In the world of research, across many different research fields, there are two main approaches that researchers employ: qualitative and quantitative research. In this article, we will explore the key differences between qualitative and quantitative research and look at what the main differences are, including their respective strengths and weaknesses, as well as some common methods used in each approach. By the end of this article, readers will have a better understanding of the two approaches and be able to choose the best approach for their research question.
Table of Contents
The core difference
Once you get started with human behavior research you soon find yourself running into the question of whether your research project is qualitative or quantitative in nature. There are inherent differences between qualitative and quantitative research methods, although their objectives and applications overlap in many ways.
In a nutshell, qualitative research generates “textual data” (non-numerical). Quantitative research, on the contrary, produces “numerical data” or information that can be converted into numbers.
In analogous terms, qualitative research is like exploring a new city. You walk around, talk to people, and soak up the atmosphere to understand the culture and the vibe. It’s all about getting a feel for the place and the people who live there.
Quantitative research, on the other hand, is more like counting cars on a busy street. You collect data, run some calculations, and analyze the numbers to understand how things work and what factors might be influencing them.
So, while qualitative research is about understanding the deeper meaning and nuances of human experiences, quantitative research is about measuring and analyzing data in a systematic way.
Both methodologies have their place in science and research in terms of understanding the world around us, but it is important to know how and when to implement them.
What is Quantitative Research?
Qualitative research is considered to be particularly suitable for exploratory research (e.g. during the pilot stage of a research project, for example). It is primarily used to discover and gain an in-depth understanding of individual experiences, thoughts, opinions, and trends, and to dig deeper into the problem at hand.
The data collection toolkit of a qualitative researcher is quite versatile, ranging from completely unstructured to semi-structured techniques.
Most common applied Qualitative Methods:
In addition, eye tracking or automatic facial expressions can be collected and analyzed qualitatively, for example in usability research, where gaze patterns (such as with heatmaps) or moments of expressions of frustration/confusion can be used to track the journey of an individual respondent within a software interface.
Check out: What is Eye Tracking and How Does it Work?
Typically, qualitative research focuses on individual cases and their subjective impressions. This requires an iterative study design – data collection and research questions are adjusted according to what is learned.
Often, qualitative projects are done with few respondents and are supposed to provide insights into the setting of a problem, serving as a source of inspiration to generate hypotheses for subsequent quantitative projects.
Advantages of qualitative research
Qualitative research methods are the tool of choice when a researcher wants to gain in-depth data from a small sample size, such as a local community, closed demographic, or situations where in-depth data lets you extrapolate responses to a broader perspective. Some of the main strengths of qualitative research are;
- Provides in-depth understanding: Qualitative research allows researchers to gain a detailed and comprehensive understanding of a particular phenomenon or concept. By gathering rich and detailed data, qualitative research can provide insights into complex and multifaceted issues.
- Flexibility: By working with much smaller sample sizes, qualitative research is flexible, adaptable, and agile. It can be adjusted as the research progresses to explore emerging themes and ideas that were not initially anticipated.
- Emphasis on context: Qualitative research emphasizes the importance of understanding the social and cultural context in which a phenomenon occurs. This allows for a more nuanced interpretation of the data and a deeper understanding of the factors that influence behavior.
- Participant perspectives: Qualitative research often involves direct engagement with participants, which allows researchers to explore their perspectives, experiences, and attitudes toward the phenomenon being studied.
- Generates new theories: Qualitative research is often used in exploratory studies and can generate new theories and hypotheses. It can be particularly useful in situations where little is known about a phenomenon and more research is needed to generate insights and understanding.
Limitations of qualitative research
- Subjectivity: Qualitative research can be more subjective than is advisable, as it relies heavily on the researcher’s interpretation and understanding of the data. This can lead to bias and affect the validity and reliability of the findings.
- Limited generalizability: Qualitative research is typically based on a small sample size and is context-specific, which means the findings may not be generalizable to other populations or settings.
- Time-consuming: Qualitative research can be time-consuming and require significant resources. Collecting and analyzing data can take a long time, and the data may be difficult to analyze due to its complexity.
- Lack of standardization: Qualitative research methods are often not standardized, which can make it difficult to compare and replicate studies. This can also affect the reliability and validity of the findings.
