Kooperativer Bibliotheksverbund

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Format: 1 online resource (377 p.)

Series Statement: Studies in Manuscript Cultures ; 12

Content: During the last two decades, the (re-)discovery of thousands of manuscripts in different regions of sub-Saharan Africa has questioned the long-standing approach of Africa as a continent only characterized by orality and legitimately assigned to the continent the status of a civilization of written literacy. However, most of the existing studies mainly aim at serving literary and historical purposes, and focus only on the textual dimension of the manuscripts. This book advances on the contrary a holistic approach to the study of these manuscripts and gather contributions on the different dimensions of the manuscript, i.e. the materials, the technologies, the practices and the communities involved in the production, commercialization, circulation, preservation and consumption. The originality of this book is found in its methodological approach as well as its comparative geographic focus, presenting studies on a continental scale, including regions formerly neglected by existing scholarship, provides a unique opportunity to expand our still scanty knowledge of the different manuscript cultures that the African continent has developed and that often can still be considered as living traditions.

Note: Frontmatter -- , Acknowledgements -- , Contents -- , Introduction: African History and Islamic Manuscript Cultures / , Section 1: Writing Supports -- , New Strategies in Using Watermarks to Date Sub-Saharan Islamic Manuscripts / , Fī Lawḥin Maḥfūẓ: Towards a Phenomenological Analysis of the Quranic Tablet / , Section 2: Around the Texts -- , Islamic Education and Ample Space Layout in West African Islamic Manuscripts / , A Preliminary Appraisal of Marginalia in West African Manuscripts from the Mamma Haïdara Memorial Library Collection (Timbuktu) / , Section 3: Writing Practices and Authorship around the Continents -- , Writing in Africa: The Kilwa Chronicle and other Sixteenth-Century Portuguese Testimonies / , Bamana Texts in Arabic Characters: Some Leaves from Mali / , Arabic and Swahili Documents from the Pre-Colonial Congo and the EIC (Congo Free State, 1885-1908): Who were the Scribes? / , Section 4: Notes -- , Moodibbo Bello Aamadu Mohammadu and the Daada Maaje, a Handbook in an Indigenous Fulfulde Script / , Elements of a 'Timbuktu Manual of Style' / , Seven Gravestones at the Muslim Tana Baru Cemetery in Cape Town: A Descriptive Note / , Kaʾana Umar's 'CCI Quran': The Making of a Bornuan Manuscript in the Twenty-First Century / , Index of Place Names -- , List of Contributors , In English.

Additional Edition: ISBN 3-11-054144-0

Additional Edition: ISBN 3-11-054140-8

Language: English

DOI: 10.1515/9783110541441

URL: Volltext  (Open Access)

URL: Inhaltsverzeichnis

URL: Cover

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Format: 1 online resource (279 pages)

