Data analysis methods are tools you can use to analyze data. They come in two categories, depending on whether the data is quantitative or qualitative.
Data analysis enables you to get the most out of data. Not only does it allow you to describe past events, but it also allows you to explain them, find relationships between them, and predict them.
An essential part of data analysis is using the right data analysis methods. Depending on whether your data analysis revolves around quantitative and/or qualitative data, there are a variety of simple methods you can use to gain new insights into your data.
In this article, we'll take a close look into some of the most popular methods for analyzing data, discuss which of them to choose, and touch on whether you can combine them. First things first, what exactly are data analysis methods?
What Are Data Analysis Methods?
Data analysis methods are specific tools or techniques you can use to analyze data. They come in two broad categories, depending on whether the data is quantitative or qualitative.
Quantitative data is data that can be expressed in numbers. As a result, a big part of quantitative data analysis is using statistical methods to find objective patterns within the data. Generally speaking, this is the preferred type of data for data analysis, since the related methods leave no room for interpretation.
Qualitative data is any data that cannot be expressed in numbers. It's often based on opinions or experiences, which are difficult or impossible to quantify. Thus, qualitative data methods are much rarer and tend to be less reliable for drawing up scientific conclusions.
Quantitative Data Analysis Methods
Since quantitative data is ideal for analysis, let's start by focusing on some of the many quantitative data analysis methods. As mentioned previously, many of these methods originate in statistics.
Average
We're all familiar with the average — the central value in a set of data. In fact, there are three well-established types of average: the mean, median, and mode. The mean is what most people think of when you say the word average. It's calculated by summing up the values in a dataset, and dividing the result by the number of values. The median is the middle number in the list. The mode is the most frequently occurring number in the list.
Regardless of which type of average you use, there's a lot to be gained from knowing what the central value is in a dataset. In particular, using averages allows you to smooth out datasets and draw more accurate conclusions; without averages, you might find yourself comparing data to an unusually low or high number.
Range
The range is the gap between the lowest and highest number in a dataset. It paints a picture of how much data can vary within a dataset.
This is also a very valuable metric to have — especially in business — since fluctuations in events can be crucial in the decision making process.
Frequency
Frequency is how often a specific value occurs within a dataset. It's the metric used to calculate the mode of a dataset (since the mode is the most frequently occurring value).
Even if you're not looking to calculate the mode of a dataset, it can still be handy to look at the frequencies of certain values. This can, for example, tell you how often certain events occur.
Standard deviation
Standard deviation is an advanced statistical measure of how much a dataset varies. While the range provides similar insights, standard deviation accounts for variation throughout a dataset, and not just between the extremes.
In practice, the standard deviation is a much more popular data analysis method than the range is, since it takes into account the entire dataset.
Hypothesis testing
Hypothesis testing is any statistical method used to confirm a hypothesis. Usually, it's used to confirm the relationship between two variables, to a certain level of confidence.
This is also a very popular method in the real world, especially in academia, since it's essential to assess whether or not correlations are random.
Qualitative Data Analysis Methods
Although they are much less common, there are some techniques that can be used for qualitative data analysis.
Content analysis
Content analysis is the broad name given to the process of analyzing the content. Since content is mostly qualitative data, statistical methods are less appropriate. Instead, the content must be analyzed by an individual, who will provide a subjective opinion on its meaning, tone, or other characteristics.
As an example of content analysis, consider a person reading a letter. How they interpret the letter might be different to how another person would interpret the letter, but some analysis is still possible.
Grounded theory
Grounded theory is a data analysis method that involves creating an explanation for a pattern or event. Once again, statistical methods are left aside, and an individual must review the dataset to assess what they think might explain certain findings, using inductive reasoning.
In business, grounded theory is valuable where quantitative data is not available. If one proposes an explanation that seems probable, further data collection and analysis can always be conducted.
How to Choose a Data Analysis Method
Which data analysis method you choose will depend greatly on the dataset you are dealing with and what you intend to achieve with it. If your dataset consists of quantitative data, you'll have to use a quantitative method; if your dataset consists of qualitative data, you'll have to use a qualitative method.
In the case of quantitative data analysis methods, metrics like the average, range, and standard deviation can be used to describe datasets. Hypothesis testing is the perhaps the most interesting method, since it allows you to find relationships, which can then be used to explain or predict data.
As for qualitative data analysis methods, content analysis is the primary approach to describing textual data, while grounded theory can be used to explain or predict any qualitative data.
Final Thoughts
Data analysis often makes use of one or more of these methods. Regardless of the type of data you're dealing with, there's bound to be a method that will meet your requirements.
