Qualitative and quantitative approaches usually involve different research methods. However, the use of both approaches may be complementary in that everyone adds insight into the overall picture of the issue.

Two contrast models (paradigms) can be distinguished in the research: positivism and phenomenology. The first attempts to explain human behavior through causes and consequences, while the latter seeks to understand and interpret human activities through everyone’s reality individually. These different models generally lead to different data collection approaches: quantitative and qualitative. This distinction is not clearly separated: the methods are not exclusively part of one tradition or model of survey, eg a positivist as well as a phenomenologist can use research methods to collect data. It seems very useful to use in the bachelor thesis and to a greater extent also in diploma or rigorous work.

The following diagram illustrates the link between the research paradigm, approaches and methods .

Research Paradigm Phenomenologists Positivists

Approaches

Main differences in the concept of quantitative and qualitative research

A large number of quantitative research methods are used in the field of study of all disciplines and is beyond the scope of this guide to describe all of them. In particular, quantitative research is based on data collection, which is then analyzed by various statistical techniques. Usually, it tends to obtain a relatively small amount of information from a large number of respondents or observations. These results are often deduced / estimated in a larger population (Weaver and Lawton, 2002). Quantitative research is often considered a model for all scientific surveys as it involves a precise process of hypothetical formulation, detached observation, data collection, data analysis, and acceptance or rejection of a bachelor or master thesis hypothesis.

Quality research

Qualitative research can be defined as research that focuses on gaining a deep insight into social reality based on a relatively small number of respondents or observations. Typically, the methodology does not rely on “sampling” or involving statistical analysis. A qualitative approach is appropriate for situations where little is known about the subject to be analyzed. Sometimes, qualitative research methods are referred to as “data enhancers” (Ragin 1994: 92). Miles and Huberman (1994: 6) state: Qualitative research is conducted by intensive and / or prolonged contact with the area or life situation. These situations are usually “banal” or normal, reflecting the everyday lives of individuals, groups, companies, organizations. The explorer’s role is to get a “holistic” view of the context being studied: its logic, its arrangement, and its explicit and implicit rules.

Merkelt and Vos presented some representative examples of qualitative approaches:

  • Case Studies: defined by Yin as “empirical inquiry that explores the current phenomenon within its real life context, where the boundaries between phenomenon and context are not clearly evident and where more resources and evidence are used”.
  • Causal Theory: a research strategy that focuses on generating (generating) theory – data induction.
  • Ethnography: consists of “a description of culture and an understanding of the way of life from the perspective of their participants; it is the art and science of describing the group and culture ”. A popular method in ethnographic research is tracking participants: a group or case is tracked in this natural environment and explorer becomes part of that environment .
  • Event / Event Survey: Problem solving, cyclic and iterative method that links action / exploration . It is generally believed to include “a spiral of self-reflecting cycles of change planning, action and tracking the consequences of change, reflecting on these processes and consequences, and then re-planning, action and tracking, reflection, etc.”.

In short, a quantitative approach is usually strictly structured, collects statistical data and tests hypotheses, while a qualitative approach is more flexible, explores opinions by analyzing texts and words, and develops new theoretical insights. In particular, the difference between qualitative and quantitative research is linked to the data collection method or the amount of data analyzed. The differences between the qualitative and quantitative exploratory approach are illustrated in the following table.

  • Quantitative approach
  • Measurable facts
  • Construction of social reality
  • Focused on variables
  • Focused on interaction and events
  • Reliability is key
  • Authenticity is key
  • Free value
  • Value of Nature and Explicit
  • Independence of context
  • Situationally limited
  • Many cases
  • Thematic analysis
  • Statistical analysis
  • The researcher is separated from the subject
  • The researcher is involved in the subject

Now, according to Merkelt and Vos it can be added that the positivist model has always dominated natural sciences, but has also become common in social studies. Some experts argue that a quantitative approach is more credible than a qualitative approach, as the first one is considered to better reflect the “real world” as it is based on rigorous procedures and has the ability to infer results for a wider population. These explorers even adopted a negative approach to a “light” and “subjective” qualitative approach (Weaver and Lawton, 2002). As these two research ways are actually complementary, this approach is rather inappropriate.

Validity and invalidity of arguments

It is true that qualitative research is inductive and can be considered intuitive, but it also allows the creation of models and hypotheses that can then be tested by quantitative (or qualitative) research methods (Weaver and Lawton, 2002). Both qualitative and quantitative research have a good reason to be of paramount importance. The proposed logic can strengthen the expert’s ability to build valid and solid arguments and learn to recognize delusions in scientific studies. In the proposed logic, one or more statements are offered as support, justification, basis, reason, or evidence for further statement. What is in the proposed logic called “argument” is the way in which the truth claim is supported. However, they do not establish the truth of the conclusions. Logicians only study the correctness of reasoning, the validity of judgments, and not the truth of the statements themselves. So statements can be true or false and arguments can be valid or invalid. Suber (1997) states: “Validity refers to justification, not to assertion, while truth refers to non-justification claims. The first fundamental principle of logic is the independence of truth and validity. However, it is possible to see the different relationships between the validity of the justification and the truth of the statement. “When the reasoning is an argument is valid and all its premise is true, then it is called solid. Otherwise, the argument is rigid. If the argument is firm, then its conclusions must be true and it would be illogical if we did not believe it. ”(Suber 1997).

Categorization of arguments and their main differences

Arguments can be divided into two categories: deductive and inductive. In the case of deductive arguments, the assumptions claim that they give a solid basis for the truth of their conclusions or claim to support conclusions with necessity. In the case of inductive arguments, premises support, but do not guarantee conclusions:

The black and white categories of validity and nullity apply only to deductive arguments; inductive arguments are strong or weak. With a valid deductive argument with all true premise the conclusion / exit truth is necessary and its falsehood is impossible. With a strong inductive argument with all true premise, the truth of the conclusion is merely possible and its untruth is only improbable.

Outputs of valid deductions

The outcomes of valid deductions are not a matter of degree, but rather “all or nothing” of this kind. The induction outputs are a matter of degree: they are more or less in nature. Induction is not a bad output: “The difference between outputs and induction is not differentiated by good and bad reasoning, but it differs between two ways to promote the truth for outputs. Deduction is the subject of strictly exact science; but not induction. ”

Deductive arguments go hand in hand with the positivist model (paradigm) and quantitative research, while inductive arguments are part of the phenomenological model and qualitative research. However, the more important is the difference between fixed arguments and those that are not.

The idea / delusion is the wrong method of argument, either deductive or inductive. Arguments may be “bad” (or unclear) for several reasons: one or more of their premise may be false or irrelevant, or the reasoning they may be invalid or the language by which they are expressed may be ambiguous (ambiguous) or vague. Surely there are infinites of bad arguments; there may even be infinite ways to argue wrongly. The idea / delusion name is usually reserved for typical errors in arguments that we still find convincing. So their study is a good defense against fraud / deception. Using a proper deduction, the bachelor thesis is usually very positively evaluated.