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The type of data psychologists are collecting and how they subsequently process and analyse it is an important consideration when psychologists are planning their research design. Psychologists can choose between quantitative and qualitative research methods, and it affects many aspects of their research, including how both the psychologists and the readers can interpret the results.
Let's look at the different methods, consider some examples, and evaluate them.
Researchers can choose different methods when collecting data for their experiments, freepik.com/storyset
Quantitative and qualitative methods
Research methods can generate different types of data. Depending on the type of research, a kind of data may be preferable to another.
Quantitative research methods are methods that mainly generate numerical data. The aim is to collect objective data to look for trends, patterns or correlations between two or more variables. Statistics show common themes in quantitative data.
Quantitative methods measure by categorising and rating the data collected by the researcher, e.g., how many students in a class enjoy playing a musical instrument. It is great for objective and easy analysis of data.
Qualitative research methods are methods that mainly generate descriptive, worded data. The aim is to collect in-depth and detailed data to understand as much as possible. Thematic analysis is an example of a qualitative analysis of data.
Qualitative methods can explore ideas, motivations, or thought processes in more depth, with a more personalised reflection of the studied person, e.g. to understand why students have chosen to play musical instruments. It is excellent for detailed data analysis and identifying additional areas of research quantitative data may not highlight.
Quantitative research: examples
To better understand quantitative research methods, we can consider some examples. Examples of quantitative research methods include:
Structured interviews
Structured interviews are interviews with closed questions; neither the participants nor the researcher can add more to the question or answer. The questions are designed to produce concise answers. These are considered to be closed questions. Open questions produce qualitative data, so do not confuse the two.
An interviewer may ask, 'Do you enjoy playing sports?'. Acceptable answers may be 'Yes', 'No', or 'Sometimes'. There is no opportunity to add detail to the answer.
Questionnaires and surveys with closed questions
Questionnaires and surveys with closed questions may include multiple-choice or 'Yes or no' questions. They help the researcher obtain data that are easily collectable and analysable.
These methods may also include Likert scales, which use scaling responses to measure and collect data. A Likert scale provides a range of scaling responses a participant can choose from.
You've probably come across a Likert scale before; they are commonly used in customer satisfaction surveys. Consider the example in the image below.
Likert scales can include a range of scaled responses, including the one above. StudySmarter Originals, Shikha Shah
Polls
Polls are an easy way to collect numerical data.
For example, a teacher may ask the class whether they would prefer to do a project in groups or individually. The teacher can set up a poll, and the students can submit their preferences. The data may be represented in numbers (e.g., 20 out of 30 students wanted to work in groups) or statistics (e.g., 65% of the class wanted to work alone).
Experiments
Experiments are good for generating numerical data as they are designed to establish cause and effect relationships between two or more variables. They generally have controlled environments, although this can differ depending on the experiment conducted.
What kind of data can be generated using quantitative research?
Quantitative research can generate a variety of objective, numerical data represented in the forms of:
Bar charts
Graphs, including correlational
Pie charts
Statistics
Tallies
Tables generally show the raw data before it is analysed and can also be used to show descriptive statistics.
Qualitative research: examples
To better understand qualitative research methods, we can consider some examples. Examples of qualitative research methods include:
Unstructured interviews
Unstructured interviews are interviews with open questions; the researcher and participant can follow up on questions and answers, respectively. They are designed to obtain in-depth and detailed data.
Case studies
Case studies focus on a single person, event, phenomenon or issue; the aim is to obtain detail to understand exactly why or how something or someone works. Case studies can generate a mass amount of qualitative data, primarily if carried out across several years or on complex issues.
Questionnaires and surveys with open questions
Similarly to unstructured interviews, questionnaires and surveys with open questions allow for follow-up and detail in questions and answers. They are good for uncovering and understanding the participants' preferences or motivations.
What kind of data can be generated using qualitative research?
Qualitative research can generate a variety of descriptive, worded data represented in the forms of:
Detailed answers or statements
Chronology of events
Reports
Opinions
Images or videos
Quantitative and qualitative data have their advantages and disadvantages. Unsplash
Research design: qualitative, quantitative, and mixed methods approach
Now that you have information about qualitative and quantitative methods, we can briefly consider how a combination of these could work. This is called a mixed-methods approach.
There may be several different reasons for using a mixed-method approach. One reason may be that the research topic is too complex to only rely on one type of data; a second reason is that using a mixed-methods approach may give you a richer, more accurate picture of the research topic.
