Collection Business Science Institute

Let Your Data Speak

Quantitative and Qualitative Methods

Jean MOSCAROLA

136 boulevard du Maréchal Leclerc

14000 CAEN

© 2022. Editions EMS

All rights reserved

www.editions-ems.fr

ISBN: 978-2-37687-575-8

Table of Contents

Introduction

Find Out!

Part I. Knowledge Production in the Social Sciences

Chapter 1. The Frame of Reference for Scientific Work

1. Karl Popper and critical rationalism

2. Conceptualizing: Concepts and theories (World 3)

3. ObservING and measurING: The field (World 1)

4. Linking theory and fieldwork (World 2)

Chapter 2. The Conceptual Framework: Choosing or Building a Theory

1. Defining concepts

2. Conceiving of relations

3. Formulating theories

4. Grand theories and reference models

5. What is a good theory?

Chapter 3. Protocols for Gathering Information and Editing Data

1. Field, information and populations

2. Sources

3. Data collection and editing protocols

4. Examples

Summary of Part I

Part II. Measuring and Analyzing: Quantitative Methods

Chapter 4. Data

1. Data tables

2. Variables

3. Observations

4. From data to knowledge

Chapter 5. Describing and Interpreting

1. Univariate analyses

2. Bivariate analyses

3. Multivariate analyses

Chapter 6. Explaining and Modeling

1. Statistical estimation and sample survey theory

2. Confidence intervals

3. Statistical tests

4. Multivariate modeling

5. The uses of statistics and models

Summary of Part II

Part III. Observing and Interpreting: Qualitative Methods

Chapter 7. The Challenges of Qualitative Data Analysis

1. Corpora

2. The language system

3. Sensemaking

Chapter 8. The Classic Qualitative Approaches

1. The tradition of the humanities

2. Pure qualitative research

3. Content analysis

Chapter 9. Textual, Lexical and Semantic Data Analysis

1. History of methods and key steps

2. From corpora to textual data

3. Methods and their contributions

4. Textual data analysis and qualitative research

Summary of Part III

Part IV. Communicating

Conclusion

Choose!

Appendix. What Sphinx Software Can Do for a DBA Researcher

Abstract

1. Introduction

2. The software resources offered by Sphinx

3. Research situations

4. Communication, sharing

5. How to make the best use of Sphinx

6. Supplementary material: Glossaries

References

Introduction

Find Out!

“Let your data speak!” I am talking to researchers,1 research directors,2 analysts and students, whose job it is to produce and communicate knowledge. I am also talking to the instructors or trainers who prepare them for this work.

The data in question are texts, articles, books or other documents; numbers, whether accounting, economic or statistical; conversations, speech that is listened to or recorded; still or moving images; things that have been seen or observed. Data are external to us. We approach them with our culture, our knowledge, our convictions, our methods and our own ambitions. Whether you are professionals or apprentices in the field of research, you work on data by mobilizing your knowledge to produce new knowledge – knowledge that will help users to understand the world or take action. Whether new or old, this knowledge must be made available. Otherwise, how can we justify the cost of producing it?

“Let your data speak.” The domain we are about to examine is far from homogeneous. It is characterized by highly diverse practices, professional environments, approaches, tools and perspectives, but they are all united under the banner of knowledge creation.

Researchers in the humanities are interested in general knowledge, which is applicable in a wide variety of situations and is teachable, while research directors generally respond to a very precise, situated request. The researcher focuses on understanding, whereas the research director seeks action and reaction. What they have in common is curiosity, a will to observe, and also a taste for analysis and reflection. They are capable of maintaining a critical mindset, and they want to be understood by others.

Researchers are inheritors of the literary or ethnological tradition. They favor direct observation, interviews, document analysis, and the gradual construction of meaning. Research directors seek to measure and quantify. They build models, systematically collect a vast amount of data, and test hypotheses and models.

Another distinction we make is between qualitative and quantitative studies and analyses. A web search3 calls up the echoes of these approaches. Qualitative studies are mentioned more frequently but references to quantitative methods are better documented. All the disciplines in the humanities are concerned, in all domains, whether the focus is professional, research-oriented or educational.

