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.
That’s why we’ve decided to write an article that would throw some light on quantitative and qualitative data. Keep reading to learn:
- Quantitative and qualitative data definitions
- Types, examples, and differences of quantitative and qualitative information
- Quantitative and qualitative research methods and data collection approaches
You will also find out how to run quantitative and qualitative research for web analytics with modern tools.
- What is Data?
- Qualitative Data Definition
- Qualitative Data Types
- Quantitative Data Definition
- Quantitative Data Types
- Difference Between Qualitative Data And Quantitative Data
- What is Qualitative Research?
- When to Choose Qualitative Analysis
- What is Quantitative Research?
- When to Choose Quantitative Analysis
- Qualitative Data Collection Approaches
- Quantitative Data Collection Approaches
- Qualitative and Quantitative Data in Website Analysis
- How to Conduct Qualitative and Quantitative Research of Your Website with Plerdy
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 you any insight into a specific topic.
To get some benefits 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. The approach to the analysis and the methods used heavily depend on the data types (qualitative or quantitative). That’s what we are going to talk about in the following sections.
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. For example, when UX specialists see that users have problems with specific website elements, they ask them 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.
Generally, qualitative data is adequate for research with limited statistics. The thing is that qualitative research methods are hard to combine with most statistical approaches because of many differences.
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.
For example, 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. For example, if you know that 50% of users don’t complete the website form, what does it mean? 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.
As to the uses and methods, quantitative data types take the leading place in statistics. They are compatible with the majority of statistical analysis approaches.
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. For example, 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. For example, you can measure your 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. For example, in website analysis, qualitative data are useful at any stage. You can 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.
Another difference critical to the comparison is the data collection methodology. For gathering qualitative data, you need a limited number of participants and more flexible research conditions. On the other hand, for quantitative data analysis, many participants and strict study conditions are required.
Examples of Quantitative Data
To have a better understanding of quantitative data and distinguish it, check out several representative examples:
- Amount of money spent
- Number of clicks
- Height and width of objects
- The probability of a specific event
- Number of people in the room
- Air temperature
Examples of Qualitative Data
To distinguish which of the collected data is qualitative and choose a proper analysis methodology, view some common qualitative data examples:
- Phone number
- Likert scale
- Multiple questions responses
- Country of origin
- User behavioral patterns
- Colors of different objects
What is Qualitative Research?
Qualitative research is a research method based on collecting and analyzing 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.
For example, you may need to run qualitative research if your website is rarely visited by men, even though it offers men’s products. By gathering information on the behavioral differences of customer groups, you will find out why some of them ignore you. Surveys, web forms, and web analytics tools are perfect for such research.
When to Choose Qualitative Analysis
The primary purpose of qualitative research methods is to understand the underlying reasons for something. For example, you may need to find out why website visitors ignore a specific button or why buyers don’t like your offer. Here are some other common cases when qualitative observations, analysis, and research methodology are preferable:
- Choose qualitative methods to uncover and contrast trends in opinions
- Choose qualitative methods to understand the purpose behind people’s actions
- Choose qualitative methods to get a deeper insight into a problem
- Choose qualitative methods to develop ideas or hypotheses for future quantitative research
- Choose qualitative methods for competitive analysis techniques
- Choose qualitative methods for customer experience analysis
Overall, the qualitative methodology is effective for real-life research processes. It yields excellent results when you have a specific problem and want the big picture.
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.
For example, you can run quantitative research and analysis to display the activity on your website in graphs. This methodology is also suitable to monitor the number of purchases and measure other numerical values.
When to Choose Quantitative Analysis
The quantitative research methodology is used when you need to test a specific theory or measure something. For example, you may need to learn how many customers bounce in April compared to March. Or you may want to distinguish the differences in website traffic after web design changes. In both cases, the result will have a numerical or graphical form.
Here are several examples of cases when the quantitative research methodology brings maximum advantages:
- Choose quantitative methods to test a hypothesis.
