What are the differences and similarities between this method

By Francislê Neri de Souza, University of Aveiro – Portugal

Qualitative Research is guided by different methodologies, techniques and tools. When we study these differences, we want to reinforce the specificity, efficiency and effectiveness of the methodological design in relation to the research objectives and questions that we want to answer.

These grounds remind us that the methodology has, despite its limitations, a definitive role in distinguishing the conclusions of science from mere speculation or daily superstition. When we discuss qualitative research versus quantitative research in education and in the other human and social sciences, we usually point out the differences of these two research approaches with the laudable aim of establishing limitations and applications of each one of them. Are we not forgetting to reflect on the similarities? Were not these similarities that would integrate the different approaches and paradigms as scientific tools?

Only to recall some of the main differences between both methodological approaches, in the epistemological, axiological, ontological dimensions, etc. we list some keywords. While in quantitative research we have: the numbers, the researcher’s point of view, the distant researcher, the test of theories, statistics, generalisation, accurate and reliable data, macro, behaviours, artificial conjunctures, etc. in qualitative research we have, respectively, the words, the point of view of the participants, the close investigator, the theories that emerge, the process, the understanding of the context, the rich and deep data, the micro, the meanings, the natural conjuncture, etc.

While it is important to establish the differences and peculiarities of each, we would like to briefly focus on the issue of similarities. If they exist, and what are they? For Bryman (2012) the similarities between qualitative and quantitative research can be summarised in nine items. Both:

i) are concerned about data reduction;

ii) want to respond to research questions;

iii) seek to relate data analysis to research already published;

iv) are concerned with the variation;

v) treat frequencies as a springboard for analysis;

vi) seek to ensure that deliberate distortions do not occur;

vii) argue for the importance of transparency;

viii) address the issue of error;

ix) are concerned that research methodologies should be appropriate to research questions.

In addition to the list of this author we could add that in both approaches the researchers are looking for: i) syntheses and patterns; ii) systematisation of the analysis of the data; iii) thoroughness of the conclusions; and iv) relevant application and reflection on the implications in the specific areas of science, technology and society. Looking at both lists we recognise the standards of science itself, to which we should add that both methodological approaches are concerned with ethical and citizenship issues.

If both methodologies are concerned with so many points in common, what would make one inferior to the other? Of course there can be misapplication of methodological principles in both approaches, which turn them into a bad science regardless of whether it is quantitative or qualitative. However, it can not be said that one methodology is “less scientific” than the other if both are governed by several similar scientific parameters.

What the thesis of similarities seeks to defend is that both sciences follow different paths, but are concerned with the rigor, systematisation and “truths” proper to science. One advantage that qualitative research currently presents is the provision of software that facilitates and enhances the transition of non-numerical data to numerical data according to the researcher’s need and rationale. In the following figure (webQDA) we can see the possibility of cross-coding categories from text, counting of reference numbers, exporting to Excel® and transforming into graphs and / or statistical indexes. Note that the connection between numerical value and non-numerical (textual) content is never lost, and it is the researcher’s choice to choose the appropriate language in academic writing (Neri de Souza, Neri de Souza, & Moreira, 2016).

All this transition between the non-numeric and the numeric, even if descriptive or inferential statistics are used, does not transform qualitative research into quantitative or mixed, because this designation of the nature of research depends much more on the questions and design of research as a whole than only the final treatment and / or writing language of results.

Bibliographic references

Bryman, A. (2012). Social Research Methods (4th ed.). Oxford: Oxford University Press.

Neri de Souza, F., Neri de Souza, D., & Moreira, A. (2016). Diversidade de Contextos e Dados: Corpus Latente na Internet – Um Desafio para os Métodos Mistos. Internet Latent Corpus Journal, 6(1), 1–6.

*Learn more at:

revistas.ulusofona.pt/index.php/rleducacao/article/view/5674/3570

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data, and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data, and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.