Although in some academic fields these divisions may be very clearly established, their borders are pretty fluid. In 2007, their complementary nature led to the creation of a journal specializing in qualitative and quantitative approaches: Journal of Mixed Methods Research, the purpose of which is to allow for the publication of studies that do not correspond to a strictly qualitative or quantitative approach and to promote mixed methods.

Nevertheless, in professional contexts, the specific features of each approach are still widely used to distinguish between qualitative and quantitative services.

Thus, qualitative research in the literary tradition remains relatively low-tech. The microphone and the video have had little impact on the work of reading, listening or observation. Word-processing software is sufficient for an analysis that remains essentially dependent on the author’s culture and reading, reflection and writing skills. Things are quite different in quantitative research. Data collection tasks have always been much more technology-driven and software has long been indispensable for processing data.

As readers and writers, or as information technicians and statisticians, the tools we use have shored up the differences between us. But all that is changing now.

In the last 40 years, the digital revolution has brought us from scarcity to abundance, while substantially increasing the productivity of data collection and processing tasks. The first step was the democratization of access to calculating power thanks to the arrival of personal computers in the 1980s. The digitization of texts and the more widespread generalization of digital access followed in the 1990s. Search engines and online surveys meant that sources of data proliferated. This movement was amplified still further in the 2000s by the semantic web, social networks and big data. Today, mobility and artificial intelligence have disrupted work methods and are making our paradigms even blurrier. From smartphones to cloud computing, devices and algorithms are ubiquitous, affecting the production, processing and communication of information. And along with these new tools, new players have appeared. They are specialists in the technologies that researchers and research directors must learn to master if they are to withstand the competition from these newcomers. New terminology is appearing: analytics instead of statistics, sentiment analysis instead of content analysis, big data instead of sampling, and so on.

Figure 1 shows the changes in the last 10 years in the use of certain key terms. New terminology is gaining ground. It reveals the emergence of new approaches related to technological changes that have given researchers access to the massive processing of qualitative or quantitative data. Large numbers are no longer the sole domain of quantitative researchers, nor is exploration reserved for the qualitative world. General and subjective “likes” are in competition with indicators and cautious measurements.

Figure 1: Some major trends reflected on the web

Although these changes also reflect communication in the service of new technologies, they show the emergence of new ways of letting data speak. This finding fits well with Klaus Krippendorff’s diagnosis in the introduction to the third edition of his work on content analysis, which first appeared in 1980. He wrote: “the large volumes of electronically available data call for qualitatively different research techniques for computer aids… which participate in content analysis much as human analysts do. They become part of its methodology with transparency being a major issue” (p. 5).

Increased productivity, a faster pace, mixed approaches – technologies are opening up new perspectives. But these tools are not an end in themselves. The researcher’s role is to examine their logic and use in order to critically assess their contribution to knowledge production. This means we have to go back to the basics to gain a good understanding of the methods and characteristics of scientific work.

That is the purpose of this book. I will refer to the production of scientific knowledge: producing new knowledge, which is situated in relation to existing knowledge; basing it on data that show its relevance and allow people to criticize it; complying with good practices in the academic or business community to foster debate.

In the tradition of the classic dichotomies, I will first cover quantitative methods, and then qualitative methods, situating these methods on the basis of their information-statistical and linguistic-semantic foundations.4 I will describe how they are used and show that these approaches cannot be reduced to the classic opposition between positivism and constructivism and that they complement each other more than they compete. I will base this discussion on many examples that are presented in more detail on a companion website, where readers can find data, tools and operating methodologies: https://faireparlerlesdonnees.blog/.

I am approaching these reflections on the basis of my professional background: over 40 years’ experience as an instructor and researcher in management science but also as a player in the business world, in which I found myself engaged both as a software publisher and as a service provider.

I started my academic career just as structuralism had achieved its zenith. I am ending it in the age of hyperspecialization, globalized networks and the post-truth world. Disciplines are fragmenting, methods and tools are being globalized, and technology is disrupting everyone’s practices. Professions themselves have not (yet?) profoundly changed. In academia, the pressure to publish is higher than ever; in every business community, standards have become more of a concern. In corporations, research functions organized in specialized departments have sometimes been threatened or dispersed among the operating departments and entrusted to software programs. Competition is fiercer among service providers. Everywhere, the use of technologies that did not exist 30 years ago is changing the game by allowing new practices and new players to appear on the scene.

