The Scientific Method the Scientific Method Is a Process Used by Scientists to Study the World Around Them
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Jackson: Choosing a Methodology: Philosophical Underpinning
JACKSON: CHOOSING A METHODOLOGY: PHILOSOPHICAL UNDERPINNING Choosing a Methodology: Philosophical Practitioner Research Underpinning In Higher Education Copyright © 2013 University of Cumbria Vol 7 (1) pages 49-62 Elizabeth Jackson University of Cumbria [email protected] Abstract As a university lecturer, I find that a frequent question raised by Masters students concerns the methodology chosen for research and the rationale required in dissertations. This paper unpicks some of the philosophical coherence that can inform choices to be made regarding methodology and a well-thought out rationale that can add to the rigour of a research project. It considers the conceptual framework for research including the ontological and epistemological perspectives that are pertinent in choosing a methodology and subsequently the methods to be used. The discussion is exemplified using a concrete example of a research project in order to contextualise theory within practice. Key words Ontology; epistemology; positionality; relationality; methodology; method. Introduction This paper arises from work with students writing Masters dissertations who frequently express confusion and doubt about how appropriate methodology is chosen for research. It will be argued here that consideration of philosophical underpinning can be crucial for both shaping research design and for explaining approaches taken in order to support credibility of research outcomes. It is beneficial, within the unique context of the research, for the researcher to carefully -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
The Rhetoric of Positivism Versus Interpretivism: a Personal View1
Weber/Editor’s Comments EDITOR’S COMMENTS The Rhetoric of Positivism Versus Interpretivism: A Personal View1 Many years ago I attended a conference on interpretive research in information systems. My goal was to learn more about interpretive research. In my Ph.D. education, I had studied primarily positivist research methods—for example, experiments, surveys, and field studies. I knew little, however, about interpretive methods. I hoped to improve my knowledge of interpretive methods with a view to using them in due course in my research work. A plenary session at the conference was devoted to a panel discussion on improving the acceptance of interpretive methods within the information systems discipline. During the session, a number of speakers criticized positivist research harshly. Many members in the audience also took up the cudgel to denigrate positivist research. If any other positivistic researchers were present at the session beside me, like me they were cowed. None of us dared to rise and speak in defence of positivism. Subsequently, I came to understand better the feelings of frustration and disaffection that many early interpretive researchers in the information systems discipline experienced when they attempted to publish their work. They felt that often their research was evaluated improperly and treated unfairly. They contended that colleagues who lacked knowledge of interpretive research methods controlled most of the journals. As a result, their work was evaluated using criteria attuned to positivism rather than interpretivism. My most-vivid memory of the panel session, however, was my surprise at the way positivism was being characterized by my colleagues in the session. -
A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools
applied sciences Article A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools Tatiana Person 1,* , Iván Ruiz-Rube 1 , José Miguel Mota 1 , Manuel Jesús Cobo 1 , Alexey Tselykh 2 and Juan Manuel Dodero 1 1 Department of Informatics Engineering, University of Cadiz, 11519 Puerto Real, Spain; [email protected] (I.R.-R.); [email protected] (J.M.M.); [email protected] (M.J.C.); [email protected] (J.M.D.) 2 Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review Citation: Person, T.; Ruiz-Rube, I.; Mota, J.M.; Cobo, M.J.; Tselykh, A.; tool focused on Computer Science, to implement evidence-based systematic review processes in Dodero, J.M. -
Molecular Biology for Computer Scientists
CHAPTER 1 Molecular Biology for Computer Scientists Lawrence Hunter “Computers are to biology what mathematics is to physics.” — Harold Morowitz One of the major challenges for computer scientists who wish to work in the domain of molecular biology is becoming conversant with the daunting intri- cacies of existing biological knowledge and its extensive technical vocabu- lary. Questions about the origin, function, and structure of living systems have been pursued by nearly all cultures throughout history, and the work of the last two generations has been particularly fruitful. The knowledge of liv- ing systems resulting from this research is far too detailed and complex for any one human to comprehend. An entire scientific career can be based in the study of a single biomolecule. Nevertheless, in the following pages, I attempt to provide enough background for a computer scientist to understand much of the biology discussed in this book. This chapter provides the briefest of overviews; I can only begin to convey the depth, variety, complexity and stunning beauty of the universe of living things. Much of what follows is not about molecular biology per se. In order to 2ARTIFICIAL INTELLIGENCE & MOLECULAR BIOLOGY explain what the molecules are doing, it is often necessary to use concepts involving, for example, cells, embryological development, or evolution. Bi- ology is frustratingly holistic. Events at one level can effect and be affected by events at very different levels of scale or time. Digesting a survey of the basic background material is a prerequisite for understanding the significance of the molecular biology that is described elsewhere in the book. -