ISBN: 9783030530815

Series Statement: IEA Research for Education Ser. ; v.10

Note: Intro -- Foreword -- Contents -- About the Editor -- 1 Introduction to Reliability and Validity of International Large-Scale Assessment -- 1.1 Introduction -- 1.2 Outline of This Book -- References -- 2 Study Design and Evolution, and the Imperatives of Reliability and Validity -- 2.1 Introduction -- 2.2 Decisions Informing the Design of IEA Studies and Test Development -- 2.3 Addressing the Challenges -- 2.3.1 Governance and Representation (Validity and Fairness) -- 2.3.2 Reliability and Validity -- 2.3.3 Changing Contexts -- 2.3.4 Compositional Changes -- 2.3.5 Financial Support -- 2.3.6 Expansion in Assessment Activities -- 2.3.7 Heterogeneity -- 2.3.8 Advances in Technology -- 2.3.9 Assessment Delivery -- 2.4 Conclusions -- References -- 3 Framework Development in International Large-Scale Assessment Studies -- 3.1 Introduction -- 3.2 Assessment Frameworks -- 3.3 Contextual Frameworks -- 3.4 Design and Implementation -- 3.5 Steps in Framework Development -- 3.6 Types of Framework -- 3.6.1 Curriculum-Referenced Frameworks -- 3.6.2 Frameworks for Measuring Outcomes in Cross-Curricular Learning Areas -- 3.6.3 Frameworks for Measuring Real-Life Skills -- 3.6.4 Frameworks for Measuring Contexts -- 3.7 Conclusions -- References -- 4 Assessment Content Development -- 4.1 Introduction -- 4.2 Key Features of ILSAs that Influence Assessment Content Development -- 4.3 Validity in International Large-Scale Assessments -- 4.4 The Assessment Frameworks -- 4.5 Stimulus Material and Item Development: Quality Criteria Associated with Validity -- 4.5.1 Representation of the Construct -- 4.5.2 Technical Quality -- 4.5.3 Level of Challenge -- 4.5.4 Absence of Bias -- 4.5.5 Language and Accessibility -- 4.5.6 Cultural and Religious Contexts -- 4.5.7 Engagement of Test-Takers -- 4.5.8 Scoring Reliability -- 4.6 Stimulus and Item Material: An Overview. , 4.6.1 Stimulus Characteristics, Selection, and Development -- 4.6.2 Item Characteristics and Development -- 4.6.3 Item Types -- 4.7 Phases in the Assessment Development Process -- 4.7.1 Phase 1: Drafting and Sourcing Preliminary Content -- 4.7.2 Phase 2: Item Development -- 4.7.3 Phase 3: The Field Trial and Post Field Trial Review -- 4.7.4 Post Main Survey Test Curriculum Mapping Analysis -- 4.8 Measuring Change Over Time and Releasing Materials for Public Information -- 4.9 Conclusions -- References -- 5 Questionnaire Development in International Large-Scale Assessment Studies -- 5.1 Introduction -- 5.2 Approaches to Questionnaire Design and Framing -- 5.3 Targeting of Questionnaires to Different Groups and a Diversity of Contexts -- 5.4 Typology of Questions, Item Formats and Resulting Indicators -- 5.5 Development Procedures, Process and Quality Management -- 5.6 Questionnaire Delivery -- 5.7 Conclusions -- References -- 6 Translation: The Preparation of National Language Versions of Assessment Instruments -- 6.1 Introduction -- 6.2 Translation Related Developments in IEA Studies -- 6.3 Standards and Generalized Stages of Instrument Production -- 6.4 Source Version and Reference Version -- 6.4.1 Terms Used: Translation Versus Adaptation -- 6.4.2 Collaborative Efforts -- 6.5 Translation and Adaptation -- 6.6 Decentralized Translations and Adaptations -- 6.7 Centralized Verification -- 6.8 Translation Verifiers -- 6.9 Layout Verification -- 6.10 Development Linked to Computer-Based Assessment -- 6.11 Reviewing Results of Translation and Verification Processes -- 6.12 Procedure Chain and Timeline -- 6.13 Conclusions -- References -- 7 Sampling, Weighting, and Variance Estimation -- 7.1 Introduction -- 7.2 Defining Target Populations -- 7.3 Preparing Valid Sampling Frames for Each Sampling Stage -- 7.4 Sampling Strategies and Sampling Precision. , 7.4.1 Multiple Stage Sampling and Cluster Sampling -- 7.4.2 Stratification -- 7.4.3 Sampling with Probabilities Proportional to Size -- 7.4.4 Estimating Sampling Precision -- 7.5 Weighting and Nonresponse Adjustment -- 7.6 Sampling Adjudication -- 7.7 Conclusions -- References -- 8 Quality Control During Data Collection: Refining for Rigor -- 8.1 Introduction -- 8.2 Manuals -- 8.2.1 Early Quality Control Procedures and the Development of Manuals -- 8.2.2 Current Implementation of Manuals -- 8.3 National Quality Control Procedures -- 8.3.1 Development of National Quality Control Procedures -- 8.3.2 Implementation of National Quality Control Procedures -- 8.4 International Quality Control -- 8.4.1 Development of International Quality Control Procedures -- 8.4.2 Implementation of International Quality Control Procedures -- 8.5 Future Directions -- References -- 9 Post-collection