Combining quantitative and qualitative methods
Combining both types of research methods can have the following advantages:
Flexibility in research methods – they have more choice as to how they carry out the research
The 'best of both worlds' – using mixed methods can decrease the impact of any weaknesses of certain research methods
A wide range of data is produced
An example of a mixed-method approach
We can understand how a mixed-method approach works by considering a simple example.
A teacher wants to find out why her students have not done very well in their recent Geography exams. She suspects that it is because the students do not like the subject and, as a result, do not put effort into studying. To test her hypothesis, she posits the following question:
'To what extent do students' attitudes towards the subject of Geography affect their examination results?'
This is an example of a research question that would result in a mixed-methods approach. Let's break it down slightly to understand how this is the case.
'To what extent do students' attitudes (qualitative) towards the subject of Geography affect their examination results (quantitative)?'
The above example is likely to result in a mixed-methods approach. The teacher could use interviews, questionnaires with open or closed questions, polls and statistical analysis to determine whether there is a link between personal attitudes and examination results.
Evaluation of quantitative research
Let's consider the advantages and disadvantages of quantitative research.
| Advantages | Disadvantages |
| Quantitative data is easier to collect, analyse and interpret | Many participants may be required to make generalisations or to establish trends, patterns and correlations - this may not always be possible |
| Research methods such as questionnaires are inexpensive and easy to set up | Quantitative data lacks the detail and insight provided by qualitative data (although that's not to say quantitative data cannot provide detailed data overall) |
| Quantitative methods can be used to make generalisations, establish trends, patterns and correlations | Quantitative data may oversimplify human behaviours or thoughts |
| The data is objective, scientific and rational | Some quantitative methods, such as experiments, can lack validity if they are done in artificial settings. |
| As the data is numerical, it can be replicated and checked for reliability | Researchers may be biased if they are too focused on testing an existing hypothesis; they may miss important details that require new hypothesis generation, known as confirmation bias. |
Evaluation of qualitative research
Let's consider the advantages and disadvantages of quantitative research.
| Advantages | Disadvantages |
| Qualitative data can produce a lot of detail, depth and insight, which can help researchers find conclusive answers to their questions. | Generating qualitative data can be very time-consuming and expensive at every stage: collection, analysis and interpretation. |
| Qualitative data is better for representing the complexities of human behaviour, thought and feelings. | Qualitative data does not generally come from large sample sizes due to practicalities such as time and cost. This reduces the generalisability of the data. |
| It is high in validity as researchers can find out 'why' something happened. | As the data is subjective, it cannot be easily replicated. |
| Researchers can spot details that can be missed by statistics or scientific analysis. | Due to the subjectivity of the data, there is also the problem of interpretation; researchers may interpret data differently from each other, and this could decrease the validity |
How to decide whether to use quantitative or qualitative methods
It can be difficult to conclude which type of method is 'better' with these considerations. However, the answer is that there is no 'better' method – it depends entirely on what type of research you want to do.
Generally, you could take this approach:
If you want to test an existing theory, hypothesis or relationship, use quantitative methods
If you want to understand or study an issue, person or event to conclude, use qualitative methods
As mentioned above, there is no right answer, and multiple factors should be considered, including whether mixed methods would be beneficial.
Quantitative and Qualitative Methods - Key takeaways
- Quantitative research methods are methods that mainly generate numerical data. Qualitative research methods are methods that mainly generate descriptive, worded data.
- Examples of quantitative methods are structured interviews, questionnaires and surveys with closed questions, polls and experiments.
- Examples of qualitative methods are unstructured interviews, case studies and questionnaires and surveys with open questions.
- A mixed-method approach can provide the 'best of both worlds' by combining numerical and worded data.
- It is important to consider the advantages and disadvantages of quantitative and qualitative methods.
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Frequently Asked Questions about Quantitative and Qualitative Methods
What is an example of quantitative and qualitative data?
An example of quantitative data is a graph showing the correlation between two variables, e.g. the correlation between warmer temperatures and ice-cream sales. An example of qualitative data is a person's interview answers about their experiences during a traumatic event.
What does qualitative data mean?
Qualitative data means the data is descriptive and worded rather than numerical.
Why does action research use both qualitative and quantitative methods?
Action research can use mixed-method approaches, which means that qualitative and quantitative methods are used. This may be because the research topic is too complex to rely on one type of data or that using mixed methods provides a richer, more accurate picture of the research topic.
What is the difference between quantitative and qualitative methods?
Quantitative methods generate numerical data whilst qualitative methods generated worded data.
What are quantitative methods?
Quantitative methods are research methods that generate numerical data. This data can be represented in the form of bars, graphs, correlations, statistics and tallies.
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