Livro - Analise de Conteudo - Laurence Bardin. Uploaded by reolopes Download as PDF or read online from Scribd. Flag for inappropriate content. Download. RESENHA. ANÁLISE DE CONTEÚDO: A VISÃO DE LAURENCE BARDIN. [ BARDIN, Laurence. Análise de conteúdo. São Paulo: Edições 70, , p.]. Help Center; less. pdf. Análise de Conteudo: a visão de Laurence Bardin Bardin. Uploaded by. Matheus Barros. connect to download. Get pdf. Academia. edu.

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Bardin Laurence Analise de Conteudo. Andre Martins. Loading Preview. Sorry, preview is currently unavailable. You can download the paper by clicking the. Análise de conteúdo. BARDIN, LAURENCE. Jean Rodrigues. Uploaded by. Jean Rodrigues. Download with Google Download with Facebook or download with. Carvalho, João Conrado de Amorim, Sabino, Emmanuel M.C.B. Análise de conteúdo: a visão de Laurence Bardin. Retrieved from niticahonu.tkharmer .com/sites/default/files/ Silva, A. H., & Fossá, M. I. T.

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Steps to a ecology of mind: Analyzing talk and text.

Como afirmam Bauer e Gaskellp. site Rapids Fun stories for kids on the go. Qualitative researching with text, image, and sound. Just a moment while we sign you in to your Goodreads account. The above steps allow us to state that a basic principle of the operation of categorical AC is the data reduction through two key processes: encoding and categorizing of the contents of a given corpus of interest. Two fundamental mechanisms underlie the process of coding and categorizing the contents of a corpus: on the one hand, a mechanism for induction; the other, a deduction.

These two mechanisms may vary and be combined in different ways, and their presence underpins the process of a standard categorical CA. To analyze qualitative data, the researcher can use predefined categories based on the theoretical referential - backed induction by theory of defined based. The own Bardin suggests this possibility. For example, a common practice is the use of structured or semi-structured script from which the researcher conducts the interview.

This same script, by encouraging the participants to talk about certain subjects, provides the researcher priori categories for analyzing its data, especially in the categorization process.

If, on the one hand, the pre-categorization facilitates the alignment between the purpose of the research and the interpretation, on the other hand, it can limit the alternative possibilities of corpus analysis in the coding phase and thus, it discourages the creativity of the researcher and the exploration of alternative ways of analysis.

The other side of the coding and categorization system is the deduction. In a way, the validity of an induction can be tested by the successful deduction. As the researcher makes use of induction to encode the raw data first data reduction , to be able to identify an organizational structure of these codes by creating categories that further reduce the significant elements of corpus, he will have to resort to deductive elements, notably the conceptual and theoretical resources.

At this point, the induction is linked to the deduction. The deduction serves, then, to the purpose of testing the adequacy of induction. As a result, in the case of a prior hypothesis testing, there is the dependence of the interpretation of a theoretical and conceptual system as its basis.

The defenders of a more atheoretical and inductive perspective argues that the categories should emerge from the data e.

That being so, it would be minimized theoretical recursion problems, which constitutes in the data according to the underlying theory of the researcher with the data distortion. If the categories emerging from the data, not from the theory, then one can say that these categories are grounded in the data, i. Only after identifying these basis categories, the process of theorizing would follow, and then the researcher would have to resort to the deduction. This "naive" realist perspective has been criticized in the broader context of the philosophy of science for decades e.

In the social sciences is a radical posture still advocated in some research traditions, such as researchers who are members of the Grounded Theory Method, particularly the followers of Glaser Prospects not so radical advocate the relevance of using the theory concurrently with the categorization process e. In the next section we deepen the discussion of inference, including, beyond the induction and the deduction, the adductive reasoning.

In the empirical sciences, inference is a fundamental process, as it seeks from the factual knowledge to reach the awareness of the reasons for this fact.

The abduction consists of assertions about unobservable to explain the observable which, without losing the connection to the sensory experience, transcend it in pursuit of rationality.

Interestingly, the deduction starts from the rule to the datum to extract the conclusion inference , while in the case of the induction, it starts from the datum in order to complete something, and then make the leap to the rule theory. In the case of the abduction, it starts from a rule, which can be a provisional theory; and venturing a conclusion hypothesized and then analyze the datum.

For Peirce, what differentiates the abduction of the induction is precisely the interpretive risk that the former takes when thinking of explanatory alternatives, rather than just establish a new general rule that represents the particular case.

We defend the hypothesis that the analysts of CA using the categorical analysis only as a technique for data reduction feature ad hoc overlook critical aspects that enable them to take better advantage of interpretive inference abduction via.