- Difficulty in data analysis: Qualitative research often involves the analysis of large amounts of data, which can be challenging and time-consuming. It can be difficult to identify patterns and themes in the data, and the findings may be difficult to interpret.
What is quantitative research and how to measure it
Simply put, quantitative research is all about numbers and figures. It is used to quantify opinions, attitudes, behaviors, and other defined variables with the goal to support or refute hypotheses about a specific phenomenon, and potentially contextualize the results from the study sample in a wider population (or specific groups).
As quantitative research explicitly specifies what is measured and how it is measured in order to uncover patterns in – for example – behavior, motivation, emotion, and cognition, quantitative data collection is considered to be much more structured than qualitative methods.
Advantages of quantitative research
As should be apparent by now, quantitative research should be your method of choice if you are looking to work with a large amount of data. Spotting trends, fluctuations across demographics, and objective generalizations in responses is where the quantitative research methods shine. Here is a list of the best reasons why you should choose quantitative research as your method.
- Objectivity: Quantitative research is often viewed as more objective and reliable than qualitative research due to its focus on numerical data and statistical analysis. In short, if you have done your data collection properly, the data will not lie.
- Generalizability: Quantitative research allows for the generalization of results to larger populations because it relies on representative samples and statistical techniques to draw conclusions and make predictions about a larger group.
- Replication: The use of standardized and objective measures in quantitative research allows for easy replication of studies, enabling other researchers to verify and build upon existing findings.
- Easy to analyze: Quantitative data is often straightforward to analyze using statistical software, allowing researchers to quickly and efficiently identify trends and patterns in the data.
Limitations of quantitative research
Research and data collection methods focused on quantity rather than quality, will inevitably come with certain drawbacks and limitations. These are dependent on the research scope, but at the very least they should be considered when building a study design. Following here is a list of the main limitations or considerations of using quantitative research as a method.
- Limited scope: Quantitative research is focused on data and statistical analysis, which can be limiting in terms of the range of topics it can explore and the depth of insights it can obtain.
- Lack of context: Quantitative research may not provide the depth of information and context that qualitative research can offer, as the main focus is on statistics rather than the experiences and perceptions of participants.
- Difficulty in measuring complex constructs: It can be challenging to measure complex constructs such as emotions, attitudes, and beliefs using quantitative methods, as they are often difficult to define and measure precisely when dealing with large datasets.
- Potential for researcher bias: The collection and analysis of numerical data can be influenced by researcher bias, leading to inaccurate or incomplete results.
- Limited ability to capture individual experiences: Quantitative research may not be able to capture the unique experiences and perspectives of individual participants, as it typically focuses on group-level trends and patterns.
Quantitative research techniques
Quantitative techniques typically comprise various forms of questionnaires and surveys, structured interviews as well as a behavioral observation based on explicit coding and categorization schemes.
In addition to these traditional techniques, biosensor recordings such as eye tracking, EEG, EDA / GSR, EMG, and ECG, as well as computer-guided automatic facial expression analysis procedures, are used.
All of these quantify the behavioral processes in such a way that numerical results can be obtained – for example, fixation duration from eye tracking (representing the amount of visual attention), the number of GSR peaks (indicating the amount of physiological arousal) or the power of a specific EEG band.
After data collection, quantitative analysis techniques and statistics can be applied, such as t-tests and ANOVAs, to non-parametric methods. This often necessitates much bigger sample sizes compared to qualitative research but allows you to make more solid conclusions, that are backed up with data.
Ultimately, whether to pursue a qualitative or a quantitative study approach is up to you – however, be sure to base your decision on the nature of your project and the kind of information you seek in the context of your study, and the resources available to you. Qualitative will offer you an in-depth understanding of your research problem and hopefully help answer your hypothesis. Quantitative will allow you to scale your research to provide larger sets of data for reliability and validity. A combination of the two provides you with objectivity.
Measurements to avoid bias
This is generally described with respect to the following criteria:
Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.
A measure is said to have a high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.
This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.
If you would like to learn more about qualitative and quantitative research designs, contact our experts at iMotions. We’re happy to help!
Psychology Research with iMotions
The world’s leading human behavior software