Data Cleaning in Process of Data Analysis – How to Do?

Data analysis is a technical process in dissertation writing. It involves cleansing, inspecting, summarising, and modelling data collected by using various means. Like different types of data collection, methods of analysis of dissertation data also vary greatly. Basically, it is the form of data that specifies which process of data analysis can best solve a scientific mystery. Based on data classification, the analysis can be qualitative or quantitative. Examples of qualitative analysis include content analysis, discourse analysis, thematic analysis, narrative analysis, and phenomenological analysis. Likewise, for quantitative analysis, you can conduct descriptive statistics and inferential statistics. No matter which method of analysis you use, data trimming or cleaning is necessary to perform, and this article is all about sharing some data-cleaning techniques.

Process of data analysis- An overview:

Data analysis is an extensive process, and to get proposed outcomes, you need to follow a cascade of events. Actually, the process of data analysis has its roots in the process of data collection. Even in some guides, the data collection is the first step of data analysis. Later on, the process passes by cleaning and trimming the data to remain on the safe side thought out the analysis. The unorganised and untrimmed data raises many difficulties in the data interpretation phase of analysis. After data trimming, the next step must be to run the decided techniques on cleaned data and get results that can meet your expectations. Lastly, giving descriptive details of what you have found is the major task that is left behind.

  1. Data collection: Depending on the plan of your study, it is the stage to collect the data from all available sources. Such sources include surveys, questionnaires, interviews, experimentations, and focus groups.
  2. Data cleaning:  It is not necessary that the data you gathered from the target audience is useful. Most of the time, it includes junk material that needs to be eliminated before moving on to the next phase of the process of data analysis. Thus, the sole purpose of the data cleaning technique is to eliminate those junks that may be in the form of duplicate data, extra spaces, and outliers.
  3. Qualitative or quantitative analysis: It is the process of applying any statistical and non-statistical technique to observe the trends in the collected data. These trends and patterns in the recorded data provide the basis for answering the research questions in a logical manner. 
  4. Data visualisation and interpretation: After analysis, you will be almost done with your aims and goals of the process of data analysis. The only thing to do to finally finish the task is reporting the results of a study. Analysis often ends by giving the tables and graphics so the data visualisation and interpretation will no longer be a problem for data analytics.

This is a brief description of the general steps to complete a process of data analysis. Additionally, you must feel free to contact one of the leading dissertation writing services to Buy Assignment Online at amazingly affordable rates.

Data cleaning process-A basic overview:

Mostly the data collected from widely scattered sources are present in an unorganised form. However, running analysis techniques on the scattered data ends up giving vague results that compromise the dissertation quality. Thus, the following are some steps to clean or run the data analysis in more systematic fashion:

  1. Remove duplicated data
  2. Find and fix the contextual or spelling errors
  3. Deliberately remove the unwanted outliers
  4. Try to logically fill out the empty spaces
  5. Validate some concerns related to data quality

Though all these steps are vital to increasing the quality of data for interpreting better outcomes, still the last step pays special attention to quality concerns. This step is all about asking some questions after cleaning the data. For example:

  • Does the collected data capable enough to make a meaningful story?
  • Does any academic misconduct, such as data fabrication and biasness, affect the quality of data?
  • Does the data provide the supporting or opposing details of the main research claim?
  • Will you be able to find any theoretical or statistical evidence that can warrant your data?

Final Thoughts:

After all is said and done, we must say that the process of data analysis aims to turn the raw data into informational content. However, data cleaning is all about improving the data quality by ensuring validity, uniformity, consistency, accuracy, and completeness in the dataset. Both processes collectively help researchers contribute to the existing body of literature.