These analysts do not dare to critically analyze the effect to hypothesize an antecedent event "cause" that can reorient the relationship between datum and theory, making it more complex from the abstract point of view, with repercussions on attempts of exploiting in the future the design of the phenomenon that binds the datum to a more complex theoretical elaboration.

Then came the time to clarify what we mean by the phenomenon and how it can bound the datum to the theory, helping in the inclusion of abduction as a form of reasoning that extends the interpretive possibilities. Here, in contrast with the approach of the "observations laden by theory ", the authors distinguish the datum phenomenon.

Data are observable and collected in certain contexts, therefore they are situational, and as such, they suffer perceptual biases.

The phenomenon, however, is an extension of the datum, something that is stable and not always observable. The data would be the means by which one accesses the phenomenon; but for that to happen we need to ensure that data are reliable - and here, Bogen and Woodward highlight the importance of experimental and statistical methods, which could ensure an inferential leap from data to phenomena. For Bogen and Woodward , the theory does not play a key role in his passage from datum to phenomenon.

According to them, the theories are constructed to explain the data, and not the reverse. Remember that the history of science is replete with examples in which the phenomena are detected in process of observation without a prior theory to explain them or to provide them.

This positioning of treating the phenomenon as emerging from the data, neglecting the role of theory is not shared by other authors, translating into focus of controversy in the philosophy of science.

For example, Massimi states that the phenomena are not images or shadows of the real world, but objects of experience to which we have access only through scientific theories. In the same direction, Apel considers that the phenomenon corresponds to a "superstructure" of the general theory used.

The author makes a distinction among statements about the phenomenon, which may be observable e.

More recently, Bendassolli argues that the phenomena, especially in the field of psychology, are not merely "accessed" by the theory and the method, but empirically reconstructed, situation in which theory plays a fundamental role.

On the other hand, a perspective that repositions the relationships among data, theory and phenomenon and that is closer to our understanding - so it is quite helpful for the purposes of this article - is that was brought by Bailer-Jones The Figure 1 , with some adaptations, illustrates the view of this author, who introduces a model notion in the composition of the datum- phenomenon-theory triad, presenting it as a central element for the indirect test of theory and the mediation between and data and theory.

Model is an interpretative description of the phenomenon that facilitates the intellectual or perceptual access to it. They are partial and synthetic descriptions as highlight some aspects over others, for simplification of the phenomenon.

The models allow the connection between the abstract theory general and the nearest concrete phenomenon, because they operate at a lower level of formalization. We take the concept of work, which is abstract.


To make this concept a phenomenon we must describe it more concretely and translate it as a model, presenting the synthetic information of what is essential to it and what distinguish it - e. In the statistical procedures, an area in which models have become seemingly more common, the models are relational constructs, proposing hypothetical relations of the data among themselves, which are tested in an empirical reality.

The model can also be analogously designed as a "gestalt", which dynamically changes as the links and refers to a representation which allows the apprehension of the whole from its constituent parts.

As can be seen from the Bailer-Jones approach, the data are a form of expression of the phenomenon that deal with the theoretical model, being this the route by which the abstract theory more general would approach in a more concrete way to be put to the test or else to be discussed in defining the phenomenon; but the data, because they are circumstantial, could be interpreted differently, depending on how the phenomenon position itself in a theoretical model, because the data are facets of the phenomenon, elements from which it is represented theoretically.

In this direction, Basu states that the same data can build distinct evidence for or against various theories. More widely in the philosophy of science, this is known as the problem of underdetermination of theories Quine Thus, if the same set of data can serve as evidence for various theories, it is essential to have in mind the phenomenon to analyze the data Bendassolli, The model defines what kind of simplification will be made of the phenomenon, leaving aside some relevant aspects and emphasizing others, which allows to look at the data in a more targeted way.

The phenomenon and the theoretical model remain strongly connected, although the test or the questioning of the phenomenon model occurs at the level of generation and analysis of the data, as figure 1 shows Bailer-Jones, It is in this context that the researcher can, in an exploratory way , risk new ways, new rules, new possibilities to explain the phenomenon. Let's look at this more closely in the next section, bringing the discussion to the field of CA.

It is important that there is an alignment.

Data, alone, are circumstantial, and to interpret them we must guide the analysis by understanding what is the phenomenon investigated. In turn, a first way of representing or operationalizing the phenomenon is to adopt a theoretical model that serves as proxy representative of the larger, abstract theory.

