Products related to Complexity:
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Innovation in Mixed Methods Research : A Practical Guide to Integrative Thinking with Complexity
Explaining both why and how to use mixed methods for discovering solutions to complex research problems, this guide gives readers the tools to adapt approaches to suit their own research conditions.Written in a warm, encouraging tone and packed with helpful diagrams and visual organizers, it provides an easy-to-follow map to the mixed methods process, covering everything from ‘what is mixed methods research?’ to framing, integrating, and describing a complexity-sensitive mixed methods approach. Features include:Key questions to navigate the important concepts of each chapterPractice alerts to provide practical tips on working in the fieldChapter check-ins to assess development of key skillsFurther reading to expand and deepen knowledge of mixed methods practicesAn annotated glossary to get to grips with foundational terms and revise for examsSupported throughout by real-world examples and advice from the author and other mixed methods experts, this book helps readers succeed in their projects and think innovatively about the methods they use.
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Complexity in Organizations : A Research Overview
Written with pace and clarity, this book is a comprehensive and compact overview and introduction to the research landscape of complexity in organizations.In addition to conveying a gripping history of how complexity has influenced organizational ideas, theories, and practices throughout the 20th century and into our present age, the book sheds light on how ground-breaking ideas in chaos and complexity research have emerged and challenged the very foundations of science into a changed vision of nature, society, and human organizations.As well as being an exciting investigation into complexity research in organizations, the book shows how, in the past, researchers who were immersed in the power politics of their day grappled with the theme of complexity in their quest to understand the dynamics of organization in nature and society.By welding fundamental theoretical themes and practical implications into the political and social contexts in which they emerged, this overview provides both depth and breadth to the history, as well as the future, of studies of complexity in organized activity.The book is a lucid and essential study of a topic that will be of interest to scholars, researchers, and students in the fields of business and management, especially those with an interest in the ways that complexity affects and transforms organizations.
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Big Data Mining and Complexity
This book offers a much needed critical introduction to data mining and ‘big data’.Supported by multiple case studies and examples, the authors provide: Digestible overviews of key terms and concepts relevant to using social media data in quantitative research. A critical review of data mining and ‘big data’ from a complexity science perspective, including its future potential and limitationsA practical exploration of the challenges of putting together and managing a ‘big data’ database An evaluation of the core mathematical and conceptual frameworks, grounded in a case-based computational modeling perspective, which form the foundations of all data mining techniques Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
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Reflexive Mixed Methods Research in Comparative and International Education : Context, Complexity, and Transdisciplinarity
Responding to both the increase of mixed methods research (MMR) and the lack of dialogue about its philosophical and methodological practices in comparative and international education (CIE) Reflexive Mixed Methods Research in Comparative and International Education: Context, Complexity, and Transdisciplinarity offers readers an overview of different philosophical approaches to mixed methods, as well as the practical processes to engage in MMR with reflexivity. Comparing reflexivity to a kaleidoscope, the author describes how it can be used throughout the research process to engage with different paradigms and methods, as well as to integrate findings and consider emerging interconnections.The book discusses various ways to reflexively adapt designs, methods, and analyses, and integrate findings in MMR to better contextualize knowledge in the field of CIE.It illustrates different approaches to MMR through various case summaries, and each chapter poses reflexive questions for researchers to consider during their research projects.It emphasizes a critical realist approach, particularly for how it involves epistemological pluralism while also offering contingent explanations to educational questions.Through a reflexive approach, the book offers possibilities for conducting better and more epistemically just MMR. This is fascinating reading for both new and more experienced researchers to expand the craft of researching with MM.Interdisciplinary and transdisciplinary teams, as well as sole researchers, will find concrete research practices in the book for employing reflexive MMR to understand complex educational issues.
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What is the complexity of semiconductor technology or microsystems technology?