Data Capture, Scoring, and Processing -- 9.1 Introduction -- 9.2 Manual Post-collection Data Capture and Management Training -- 9.2.1 Data Capture from Paper-Based Instruments -- 9.2.2 Software Used for Data Capture -- 9.2.3 Quality Control: Data Entry -- 9.3 Scoring Cognitive Data: Test Booklets -- 9.3.1 Process of Scoring Constructed-Response Cognitive Items -- 9.3.2 Software Used for Scoring Data -- 9.3.3 Quality Control -- 9.4 Coding Data -- 9.4.1 Process of Coding Data -- 9.4.2 Software Used for Coding Data -- 9.4.3 Quality Control -- 9.5 International Data Processing -- 9.5.1 Processes in International Data Processing -- 9.5.2 Software Used for International Data Processing and Analysis -- 9.5.3 Quality Control -- 9.6 Conclusions -- References -- 10 Technology and Assessment -- 10.1 Introduction -- 10.2 Technology in Education -- 10.3 Promises and Successes of Technology to Reform Assessment Data Collection -- 10.3.1 Efficiency. , 10.3.2 Increased Reliability: Direct Data Capture -- 10.3.3 Inclusion of More Comprehensive Measures of the Overall Construct -- 10.3.4 Reading: Additional Competencies Needed in the Information Age -- 10.3.5 Mathematics and Science: Inclusion of Innovative Problem-Solving Strategies -- 10.3.6 Computational Thinking: Developing Algorithmic Solutions -- 10.3.7 Increased Reliability: Use of Log-File Data -- 10.3.8 Development of More Engaging and Better Matching Assessments -- 10.4 The Transition -- 10.4.1 Delivering Questionnaires Online -- 10.4.2 Computer-Based Assessment -- 10.5 Challenges -- 10.6 The Future: Guiding Principles for the Design of an EAssessment Software -- 10.6.1 Adaptive Testing -- 10.6.2 Translation -- 10.6.3 Printing -- 10.6.4 Web-Based Delivery -- 10.6.5 General Considerations -- 10.7 Conclusions -- References -- 11 Ensuring Validity in International Comparisons Using State-of-the-Art Psychometric Methodologies -- 11.1 Introduction -- 11.2 Modern Educational Measurement: Item Response Theory -- 11.2.1 From Chess Ranking to the Rasch Model -- 11.2.2 Characteristics of the Rasch Model -- 11.2.3 More General IRT Models -- 11.2.4 Central Assumptions of IRT and Their Importance -- 11.2.5 Unidimensionality -- 11.2.6 Local Independence -- 11.2.7 Population Homogeneity/ Measurement Invariance -- 11.3 Simultaneous Modeling of Individual and Group Differences -- 11.4 Statistical Modeling of Individual and Group Differences in IEA Survey Data -- 11.4.1 Comparability as Generalized Measurement Invariance -- 11.4.2 Multiple-Group IRT Models -- 11.4.3 Population Models Integrating Test and Background Data -- 11.4.4 Group Ability Distributions and Plausible Values -- 11.5 Conclusions -- References -- 12 Publications and Dissemination -- 12.1 Introduction -- 12.2 Core Project Publications -- 12.3 Project-Related Publications. , 12.4 Academic Journal -- 12.5 IEA International Research Conference (IRC) -- 12.6 IEA Compass Briefs -- 12.7 Quality Assurance in Publications -- 12.8 Public Dissemination of IEA's Work -- 12.9 Conclusions -- References -- 13 Consequential Validity: Data Access, Data Use, Analytical Support, and Training -- 13.1 Introduction -- 13.2 Data Access -- 13.3 Facilitating Analysis: The IEA IDB Analyzer -- 13.4 Capacity Building: Workshops -- 13.4.1 Promoting High-Quality Research Based on Large-Scale Assessment Data -- 13.4.2 The IEA-ETS Research Institute (IERI) -- 13.4.3 IEA International Research Conference -- 13.4.4 Academic Visitors/Scholars -- 13.4.5 IEA Awards -- 13.5 Conclusions -- References -- 14 Using IEA Studies to Inform Policymaking and Program Development: The Case of Singapore -- 14.1 Introduction -- 14.2 Why Singapore Participates in International Large-Scale Assessments -- 14.2.1 Participating in International Large-Scale Assessment Facilitates Benchmarking of Student Developmental Outcomes and Educator Practices -- 14.2.2 Participating in International Large-Scale Assessment Provides Additional High-Quality Rich Data Sources for Secondary Analyses -- 14.2.3 Participating in International Large-Scale Assessment Builds International Networks of Educationists and Experts -- 14.3 How MOE Has Used Large-Scale Assessment Data -- 14.3.1 STELLAR: "We Must and Can Do Better!" -- 14.3.2 A New Pedagogical Approach to Learning Science: "We Tried a Different Method, Did It Materialize?" -- 14.3.3 Bold Curricular and Pedagogical Shifts: "We Made Some Trade-Offs, What Did We Sacrifice?" -- 14.4 Some Principles Underpinning MOE's Use of Large-Scale Assessment Data -- References -- 15 Understanding the Policy Influence of International Large-Scale Assessments in Education -- 15.1 Introduction -- 15.2 Impact, Influence, and Education Policy. , 15.3 Policy Influence?.

