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Data analysis is common when researchers are reviewing information gathered for a particular study. Researchers gather many different types of data. The best tips for analyzing data include creating a plan for data gathering, separating data into groups, organizing data once retrieved, and computing descriptive statistics. Researchers often have the liberty to work with their data as they desire because they have the most control over the research process. Analyzing data can take different amounts of time depending on the size of the data group.
Starting a research process begins with deciding which type of data will help support a research hypothesis. Researchers need to create a plan for which data to gather and how they will gather it. This plan will have complete steps that guide the entire data information process from start to finish. Alterations to the plan may occur when a researcher discovers new or alternative data during the study. Analyzing data may also change if a researcher decides to alter the plan for gathering data.
Once a researcher has data in hand, analyzing data begins with separating the information into groups. The two most common data types are qualitative and quantitative. The former style is less mathematical and can be a bit more difficult to analyze. Quantitative data allows for a more mathematical approach in the analysis stage. Separating gathered data into these two groups allows researchers to decide which tools to use during analysis.
Organizing data once separated into groups is often among the most difficult processes when analyzing data. Researchers need to decide which data needs inclusion and which individual datum needs to be included in tables or other analysis tools. For example, a researcher studying demographics may organize data by race, sex, income, and so on. How analyzing the data will affect the study may also play a role in its organization. In short, a proper plan for analyzing data is necessary to prevent errors or bias from being introduced into the study.
Descriptive statistics are among the most common output when analyzing data. These statistics often include mean, median, and mode along with standard deviation and variance. These data groups allow researchers to have a base for further analysis. The nature of these statistics is just like their name; the individual statistics are meant to describe the information gathered through the research methods. Once researchers compute the initial statistics, they can go further into the analysis if necessary with the same data set.
@Charred - I worked in the reporting department of a telecommunications company. Sometimes we used statistics, and sometimes we didn't – or it was very basic stuff.
The most important thing in data analysis is properly defining your question. What is it that you’re looking for? Start with this top down approach, and then it will be easier to identify the particular data sets that you will have to query in order to find the answer that you need.
Sometimes you need to drill down into even deeper levels of granularity to find the answers you need. What you want is to continually whittle down your data sets so that they are small enough not to drain your server resources yet large enough to give you the information you need. As you can imagine, in telecommunications you have gobs of data to work with.
I work at a software company where we develop software for the energy industry. Our software is feature filled, but the most important feature in my opinion is its reporting functionality.
I would say it’s the statistical reports that add the most value to the whole product. Frankly, without the reporting the entire application would be worthless.
Our customers are called upon to deliver these reports to auditors who visit their facilities and ask for information about tests run on the electrical equipment.
The statistical reports also include charts and plot graphs as ways of displaying the information. This makes it immediately accessible to everyone in a way that they can understand.
Without this ability to analyze the data, they would be forced to run queries against the database to get the information they need, which is not as easy.
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