Every research paper is founded on, well, research. And as anyone who’s ever written a research paper knows, there are countless methods of conducting research, and many different types of data that you can acquire from each research method.
When it boils down to it, though, there are exactly two types of research: quantitative, and qualitative. These two methods of research couldn’t be more different—they have completely different data sets and methodologies, and are synthesized in unique ways across various fields of inquiry.
Knowing how to use each research method is key to developing the right insights for your paper. In this article, we define what is the difference between qualitative and quantitative data and illustrate how to use each, both individually and to complement one another, when you’re writing a research paper for different fields of study.
What is the difference between quantitative and qualitative data?
In a nutshell, the difference between quantitative and qualitative research is as follows:
- Quantitative research collects and analyzes measurable, empirical data to test and confirm hypotheses.
- Qualitative research uses non-measurable, subjective data that is primarily expressed in words, and is used to explore ideas and develop insights into problems and hypotheses. It can also be used to develop or interpret data collected in quantitative research.
What kind of data is used in qualitative vs. quantitative research?
Performing qualitative vs. quantitative research results in very different kinds of data, which is used in unique ways depending on the field of study and the hypothesis you’re trying to prove.
Quantitative research produces measurable data that is expressed in raw numbers, graphs, tables, and charts. Essentially, it answers exactly “what” something is when it comes to your research problem.
Some examples of quantitative data sets include:
- Average monthly temperatures measured in a single city over a period of 20 years
- Amount of foot traffic a mall receives on each day of the week
Consider a hypothetical study:
“What is the scariest horror movie?”
The experimentation in this problem would involve observing and interviewing participants as they watch a list of horror movies.
To get objective data about our hypothetical horror problem, you need to find things that are numerical and measurable, which are related to how viewers respond to horror. Some examples would be:
- Average heart rate and blood oxygen saturation levels for viewers
- Number of times viewers closed their eyes for longer than five seconds
- Number of times viewers screamed past a certain decibel level
- A post-movie survey that asks viewers to rate the “scariness” of the movie they watched, from 1 to 5 (this is a way of translating qualitative research into quantitative data).
These are all objective numbers that you can correlate with the horror movie being watched, and get a “horror score” that you can associate with each movie.
On the other hand, qualitative research answers “why” something is, and is often expressed in narrative, textual form.
Examples of qualitative data include:
- Written or verbal opinions of residents of a town, about their quality of life over the years
- Field notes based on your observations of a certain animal species
- Interviews with employees of a corporation about their experiences
To use the hypothetical horror movie example again, you might have gathered raw quantitative numbers about each horror movie, but there are still ways to explore your problem further through qualitative research.
For example, you can interview each viewer immediately after they watch a movie, then again a few days later, and ask open-ended questions such as:
- Why do you think the movie was so scary?
- What kind of dreams did you have after you watched the movie?
- How has your normal daily life been impacted by the movie
Even if you have empirical data about their reactions to each movie, you won’t be able to analyze its longer-term impacts on people. You need qualitative data to do this. A movie with a lot of jump scares will definitely be able to elicit fast heart rates and screams from viewers, but you could also argue that movies which “stick with you” long after you’ve left the cinema are even scarier.
Qualitative vs. quantitative research and analysis methods
Quantitative and qualitative research are carried out in very different ways, due to the kind of data that they use.
Quantitative research is collected via highly structured methods, such as:
There are also different approaches to quantitative research which you can use depending on what your goal is with your research problem. Let’s go through a few of them.
Descriptive research aims to describe something through the raw data variables that you’ve collected.
For example, “What is the most-visited city in America?” can be answered by comparing empirical figures on the number of people who’ve visited each city.
Correlational research identifies the relationship between variables that you’ve identified in your research problem.
For example, the question, “Does the amount of coffee a person consumes daily affect their life expectancy?” can be answered by looking at two variables (daily coffee consumption and life expectancy) and determining whether they have positive, negative, or no correlation.
On the other hand, qualitative research is collected using less structured methods, such as:
Once you have your qualitative data, there are multiple approaches to analyzing it, depending on what you need to achieve in your research.
Here are just a few methods for processing qualitative data:
Content analysis is used to determine if there are certain patterns of word or phrase usage in the content of your qualitative data. It typically involves analyzing the raw text and categorizing words and ideas within it according to your needs. Content analysis can often be a hybrid of qualitative and quantitative research.