All this is making it more complicated to teach methods and train researchers and research directors. Although, in essence, the fundamentals have not changed much, the new techniques and practices have completely changed our world. The internet makes the worldwide “library” immediately accessible; increased calculating power and decreased costs are accelerating the obsolescence of statistical techniques developed in an economy of “digital poverty”; and the progress of artificial intelligence and automatic language processing is opening up new prospects.

I have based this work on my experience, enhanced in recent years by my contacts with the professors and researchers at the Business Science Institute (BSI). I have been teaching them methods that will enable them as managers to contribute to knowledge creation. In return, contact with them has helped me formalize this book by selecting the essential contributions from the scientific tradition, methodologies and tools. My goal is to describe the realm of the possible and to provide sufficient intelligence to guide readers’ choices and help them apply appropriate techniques and tools. To do this, I have benefited from the wide variety of topics examined by researchers at the Business Science Institute and from our collaboration with the publisher of the Sphinx software tools.

I am grateful to them all!

This book joins the series of books in the Business Science Institute collection put out by Éditions EMS (e.g., Beaulieu & Kalika, 2015; Kalika, 2017; Chevalier et al., 2018).

General outline of the book

I. Knowledge production in the social sciences

II. Measuring and analyzing: Quantitative methods

III. Observing and interpreting: Qualitative methods

IV. Communicating

The companion website – https://infos.lesphinx.eu/dataspeak.htm – presents numerous examples and the operating methods needed to apply them using the Sphinx suite of software.

I shall depart from the tradition of leading off with qualitative methods, since quantitative methods are better known and documented and thus are easier to present. But most of all, this expresses my conviction that qualitative methods can reasonably be presented as an outcome rather than as a mere preliminary!

1. What I mean by researchers is members of academia or major private- or public-sector research institutes who have a degree of freedom to choose their research topics.

2. I use the term research directors for employees of a government department or industrial, commercial or service enterprise, who work for a client in response to a request and in accordance with precise specifications. They are also called project managers.

3. Search with Google Analytics in French and English on the web, as well as on the Amazon and Sage Publications sites. For more information, see the companion website to this book.

4. I would remind readers that computerized techniques structure statistical thinking, just as language structures sensemaking!

Part I. Knowledge Production in the Social Sciences

Chapter 1. The Frame of Reference for Scientific Work1

What is scientific knowledge? And how can we claim to contribute to this kind of knowledge?

Three kinds of answers can be given to these questions.

The first, which is epistemological in nature, consists in defining the essence of a scientific statement in its relation to conceptions of truth. The second, or conventionalist, kind consists in considering knowledge to be scientific if it comes from a scientific community and complies with this community’s paradigms. The third, or methodological, kind comes down to assessing the scientific nature of knowledge based on how it was produced.

It is this last kind of answer that we will develop here,2 by presenting the principles of critical rationalism. They have the benefit of giving researchers a method and operating practices that are compatible with positivist or constructivist epistemology; in addition, they can be applied to qualitative, quantitative or mixed approaches.

Scientific researchers differ from their competitors – consultants or journalists – in their practices and methods. Consultants seek to promote action and journalists want to inform, whereas scientists take pride in subjecting their statements to the control of reason and exposing them to the criticism of facts and of their peers.

This exceptional attitude is difficult to adopt for people who focus on action and are mainly concerned with finding solutions, convincing others and succeeding.

The aim of this chapter is to help readers understand the requirements of scientific work by describing its methods and showing the value of the approach that involves the reasoned collection of data, critical examination thereof and acceptance of debate.

1. Karl Popper and critical rationalism

1.1. Refutation

In his book Conjectures and Refutations: The Growth of Scientific Knowledge (1963), Karl Popper analyzed scientific work as a particular type of intersection between the world of ideas and the world of phenomena. In his view, theories can proceed from observation (induction-conjecture) but they cannot be considered scientific unless the statements to which they lead can be contradicted by logic and by facts (deduction-refutation). This is what Popper calls critical rationalism: to be considered scientific, a statement must be able to be refuted or falsified. If this possibility exists, the statement can be considered to be true as long as it has not been shown to be false (falsification) by observation or experiment. From this perspective, all knowledge is only provisionally true. The scientist’s work is to test validity by observation and experiment. Thus, beliefs and firm convictions, no matter how useful, can only become scientific knowledge if it is possible to test them by observation.

