Analyzing research data is a process used by researchers to reduce data to a story and interpret it in order to gain insights. In this process, a large amount of data is reduced into small pieces to make sense of it.
To reach the data analysis stage , it is necessary to have previously defined the research problem, developed and implemented a sampling plan, a structure design, methods and tools, so this is considered an easier step to carry out in an investigation.
If you have any doubts about how to carry out your analysis correctly, in this article we will present the main points that you should take into account.
What is the importance of analyzing research data before presenting results?
The purpose of analyzing research data is to obtain information that can be useful for your work and allows you to:
Describe and summarize the data
Identify the relationship between variables
Compare variables
Identify the difference between variables
Predicting results
How to perform data analysis in research?
There are three elements to analyzing research data: organizing the data, reducing the ukraine phone number data through integration, and categorizing it so that patterns and themes can be easily identified and linked.
Below, we have for you the process of carrying out a data analysis, which consists of three main phases:
Phase I: Data validation
Data validation is done to understand whether the collected information is in accordance with pre-established standards or whether it is a biased data sample. This phase is divided into four different aspects:
Fraud: Ensuring that each survey or questionnaire response is recorded by a real human being.
Screening: Ensuring that each participant or respondent is selected or chosen according to the research criteria.
Procedure: Ensure that ethical standards were maintained while collecting sample data.
Completeness: Ensuring that the respondent answered all the questions in the online survey or that the interviewer asked all the questions included in the questionnaire.
Phase II: Data editing
More often than not, a large sample of research data is riddled with errors. Respondents sometimes fill out fields incorrectly or sometimes accidentally leave them out.
Data editing is a process in which researchers have to confirm that the data provided is free from such errors and for this, they need to perform basic and outlier checks to edit the raw data and prepare it for analysis.
Phase III: Data coding
This is the most important phase of data preparation, as it is associated with grouping and assigning values to survey responses.
Suppose a survey is completed with a sample size of 1000, then the researcher will create an age range to distinguish the respondents based on their age. Therefore, it is easier to analyze small cubes of data than dealing with the huge pile of data.
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