Additional Edition: Print version: Wagemaker, Hans Reliability and Validity of International Large-Scale Assessment Cham : Springer International Publishing AG,c2020 ISBN 9783030530808

Language: English

Keywords: Electronic books. ; Electronic books. ; Electronic books.

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Format: IX, 108 S.

Language: English

Subjects: Comparative Studies. Non-European Languages/Literatures , Theology

Keywords: Textkritik ; Einführung ; Einführung

UID:

Format: 1 online resource (374 pages)

Edition: 1st ed.

ISBN: 3-11-138182-X

Series Statement: Dependency and Slavery Studies ; v.11

Content: Given that strong asymmetrical dependencies have shaped human societies throughout history, this kind of social relation has also left its traces in many types of texts. Using written and oral narratives in attempts to reconstruct the history of asymmetrical dependency comes along with various methodological challenges, as the 15 articles in this interdisciplinary volume illustrate. They focus on a wide range of different (factual and fictional) text types, including inscriptions from Egyptian tombs, biblical stories, novels from antiquity, the Middle High German Rolandslied, Ottoman court records, captivity narratives, travelogues, the American gift book The Liberty Bell, and oral narratives by Caribbean Hindu women. Most of the texts discussed in this volume have so far received comparatively little attention in slavery and dependency studies. The volume thus also seeks to broaden the archive of texts that are deemed relevant in research on the histories of asymmetrical dependencies, bringing together perspectives from disciplines such as Egyptology, theology, literary studies, history, and anthropology.

Note: Frontmatter -- , Contents -- , Introduction – Narratives of Dependency: Examining the History of Strong Asymmetrical Dependencies through the Lens of Narrative Texts -- , ‘I Am the Champion Who Has No Peer!’: The Language of Dependency in the Tomb ‘Biographies’ of Two Ancient Egyptian Nomarchs -- , Narrating Dependency: The Relationship between David and Solomon of Jerusalem and Hiram of Tyre in Hebrew Bible Traditions -- , Transforming Exodus – Second Temple Liberation Narratives from the Perspective of Historical Narratology -- , Slavery and its Narratives in Ancient Novels – Stories of ‘Decline and Fall’? -- , The Dark Side of Proximity: Advice and Betrayal in the Middle High German Rolandslied -- , Dependency Narrated in a Biographic Manual from the Mamluk Sultanate: The al-Ḍawʾ al-lāmiʿ fī aʿyān al-qarn al-tāsiʿ by al-Sakhāwī (1427–1497) -- , Slave Voices in Ottoman Court Records – A Narrative Analysis of the Istanbul Registers from the Sixteenth and Seventeenth Centuries -- , Narrativity and Dependency: The Captivity of an Ottoman Official in Saint Petersburg (1771–1775) -- , ‘The Suffering of the Russians’: The Narration of Captivity and Suffering in the Imaginations about the Ottoman Stranger in Pavel Levašov’s Writings -- , Context Matters – The Importance of the Narrative Situation and Actors who Transmit Information for Representations of Experienced Captivity: The Case of the Enslaved Russian Captive Iakov Zinov’ev (1838) -- , Narrative, Affective Communities, and Abolitionist Cosmopolitanism in the American Gift Book The Liberty Bell (1839–1858) -- , Narrative Self-Representations of Enslaved People under Slavery Regimes – Myth or Reality? -- , Reading Asymmetrical Dependencies in the Narratives of Nineteenth-Century Women Travelers in Ottoman Lands -- , Hierarchies and Dependency as a Narrative Legitimation Strategy for Female Leadership in an Islamist Framework: A Case-Study of Zainab al-Ghazali’s Prison Memoirs -- , Tattooed Dependencies: Sensory Memory, Structural Violence and Narratives of Suffering among Caribbean Hindu Women -- , Index , Issued also in print.

