If you are involved in statistics, marketing or data science, it is essential to know what is the difference between qualitative and quantitative data and analysis.
They are different approaches to understanding data and to perform analysis of information.
We will define, explain, compare, and give examples of qualitative data versus quantitative data, variables, and analysis.
On this page:
- Qualitative vs quantitative data: definition, examples, characteristics, contrast, similarities, and differences.
- What is quantitative data analysis? Steps and types.
- What is qualitative data analysis? Steps and types.
- Comparison chart in PDF (infographic).
What is quantitative data?
Quantitative data seems to be simpler to define and identify.
Quantitative data are easily amenable to statistical manipulation and can be represented with a wide variety of statistical types of graphs and chards such as line, graph, bar graph, scatter plot, box and whisker plot and etc.
Key characteristics of quantitative data:
- It can be quantified and verified.
- Data can be counted.
- Data type: number and statistics.
- It answers questions such as “how many, “how much” and “how often”.
Examples of quantitative data:
- Scores on tests and exams e.g. 85, 67, 90 and etc.
- The weight of a person or a subject.
- The number of hours of study.
- Your shoe size.
- The square feet of an apartment.
- The temperature in a room.
- The volume of a gas and etc.
Types of quantitative data:
There are 2 general types of quantitative data:
- Discrete data – a count that involves integers. Only a limited number of values is possible. The discrete values cannot be subdivided into parts. For example, the number of children in a school is discrete data. You can count whole individuals. You can’t count 1.5 kids.
- Continuous data – information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value. For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc. More on the topic see in our post discrete vs continuous data.
What is qualitative data?
As you might guess qualitative data is information that can’t be expressed as a number and can’t be measured.
Qualitative data is also called categorical data. The reason is that the information can be sorted by category, not by number. Qualitative data is analyzed to look for common themes.
Key characteristics of qualitative data:
- It cannot be quantified and verified.
- Data cannot be counted.
- Data type: words, objects, pictures, observations, and symbols.
- It answers questions such as “how this has happened” or and “why this has happened”.
Examples of qualitative data:
- Your socioeconomic status
- Colors e.g. the color of the sea
- The Smell e.g. aromatic, buttery, camphoric and etc.
- Your favorite holiday destination such as Hawaii, New Zealand and etc.
- Names as John, Patricia,…..
- Sounds like bang and blare.
- Ethnicity such as American Indian, Asian, etc.
Quantitative Data Analysis: Meaning, Steps, and Types
Quantitative data analysis ends with easy to understand and quantifiable results. You can analyze it in many different ways. But before starting the analysis you have to define the level of measurement involved in the quantitative data.
Let’s see the steps in the process of analyzing quantitative variables. It will help us to see better the difference between qualitative and quantitative data analysis.
Step 1: Identify the level of measurement
There are 4 scales/levels of measurement:
- Nominal – data scales used simply for labeling variables, without quantitative value. The nominal data just name a thing without applying it to an order. Even though we can use the numbers, they do not denote quantity. Examples of nominal data: hair color (Blonde, Brown, Brunette, etc.).
- Ordinal. Ordinal data is placed into some kind of order by their position on the scale. They often indicate superiority. Example of ordinal data: the first, second and third person in a competition. To understand better see our post nominal vs ordinal data.
- Interval – numerical scales that show information about an order. In interval scales, the intervals between each data value are the same. A popular example here is the temperature in centigrade, where, for instance, the interval between 930C and 950C is the same as the distance between 1060C and 1080C. However, there isn’t a starting point in the interval scales. See more interval data examples.
- Ratio – not only show order and have equal intervals, but they can also have a value of zero.
Identifying the levels of measurement where a dataset falls under, will help you decide whether or not the data is useful in making calculations. The scales of measurement are very important because they determine the types of data analysis that can be performed.
We should note that the nominal level is just used to classify data while the interval and ratio measurement levels are much more exact and more used for quantitative data analysis.
After determining the measurement level, the next step in quantitative data analysis is to enter the data into a spreadsheet and organize it or to “code” in some way that will give meaning to data.