Outline of Science
Outline of science The following outline is provided as a topical overview of • Empirical method – science: • Experimental method – The steps involved in order Science – systematic effort of acquiring knowledge— to produce a reliable and logical conclusion include: through observation and experimentation coupled with logic and reasoning to find out what can be proved or 1. Asking a question about a natural phenomenon not proved—and the knowledge thus acquired. The word 2. Making observations of the phenomenon “science” comes from the Latin word “scientia” mean- 3. Forming a hypothesis – proposed explanation ing knowledge. A practitioner of science is called a for a phenomenon. For a hypothesis to be a "scientist". Modern science respects objective logical rea- scientific hypothesis, the scientific method re- soning, and follows a set of core procedures or rules in or- quires that one can test it. Scientists generally der to determine the nature and underlying natural laws of base scientific hypotheses on previous obser- the universe and everything in it. Some scientists do not vations that cannot satisfactorily be explained know of the rules themselves, but follow them through with the available scientific theories. research policies. These procedures are known as the 4. Predicting a logical consequence of the hy- scientific method. pothesis 5. Testing the hypothesis through an experiment – methodical procedure carried out with the 1 Essence of science goal of verifying, falsifying, or establishing the validity of a hypothesis. The 3 types of -
Principal Scientist, Pharmacology (DMPK) at Entrada Therapeutics Full-Time Opportunity in Boston, MA
Principal Scientist, Pharmacology (DMPK) at Entrada Therapeutics Full-Time Opportunity in Boston, MA Company Overview Entrada Therapeutics is a biotechnology company dedicated to transforming the treatment of devastating diseases through the intracellular delivery of biologics. Entrada’s technology enables the efficient intracellular delivery of proteins, peptides and nucleic acids, thus allowing for the development of programs across several intracellular target classes. The Company’s novel approach addresses current challenges associated with both large and small molecule therapeutics and represents a fundamental advancement in the delivery of molecules into the cytosol. Entrada is comprised of experts and leaders in both biologics development and the rare disease space. In December 2018, Entrada closed a $59 million Series A from 5AM Ventures, MPM Capital, Roche Venture Fund, MRL Ventures Fund and Agent Capital. Position Summary Entrada is hiring for a highly motivated, collaborative and innovative Principal Scientist, specializing in pharmacokinetics and pharmacology to join the Company’s intracellular biologics discovery and development efforts. The candidate will work with a world-class, multidisciplinary team of drug discovery scientists and lead the in vitro and in vivo ADME, PK, PD and toxicokinetic efforts. Specifically, the candidate will design, manage and execute in vitro and in vivo programs both in- house and at contract research organizations. The candidate will also be responsible for analyzing, interpreting and modeling -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
Reliability Engineering: Today and Beyond
Reliability Engineering: Today and Beyond Keynote Talk at the 6th Annual Conference of the Institute for Quality and Reliability Tsinghua University People's Republic of China by Professor Mohammad Modarres Director, Center for Risk and Reliability Department of Mechanical Engineering Outline – A New Era in Reliability Engineering – Reliability Engineering Timeline and Research Frontiers – Prognostics and Health Management – Physics of Failure – Data-driven Approaches in PHM – Hybrid Methods – Conclusions New Era in Reliability Sciences and Engineering • Started as an afterthought analysis – In enduing years dismissed as a legitimate field of science and engineering – Worked with small data • Three advances transformed reliability into a legitimate science: – 1. Availability of inexpensive sensors and information systems – 2. Ability to better described physics of damage, degradation, and failure time using empirical and theoretical sciences – 3. Access to big data and PHM techniques for diagnosing faults and incipient failures • Today we can predict abnormalities, offer just-in-time remedies to avert failures, and making systems robust and resilient to failures Seventy Years of Reliability Engineering – Reliability Engineering Initiatives in 1950’s • Weakest link • Exponential life model • Reliability Block Diagrams (RBDs) – Beyond Exp. Dist. & Birth of System Reliability in 1960’s • Birth of Physics of Failure (POF) • Uses of more proper distributions (Weibull, etc.) • Reliability growth • Life testing • Failure Mode and Effect Analysis -