Additional Edition: ISBN 3-11-137980-9

Language: English

DOI: 10.1515/9783111381824

URL: lizenzpflichtig

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Format: 1 online resource (434 pages)

Edition: Second edition.

ISBN: 0-12-816917-6

Content: Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture

Note: Includes index. , Front Cover -- Data Architecture: A Primer for the Data Scientist -- Copyright -- Dedication -- Contents -- Chapter 1.1: An Introduction to Data Architecture -- Subdividing Data -- Repetitive/Nonrepetitive Unstructured Data -- The Great Divide of Data -- Textual/Nontextual Data -- The Different Forms of Data -- Business Value -- Chapter 1.2: The Data Infrastructure -- Two Types of Repetitive Data -- Repetitive Structured Data -- Repetitive Big Data -- The Two Infrastructures -- What's Being Optimized? -- Comparing the Two Infrastructures -- Chapter 1.3: The ``Great Divide´´ -- Classifying Corporate Data -- The ``Great Divide´´ -- Repetitive Unstructured Data -- Nonrepetitive Unstructured Data -- Different Worlds -- Chapter 1.4: Demographics of Corporate Data -- Chapter 1.5: Corporate Data Analysis -- Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time -- Chapter 1.7: A Brief History of Data -- Paper Tape and Punch Cards -- Magnetic Tapes -- Disk Storage -- Data Base Management System (DBMS) -- Coupled Processors -- Online Transaction Processing -- Data Warehouse -- Parallel Data Management -- Data Vault -- Big Data -- The Great Divide -- Chapter 2.1: The End-State Architecture-The ``World Map´´ -- Architectural Components -- Different Kinds of Data in the End State Architecture -- Shaping the Data Through Models -- Where Is the Data Warehouse? -- Where Different Types of Questions Are Answered Across the End State Architecture -- Data in the Data Lake -- Metadata in the End State Architecture -- Networked Metadata -- An Evolutionary Experience -- The Data Lake Architecture -- Chapter 3.1: Transformations in the End-State Architecture -- Redundant Data -- Transformations -- Customizing Data -- Transforming Text -- Transforming Application Data -- Transforming Data Into a Customized State -- Transforming Data Into Bulk Storage. , Transforming Data Generated Automatically -- Transforming Bulk Data -- Transformation and Redundancy -- Chapter 4.1: A Brief History of Big Data -- An Analogy-Taking the High Ground -- Taking the High Ground -- Standardization With the 360 -- Online Transaction Processing -- Enter Teradata and MPP Processing -- Then Came Hadoop and Big Data -- IBM and Hadoop -- Holding the High Ground -- Chapter 4.2: What Is Big Data? -- Another Definition -- Large Volumes -- Inexpensive Storage -- The Roman Census Approach -- Unstructured Data -- Data in Big Data -- Context in Repetitive Data -- Nonrepetitive Data -- Context in Nonrepetitive Data -- Chapter 4.3: Parallel Processing -- Chapter 4.4: Unstructured Data -- Textual Information-Everywhere -- Decisions Based on Structured Data -- The Business Value Proposition -- Repetitive and Nonrepetitive Unstructured Information -- Ease of Analysis -- Contextualization -- Some Approaches to Contextualization -- Map Reduce -- Manual Analysis -- Chapter 4.5: Contextualizing Repetitive Unstructured Data -- Parsing Repetitive Unstructured Data -- Recasting the Output Data -- Chapter 4.6: Textual Disambiguation -- From Narrative Into an Analytical Data Base -- Input Into Textual Disambiguation -- Mapping -- Input/Output -- Document Fracturing/Named Value Processing -- Preprocessing a Document -- E-mails-A Special Case -- Spreadsheets -- Report Decompilation -- Chapter 4.7: Taxonomies -- Data Models/Taxonomies -- Applicability of Taxonomies -- What Is a Taxonomy? -- Taxonomies in Multiple Languages -- Commercial or Private Taxonomies? -- Dynamics of Taxonomies and Textual Disambiguation -- Taxonomies and Textual Disambiguation-Separate Technologies -- Different Types of Taxonomies -- Taxonomies-Maintenance Over Time -- Chapter 5.1: The Siloed Application Environment -- The Challenge of Siloed Applications. , Building Siloed Applications -- What Does a Siloed Application Look Like? -- Current Valued Data -- Minimal Historical Data -- High Availability -- Overlap Between Siloed Applications -- Frozen Business Requirements -- Dismantling Siloed Applications -- Chapter 6.1: Introduction to Data Vault 2.0 -- Data Vault Origins and Background -- The ``Old´´ Data Vault 1.0 -- The New and Updated Data Vault 2.0 -- What Is Data Vault 2.0 Modeling? -- A Business View -- A Technical View -- How Is Data Vault 2.0 Methodology Defined? -- A Business View -- A Technical View -- Why Do We Need a Data Vault 2.0 Architecture? -- Where Does Data Vault 2.0 Implementation Fit? -- What Are the Business Benefits of Data Vault 2.0? -- What Is Data Vault 1.0? -- Chapter 6.2: Introduction to Data Vault