September 28, 2022
Conducting a Survey

Are you done with data collection after performing a detailed survey? I am glad to hear that you have completed this time consuming task. Now, do you know what the next step is? Yes, the next step after conducting a survey is data refining. You need to purify your obtained results or data using different techniques. Many students are totally unaware of those techniques. They put their ears on this most important task, hence, fail to grab their desired grades. Keeping this in mind, today’s article is for students who do not know the importance of data refining and the ways of doing it. So, let’s start discussing the topic with the question below:

What is refining the data, and why is it so important?

No survey is free from errors. Refining the survey data means purifying or cleansing it from irrelevant and incorrect information. The process of refining the survey data includes identifying incorrect, irrelevant, incomplete, and dirty datasets in a dataset. Upon identification of such data, you clean or refine it using some strategies.

Importance of refining the data

In qualitative research, you conduct a survey and collect the data to answer the research questions. While collecting data or gathering responses from the respondents, encountering errors in the data is a regular thing. Such kinds of errors disturb the overall results of the survey. Obviously, you do not want this to happen after conducting a survey. Therefore, data refining becomes very important. Most of students get best dissertation help to refine the data. This way, you get rid of the unwanted and dirty errors in your dataset.

5 Primary Steps to Refine Data Obtained by Conducting a Survey

From the discussion above, you know the meaning and importance of data refining in any survey. Moving on to the main topic of discussion, let’s now discuss the steps included in the data refining process. Hence, a brief description of the 5 easy steps to follow is mentioned below:

1.     Removal of unwanted observations

One of the primary goals of refining the data is to get rid of unwanted things. So, the first step is the removal of unwanted observations. There is no doubt that sometimes, you may have collected data that is not relevant to your research questions. In such a case, you must identify and remove that data. Unwanted observations in data after conducting a survey are of two types. A brief description of both types is as follows:

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Duplicate observations

It is the first type of unwanted observation. It means that you have duplicate values or observations in your dataset. This error happens when you combine data from multiple sources. It also occurs when a survey taker submits more than one response. You need to analyse each data and replicate the observations carefully.

Irrelevant observations

The second type of unwanted observation is irrelevant observations. These are the observations or values that do not fit the context of your research problem. For example, someone mentions the price when you are only dealing with the quantity. So, eliminate such errors by looking deep into the data.

2.     Fix structural errors

The second type of refining the data obtained by conducting a survey is fixing the structural errors. This type of error occurs when you transfer data from one format to another. The structural errors include giving strange names to the survey entities, typos, and incorrect and heterogenous capitalisations. So, deeply look for the words that are misspelt and eliminate them as part of the process of refining the data. Also, ensure shortening the long category headings because too long headings become a problem when making the graph. Hence, do not let any heading poorly formatted and any words misspelt.

3.     Filter unwanted outliners

The 3rd step involves the filtering out of outliners. Outliners are the expressions, values, or observations that are completely different from other observations in the dataset. This step is very important as it increases the performance of your model or survey performance. Note that outliners are very tricky. They act like real observations, but in reality, they are completely different from other observations. For example, if a value turns out to be in 1000s in a dataset ranging between 1-10, it means the 1000 value is incorrect and an outliner.

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4.     Handle missing data

Before performing the data analysis, you must ensure that your dataset is complete. If this is not the case, handle the missing data. It is a crucial step that you must do before the actual analysis. You cannot ignore doing this because many algorithms do not run until the data is complete and consistent. A brief description of the couple of ways to deal with missing data is as follows:

  • Drop the information that is missing but be very careful. Dropping the information means you will be less on data.
  • Input the missing values on the basis of other observations. You can apply the interpolation technique.
  • Alter the way that data is used.

5.     Validate and QA

The final step of data refining obtained by conducting a survey is validating the data and having a Q&A session. To perform the basic validation of your dataset, you must be able to answer these questions:

  • Does the obtained data make some sense after refining?
  • Does the collected data follow the rules set by the instructor?
  • Does the gathered answer your research questions or not?

By answering these questions, you can simply check the validity of your data. Note that the answer to these questions must be “YES”. If there is a single “NO”, start the refining process again and eliminate the rest of the errors.

Conclusion

Conducting a survey and collecting the data is easy. The real thing is refining and cleansing the data before the actual analysis. It is important to get the best results that reflect the actual field scenarios. Also, it is necessary because many algorithms do not run unless the data is complete and consistent. Hence, follow the steps mentioned above and refine the collected data for better results.

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