Having an understanding of data analysis is increasingly important as we move into an era where almost every activity creates, collects, and processes large amounts of information.
Data analytics are everywhere around us – from social media sites to online shopping carts to smartphone apps that track our daily activities. Even our own personal information (e.g., phone numbers, emails, etc.) get stored in databases that use computer algorithms to link all these pieces together.
In fact, according to Business Insider, “data has become one of the most valuable resources for companies” due to its ability to generate insights into human behavior and market influence.1
So how can incorporating lessons in data analysis enrich the liberal arts? We will look at three examples of applications that use empirical reasoning to inspire creativity, imagination, and knowledge about the world.
Three examples: using data to enhance the creative process
Reading literary works like A Tale of Two Cities or The Grapes of Wrath requires you apply yourself and pay close attention. You have to work hard to understand what happens in each chapter and why it matters.
That’s why reading them aloud may be your best bet. By talking out loud while you read, you give your mind more time to really focus on what you just learned.
Audio readers are not only effective for literature, but they also offer benefits in other areas. For example, research suggests listening to audiobooks while commuting helps reduce stress and fatigue.
Statistical data analysis
A growing field within the liberal arts is statistical literacy or, more commonly known as quantitative literacy. Quantitative literacy looks at how numbers can be organized, manipulated, and analyzed to gain insights into the world around us.
Many universities are developing courses that teach this kind of analytical thinking. These classes typically focus on topics such as statistics for social scientists, mathematical psychology for psychologists, and computer science for information technology professionals.
However, what most fundamentally share in common with these fields is the use of mathematics to understand human behavior. This isn’t just limited to understanding why people behave like they do, but also how individuals in groups influence one another.
These concepts aren’t exclusively used by professional experts, nor are they entirely theoretical. Many industries rely heavily on math to achieve their goals, from finance to healthcare to education.
In fact, many students enter college with little-to-no formal training in math beyond basic arithmetic. But there are ways educators can help them navigate through the pitfalls of math class while still offering relevant lessons about our everyday lives.
Graphical data analysis
Another way to use math in the liberal arts is through graphical data analysis. This form of mathematical visualization can be done virtually anywhere, using almost any device!
Data graphing comes in many forms. One of the most popular types is called a scatterplot. A scatterplot uses two variables (or factors) as axes for time and something else.
For example, you could make one axis represent months and another axis represent temperature. Then, you would plot temperature against month and see what trends emerge.
You are not necessarily looking at a linear relationship- some months have warmer temperatures than others, even during the same season. But if there is a correlation, then maybe winter weather makes people happier or people feel colder in cold months so they stay indoors more.
There are several ways to analyze your findings with this type of graph. The first thing is to determine whether the trend is positive or negative – how much does it increase or decrease? If it increases consistently, then we call that a growth pattern.
A growth pattern is important because it shows us that whatever being studied correlates with the things that rise over time. For example, monthly temperatures may feel better due to improved insulation, but it also means that people need to spend more time outside because it’s too warm.
If the patterns are mostly going down, then we should probably look at why that is.
Categorical data analysis
A less commonly learned type of statistical analysis is categorical, or qualitative. This kind of analysis looks at discrete qualities (or categories) of things being studied, and how they relate to each other.
For example, we could analyze all the books in the world and determine which ones are categorized as fiction, nonfiction, young adult, etc. Then, we could compare those genres to see what percent of the market each one captures.
We could also look at different genre types within those larger categories to see what sells well and why. This way, we can learn more about the success of certain books, authors, and publishers by looking into underlying factors such as motivation for readers, and strategies for writers.
With quantitative research, there’s an emphasis on finding average answers to questions. But with qualitative studies, there’s an emphasis on individual experiences, observations, and stories- these often have moral lessons or examples that apply to others.
Data analytics can help us draw conclusions from both sets of information. So, while statistics and numbers may be used to prove or disprove theories, qualitative analyses can show you something new about people, ideas, and systems.
Multivariate data analysis
One of the most important concepts in analytical psychology is multivariable or, as some like to call it, “multiple regression.” This technique involves studying how certain variables influence one another by testing whether there is an association between two measurable indicators.
The term “regression” comes from the Latin word meaning “to go back.” In other words, when we perform multiple regression, we are creating equations that predict what item A will be influenced by item B. For example, if you were trying to determine which movie is the best superhero film, then genre would likely play a significant role in determining the winner.
There are three main reasons why performing regression analyses is so powerful. First, it can identify potential causal relationships – for example, reading comics makes you watch more superhero movies. Second, it allows us to measure the strength of those associations (or correlations). And third, it helps rule out spurious causes because it requires that effect A must be caused by effect B.
Tracy Hester, PhD, LMFT
Dr. Tracy Hester has over 16 years experience working with adolescents, adults, and families. She works primarily with depression, anxiety disorders, grief reactions, trauma symptoms, relationship issues, and career development. Dr. Hester earned her bachelor’s degree at Brigham Young University and master’s degrees from Stanford University and The George Washington University.
A large part of what makes up the liberal arts is analytical thinking. This includes reasoning, studying patterns, and gathering information through observation and discussion. In fact, many consider being able to apply systematic analysis to be one of the most important qualities in life.
There are several areas within the humanities that require significant amounts of analytical thinking. These include literature, psychology, sociology, and anthropology- all very close related fields.
In this article we will talk about some ways you can use data analytics in the field of social science. We will also look at how you can begin practicing these concepts on your own.
Data analytics for social scientists
Here we will discuss three different applications of data analytics that have direct connections with studies in the field of sociology. All three topics can be done online using free resources or softwre packages such as Google docs or Microsoft word.
1) Sentiment analysis
One popular way to do sentiment analysis is by looking at how people respond to media content, advertisements, statements, etc.
A lot of companies now use technology to scan messages and comments to determine whether people like or dislike something. For example, if there are lots of negative comments about an item, then the company may decide it would be a good idea to make similar items but with opposite colors or textures.
This saves money for the manufacturer since they do not need to create those features themselves, and it cuts down on waste.