Qualitative vs. Quantitative data:
Research is an essential part of the writing process. Through this post, I will introduce you to the two main categories of research data, qualitative, and quantitative. Data can be placed into two broad categories. So, how do we define each research method, what are its strengths, and weaknesses? Let’s find out.
Quantitative data is information about quantities, as the name suggests. It is about cold, hard facts and numbers. Such data is usually descriptive, statistical, and structured. It is data that can be counted or compared on a numerical scale like so: AUBG is conducting a survey that rates the restaurant’s food from 1 to 5. The data gathered from this survey is called quantitative data as it is statistical, structured, descriptive, and can be counted and compared on a numerical scale. So, what are the different ways we can collect quantitative data?
There are different methods to collect quantitative data. Such methods include:
- Controlled observation(s)
- Longitudinal studies
It is important to note this is how the data is collected. Data collection differs from conducting research. When conducting your secondary research, you use data that has already been collected. However, in primary research, data collection is your responsibility. Want to know more about primary and secondary research, read our comparative article about research You will encounter the following types of quantitative data during your research.
Types of quantitative data:
Descriptive data: Descriptive statistics allow you to quantify variables for a target group, e.g. How many students take the bus to school? If the variable is the bus, the target group is students.
Comparative data: This type of data compares two or more different target groups based on the listed variable(s), e.g. How many students and adults take the bus? The variable is the bus, and the compared target groups include the students and the adults.
Relationship data: This data highlights the different associations, trends, and relationships of two or more variables, e.g. What is the relationship between food diet and age? Here the two variables are age and a different diet.
Strengths of quantitative data:
Objective: As the quantitative data is of statistical significance and quantified, it tends to be less biased, leaving less room for the researcher’s subjective reasoning.
Easy to analyze: With everything presented in numbers or quantifiable and structured data, quantitative research is easier to analyze than qualitative data as we will see later in this article.
Generalizable conclusions: When conducting a study on a smaller group, in most cases quantitative data can be used to define a larger population relating to the sample study.
Weaknesses of quantitative data:
What about the how and why? Quantitative data provides an answer to the questions: what, who, and when, but doesn’t answer how or why. Therefore, combining qualitative data with quantitative data can answer the full range of questions.
Bigger sample groups: When looking for answers that can be generalized concerning a larger population, the research sample needs to be bigger. This can consume time and money, require more work, and be susceptible to error.
Confirmation bias: Even when our research is statistics-based and quantifiable, this does not safeguard us from looking for data that confirms our beliefs, decisively, or unintentionally.
Qualitative data tends to be descriptive data that try to detail a topic rather than measure it. Such data can include impressions, opinions, reviews, or different methods used to analyze the quality of a subject rather than the quantity of its parameter. In other words, AUBG conducts a survey to understand why the students are unsatisfied with their restaurant food rather than gather ratings from 1 to 5 like the previous example. So, what are some of the most known methods to collect qualitative data? Take a look.
Some of the most common qualitative research collect methods are:
- Case studies
- Secondary research
- Open-ended surveys
- Observational studies
- Focus groups
As qualitative research is descriptive and exploratory, some of the results can be open-ended. So, as a writer, how can you analyze open-ended data?
Data analysis methods:
There are two main ways you should approach open-ended data analysis when it comes to qualitative data:
Inductive approach: As the most common analysis method, it focuses on collecting the data and identifying patterns and trends within the data set, e.g. an employee satisfaction survey.
Deductive approach: This approach works by creating a hypothesis or an expectation from the results and trying to prove or disprove the expectations through data analysis, e.g. writing a research paper.
Strengths of qualitative research:
Data is reasoned: Because the language used can tell us about feelings and different aspects that numbers don’t provide, these nuances offer more information and features towards the results. Such strength can be evident when used during customer satisfaction surveys.
The human element: Qualitative data provides you with more meaningful in-depth feedback. This allows you to understand the human elements behind the answers such as a person’s reasoning and beliefs.
Fewer resources: For qualitative data, you can test smaller sample size. This translates to less time and money consumed than with quantitative research.
Weaknesses of qualitative research:
Time-consuming analysis: As mentioned before, analyzing open-ended data can require some time, so if you’re short on time, fill in this form and let our writers help.
Less generalizable: With a smaller research sample than quantitative data, qualitative data can be less generalized.
Requires good research skills: To be able to have understandable and analyzable data, it is important to have good research skills. Such skills might include asking open-ended survey questions that encourage relatable answers from the participant.
Here is a fast and concise comparison for you:
- Numerical, quantifiable, or statistical
- Collected through experiments, surveys, and polls
- 3 types of data, descriptive, relationship, and comparative data
- Easy to analyze, objective, and generalizable
- Consumes more time and money and requires larger sample groups
- Doesn’t answer the how and why
- Open-ended data
- Collected through open-ended surveys, interviews, and experts’ opinions
- Two collection approaches: inductive and deductive
- Reasoned data and fewer resources
- Has a human element
- Time-consuming analysis, less generalized, and requires good research skills
It is important for you, as a writer to understand the different types of data that you will face in your research and how to implement them correctly.