All research boils down to data. Therefore, for effective data collection, you will have to know what kind of information is required. Consequently, we want to define the main data collection methods from the start.
Generally, you will need to choose between qualitative and quantitative approaches. Qualitative is descriptive data that provides information about the characteristics of the researched topic. Quantitative is used to express a specific amount or range. Even though the two concepts are often confused because of similarities, they have significant differences. Quantitative and qualitative information is collected to learn different things and provide researchers with additional findings.
What is Data?
Let’s start with the basic definition. Data is a combination of disparate details, facts, and information collected for research or analysis. These may be words, numbers, measurements, observations, characteristics, or descriptions of things. Before processing and segmentation, data is raw information that cannot provide insight into a specific topic.
To benefit from the raw data and define what it means, you need to analyze it. The analysis is essential to group, model, and convert data units into a clear format.
Qualitative Data Definition
Qualitative data is the information that describes something. This data type is primarily about categories and groups rather than numbers. Qualitative data is also called categorical data. The main question it answers is Why?
Qualitative data may be used for a direct assessment of website usability. When UX specialists see that users have problems with specific website elements, they ask follow-up questions to understand the issue. The answers to these questions and some additional details like heatmap or scroll data are qualitative data. These findings describe how something works. Later on, UX specialists can rely on their expertise and the collected insights to figure out how to improve the problematic website elements.
Qualitative Data Types
When you analyze qualitative information, you will come across three data subtypes: binary, nominal, and ordinal. Here are their brief definitions and examples to show you the differences and similarities between these subtypes:
- Binary or dichotomous types. The data places the variable in one of two categories: yes/no, good/bad, accept/reject. These research categories are mutually exclusive.
- Nominal types. The value specifies the quality of the variable (e.g., color). This data type is descriptive and unordered
- Ordinal types. The deal is often numerical and assigned to categorize with some natural order (e.g., small/medium/big, 1 to 10 score).
Quantitative Data Definition
Quantitative data is the information provided in numbers or counts. All data set is associated with a unique numerical value. That’s why the quantitative data type is also called numerical data. The main questions it answers are: How much and How many.
It may be used for an indirect assessment of website usability. These are analytics, click-through rates, number of errors, and other website metrics. Whereas qualitative data directly describes the issues, quantitative data types are numbers that require a broader context for analysis. This is a tremendous difference. What does it mean if you know that 50% of users don’t complete the website form? Is this a good or bad indicator? To explain this during the analysis, quantitative data types are often compared to reference point data. This helps to understand whether the recorded value means positive or negative and track the differences between values.
Quantitative Data Types
Anyone gathering quantitative data for analysis and research should distinguish its subtypes, differences, and similarities. Quantitative data falls into two subtypes: discrete and continuous. Here and their definitions and examples:
- Discrete types. These data units are counting numbers only. The number of pets in a house is discrete because you cannot have 1.5 or 1.3 cats. Some other examples include the number of days in a year, the age of a person, etc.
- Continuous types. These data units can be broken into more minor levels in a meaningful way than discrete data. Measure dog’s weight in kilos, grams, milligrams, and so on. Weight, length, and speed are other common examples. The continuous data type is subdivided into ratio and interval data.
Difference Between Qualitative Data And Quantitative Data
Now when you know the definitions, it’s time to talk about differences. The critical difference is that qualitative data is descriptive and categorized based on the characteristics of the studied object. On the other hand, quantitative data is counted and expressed with numbers. In contrast, qualitative data helps to figure out why something happens, quantitative measures what happens.
These two data types are also rarely collected, compared, and contrasted at the same time. In website analysis, qualitative data are useful at any stage. Use qualitative research both to test the existing website or develop a new one from scratch. Quantitative data is collected when the product is ready, and it’s time to measure how it works and collect analytics.
What is Qualitative Research?
Qualitative research is a research method that collects and analyzes non-numerical data with questionnaires, observations, record-keeping, and other alternative approaches. It relies on descriptive qualitative data that explains why a specific phenomenon happens instead of only measuring it. For qualitative research, you will usually use a small sample size.
What is Quantitative Research?
Quantitative research is a research method based on performing statistical, mathematical, or computational analysis with the help of data sampling. The emphasis is put on using numbers for testing a hypothesis. Although the quantitative research methodology also relies on surveys and polls, their results are numerical values. To complete the analysis, these values are transformed into tables, graphs, or charts. Since the quantitative research methodology requires lots of input data for accuracy, the sample size is big.
Qualitative and Quantitative Data in Website Analysis
Qualitative and quantitative research is of the utmost importance in web analysis. However, when these techniques are mixed, learn what people do on website and why they behave like this. So let’s differentiate when to use each of the methods and have them mixed.
Run quantitative research to analyze big data and detect possible problems. Web analytics tools like Plerdy collect critical website statistics and display the data in graphs for tracking. It allows you to see weak spots and research them with qualitative analysis (e.g., surveys, questionnaires, web forms). Plerdy collects statistics and offers popup forms, event tracking, heatmaps, and other qualitative research capabilities. This makes analysis super easy.
How to Conduct Qualitative and Quantitative Research of Your Website with Plerdy
Plerdy has many benefits for marketers, UX designers, and website owners that need web analysis. First, it collects unique types of data, which differentiates it from many alternative solutions. That’s why, if you want to run mixed qualitative and quantitative research, consider using Plerdy.
To configure Plerdy tools, you will need to:
- Create a Plerdy account.
- Install the Plerdy script on the website you want to monitor.
- Use the intuitive interface to configure data collection.
Plerdy provides seven products that allow for combining mixed qualitative and quantitative research methods. Use website heatmaps, event tracking, popup forms, sales tracking, SEO checker, conversion funnel analysis, and session replay software. The data collected with these tools help track customer behavior to detect even minor changes and differences.
Our team recommends using Plerdy’s click heatmap, session recordings, event tracking, and conversion funnel analysis to collect qualitative data. Popup forms can also come in handy. For quantitative research, Use detailed analytics available in the Plerdy dashboard.
In this article, we’ve covered the main differences between qualitative and quantitative data types and research. Nevertheless, both approaches have proven importance for studies in multiple fields. Website analysis in one of them. Combining qualitative and quantitative methods with reliable tools can measure customer behavior and differentiate the underlying reasons to fix the detected problems.