of distribution studied, the researcher’s next task is to examine the pattern of relationship between the variables under study.

Bivariate analysis includes correlation analysis, regression analysis etc. Bivariate analysis measures have a limited function of establishing co-variation and its direction. It does not permit making casual relationships.

A bivariate relationship may be the result of chance or it may exist because the variables are related to a third unrevealed variable.

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Multivariate Analysis:

Multivariate analysis describes data on more than two variables. Multivariate analysis involves simultaneous analysis of more than two variables. Multivariate analysis provides more complete explanations for complex phenomenon and permits assessing relationships through statistical control.

Multivariate analysis consists of following:

1. Multiple regression analysis,
2. Multiple discriminant analysis,
3. Canonical analysis,
4. Multivariate analysis of variance,
5. Factor analysis.

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Inferential Analysis:

Inferential analysis is concerned with drawing-Inferences and conclusions from the findings of research study. Inferential analysis enables us to make decisions and draw conclusions from studies which could otherwise not be feasible because of the size of the universe or of prohibitive costs of a census survey or of destructive testing procedures as in quality control.