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Data Preparation in Social Research2

How to Conduct Data Preparation for Social Research?

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What is Data Preparation?

Data Preparation in Social Research1

Data preparation refers to the process of gathering, synthesising, and systematically consolidating data into one file or data table to prepare it for analysis. It is a pre-processing step in which raw data is converted into a form that can be effectively analysed. Although data preparation can be a time-consuming and tedious undertaking for data professionals, it is an essential step of pre-processing that provides a context to the data collected, allowing useful and reliable insights to be extracted and biases to be eliminated.

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Benefits of Data Preparation

The following are a few advantages of preparing data for social research:

  • Cost-effective and Efficient: Facilitates preparing data in a cost-effective and efficient manner.
  • Improves Data Integrity: Data preparation helps identify and fix issues within the data collected, enhancing the integrity of the data.
  • Eliminates Duplication: The data preparation process corrects any issues within the data, including duplications. By eliminating any duplication within the data sets, you can increase its reliability.
  • Improves Decision Making: Data preparation enhances the quality of data used in research, allowing for better-informed decisions and conclusions to be made. 

Steps to Conduct Data Preparation for Social Research

Data Preparation in Social Research

There are six key steps that must be carried out when preparing data:

  • Collection: The first step is to collect the data relevant to your topic of social research. There are many different sources of data used in social research, whether they be primary sources (observations, for example) or secondary sources (case studies, for example). It is important that the data collected comes from reliable sources and is relevant and in line with the objectives of your study. 
  • Discovery and Profiling: This step involves examining, analyzing, and synthesising the collected data. In this step, you must aim to gain a better understanding of the social phenomenon being studied so that you can identify what needs to be done to prepare the data for its intended use. You should also identify any patterns, inconsistencies, gaps, and anomalies in the data that must be addressed. 
  • Data Cleansing: Data cleansing refers to the process of identifying and correcting corrupt, inaccurate, incorrectly formatted, duplicated, incomplete, or irrelevant data in a data set. This step helps ensure that data errors are corrected so that the data set is ready to be analyzed accurately. This is an integral step in the data preparation process, and if not carried out effectively, it can result in the results or answer to the social problem being inaccurate and futile.

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  • Structuring of Data: In the fourth step, you must structure and model all the data collected through your social research in an organized and unified format. While doing so, it is important to keep in mind the methods of analysis you plan to employ to analyse the data, as your data will need to be structured accordingly. 
  • Transformation and Enrichment: Once you’ve structured your data, you can begin with data transformation and enrichment. Data transformation involves turning the data into usable information. There are many different types of data transformation such as item reversals, missing values, scale totals, etc. You must then also conduct data enrichment. This involves enhancing existing information by supplementing the missing or incomplete data within the data sets. 
  • Data Validation and Publishing: The final step in the process of data preparation is data validation and publishing. In this step, you must run automated routines against your data to validate its consistency, wholeness, and accuracy. Once your data has been validated, it can then be published and made available for use in forums or repositories for social research.

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FAQs on Data Preparation

Data preparation is the process of gathering, synthesising, and consolidating raw data into a form that can be readily and effectively analysed to draw useful insights.

Data cleansing is an important step in the process of data preparation. It involves improving the data collected by removing incorrect, duplicated, corrupted, irrelevant, incorrectly formatted, or incomplete data within a data set. It helps ensure the output or conclusions drawn from the research are useful and reliable.

A few key steps in the process of data preparation are: data collection, data profiling, data cleansing, data structuring, data enrichment, and data validation.

Data enrichment refers to the process of refining and enhancing data by supplementing missing or incomplete data. It is used to fill any gaps in the data collected to increase the completeness of the data sets. 

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