Data Interpretation

The step that is considered the first step to data interpretation is data analysis; it is the way of categorizing, ordering, summarizing, and manipulating data for obtaining the results of the research questions.  Data interpretation should be done properly as it is a very important part. 

Data interpretation and logical reasoning

Data interpretation

For answering difficult questions, data interpretation helps researchers to manipulate, categorize, and summarize the situation. To come to a conclusion, data interpretation is a process of using various logical methods for reviewing data. The data interpretation’s importance is apparent and this is the reason why data interpretation should be performed properly. Varying scales of data interpretation are –

Ordinal Scale: The meaning of ‘ordinal’ in ordinal scale is ‘order’. The ordinal scale only allows for calculating the values at a lower or higher scale. The accurate comparison between the two groupings is not possible by ordinal scale. 

Nominal Scale: Quantitatively the non-numeric categories are not possible to be compared or ranked but there are distinctive identifiers to mark such categories.

Interval Scale: In the interval scale always the arbitrary point zero is present. The data is grouped in this type of scale with equal distance and orderly between the classifications.

Ratio Scale: The ratio scale consists of the features of all three scales.

Logical Reasoning

Logical reasoning means analyzing the situation or the problem very minutely and sincerely and after that coming to a solution with logical answers to the problem. For analyzing a problem one uses logical thinking skills for considering the problem distantly. There are two types of logical reasoning categorized on artificial intelligence; Deductive reasoning and inductive reasoning. In simple terms, logical reasoning means analyzing a situation logically and then coming to a conclusion. Data interpretation and logical reasoning are the sections that are included in every Government exam, it is a very vital part of the syllabus. In verbal reasoning and non-verbal reasoning, the syllabus of logical reasoning can be divided. 

The topics that come under data interpretation and logical reasoning are as follows –

• Data interpretation:

• Line Charts
• Pie Charts
• Bar Graphs
• Radar Graphs
• Tables
• Mixed Graphs, etc.

• Logical reasoning:

• Odd Pair
• Missing character
• Symbols
• Arrangement
• Puzzles
• Syllogism
• Clock and calendar
• Seating Arrangement
• Analogy
• Mathematical operations
• Clock and Calendar
• Figure matrix
• Cubes and Dice
• Reflection of mirror and water
• Fitting pieces, etc.

Data interpretation example

Example 1: Based on the table given below answer the question that follows.

Disbursement of one Company (in Lakh) annually in the year given

Question 1. 

Write the total expenditure/disbursement of the company in the year 2002.

1. Rs. 713.36
2. Rs. 813.36
3. Rs. 719.43
4. Rs. 515.15
5. Rs. 714.36

Reason for the answer: 

Total expenditure in the year 2002 = Rs. (142+420+49.4+3.96+98) lakhs

= Rs. 713.36 lakhs

Question 2.

Calculate the average bonus in these years from 2000 to 2004.

1. 4.5
2. 3.4
3. 3.8
4. 5.2
5. 4.83

Reason of the answer:

Average Bonus = Rs. 3.84+3.00+3.96+2.52+3.685

= Rs. 3.4

Question 3.

Calculate the Approximate total expenditure percentage of the year 2000 to the year 2004.

1. 68%
2. 60.65%
3. 69.12%
4. 91%
5. 62.87%

Reason of the Answer:

The required percentage = {[(101+324+41.6+3.84+74)/(133+336+36.4+3.68+88)]x100} %

= [{544.44/597.08} X 100%]

= 91.18% (approx.)

Conclusion

It is to conclude that for answering difficult questions, data interpretation helps researchers to manipulate, categorize and summarize the situation. To come to a conclusion data interpretation is a process of using various logical methods for reviewing data. The data interpretation’s importance is apparent and this is the reason why data interpretation should be performed properly. Varying scales of data interpretation are; nominal scale, ordinal scale, interval scale, and ratio scale.