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The Choice for Sustainability
Published in Jean Russ, Sustainability and Design Ethics, 2018
Once scientists have collected their observations, they have to determine what those observations mean. This requires the scientists to draw inferences from the data based on logical reasoning. Deductive reasoning is logically valid. It is the fundamental method used in mathematical proofs. Deductive reasoning proceeds from the general rule to the particular, whereas inductive reasoning proceeds from the particular to a general rule. In inductive reasoning, we say a general principle is true because all of the examples or samples we have seen are true. Of course, inductive logic is not logically valid, because the limited number of observation is insufficient to guarantee a general rule. But inductive logic is consistently used in science and even in day-to-day life.
Solving Problems and Making Decisions
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
Induction differs from deduction in that an inductive conclusion is not necessarily true if the premises are true, as is the case with valid deductions. Inductive reasoning is accomplished by drawing a general conclusion from particular conditions. We do this every day without using any formal rules of logic. For example, a student may arrive at the inductive conclusion that all midterm exams are held in middle week of the semester because all of hers have been held at this time. Although this conclusion may be generally true, the student may take a class next semester for which the midterm exam is given at some other time. Inductive reasoning involves processes like categorization, reasoning about rules and events, and problem solving (Holyoak & Nisbett, 1988).
Packaging Localisation
Published in Huda Khan, Richard Lee, Polymeros Chrysochou, Consumer Packaging Strategy, 2023
We used an abductive reasoning approach to generate insights into the intended meanings of the captured packaging elements (Glaser, 2008; Walton, 2004). Abductive reasoning is appropriate for this study because it provides a suitable data processing procedure (data fishing and theoretical sampling) to facilitate the grouping of packaging elements with similar meanings. By focusing on the likeliest or the best possible explanation for the observed fact, abductive reasoning is able to identify the explanation most preferred over the others in the inference procedure (Timmermans & Tavory, 2012). Abductive analysis shares criteria with grounded theory, such as focusing on theoretical sampling, requiring theoretical sensitivity and developing new theories. However, the logical form of abductive reasoning is distinct from grounded theory. In detail, grounded theory is developed based on the inductive method and lets the theories emerge inductively rather than applying any analytical strategy. Researchers criticise the ‘induction focus’ as the key weakness of grounded theory. Abduction, instead, building on grounded theory, aims to analyse qualitative data for theoretical construction and innovation. This approach rests on the systematic analysis of existing theories and evidence and tends to reveal fresh empirical findings to foster theoretical innovation (Kelle, 2010; Locke, 2007). Abduction also logically differs from the other types of reasoning, such as induction and deduction. Inductive reasoning starts with observing specific objectives in a limited scope and tends to generate conclusions that are likely rather than certain. The results found by adopting this approach are operative in the scope but lack universal generalisation.
Data-driven zero-carbon transition analysis in the industrial and manufacturing sectors: a world-regional perspective
Published in International Journal of Logistics Research and Applications, 2023
Tat-Dat Bui, Viqi Ardaniah, Qinghua Zhu, Mohammad Iranmanesh, Ming-Lang Tseng
Content analysis is considered to identify information and communication entities; such an analysis is systematically conducted by observing and reading texts or objects and is viewed to offer replicable and arduous prior studies to investigate the distribution of documents (Wimbadi and Djalante 2020). Content analysis is critical in structurally assessing a large amount of information by accurately apprehending pertinent data to determine significant and actual issues, approaches, and arguments. The analysis consists of two types: inductive and deductive reasoning processes (Laibach, Börner, and Bröring 2019). The inductive reasoning process refers to abstracting, reducing, and grouping the data by applying concepts, categories, or themes to meet the objectives of this study, while the deductive reasoning process denotes retesting existing theory with the collected data. In this study, deductive content analysis is first employed to determine the predefined keywords from the Scopus database that drive the ZCT literature. Then, inductive content analysis is applied to identify those data that present ZCT across geographic regions.
Engineering judgement in undergraduate structural design education: enhancing learning with failure case studies
Published in European Journal of Engineering Education, 2022
Vikki Edmondson, Fred Sherratt
Judgement has been defined as having ‘three fundamental attributes – it has a diagnostic character in problem definition, and inductive character in combination of evidence, and interpretative character in providing meaning and context to predictive conclusion’ (Vicks 2002, 100). Examining this definition, it is furthermore apparent that judgement is a process that occurs at key stages of structural engineers’ design practice. Judgement serves in the diagnostic forming of a hypothesis of how a structure will behave. Inductive reasoning gathers data and selects theories and analytical methods that are applicable to the problem. These could be characteristics of the form of the structure, the loading conditions, the applicability of elastic or plastic analysis, or the properties of the ground. Finally, interpretive judgement contextualises the results with wider understandings and the intuition of experience.
How education background affects design outcome: teaching product development to mechanical engineers, industrial designers and managers
Published in European Journal of Engineering Education, 2019
Arlindo Silva, Marco Leite, João Vilas-Boas, Ricardo Simões
Finally, the authors do believe that addressing the topics introduced by the previous research questions will make a difference, as follows: (i) between schools that teach engineering and design methods and schools that educate engineers and designers to face the new challenges; and (ii) between business schools that teach entrepreneurship and the ones that create entrepreneurs. Therefore, it is argued for the potential relevance and usefulness of the outcomes of this research for other schools concerned with engineering, design and business education. Nevertheless, it should be acknowledged as a limitation that the study conclusions concerning the initial research questions can only be taken as a suggestion of truth, but they cannot assure it, as a consequence of the inductive reasoning.