As Gaston Bachelard showed in his book The Philosophy of No (1968), only the false can be proven. For Popper, this constitutes the domain of scientific progress since, in science, all knowledge deemed to be true must be able to be refuted, thereby allowing new conjectures to be stated, in a form in which they too can be refuted.

This idea is the basis of the empirical-formal sciences (Piaget, 1972), which establish a relation between formalism (theory) and empiricism (observation). Conjecture gives an abstract form to empirical observations by proposing concepts and theories. This is the inductive, creative phase of research work. It leads to the formulation of a hypothesis, which is subjected to refutation by experiment, or empirical testing, in the hypothetico-deductive phase. Thus, any hypothesis can be invalidated by facts and stimulate a new conjecture, contributing to the growth of knowledge.

Although it is much debated (Alker, 2013), Popper’s vision provides a reference framework for the social sciences.

1.2. Popper’s three worlds

To analyze scientific activity, Popper distinguishes between three worlds:

World 1: the world of nature, the physical world and behavior: objects, organizations, the economy, the world of work and business.

World 2: the world of the senses and observation tools: experimental devices, measurement tools, questionnaires, statistics, etc.

World 3: the world of knowledge, ideas, theories and culture: the library, articles and books.

Figure 2: Conjecture and refutation

As Figure 2 shows, these worlds describe the mechanisms of observation. They allow us to understand that what we take to be the result of an observation bears the signs not only of the phenomena observed but of the knowledge we have. This knowledge filters and formats what our senses transmit to us. Thus, for Christopher Columbus, the shores he reached were those of the Indies, since the maps of that era showed no other land there.

But the tools of World 2 also play a determining role: when we talk about growth, we base our perceptions on very specific calculation procedures (growth in GDP, in employment, etc.).

For scientific research activities, World 3 refers to the scientific literature, concepts, theories and all the references of a discipline. World 2 is the world of the laboratory, with its equipment and measurement tools; it is the world of researchers. Thanks to their instruments, they penetrate World 1, which mere mortals cannot access, to measure what their theories allow them to conceive of. The process is different in the social sciences.

1.3. Epistemological rupture

The knowledge produced in this way is different from common knowledge. It is distinguished by a specific language: the language of theories that use abstract concepts and are based on the results of observations and measurements produced by World 2 tools. Philosophers of science talk about epistemological rupture (Bachelard, 1938) to describe this characteristic of scientific knowledge. This rupture is evident in the natural sciences. It is much less clear in the social and management sciences, where concepts are expressed in everyday terms (e.g., organization, efficiency, etc.) and where the observation tools seem considerably less specialized (interviews, questionnaires, statistical methods, etc.).

Thus, the specificity of work in the management sciences is much more difficult to establish, especially for researchers who are engaged in World 1 and seem to be familiar with the methods they will have to use.

They must first understand that their role is no longer to find solutions for their specific situation in World 1 but to produce general knowledge that can be received in World 3 as scientific contributions. This characteristic depends on their ability in World 2 to execute an epistemological rupture, which distinguishes their contributions from those of the expert, the consultant or the journalist.

Thus, for the researcher, notions such as efficiency, satisfaction or workplace wellness are defined within the framework of reference theories and are the object of observation or measurement, which clarifies their meaning. This is inherent in scientific work: conjecture, formalization, confrontation and refutation. Applied to the management sciences, these principles can usefully guide a researcher’s work. That is what we are now going to show, by presenting some advice inspired by critical rationalism.

We will in turn examine the requirements of formalism (World 3) and the variety of empirical manifestations (World 1), and finally consider how they are connected (World 2).

2. Conceptualizing: Concepts and theories (World 3)

The purpose of research is to produce concepts and theories that constitute knowledge of a field. These concepts must possess a certain number of properties summarized in Figure 3.

