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Overview
Published in Song S. Qian, Mark R. DuFour, Ibrahim Alameddine, Bayesian Applications in Environmental and Ecological Studies with R and Stan, 2023
Song S. Qian, Mark R. DuFour, Ibrahim Alameddine
The above example illustrates the difference between deductive and inductive reasoning. Using deductive reasoning, we start from what we know to predict the outcome. As long as what we know is correct, the prediction will be correct. If we know the probability of success is , we can easily calculate the likelihood of observing successes in trials (dbinom(x=3, n=10, p=0.3)). Induction is the inverse process of figuring out the likely value of the probability of success when observing the data. In the binomial example, we start the process by providing an initial guess (the prior) and Bayes' theorem updates the prior with data. The updating process can be iterative.
Designing and Delivering a DTx Clinical Research Program: No Need to Re-invent the Wheel
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Colin A. Espie, Alasdair L. Henry
There is a fundamental contrast between an intervention being “evidence-based” versus one that is “evidence-informed.” To be clear, the requirement to generate clinically meaningful evidence on a DTx is exclusively related to the product itself being evidence-based. An evidence-informed product does not have clinically meaningful evidence, and such a product is not evidence-based. It is, therefore, not a digital therapeutic. One becomes familiar with an unhelpful form of “inductive reasoning” along the lines of: X treats Y effectively.This new product contains X.Therefore, this new product treats Y effectively.
Medicine making sense
Published in Alan Bleakley, Educating Doctors’ Senses Through the Medical Humanities, 2020
Inductive reasoning involves working forward from evidence to set up a hypothesis. Deductive reasoning involves testing a hypothesis through gathering evidence. There is a third way of knowing – ‘abduction’ or abductive reasoning, first described by Charles Peirce (1931) as a knowing in the senses (Schleifer and Vannatta 2013). Peirce described abductive reasoning as “the operations by which theories … are given birth”, in other words, the embodiment of proto-theory in practical acts. Something is done, and an idea follows that is contained in the arc of the act. Theory is then performative, often muscular and sometimes nervy – in itself an embodied activity. Donald Schön (1990) famously described ‘reflection-in-action’ as the moment-to-moment adjustment that we make as we are faced with novelty or uncertainty in activity. This is a reflex in the human, who is naturally predictive (Clark 2016). As the blurb to Andy Clark’s (ibid) book Surfing Uncertainty: Prediction, Action, and the Embodied Mind promises the reader: This title brings together work on embodiment, action, and the predictive mind. At the core is the vision of human minds as prediction machines – devices that constantly try to stay one step ahead of the breaking waves of sensory stimulation, by actively predicting the incoming flow.
“As a Trans Person You Don’t Live. You Merely Try to Survive and Apologize Every Day for Who You Are” – Discrimination Experiences Among Trans Individuals in Greece”
Published in Journal of Homosexuality, 2023
Vasileia Papadaki, Andreas Ntiken
Data were collected from December 2017 to May 2018 through semi-structured interviews conducted in person with the participants. The average length of the interviews was one hour; they were audio-recorded and transcribed verbatim. During the translation of the written narratives into English, special care was taken in order to make sure that the meaning of the original texts remained unaltered. Qualitative analysis uses inductive reasoning, by which themes and categories emerge from the data through the researcher’s careful examination and constant comparison (Patton, 2002). Thematic analysis was utilized by reading the transcript multiple times and using coding in order to identify themes and sub-themes. The approach of structural coding (Guest, MacQueen, & Namey, 2012) was used to identify the structure imposed on the data-set by the research questions and design; the interview text was segmented based on the questions or prompts asked by the researcher together with the responses from the research participants. The technique of member checking—taking data and interpretations back to the participants in the study so that they can confirm the credibility of the information and narrative account (Lincoln & Guba, 1985)—was used as a measure of validity, credibility and trustworthiness of this research project.
Exploring the applicability of occupational therapy transition assessments for students with disabilities
Published in World Federation of Occupational Therapists Bulletin, 2019
Christopher Trujillo, Meghan Poach, Mikaela Carr
There were 33 individuals who answered the open-ended anonymous comments section at the end of the survey. This data was examined systematically, line by line, and through the analytic process common ideas began to emerge. Using participant’s own words, these initial 12 ideas were categorised using the participant’s own words. These words were then coded numerically and categorised using an open coding approach. During secondary analysis of categories, related concepts were grouped into themes that researchers agreed on and named. Inductive reasoning was applied to understand the themes of the data. The results revealed three main themes: (1) positive statements about occupational therapy transition support and assessment, (2) the need for increased transition training for special education teams, and (3) transition team members desire for their input to be included in a transition assessment.
Is the premise ‘occupation promotes health’ logical? A syllogistic analysis
Published in Journal of Occupational Science, 2018
Rationalism and empiricism play different roles in scientific inquiry. Empiricism tests how well a postulate corresponds to a phenomenon with inductive reasoning, whereas rationalism validates the logical connection of concepts in the postulation using deductive reasoning. According to philosophyterms.com, “While deductive reasoning implies logical certainty, inductive reasoning only gives you the reasonable probability” (Philosophy terms, n.d., Para. 8). The strength of rationalism is that it enhances scientific credibility through the use of deductive reasoning skills, allowing the formulation of logically certain premises upon which theories can be built. Occupational science requires both empiricism and rationalism. In order to expedite the epistemic development of the discipline, I advocate the need for occupational science to be bifurcated into rationalistic occupational science and empirical occupational science, and the inclusion of various rationalist methods in occupational science curricula.