Modeling -- What Is a Data Vault Model Concept? -- Data Vault Model Defined -- Components of a Data Vault Model -- What Makes Business Keys So Interesting? -- What Does This Have to Do With Data Vault and Data Warehousing? -- How Does This Translate to Data Vault Modeling? -- Why Restructure the Data From the Staging Area? -- What Are the Basic Rules of the Data Vault Model? -- Why Do We Need Many to Many Link Structures? -- Primary Key Options for Data Vault 2.0 -- Sequence Numbers -- Hash Keys -- Business Keys -- Source System Sequence Business Keys -- Multipart Source Business Keys -- Chapter 6.3: Introduction to Data Vault Architecture -- What Is a Data Vault 2.0 Architecture? -- How Does NoSQL Fit in to the Architecture? -- What Are the Objectives of the Data Vault 2.0 Architecture? -- What Is the Objective of the Data Vault 2.0 Model? -- What Are Hard and Soft Business Rules? -- How Does Managed Self Service BI Fit in the Architecture? -- Chapter 6.4: Introduction to Data Vault Methodology -- Data Vault 2.0 Methodology Overview -- How Does CMMI Contribute to the Methodology?. , If CMMI Is So Great, Why Should We Care About Agility Then? -- Why Include PMP, SDLC If CMMI and Agile Should Be All That's Needed? -- So Then, What Does Six Sigma Contribute to the Data Vault 2 Methodology? -- Where Does TQM (Total Quality Management) Fit in to All of This? -- Chapter 6.5: Introduction to Data Vault Implementation -- Implementation Overview -- What's So Important About Patterns? -- Why Does Reengineering Happen Because of Big Data? -- Why Do We Need to Virtualize Our Data Marts? -- What Is Managed Self-Service BI? -- Chapter 7.1: The Operational Environment: A Short History -- Commercial Uses of the Computer -- The First Applications -- Ed Yourdon and the Structured Revolution -- The SDLC -- Disk Technology -- Enter the DBMS -- Response Time and Availability -- Corporate Computing Today -- Chapter 7.2: The Standard Work Unit -- Elements of Response Time -- An Hourglass Analogy -- The Racetrack Analogy -- Your Vehicle Runs as Fast as the Vehicle in Front of It -- The Standard Work Unit -- The SLA -- Chapter 7.3: Data Modeling for the Structured Environment -- The Purpose of the Roadmap -- Granular Data Only -- The ERD -- The Dis -- Physical Data Base Design -- Relating the Different Levels of the Data Model -- An Example of the Linkage -- Generic Data Models -- Operational Data Models/Data Warehouse Data Models -- Chapter 8.1: A Brief History of Data Architecture -- Chapter 8.2: Big Data/Existing System Interface -- The Big Data/Existing Systems Interface -- The Repetitive Raw Big Data/Existing Systems Interface -- Exception Based Data -- The Nonrepetitive Raw Big Data/Existing Systems Interface -- Into the Existing Systems Environment -- The ``Context Enriched´´ Big Data Environment -- Analyzing Structured Data/Unstructured Data Together -- Chapter 8.3: The Data Warehouse/Operational Environment Interface. , The Operational/Data Warehouse Interface -- The Classical ETL Interface -- The ODS and the ETL Interface -- The Staging Area -- Changed Data Capture -- Inline Transformation -- ELT Processing -- Chapter 8.4: Data Architecture: A High-Level Perspective -- A High Level Perspective -- Redundancy -- The System of Record -- Different Types of Questions -- Different Communities -- Chapter 9.1: Repetitive Analytics: Some Basics -- Different Kinds of Analysis -- Looking for Patterns -- Heuristic Processing -- Freezing Data -- The Sandbox -- The ``Normal´´ Profile -- Distillation, Filtering -- Subsetting Data -- Bias of the Sample -- Filtering Data -- Repetitive Data and Context -- Linking Repetitive Records -- Log Tape Records -- Analyzing Points of Data -- Outliers -- Data Over Time -- Chapter 9.2: Analyzing Repetitive Data -- Log Data -- Active/Passive Indexing of Data -- Summary/Detailed Data -- Metadata in Big Data -- Linking Data -- Chapter 9.3: Repetitive Analysis -- Internal, External Data -- Universal Identifiers -- Security -- Filtering, Distillation -- Archiving Results -- Metrics -- Chapter 10.1: Nonrepetitive Data -- Inline Contextualization -- Taxonomy/Ontology Processing -- Custom Variables -- Homographic Resolution -- Acronym Resolution -- Negation Analysis -- Numeric Tagging -- Date Tagging -- Date Standardization -- List Processing -- Associative Word Processing -- Stop Word Processing -- Word Stemming -- Document Metadata -- Document Classification -- Proximity Analysis -- Functional Sequencing Within Textual ETL -- Internal Referential Integrity -- Preprocessing, Postprocessing -- Chapter 10.2: Mapping -- Chapter 10.3: Analytics From Nonrepetitive Data -- Call Center Information -- Medical Records -- Chapter 11.1: Operational Analytics: Response Time -- Transaction Response Time -- Chapter 12.1: Operational Analytics. , Different Perspectives of Data.