Figure 3: Concepts and theory

2.1. Define and document

Define: Concepts are abstract, general terms, which can be used in a wide variety of local situations. Efficiency, satisfaction, resources, and involvement are concepts. They denote facts, phenomena or opinions and are the object of precise definitions that may vary depending on authors and points of view. The essential thing is that they are defined, which distinguishes them from ideas or notions, which may be evoked vaguely or ambiguously. A concept must be understood in a single specific way. The precision of concepts is a marker of scientific language. It is based on definition and the possibility of observing and measuring.

Document and measure: A concept must be able to be illustrated and concretely described by a state or a measurement. A strategy can be short- or long-term, workplace wellness can be measured by empirical indicators or answers to questions. Mentioning an example is not enough to define a concept; we must go further by indicating how to systematically identify it or measure it. That is the purpose of work in World 2, where the researcher operationalizes concepts by formulating constructs,3 which empirically clarify their meaning.

2.2. Classify and relate

Classify: Science progresses by its capacity to group detailed observations into more general categories. Thus, the typology of needs or of management styles provides a more general understanding than mere observation of consumers’ or managers’ behavior. Conversely, a very general concept may be divided into different concepts at a more detailed level.

Concepts can be thought of in a tree structure with different levels of granularity. Moving up in the hierarchy of concepts by combining several concepts proposed by different authors or studies is itself a contribution to knowledge.

Relate: A concept must be able to be included in a reasoning process, help to understand or explain, or contribute to a theory. Based on the linking of several concepts, theories are the most complete forms of knowledge production. They make it possible to understand, explain and predict.

Concepts are involved in causal relations. In management sciences, most theories amount to one of the following explanatory models: if antecedent, then consequent; if cause, then effect; if resource, then result or product; and so on.

2.3. Situate and reference

Concepts and theories must be situated in relation to existing knowledge and the scientific literature in the discipline or other disciplines to which it may refer. This goes without saying when research aims to prove an established or modified theory. But a proposed new theory or a conjecture elicited by the field must also be situated in relation to existing knowledge to prove its relevance and value. A thesis committee or a journal’s referees will reject a contribution by someone who does not make an effort to situate it in relation to the knowledge of his or her teachers or colleagues. In the sciences, difference is cultivated in referencing. The same is true of the overall progress of knowledge.

Respecting previous conditions is a condition for publishing a study. It ensures a common understanding among researchers in World 3 and authorizes criticism of the work.

3. ObservING and measurING: The field (World 1)

According to the schema of critical rationalism, research is based on the observation of empirical phenomena that must allow us to formulate (conjecture) or test (refutation) the knowledge produced.

3.1. The field

There can be no research without a field. One must choose a field, that is, a concrete, observable situation in which a researcher can find elements of an answer to his or her research question. In management sciences, that generally involves examining one or more companies, an economic sector, or a population of customers or employees. For example, to study governance issues, Guelmami (2015) investigated some 20 clinics in which he interviewed managers, patients and physicians. Bogaert (2017) studied an industrial company’s strategy over 30 years. Favre (2015) studied the adaptiveness of a population of managers in a large distribution company. And Hassoumi (2017) examined telecommunications regulation policies in seven West African nations.

3.2. Populations and levels of observation

The previous examples feature different types of populations. A single research question can be studied at different aggregation levels: at the level of a clinic, seen as a whole from the perspective of its organization, or at the level of the physicians who express their involvement in the clinic’s management, or at the level of patients who may or may not be satisfied. Similarly, regulatory policies may be assessed at the macroeconomic level of the nation or from the perspective of operators.

The choice of a level of observation is a determining factor, particularly in management sciences, where, to take the organizational fact into account, several levels of observation must be considered. What is observed depends on the level considered. Thus, where economists see the firm as an abstract, rational agent, managers see it as a concrete organization, animated by stakeholders. The economist is interested in facts, while the manager must also take opinions into consideration.

3.3. Facts and opinions

A fact refers to an object or an identifiable event that exists beyond human perception or evaluation. For example, a person’s age is a fact, as is the legal form of a company or a sales figure. The physical sciences are interested only in facts that can be grasped with measurement tools.

An opinion is the individual expression of a person’s feelings or beliefs. It provides access to information that can only be expressed by a subject. For example, saying that one is not lying is the expression of an opinion, whereas the result of a polygraph establishes a fact about a person’s neurological condition.

