Inductive And Deductive Theory Pdf

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View Deductive-and-Inductive-Reasoning. If one of the premises is false, the conclusion will be false. Inductive vs.

The purpose of this paper is to explain the rationale for choosing the qualitative approach to research human resources practices, namely, recruitment and selection, training and development, performance management, rewards management, employee communication and participation, diversity management and work and life balance using deductive and inductive approaches to analyse data. The paper adopts an emic perspective that favours the study of transfer of human resource management practices from the point of view of employees and host country managers in subsidiaries of western multinational enterprises in Ghana. Despite the numerous examples of qualitative methods of data generation, little is known particularly to the novice researcher about how to analyse qualitative data.

Practitioner Research in Health Care pp Cite as. The first two chapters of this book set out what we believe are the distinguishing characteristics of practitioner research. Unable to display preview.

Qualitative research: deductive and inductive approaches to data analysis

Understanding the causes of land use change is of great importance for issues of tropical deforestation, agricultural development and biodiversity conservation. This can be sound science but we here aim to exemplify that there is also scope for more deductive approaches that test a pre-defined explanatory theory. The paper first introduces the principles and merits of inductive and more deductive types of land use modeling.

It then presents one integrated causal model that is subsequently specified to predict land use in an area in northeastern Philippines in a deductive manner, and tested against the observed land use in that area. The same set of land use data is also used in an inductive multinomial regression approach. Because the deductive model explicitly contains not only the causal factors but also the causal mechanisms that explain land use, the deductive model then provides a more truly causal, as well as more theory-connected, understanding of land use.

This provides land use scholarship with an invitation to add more deductive theory-driven and theory-building daring to its methodological repertoire. The face of the earth is rapidly changing, with great consequences for rural livelihoods, biodiversity conservation, urban quality of life and the global climate.

Understanding land use change is therefore a matter of obvious import and urgency. How can such complexity be handled scientifically? One approach is to focus on only one or a few factors, and accept that explanations can only be partial. As shown, for instance, in overviews of Walker et al. In the present paper, we make a case that the present state of the art allows us to perform integrated research and yet use a more deductive epistemology, and that this option, in interaction with inductive work, will enhance causal insight and cumulative scientific progress in land use science.

We aim to strengthen our case by showing and discussing the performance of a deductive and an inductive approach, applied parallel to each other to explain land use in a single example region. The ensuing discussion shows the value of the deductive modeling approach within a range of approaches from inductive to deductive. First there is a theory; then a concrete predictive hypothesis is deduced from that theory.

Then this hypothesis is tested in the real world and with that result, the theory is either falsified or strengthened. This, in short, is deductive epistemology. Contrasting with this approach, inductive methodology begins with observations of reality and then tries to find regularities in these data.

This regularity is then declared to be a general pattern a model, a theory , which again can be tested in practice. The present paper does not find fault in this basic epistemological scheme. We do, however, think that for a proper understanding of how land use science proceeds in practice, it is necessary to define a number of methodological positions that lie in-between the deductive and inductive extremes.

On the one hand, there is extreme deduction Popper, in which the empirical cycle is followed strictly and theory falsification rather than verification is seen as the key to progress. On the other hand, there is extreme induction, in which the researcher aims to find patterns in large datasets without any theoretical guide.

Both extremes have their advantages in some cases, depending on the availability of theory. Both have disadvantages too, however. In the social and economic sciences, extreme deductivism would lead to an endless rejection of theories because simply none of them is able to grasp the full complexity of the system described.

Extreme inductivism, on the other hand, leads to an immense amount of correlations that cannot be interpreted as causes and never accumulate into a coherent theory. In most research practice, researchers find a less daunting solution by adopting a position, usually implicit and led by disciplinary traditions, somewhere on the continuum between extreme induction and extreme deduction.

For the present paper and including the two extremes, we may define six of these positions. We concentrate here mainly on quantitative work. Extreme induction.

The first case is that a researcher has no model or theory at all. Unstructured factors induction. Under this term we subsume all research approaches that apply a broad conceptual framework of some kind, usually derived from common sense or literature overview, in order to specify a usually long list of factors or proxies that are candidate to help explain land use or land use change.

Alternatively, some kind of theory may be invoked as well, e. Nelson et al. The studies then leave it to the procedures of statistical inference to find the correlations between these variables. Characteristically, these studies do not end with a discussion of theoretical perspectives but with a discussion of the significance of correlation coefficients and such like in the specific case studied.

Many land use change studies fall into this category e. Serneels and Lambin, ; Overmars and Verburg, Theory-guided factors induction. This term denotes studies that take an explicit theory of land use change as point of departure to critically specify a theory-connected usually shorter list of explanatory variables.

Strictly speaking, this list is still unstructured without specification of how the variables are supposed to interact. One quantitative example is in Perz and Walker , focusing on secondary forest growth in Amazonia in connection with Chayanovian theory. Interesting results have also been reached in a more qualitative manner, exemplified by Ostrom who arrived at her well-known conditions for successful common property management by a stepwise induction of case studies.

Characteristic for all studies of theory-guided induction is that the relevance of the results is wider than those of type 1 and type 2 studies. Guided by theory, induction can become theory building.

Imposed theory structure. According to Elster , it is only then that true explanation comes within reach, because true explanation requires insight not only in the factors but also in the mechanisms.