Additional Edition: ISBN 0-12-816916-8

Language: English

UID:

Format: 1 online resource (xi, 326 pages) : , digital, PDF file(s).

Edition: 1st ed.

ISBN: 1-107-13313-0 , 1-280-42153-3 , 0-511-17780-1 , 0-511-04236-1 , 0-511-14823-2 , 0-511-32580-0 , 0-511-49062-3 , 0-511-04539-5

Series Statement: Ideas in context ; 63

Content: Peter Mack examines the impact of humanist training in rhetoric and argument on a range of Elizabethan prose texts, including political orations, histories, romances, conduct manuals, privy council debates and personal letters. Elizabethan Rhetoric reconstructs the knowledge, skills and approaches which an Elizabethan would have acquired in order to participate in the political and religious debates of the time: the approaches to an audience, analysis and replication of textual structures, organisation of arguments and tactics for disputation. Study of the rhetorical codes and conventions in terms of which debates were conducted is currently a major area of historical and literary enquiry, and Mack provides a wealth of new information about what was taught and how these conventions were exploited in personal memoranda, court depositions, sermons and political and religious pamphlets. This important book will be invaluable for all those interested in the culture, literature and political history of the period.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover; Half-title; Series-title; Title; Copyright; Dedication; Contents; Acknowledgements; Introduction; 1 Rhetoric in the grammar school; 2 Rhetoric and dialectic at Oxford and Cambridge; 3 English-language manuals of rhetoric and dialectic; 4 Everyday writing: notebooks, letters, narratives; 5 Histories, conduct manuals, romances; 6 Political argument; 7 Elizabethan parliamentary oratory; 8 Religious discourse; Conclusion; Bibliography; Index of rhetorical and dialectical terms; General index , English