Opinions, beliefs and preferences play a major role in decision-making and in the understanding of action. Thus, being aware of them is important in management sciences, in the same way as knowing about the external facts that condition a subject’s judgments and opinions.

Facts can be observed directly by an observer who is outside a situation or collected in legal, business, or accounting documents or any other archive that may be available. For example, the business decisions studied by Bogaert (2017) came from managerial and media reports.

On the other hand, opinions can only be discovered by asking people to express them or by looking for traces of them in documentation.

3.4. Types of information and data

Let us now consider the kinds of information that enable us to document facts and opinions. They determine the nature of the data collected in World 1 and the methods applied in World 2.

From vague, rich and complex to highly focused and precise, this information reflects the variety of tools and methods used and results in very different kinds of knowledge.

Figure 4 sums up the variety of situations that a researcher may encounter.

Figure 4: From observation to knowledge

Researchers can use images, texts and nomenclature systems to describe the situations they are studying and characterize them more precisely by measuring their physical or economic size. To collect opinions, orders of magnitude expressed with scales are most appropriate.

This information leads to different kinds of data. On one hand, there are qualitative data, texts and images, whose meaning must be discovered. On the other, there are quantitative data, defined with a determined unit of measurement and varying like numbers. Lists or nomenclature systems are located between these two extremes. Unlike texts, their meaning is determined a priori, but they do not have the precision or properties of numbers.

3.5. Number of observations

The quality of research depends a good deal on the observation effort and relates to the question that every researcher must ask: How many cases should I study? How many people should I survey? Of course, that depends on the available resources and time.

Without referring to the science of surveys at this point (see Chapter 4), we must remember that increasing the number of observations and sources allows us to:

increase our confidence in making generalizations about repeated phenomena: All swans are white.

increase variety and make use of the differences in order to compare and explain: we need to observe both successes and failures if we want to understand the reasons for them.

When data are quantitative, the more observations there are, the better the conditions for refutation will be, based on statistical estimation methods. From this perspective, the emphasis is on the number of observations.

When data are qualitative and we have to find the meaning of the information we have collected, variety is what really counts. There is no purpose in collecting a large number of interviews or texts that all say the same thing; rather, it is important to describe infrequent cases or collect minority opinions. When data are qualitative, the more diverse and varied they are, the richer our conjectures and interpretations will be.

This distinction leads to ambitions of different scopes, depending on whether we want to interpret data or generalize results, produce meaning or validate hypotheses. This relates to the nature of work in World 2 and the tools and methods to be applied.

4. Linking theory and fieldwork (World 2)

4.1. Approaches: Inductive or deductive?

Regardless of the nature of the data or the size of the samples, research work consists in linking the field to the theory or the theory to the field.

When we start with the field, we proceed by means of induction: concepts and theories are discovered or clarified by observation and are gradually formalized (conjecture). We seek to discover new theories, as Isabelle Walsh (2017) says in her book of that name.

When we start with a theory, we apply a hypothetico-deductive method to test it. The field is chosen with the aim of documenting concepts and confronting the theory with data from observation (refutation). We want to test hypotheses or a model.

In all cases, a trip to the library is indispensable to situate and justify the research question, but immersion in fieldwork is also necessary to give body to concepts and identify sources of information. Finally, the choice of whether to apply an inductive or deductive approach may depend as much on the researcher’s taste for a theory as on the sources he or she can easily access.

The most important thing is to start quickly, which allows us to conduct several short cycles: start inductively, formalize a theory and then test it; or start by testing a model and then look inductively for the reasons why it is refuted. By working in this way, we also open up the possibility of reorienting a research project by progressing serendipitously.4

It is perfectly possible to engage in an inductive and a deductive approach simultaneously, by collecting information in the form of unstructured utterances at the same time as measurements in accordance with a model defined a priori. This kind of mixed approach is made much easier by the use of online surveys (Moscarola, 2019b).

Finally, we must not neglect the apprentice researcher’s approach: constructing an intuitive questionnaire based on one’s experience and knowledge that may be only moderately related to the literature. A researcher may actually learn faster by doing this, as the example of Vincent Mottet’s (2016) study of the perception of environmental policies shows.

