# Quantitative Data Analysis: A Versatile and Beneficial Process

Quantitative Data Analysis is widely used in many fields, including economics, sociology, psychology, market research, health development, and many different branches of science. Quantitative data is generally more reliable than qualitative data, and good analysis relies on good data. Quantitative data refers to numbers and statistics, and is very useful in finding patterns of behaviour or overriding themes. This is more useful than qualitative research, as the latter can be very vague, depending on the methods used to collect it. For a brief overview of data analysis techniques and forms of data, try this course:Data & Analysis.

By definition, Quantitative data is numerical. This means you can gather percentages and statistics and analyse your results using graphs and charts. Most data discovered by Quantitative methods is less prone to bias and can often be extrapolated to fit a larger sample size than the data was collected from. Think of gathering data from a single area of a town and using this data to influence decisions that affect the whole town.

Quantitative research can generate large amounts of data. Quantitative Data Analysis is applied to the raw data so that the research can be displayed in a friendlier fashion, especially to those who do not understand the area the statistics refer to. Quantitative Data Analysis usually starts with taking a sample size and minimum / maximum values, in addition to any preliminary tables that have been drawn up. From here, statisticians will often create averages and figure out the deviation from the expected result – and this helps inform whether or not hypotheses are correct. Dispersion is a very important part of data analysis, along with the aforementioned averages.

Quantitative Data Analysis is used in many fields, from Science to Finance. In fact, Finance is a great area to look into. Stock traders use data analysis every day, looking at charts that are covered in Technical Analysis, Moving Averages and Price Oscillations. For a better overview of these aspects of data analysis you can take this course: Technical Analysis in Finance

Numerical Data Analysis usually relies on a set of data that has to be a “fair representation” of the areas that it is concerned with. This means that if you are trying to create a survey on any given subject, you must strive to get the most accurate data in the first instance, as any extrapolation from data with errors will lead to larger errors down the road. The larger the sample size, the more accurate your findings will be, and the more effective your data analysis will be.

Numerical Data Analysis allows us to make sense of any data that is currently available to us. Using Quantitative Data Analysis allows us to organise and summarize and prepare the data for dissemination to others. In addition to these common uses of Quantitative Data Analysis, it is also used to find patterns in data.

If one was to poll a large cross section of students, you wouldn’t be surprised to see that over 75% of students attend the cafeteria four or five times a week, for example. Without Quantitative Data Analysis, you would have to sift through your data every time you wanted to make a statement. Quantitative Data Analysis will find differences as well as similarities and most other patterns in any data you analyse. This allows you to extrapolate and ask questions about the data to see new patterns you wouldn’t see without analyzing the data. Using our cafeteria example, perhaps your data shows that during exam times, the cafeteria attendance goes up!

Quantitative methods are ideal for finding out when and where, who and what and any relationships and patterns between them. They also allow broad study with large sample sizes, which makes for greater accuracy when extrapolating, and can help to improve accuracy of the general result. If you have the choice of surveying 5,000 people or 50,000 people, it’s not much more work to get the extra surveys done by outsourcing the research. The greater the survey pool of participants, the more general and representative the results will turn out to be. Quantitative Data Analysis allows you to analyse your data in a highly objective manner, as quantitative research takes into account very few variables and huge amounts of data.

Assuming your data collection methods are acceptable and standards are met, using Quantitative Data Analysis can allow your research to be replicated and analysed to arrive at the same results. The Scientific Method espouses repeatable results, and this can improve the validity of your claims from your evidence. Quantitative Data Analysis can be quite math-heavy, so taking a course in using a computer program for statistical analysis like Microsoft Excel to do the heavy lifting for you is highly advisable.

Quantitative Data Analysis offers another benefit that generally isn’t found with other methods of experimentation. It helps your results be more accurate, as it is very difficult to add personal bias to numbers obtained when the correct data gathering procedures have been followed. Avoiding personal bias and confirmation bias in the case of analysis means that your data will be much more accurate, as bias happens even when you try to make sure that no bias affects the results. By using purely mathematical analysis on your data, your personal opinion doesn’t matter; and the data remains truthful, as it cannot share your opinion!

Data Analysis allows you to keep aloof from the experiments by hiring unknowns to gather the data for you. This is helpful for so many fields – not just the sciences. Some of the most useful Quantitative Data Analysis techniques are applied for business profit projections and many other things. Data analysis is a highly important skill, and there isn’t a job out there that wouldn’t benefit from the employee knowing at least rudimentary

Quantitative Data Analysis. You can teach yourself easily and quickly with this course on business analysis.