Data Types Are Values
Data Types Are Values James Donahue Alan Demers Data Types Are Values James Donahue Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 Alan Demers Computer Science Department Cornell University Ithaca, New York 14853 CSL -83-5 March 1984 [P83-00005] © Copyright 1984 ACM. All rights reserved. Reprinted with permission. A bst ract: An important goal of programming language research is to isolate the fundamental concepts of languages, those basic ideas that allow us to understand the relationship among various language features. This paper examines one of these underlying notions, data type, with particular attention to the treatment of generic or polymorphic procedures and static type-checking. A version of this paper will appear in the ACM Transact~ons on Programming Languages and Systems. CR Categories and Subject Descriptors: 0.3 (Programming Languages), 0.3.1 (Formal Definitions and Theory), 0.3.3 (Language Constructs), F.3.2 (Semantics of Programming Languages) Additional Keywords and Phrases: data types, polymorphism XEROX Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 DATA TYPES ARE VALVES 1 1. Introduction An important goal of programming language research is to isolate the fundamental concepts of languages, those basic ideas that allow us to understand the relationship among various language features. This paper examines one of these underlying notions, data type, and presents a meaning for this term that allows us to: describe a simple treatment of generic or polymorphic procedures that preserves full static type-checking and allows unrestricted use of recursion; and give a precise meaning to the phrase strong typing, so that Language X is strongly typed can be interpreted as a critically important theorem about the semantics of the language. -
PDF Download Starting with Science Strategies for Introducing Young Children to Inquiry 1St Edition Ebook
STARTING WITH SCIENCE STRATEGIES FOR INTRODUCING YOUNG CHILDREN TO INQUIRY 1ST EDITION PDF, EPUB, EBOOK Marcia Talhelm Edson | 9781571108074 | | | | | Starting with Science Strategies for Introducing Young Children to Inquiry 1st edition PDF Book The presentation of the material is as good as the material utilizing star trek analogies, ancient wisdom and literature and so much more. Using Multivariate Statistics. Michael Gramling examines the impact of policy on practice in early childhood education. Part of a series on. Schauble and colleagues , for example, found that fifth grade students designed better experiments after instruction about the purpose of experimentation. For example, some suggest that learning about NoS enables children to understand the tentative and developmental NoS and science as a human activity, which makes science more interesting for children to learn Abd-El-Khalick a ; Driver et al. Research on teaching and learning of nature of science. The authors begin with theory in a cultural context as a foundation. What makes professional development effective? Frequently, the term NoS is utilised when considering matters about science. This book is a documentary account of a young intern who worked in the Reggio system in Italy and how she brought this pedagogy home to her school in St. Taking Science to School answers such questions as:. The content of the inquiries in science in the professional development programme was based on the different strands of the primary science curriculum, namely Living Things, Energy and Forces, Materials and Environmental Awareness and Care DES Exit interview. Begin to address the necessity of understanding other usually peer positions before they can discuss or comment on those positions. -
Using the Scientific Method
Using the Scientific Method 2002 and 2014 GED Content Area: Science Focus: Scientific Method (2002) and Scientific Hypothesis and Investigation(2014) Activity Type: Graphic Organizer and GED Practice Objectives Students will be able to: Appreciate the purpose of the Scientific Method Understand key terms related to the Scientific Method: observation, hypothesis, test, experiment, result, conclusion Relate the Scientific Method to an experiment Answer GED questions based on the Scientific Method Directions 1. Print the handout “Using the Scientific Method” (next page). Pass out the handout to the class. 2. Explain that the scientific method is the way scientists learn about the world around us. This involves several steps, often in the form of experiments. Discuss the 5 steps in the chart on the handout and define the highlighted words. 3. Have a student or students read the first passage out loud. Ask the class to fill in the chart. They can fill in the chart individually or in pairs (discussing these concepts can help students develop their thinking skills). 4. Discuss the students’ answers. Samples: 1. Observation: Where there was Penicillium mold, there were also dead bacteria. 2. Hypothesis: The mold must produce a chemical that kills the bacteria. 3. Test: Grow more of the mold separately and then return it to the bacteria. 4. Result: When the material is returned to the mold, the bacteria died. 5. Conclusion: Penicillium kills bacteria. 5. Have students read the passage at the bottom of the page and answer the GED practice question. Choice (4) is correct because the doctor saw that when the chickens ate whole‐grain rice with thiamine, they did not have the disease.