The complexity of semiconductor technology or microsystems technology is high due to the intricate processes involved in designing, manufacturing, and integrating tiny electronic components. These technologies require precise control at the nanoscale level, involving complex materials, intricate fabrication techniques, and sophisticated equipment. Additionally, the rapid pace of innovation and the need for continuous improvement in performance and miniaturization add to the complexity of these technologies. As a result, semiconductor and microsystems technology require significant expertise, resources, and investment to develop and produce advanced electronic devices.
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Can complexity be objectively measured?
Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity.
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What is the complexity of Mergesort?
The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets.
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How can one get rid of complexity?
One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations.
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Research Methods for Complexity Theory in Applied Linguistics
This book provides practical guidance on research methods and designs that can be applied to Complex Dynamic Systems Theory (CDST) research.It discusses the contribution of CDST to the field of applied linguistics, examines what this perspective entails for research and introduces practical methods and templates, both qualitative and quantitative, for how applied linguistics researchers can design and conduct research using the CDST framework.Introduced in the book are methods ranging from those in widespread use in social complexity, to more familiar methods in use throughout applied linguistics.All are inherently suited to studying both dynamic change in context and interconnectedness.This accessible introduction to CDST research will equip readers with the knowledge to ensure compatibility between empirical research designs and the theoretical tenets of complexity.It will be of value to researchers working in the areas of applied linguistics, language pedagogy and educational linguistics and to scholars and professionals with an interest in second/foreign language acquisition and complexity theory.
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Think Complexity : Complexity Science and Computational Modeling
Complexity science uses computation to explore the physical and social sciences.In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
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Complexity and Values in Nurse Education : Dialogues on Professional Education
This work explores the interplay of complexity and values in nurse education from a variety of vantages.Contributors, who come from a range of international and disciplinary backgrounds, critically engage important and problematic topics that are under-investigated elsewhere.Taking an innovative approach each chapter is followed by one or more responses and, on occasion, a reply to responses.This novel dialogic feature of the work tests, animates, and enriches the arguments being presented.Thought-provoking, challenging and occasionally rumbustious in tone, this volume has something to say to both nurse educators (who may find cherished practices questioned) and students.Given the breadth and nature of subjects covered, the book will also appeal to anyone concerned about and interested in nursing’s professional development/trajectory.
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Developmental Evaluation : Applying Complexity Concepts to Enhance Innovation and Use
Developmental evaluation (DE) offers a powerful approach to monitoring and supporting social innovations by working in partnership with program decision makers.In this book, eminent authority Michael Quinn Patton shows how to conduct evaluations within a DE framework.Patton draws on insights about complex dynamic systems, uncertainty, nonlinearity, and emergence.He illustrates how DE can be used for a range of purposes: ongoing program development, adapting effective principles of practice to local contexts, generating innovations and taking them to scale, and facilitating rapid response in crisis situations.Students and practicing evaluators will appreciate the book's extensive case examples and stories, cartoons, clear writing style, "closer look" sidebars, and summary tables.Provided is essential guidance for making evaluations useful, practical, and credible in support of social change. See also Developmental Evaluation Exemplars, edited by Michael Quinn Patton, Kate McKegg, and Nan Wehipeihana, which presents 12 in-depth case studies.
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What is the complexity of composing two functions?
Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input.
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What are the Landau symbols for the time complexity?
The Landau symbols for time complexity are commonly used to describe the upper and lower bounds of an algorithm's running time. The most commonly used Landau symbols for time complexity are O (big O) for upper bound, Ω (big omega) for lower bound, and Θ (big theta) for both upper and lower bounds. These symbols are used to express the growth rate of an algorithm's running time in terms of the input size. For example, if an algorithm has a time complexity of O(n^2), it means that the running time of the algorithm grows no faster than n^2 as the input size increases.
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What are the Big O notations for time complexity?
The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms.
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How do you determine the complexity of a function?
The complexity of a function can be determined by analyzing its time and space requirements. This can be done by examining the number of operations the function performs and the amount of memory it uses. Additionally, the complexity can be influenced by the size of the input data and the efficiency of the algorithm used in the function. By considering these factors, one can determine the complexity of a function, which is often expressed using Big O notation.
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