- Choose quantitative methods when you need statistical analysis
- Choose quantitative methods for extensive data sampling
- Choose quantitative methods to generate graphs and charts representative of a specific problem
- Choose quantitative methods for laboratory-based research
- Choose quantitative methods for scientific research
Quantitative sampling, research, and analysis are most advisable for gathering and processing statistics. The methodology shows how many events happened with a specific object and how the trends change over time.
Qualitative and Quantitative Research Methods
Even though qualitative and quantitative research methods are rarely used simultaneously, they aren’t mutually exclusive. You can run both types of analysis at different stages of research for a more comprehensive study. Qualitative research and data gathering techniques, including personal interviews, focus groups, questionnaires, and observations, help to get a bird’s eye view on the subject. It’s a great way to estimate the overall situation, map out the possible problems, and create hypotheses for further testing. When you are done with the hypotheses, quantitative analysis is better to check them and have reliable data-driven results.
To get a better idea of how qualitative and quantitative research techniques work, learn more about the approaches they involve.
Qualitative Data Collection Approaches
Qualitative data collection and analysis approaches are time-consuming and require specific expertise. Therefore, if you want to reap the benefits of qualitative research, you will need to use one of these techniques:
Face-to-Face Personal Interviews
It’s one of the most common qualitative data collection types. You ask interviewee questions for data collection, comparison, and study. The answers don’t have a clear structure and may have differences. This methodology usually looks like a set of random open-ended questions with a broad purpose.
These are web-based or paper surveys and questionnaires comprising short questions for data collection. They allow you to learn the opinions of different people quickly and compare their ideas.
In this case, descriptive data collection happens during a group discussion. A focus group study is limited to 6-10 people that have a common characteristic (e.g., they are doctors) for comparison. They talk about different questions under research.
The document revision definition means revising previously existing documents and other data sources in the new research.
In the observation approaches, you will need to immerse yourself in a specific setting and see how people behave. During observations, you can make notes, take photos, and use other tools to collect information. Later on, these findings will help you explain the research phenomenon, compare and contrast several hypotheses.
These data collection techniques can take years or even decades. You will need to repeatedly collect data on the same data sources to find correlations and answers to your questions. Longitudinal studies are a popular solution for anthropological research (e.g., to check how people’s behavior, traits, characteristics change over time).
To collect, compare, and contrast data, you will need to analyze a specific case. The case study may be an individual, group, or organization. One of the key benefits of the case study approach is flexibility. You can use it to study both simple and complex units.
Quantitative Data Collection Approaches
To use quantitative research techniques, you will need to start with data collection. Quantitative approaches have many differences from qualitative. For example, they require different data for sampling, analysis, and comparison. For this purpose, it’s essential to learn the definitions of the top quantitative data collection approaches to distinguish them and use the proper techniques.
Qualitative and quantitative surveys shouldn’t be mixed since, apart from similarities, they have many differences. Unlike open-ended questions, quantitative surveys include closed questions with answer options meaning that people don’t have to write anything. Such questionnaires are targeted at a broader audience than qualitative questionnaires and are more suitable to collect generalized statistics.
Personal interviews are another data collection methodology that can be used for qualitative and quantitative research. Despite many similarities, it’s essential to explain and differentiate how to apply this method in both cases. Qualitative interviews are more detailed and talk-like. Quantitative interviews have a clear structure and a set of questions. The processing of interview results and data samples also have considerable differences. Quantitative interviews are often automated and web-based.
Quantitative observation meaning implies that a researcher makes systematic observations to record WHAT happens instead of WHY. For example, you can count the number of brick-and-mortar store visitors over several periods to do sampling and convert them to a graph. The data comparison will explain when you get the most buyers.
The meaning, benefits, and importance of experiments for scientific studies are enormous. To run an experiment, you need to create a test combining an independent variable and controls. The change of the dependent variable will allow you to confirm or refute the tested hypothesis. The possible uses of experiments are unlimited and vary from medical studies to web analytics. Therefore, the similarities and differences between experiments are also significant.
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, you can learn what people do on your website and why they behave like this. So let’s differentiate when to use each of the methods and have them mixed.
For example, you can 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 in detail 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. For example, you can 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, you can 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 backed up with reliable tools can measure customer behavior and differentiate the underlying reasons to fix the detected problems.