Imposed theory. A purely deductive approach is reached when a land use theory is specified for a real world case in terms of both structure and parameters, and the land use thus predicted is tested against real land use. It is only then that true prediction, hence true deduction, is possible. The case study of the present paper will be an example. Extreme deduction.

A few technical remarks are in order here. First, induction, deduction and the continuum between them, even though central tenets of epistemology, do not cover the full spectrum of scientific methodology. Researchers may also find intermediate and mixed positions, or work sequentially, for instance. Overlooking the field of explanatory land use studies, we find many examples of unstructured factors induction. Theory-guided factors induction is present in much smaller numbers.

Imposition of theory structure is virtually non-existent. This may have historical and cultural reasons. To begin with, strong theories that may be tested are simply not massively present in any young scientific field. Furthermore, theories and deduction are not really en vogue in post-modern times they are top-down, they turn a blind eye to the multiple complexities and voices of social realities, etc. And finally, the attraction that land use studies appear to have had to econometrists and GIS-based geographic data technology may block growth towards more deductive, theory-guided work.

In our opinion, explanatory land use studies could become more deductive. And we have more general explanatory theories waiting to be applied and tested on land use situations, such as rational choice theory, cultural theory, theories of collective action and common property management.

Furthermore, much knowledge has accumulated in datasets for inductive analyses, which may be conceptually re-used in more deductive ways. In the present paper, our example shows that nothing conceptually difficult is at stake here. There are two main advantages of using deductive methods. First, deduction yields the intrinsically better proof of causality, i. Let us take Nelson et al.

Their causal model is that on each site, the most profitable crop is grown. However, this is not tested as such because, as Nelson et al. Instead, factors such as slope are used as explanatory variables. If, say, maize is found to be associated with medium slopes, would that be because of its relative profitability there?

It could also be that traditions do not allow maize elsewhere, or because of risk aversion. If, however, Nelson et al. The second benefit of a more deductive approach is that it better facilitates the accumulation of insight on the level of the discipline as a whole.

Referring back again to the example of Nelson et al. Conclusions then necessarily tend to remain largely stuck on that level, e. In order to reach some degree of generalization, such studies then have to wait until enough of them have accumulated to themselves become data in a meta-analysis such as that of Geist and Lambin who, characteristically for an inductive approach in the meta-analysis of inductive studies, come up with a generalized and regionally patterned listing of proximate factors and underlying driving forces of tropical deforestation.

Obviously useful as this may be, more progress would be made if not only the incidental meta-analyst but also the researchers themselves, in their own studies, were able to participate in a permanent intertrade of generalization. This can be achieved if these studies were more deductive, i. Theory-led work, feeding back into theory, leads to theory building.

In all this, we assume that empirically-based theories are good to have. In other words, we assume that land use scientists do not become addicted to theories, especially their own, to a degree that theories begin to block entry for the surprises of reality Vayda, or become objects of counterproductive controversy Brox, For illustrating the deductive approach, we have chosen to test a broad model that is able to take up all factors that should be comprised in an integrated approach, hence including cultural, economic and biophysical data.

The model has been taken from De Groot , and may be characterized as broad rational choice. For the inductive approach a multinomial logistic regression model is applied. In the remainder of this paper these two approaches are referred to as the inductive and deductive approach or model, respectively. We put all emphasis on the comparison and not on the cultural or land use intricacies of the study area.

Action-in-Context AiC De Groot, is a framework designed for the explanation of human actions, especially in the environmental field. With that, AiC is a fully actor-based framework, which is a logical choice for explanatory work because actors, not systems, are the social entities that cause change directly. Footnote 1 AiC may be used as a framework to guide the research process, but can also be used as a template for models.

These models can be, for example, detailed multi-agent models that model individual agents Huigen, , or models that explain the choices of a smaller number of large actor categories. The latter is of course much simpler to implement and the way we will proceed in this study. Action-in-Context has four interconnected components. The other components of AiC are elaborations of the core element.

6.3 Inductive and deductive reasoning

The main difference between inductive and deductive approaches to research is that whilst a deductive approach is aimed and testing theory, an inductive approach is concerned with the generation of new theory emerging from the data. A deductive approach usually begins with a hypothesis, whilst an inductive approach will usually use research questions to narrow the scope of the study. For deductive approaches the emphasis is generally on causality, whilst for inductive approaches the aim is usually focused on exploring new phenomena or looking at previously researched phenomena from a different perspective. Inductive approaches are generally associated with qualitative research, whilst deductive approaches are more commonly associated with quantitative research. However, there are no set rules and some qualitative studies may have a deductive orientation.

Deducing hypothesis from theory as outlined by Robinson () in deductive logic in research follows the following procedure.

Inductive vs. deductive reasoning

Reasoning in artificial intelligence has two important forms, Inductive reasoning, and Deductive reasoning. Both reasoning forms have premises and conclusions, but both reasoning are contradictory to each other. Following is a list for comparison between inductive and deductive reasoning:. The differences between inductive and deductive can be explained using the below diagram on the basis of arguments:.

Theories structure and inform sociological research. So, too, does research structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students new to these topics when they consider the relationships between theory and research in inductive and deductive approaches to research.

Theory structures and informs social work research. Conversely, social work research structures and informs theory. Students become aware of the reciprocal relationship between theory and research when they consider the relationships between the two in inductive and deductive approaches.

Difference between Inductive and Deductive reasoning

Understanding the causes of land use change is of great importance for issues of tropical deforestation, agricultural development and biodiversity conservation.

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The contribution of inductive and deductive theory to the development of practitioner knowledge

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