In carrying out any form of research, a researcher is present with two kinds of data to work with.
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Qualitative data deals with qualities that cannot be measur and are not tangible e.g. Auditing  character traits like age, sex and ethnicity. Quantitative data deals with the quantity i.e. amount like 100 kgs, 50 cm, etc. The process undertaken to collect, compile and organize the collect data is referr to as data analysis.

In analyzing the already collect and previous data, the researcher has to come up with a summary of the data. This is referr to as secondary research. The latter-going to the field is primary research. Data analysis and secondary research are the backbone of any research. When the two are us together, they enhance a successive research.

Auditing Any time of research:

Starts with first coming up with a topic that you wish to research on. Since no one will believe your research without a candid report, you ne to start by collecting data. This can either done using the primary or secondary way of data collection. Primary data collection requires that you go to the field and collect raw data.
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This will be us later on in interpreting the research findings.
The other way would be to use secondary data collection. This is a collection of already done research. It can get from newspaper reviews, annual reports or any other already publish journal. Data analysis then do to prove the hypothesis. Since the research is meant to prove a point, the collect data is us to falsify or accept the hypothesis.

Data analysis is representing:

Using empirical graphs and chats to show the research findings. The most popular and reliable way of representing these findings is by the use of the SPSS program. It gives detail and summariz representations of all quantitative findings. Qualitative data analysis is us in making inferences about the research done. This is because every respondent gives a different answer to the questions being ask.

The final stage of compiling your research findings is done through the implementation and audit support. Data analysis implementation gives the conclusion and inferences of the research findings. Implementation and audit support gives the base of the research and helps in making recommendations and the way forward regarding the problem at hand.

The research findings are then Auditing:

In the audit and the cause of action adopt. The importance of an audit is that it gives cribility as well as adding value to the research. If it’s in a company, the research and analysis of data best do by an outside body. The audit will then give confidence to both the employees and the share holders of the company. Implementation and audit support in data analysis is very important to every other company/organizations and companies.

If people have doubts about any activity that is being carri out, the audit is us to clear up these claims. It’s very important that after carrying out a research, that you produce an audit. It verifies details, prevents the occurrence or errors, makes it easy to do evaluation and also make future prictions.