The best way of doing that is with specialized data software.
As you have the raw data, you cannot just sit and look at it. You need to take actions to identify some patterns or to visualize what the data is showing.
Step 2: Perform descriptive statistics
Descriptive statistics are used to describes and summarizes basic features of a data set. Commonly used descriptive statistics are:
- Central tendency (mean, mode, and median).
- Dispersion (range, quartiles, variance, and standard deviation)
Step 3: Perform inferential statistics
Inferential statistics are used to draw conclusions and trends about a large population based on a sample taken from it. Inferential statistics study the relationships between variables within a sample.
Inferential statistics allow you to test different hypotheses and to generalize the gained results to the population as a whole.
Key inferential techniques, methods, and types of calculations are:
- Linear regression models
- Logistic regression
- Analysis of Variance (ANOVA)
- Analysis of Covariance (ANCOVA)
- Statistical significance (T-Test)
- Correlation analysis
Step 4: Define statistical significance
Finally, you need to look for statistical significance. Statistical significance is captured through a ‘p-value’, which evaluate the probability that your discovering for the data are reliable results, not a coincidence. The lower the p-value, the more confident you can be that your findings are reliable.
As you see when it comes to quantitative data analysis, there are many techniques and methods you can use.
The next step in our post for the difference between qualitative and quantitative data is to see what qualitative data analysis involves.
Qualitative Data Analysis: Definition, steps, and types
It is harder to perform Qualitative Data Analysis (QDA) in comparison with quantitative one.
QDA includes the processes and methods for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data.
Qualitative data analysis is very important because it allows data sciences and statisticians to form parameters for observing and analyzing larger sets of data.
For example, if a company need to identify the diversity of its personnel, it would look at qualitative data such as ethnicity and race of its employees.
For comparison, quantitative data, in this case, could be the frequency of workers to belong to those ethnicities and races.
In general, the qualitative data analysis has the following steps:
Step 1: Become familiar with your data
As a data scientist or researcher, you have to read and re-read the data, record detailed notes and impressions, and deciding which pieces of data possess value.
Step 2: Define the key questions that they need to answer through the QDA
Each QDA has specific questions, problems or topics. Find out which questions do you need to answer.
Step 3: Reduce and code the data into themes
This means to create categories and subcategories. These categories are very likely to get bigger as you work through your data. The list of build themes represents your first set of codes.
Step 4: Search for patterns and connections
This step involves looking for the relative importance of data, identifying relationships between data sets or themes, and trying to find explanations from the available data.
Step 5: Interpret the data and draw conclusions
After you identify the themes, connections, and patterns, now you need to assign meaning and importance to the data.
It is very likely to find out much more than you could need, so you will have to decide what is most significant data and results.
Note: qualitative data do not drive conclusions and generalizations across a population. This is an important difference between qualitative and quantitative data analysis. In contrast, quantitative analysis can lead to conclusions or trends about a large population based on a sample taken from it.
There is a range of approaches to qualitative data analysis. Some of the key approaches are:
- Content analysis – a technique to make inferences by interpreting and coding textual information (e.g., documents, graphics, oral communications).
- Thematic analysis – a widely-used QDA method that involves grouping the data into themes to define patterned meaning across a dataset.
- Discourse analysis – includes a group of approaches for analyzing written or vocal interactions or any significant semiotic event. The method focuses on the social context in which the communication happened. It seeks to understand how people express themselves.
- Grounded theory – allows you to look for latent social patterns and structures.
As you see the difference between qualitative and quantitative data is significant, not only when it comes to the nature of data but also the methods and techniques for analysis are quite different.
Both qualitative and quantitative data analysis have a vital place in statistics, data science, and market research. The two data analysis types work great together to help organizations build much more successful data driven decision making process.
Working in data management area involves familiarizing yourself with the suitable software solutions. There is a wide range of tools, dashboards, and programs that you can use to make the whole data analysis process effective and profitable.
Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.
Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.