The consideration of this role model seems essential, because in its absence, the researcher is faced with a series of data which, by themselves, are not able to say much about a more stable phenomenon, although this stability, in the case of research in psychology, is provisional. At this point, we can replace the relationship among induction, deduction and abduction in the categorical CA.


The inductive leap, when starts from the "gross" datum to a concept, occurs from a structural alignment among theory, model theory and phenomenon. This alignment is that it allows the researcher to select portions of the empirical material as those containing relevant information about the phenomenon.

As such, there would not be a pure induction, in the naive realist sense: not start from the data blindly, as these, strictly speaking, only start to make sense as corpus from the moment that defines the logical framework, of deductive nature, formed by the theory, the model and the phenomenon - including subsidizing the design itself of the research as a whole Bendassolli, With the phenomenon on the horizon becomes possible the empirical test, which, in the case of AC, is the deductive process by which categories are confronted with the theory, conducting the empirical adjustment and acting on its explanatory power.

As we said at another time, the induction is ratified by deduction; but, where to situate the abduction? The abduction could be used by the researcher when skipping from the theoretical model 1 to the theoretical model 2 see Figure 1 , in an attempt to find a better alignment for the same data and the same phenomenon. This could be a conciliatory way between, on one hand, the search for empirical adequacy of the theoretical scaling process that may involve the proposal of a new theory, perhaps more suitable to understand the data induction , and on the other hand, the attempt of "theory test", which consists in an effort to conform the data to the prior theory that provided the frame to the theoretical model and the empirical research deduction.

Additionally, other simplifying elements of the fundamental aspects of the phenomenon are taken into account for the new alignment, being necessary to resort to the theoretical model 2 and to the abstract theory 2.

Thus, it is assumed that phenomena have relative stability and, depending on the theoretical model and the abstract theory of which it is a simplification, it can be analyzed from various perspectives; but as much of the researches that use CA, it does not take into account the clear existence of a phenomenon, this point can be difficult to understand.

In fact, the exchange of a model by other means involves, to some extent, some theoretical development. If the search is conducted with an CA as a resource ad hoc, in the end, there are empirical generalizations of inductive nature that may, in the absence of a seated robust phenomenon in the phenomenon-theory-datum axis, result in spray the findings, expanding the empirical base without a consideration of theoretical development.

There are two possibilities when it carries out an CA: to produce new theories or models, either to confirm or "test", or to consolidate, or to enlarge, etc. The choice of one or other of these pathways affects the process of operationalization, as in the decision of using categories a priori or not; but in both cases, they are at stake conceptions about the statutes of empirical data in support of a theory and the independence or dependence of the latter in relation to those same empirical data.

In a "naive" approach, the theory appears only a posteriori, after the data "speak" to the researcher Bendassolli, In this case, the weight assigned to the empirical is crucial, but strictly speaking, the phenomenon is not presented without lens that allows watching it theoretical model, simplifying of the abstract theory.

The datum, to be interpreted, needs a spotlight, which is fostered by the theoretical perspective, which makes it apprehensible by the perception in space and time. In an opposite perspective, the theory appears as a great backdrop, as a horizon which provides for the interpretation of data. While upholding the view that the phenomenon, through the lens of theory and from the model, ensures the prospect of watching of a portion of the world, the CA should look into the data as associated to an understandable phenomenon by a given theoretical perspective, creating the phenomenon-theory-datum axis, which is the support of the research design.

If the path adopted is possible without a prior theory, it is understood that the phenomenon can be grasped from the datum - and only then, the theory that represents it is sought empirical adequacy ; but as we have seen, it seems problematic and questionable the attempt to analyze data without having in mind a phenomenon.

Computer Assisted Qualitative Data Analysis with MAXQDA

FINAL THOUGHTS The main argument in this article is that the use of CA by researchers in psychology often ignores its potential for generating alternative explanatory theories, which can avoid both the trap of "corroboration" deduction or imposition of the theory to the datum, as the "reification of the datum" induction - building a punctual theory that will not go beyond the datum itself.

Our aim was solely to alert researchers about the importance of these aspects when using of the AC, as obviously is made in any analysis of qualitative data. By the way, this is the main contribution of qualitative data analysis: to offer new interpretive possibilities that go beyond the statistical inferences.As described, we began by quantifying the sample by rules 1 to 5.

E It is not sufficient that you talk to the professors and tell them that they need to refer to the healthcare network system Ohno, Taiichi. Bardin, Laurence.

It is argued this method of analysis must go beyond the mere description or organization of data through categorization, focusing instead in a theorization of the psychological phenomenon under investigation.

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