Analysis and Interpretation

Custom Student Mr. Teacher ENG 1001-04 26 September 2016

Analysis and Interpretation

Data analysis and data interpretation are closely related, but have different meanings. Lets first define the meaning of the word data. Data is defined as factual information that can be used as a basis for reasoning, discussion, or calculation. There are several different types of data. It can also be defined as information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful.

Now that data has been defined, in order to put together the meaning and uses of data analysis and interpretation, analysis and interpretation would need meaning and definition as well. Analysis is a word that is used to define separation or breakdown of something whole into its separate components. In reference to data, data analysis is a breakdown of information and facts that were compiled or processed to form data. Data analysis includes inspection of data, cleaning, transforming, and modeling data to form supportive information.

Data analysis is a process that contains several phases. There are two parts that are clearly defined, that is initial and main data analysis. Data cleaning is a relevant procedure that is is used to ensure the high quality of data and the opportunity to make corrections to any incorrect or improper data. During this process data is documented, corrected, and saved. An example of data analysis would be individuals’ submitted answers to a survey or poll. Their submissions would be processed to form data. So all of their submissions would be the data analysis.

The results of the poll or survey would lead to interpretation. Interpretation involves action. Data interpretation is applying statistical procedure to analyze specific facts from a study of body of research. It is the act or result of interpreting. It can be described as an explanation of results or reasoning. Interpretation is used to define data or justify actions due to received data. It is often used in the same sense as the word translation or decoding. Initial data analysis is the process of taking collected or gathered data, process it and develop conclusions and useful information.

There are some guidance questions that can be used to frame this process. The first question is, what is the quality of the data,. The quality of data is very pertinent to leading to the interpretation of data. We would want to review the quality of the data as early on in the process as possible. Next, we want to know the quality of measurements. According to the data being used, we want to be sure to use the proper method of measurement for accuracy. Keeping in mind, the intentions and purpose of collecting the data, we should check the success of data analysis.

If the purpose was not met, one should check data sampling and make sure none of the data was compromised to manipulate the results. If the process was successful, the results can be interpreted in a form that will present either a confirmation or exploratory approach. It is best to do this prior to collecting the data. Confirmatory would present clear hypotheses about the data, while exploratory can involve multiple models used to find ideas for a theory, but not to test that theory. Confirmatory can be used to test theory and can prove to be more informative.

Interpretation of data involves taking the raw facts, explaining the meaning or significance of data gathered. When the data is gathered and has been analyzed, one can take the findings and process them to have meaning. What that analyzed information means is the interpretation. There are different examples of data analysis and interpretation. As mentioned earlier, poll and survey, research studies of data samples. The results of data interpretation can be presented as a number, a statement, an explanation, or visually on a chart or graph for easier comparison.

This method can be said to be a correlation method. This occurs when two or more sets of data are compared to see if there is in fact some relationship between two or more sets of data. Descriptive statistics is another type of interpretation. Mathematically referred to as the average number in a set. This method is used to get an individual’s final grade, as an example. This can also be used as a guide on a growth/height chart. Referenced when many people shop to make sure they are not paying or offering above or below average.

Regression coefficient is used to establish the cause of correlation or the relationship between two sets of data. Many professional employed by major companies, financial, medical, and, educational field use this method for different uses. Financial companies use data analysis and interpretation to set rates and increase productivity. Medical professionals perform data analysis on individuals and on large population of people. They use this information to keep up with average age, weight, or even regional location of a specific disease or disorder.

Educators use data analysis and interpretation not only to evaluate existing or potential students, but also as a way to keep track of the enrollment of students. They track how many students enroll and transfer to another school, the graduation and dropout rate. They also are able to compile data to accommodate students and set tuition rate and introduce or discontinue certain courses. All three of the aforementioned fields, also use this data method to present themselves to the public.

Their funding also depends upon the information gathered from research and planning according to their results of data analysis and interpretation. We see evidence of data analyst and interpretation numerous times a day. Insurance quotes, medicine dosage, gas prices, even the order in which items are placed on the shelves in grocery stores. A study has been done to find out how to make consumers spend more when they shop. Grocers and retailers have used this study to arrange their products and to make changes to their store layout, to become more appealing to shoppers.


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  • University/College: University of California

  • Type of paper: Thesis/Dissertation Chapter

  • Date: 26 September 2016

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