For example, say you want to test the hypothesis that female professionals are more frequently treated with condescension than male professionals when communicating with clients by email. To do this, you gather emails sent to both male and female professionals, analyze the words used in each email, and then categorize the text according to the use of “condescending” language. By counting the instances of this use of language and correlating it with the sex of the recipient, you can confirm or debunk your hypothesis.
Thematic analysis is used to identify whether there are certain themes in a qualitative data set. Rather than looking at individual words or ideas in each text, as with content analysis, thematic analysis looks at the overall sentiment of the text and determines whether there are overarching themes in the data set.
For example, if you want to identify how students at a certain school feel about the school’s atmosphere and culture, you could collect surveys and interviews with students, and interpret each interview on the basis of sentiment. You would then get a big picture of how the student body generally feels about school life.
In discourse analysis, you look at how language is used in various social contexts. Discourse analysis involves looking at entire texts, including conversations, newspaper articles, and speeches, and analyzing how this communication is leveraged to achieve certain goals, such as convincing a population or eliciting emotion.
One example of discourse analysis is looking at the way the language of advertising companies evolves. By looking at various advertisements over the years, you might observe that over a period of a decade, technology companies have shifted from selling products on the basis of features and specifications to associating them with lifestyle and luxury choices.
How are Qualitative and Quantitative Research used in Different Fields?
Each humanities or scientific field has its own needs for either qualitative or quantitative research. Some fields have more extensive use of one type versus the other.
In general, the hard sciences, such as physics and chemistry, make extensive use of quantitative methods. Because they observe objective processes in the physical world, the hard sciences require a high degree of experimental reproducibility, and conclusions are always backed up by numerical methods. But observations gleaned from experiments themselves can also constitute a form of qualitative data.
On the other hand, the social sciences, such as anthropology and sociology, often have a more balanced use of both qualitative and quantitative data. Exploring human sentiment and experience often requires subjective assessment, such as interviews or textual review, and this will require qualitative research methods.
At the same time, the social sciences will also require quantitative methods to get standardized data from large numbers of human respondents. Many social scientists are familiar with the classic quantitative survey with multiple-choice methods.
History is a field where qualitative and quantitative research methods are often used in concert. One example would be an analysis of the Great Depression’s impact on society. A quantitative approach could look at the availability of basic goods and services in different regions of the United States at the peak of the Great Depression. By looking at regions where availability is lowest, a qualitative approach can then be undertaken, analyzing first-hand historical accounts in those regions to see what life was like at its worst.
How Flowcite Supports Qualitative and Quantitative Research
Whether you’re using qualitative, quantitative, or both types of research at once, it’s important to be able to manage your sources of data and to cite them correctly for attribution.
Flowcite provides you with a platform that lets you search for your secondary sources, manage your research, and cite sources in your paper instantly. The platform’s core and add-on services provide you with everything you need to write a paper from start to finish, including:
- A Knowledge Library with over 25 million credible academic sources
- An AI-driven Article Summarizer
- A Browser Plugin for web source referencing
- Up to 30 GB Personal Library storage for references and citations
- A Writing Extension with over 7,000 citation styles (compatible with MS Word, LaTeX, and CKEditor)
The platform includes a built-in LaTeX text editor, which allows you to easily write formulas, tables, and other forms of quantitative data right in your document editor, without having to worry about styling and formatting. LaTeX is the typesetting system of choice in the world of scientific research, with as many as 18% of researchers using it.
Flowcite’s editor also allows you and your peers to work on your paper together. This is especially helpful if you have peers who are working separately with qualitative and quantitative research methods.
And it doesn’t stop with writing your paper. Flowcite supports you throughout the lifecycle of your research with additional services that help you review, finalize, and publish your paper, including:
- Professional Similarity Checking, Proofreading, and Journal Matchmaking services
- Peer Review and Manuscript Submission support
- Printing Service with free worldwide express delivery
Flowcite combines many tools that you’d otherwise use separately into a single unified platform, making it easier to coordinate different parts of your work. In fact, it can save as much as 80% of the time you spend on tasks that aren’t writing your paper, such as project and software management.
Qualitative and quantitative research have very specific use cases for each field and research problem. But that doesn’t mean that they’re two opposing kinds of research, to be used in isolation. Rather, they’re often two sides of the same coin, complementing each other, reinforcing hypotheses, and providing new contexts to explore.
Knowing how and when to use these types of data is key to growing as a scientist. And it’s just as important to use the right tools to maximize your collection and organization of this data so that nothing ever gets lost in the process.
Get started with Flowcite today for free and start conducting your research in minutes, not hours.