Figure 5 illustrates these back-and-forth transitions between empiricism and theory (vertical arrows) and between informal (qualitative) and highly formal (quantitative) work (horizontal arrows). It highlights all the elements that may be contained in a research study involving several short cycles.

Figure 5: Several short cycles

4.2. Methods: Descriptive or explanatory

The connection between empirical data and theoretical knowledge is the focus of methods that allow us to assess the researcher’s work. They can be divided into two large families depending on whether they are used to produce knowledge or to test it.

From compilation to interpretation

Compile: Compilation consists in publishing empirical data: qualitative or quantitative, interview transcripts or data from a questionnaire. Researchers who do this expose themselves to the criticism of their peers, who can reproduce the analyses and contest the results.

Reduce: Overly detailed or very voluminous raw data can undergo extraction to facilitate knowledge creation by producing percentages, means, lists of keywords, transcripts or themes for a content analysis. This reduces the volume of data. These objectively produced elements5 can be communicated more easily and may be tagged with descriptive comments, established variable by variable or corpus by corpus. These first-level comments are purely descriptive. They can lead to interpretations, which remain subjective without a more in-depth examination of the data’s structure.

Synthesize: Synthesis represents another level of contribution to knowledge. It consists in using quantitative structural analysis methods to highlight typologies, hidden dimensions or lexical universes. As with grounded theory (Glaser, 1978), these synthetic categories emerge from the data themselves and are likely to make sense, insofar as the researcher is able to interpret them. The researcher then contributes to knowledge by proposing concepts that can be used to name and empirically document the categories revealed by the analysis: groups of individuals with homogeneous behavior or sets of interdependent criteria or opinions.

These methods, which are presented in more detail in Chapter 5, have no external validity; that is, they provide information only about the sample being studied. In addition, although they allow us to move up higher in the hierarchy of concepts, they have no explanatory power. To go further, we have to be able to generalize, link and test data.

From estimation to model testing

Statistical science establishes the conditions for external validity. Based on probability theory, it makes it possible to assess the risk we assume if we generalize the results obtained on the basis of the available data.6

Estimation is intended to establish the precision of an observation by indicating how to move from a certain measurement, carried out on the available data (sample), to knowledge of a larger set (population). It is associated with a range of uncertainty (confidence interval and associated risk).

Hypothesis testing has the purpose of establishing the existence of a relation between two concepts on the basis of an examination of the data documenting them. It is based on assessment of the risk assumed to refute this hypothesis. Model testing is a generalization of hypothesis testing applied to several concepts and the systems of relations among them. It represents the most complete objective of deductive approaches since it enables us to empirically test a theory formalized by a model.

These methods are presented in more detail in Chapter 6.

4.3. Mixed approaches

Research traditions have posited a division between qualitative and quantitative studies, based on the following sets of characteristics.

Qualitative: induction, weakly structured data, individual cases or small samples, descriptive and interpretive method.

Quantitative: deduction, highly structured data, large samples, statistical methods, hypothesis testing and modeling.

This division is treated as a paradigm, but it does not restrict researchers, who are increasingly developing mixed approaches.7 This development has been greatly stimulated by technological changes, which make it easier to access and process textual data.

Thus, quantitative methods, data counting and analysis, can be applied to qualitative data, just as exploratory and interpretive methods can be used with quantitative data. A researcher working in grounded theory can work with quantitative data, while hypothesis testing can be applied to qualitative data.

1. This chapter is a revised version of Chapter 3 of the anthology titled Le projet de thèse de DBA, P. Beaulieu and M. Kalika (Eds.), 2017.

2. Researchers in management sciences habitually refer to epistemology (Martinet & Pesqueux, 2013). Conventionalist approaches (Kuhn, 1962) are mainly of interest to historians of science.

3. The construct operationalizes the concept and makes it possible to obtain the variable that measures it.

4. Serendipity is finding something other than what one was looking for (Van Andl & Bourcier, 2009).

5. Except in the case of content analysis by means of human reading and interpretation.

6. When the study concerns a whole population, the risk is related to variables due to the time or circumstances of the observation.

7. As documented in the Journal of Mixed Methods Research.

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