When you plan to appear for an entrance exam like SSC or CAT, you will come across different subjects or syllabus. You need to prepare for each according to their importance. Like Reasoning SSC is an important subject which you need to be very calculative and prepare well in advance. Similarly, data interpretation is an important subject where you will be tasked to review data given on the question paper. In government jobs, where conducting surveys is quite common, aspirants with good data interpretation techniques have higher chance to get the job.
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What is Data Interpretation?
Data interpretation is the process of reviewing data using calculation which will help organizations to assign tasks and come up with the complete set of data. This also gives some valid meaning to the data before arriving at the conclusion.
If you have already done the data interpretation course, then you will surely be able to do the data analysis implementing different steps.
Data analysis is a strategic process that involves ordering, categorizing, and summarizing the complete data. Data analysis is the first step taken for the interpretation of data.
Preparing for the data interpretation:
When you work for the data interpretation, you need to know the methods followed. The methods are about how analysts help people make sense of using numerical data collected and presented. For a layman, raw data can be confusing, so by using the interpretation method, you can explain the complete set. This is what is examined during the data interpretation subject while giving the entrance exam. The examiner will check how well you can use different methods for evaluating and explaining the complete data.
Two methods of Interpretation Methods:
1. Qualitative Data Interpretation Method
Under this method, the user analyses qualitative data which is also known as categorical data. Under this method, the user uses texts rather than numbers or patterns. In this method, the data is gathered using different methods of person-to-person techniques, which otherwise is quite difficult to analyse.
Under qualitative data, the user needs to first code the data into numbers before analysing. This is because most of the text is not arranged and takes more time to get the result.
2. Quantitative Data Interpretation Method
This method is used for analysing quantitative data, which is also known as numerical data. There are numbers, so the user needs to analyse using numbers. Under quantitative data, there are two types- discrete and continuous data. Since there are numbers, analysts do not have to employ coding techniques. The process involves statistical modelling techniques like the standard deviation, mean and median.
Some other tips for Data Interpretation
When you studying data interpretation for the exam, there are certain things to consider:
1. Identify the Data Type
As a researcher, you need to identify the type of data that is necessary for a particular survey or research. While conducting data interpretation, you need to understand the research question, so you can identify the data required for the research work.
2. No Biases Data
During the interpretation of data, the researcher may encounter different biases which usually comes from the respondent. There are two different types of biases- response bias and non-response bias. You need to be very accurate in eliminating any of these biases.
3. Using Close Ended Surveys
Close-ended surveys, restrict respondents’ answers regarding some predefined options along with eliminating irrelevant data. This will allow researchers to analyse and interpret data.
When you are practicing a data interpretation course, you will come across different aspects of this subject. You can use techniques learned through the course while appearing for the entrance exam. When practicing data interpretation, you will also learn different areas of data collection and preparing the final draft. Bar graph, pie-chart, tables, line graph, and others. You need to be mentally prepared with these areas when appearing for the exam. The questionnaire may ask to interoperate the data in any of the mentioned forms.