Additional Edition: ISBN 0-521-02099-9

Additional Edition: ISBN 0-521-81292-5

Language: English

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/CBO9780511490620

UID:

Format: 1 online resource (378 p.)

Edition: 1st edition

ISBN: 0-12-802091-1

Content: Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or a

Note: Includes index. , Cover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data , Different Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing , Enter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization , Some Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages , Dynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology , The Challenges of Integration , English

Additional Edition: ISBN 0-12-802044-X

Additional Edition: ISBN 1-322-34768-9

Language: English

Keywords: Electronic books.

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UID:

Format: 1 online resource (xv, 280 pages) : , illustrations.

ISBN: 1-283-95884-8 , 0-12-398279-0

Series Statement: Practical resources for the mental health professional

Content: Individual Case Formulation presents formulation as a process that can be taught systematically to trainee therapists. The book begins by discussing assorted theories of case formulation, and critiques their ability to be applied in real world situations. The individual case formulation approach is then defined and discussed as a way to integrate the best of what different theoretical orientations have to offer in conjunction with the expertise and clinical judgment of the therapist. The book proposes a systemic/functional framework that focuses on difficulties as defined by the client and

Note: Description based upon print version of record. , Front Cover; Individual Case Formulation; Copyright; Contents; Preface; Acknowledgments; Chapter1 - Formulation-the Main Issues; CASE FORMULATION IN CONTEXT; DIFFERENT MODELS OF CASE FORMULATION; "GOOD AND BAD" THERAPISTS; CASE FORMULATION AND THE MEDICAL MODEL; REASONING ABOUT INDIVIDUAL CASES; Chapter2 - Conceptual Frameworks for Case Formulation; RECONCILING NOMOTHETIC PRINCIPLES WITH AN IDIOGRAPHIC ANALYSIS; IS A THERAPIST'S COMPETENCE RELATED TO THERAPEUTIC SUCCESS?; HAS PROGRESS BEEN MADE IN PSYCHOTHERAPY?; A FRAMEWORK FOR DESCRIBING PROBLEMS , LITERAL DESCRIPTION VERSUS INTERPRETATION/HYPOTHESISWHO OWNS THE FORMULATION: THERAPIST OR CLIENT?; A COMMON LANGUAGE FOR PROBLEM DESCRIPTION; HOW DO THERAPISTS ACQUIRE THEIR SKILLS?; DIFFERENT MODELS OF FORMULATION AND THEIR RELATIONSHIP TO INTERVENTION; SOME INITIAL PROPOSALS FOR ICF; Chapter3 - Evidence-Based Practice: Diagnostic and Transdiagnostic Approaches; CLIENT'S PROBLEMS AS AN EXPRESSION OF NATURAL DYSFUNCTIONS (DISORDERS); THE EFFECT OF DISORDER-SPECIFIC MODELS ON PRACTICE; A CRITIQUE OF RANDOMIZED CONTROL TRIALS AND MANUALS AS THE BASIS FOR PRACTICE , MANUALS AS A MODEL FOR ROUTINE PRACTICE (MEBP)DOES MEBP SERVE BEST PRACTICE OR OTHER PURPOSES?; THE MOVE TOWARD TRANSDIAGNOSTIC MODELS; Chapter4 - Theory and Evidence in Individual Case Formulation; GROUNDS FOR EMPHASIZING COMMON FACTORS IN THERAPY; ARGUMENTS FOR CHANGE METHODS BASED ON SPECIFIC THEORETICAL PRINCIPLES; CURRENT STATUS OF INDIVIDUALIZED APPROACHES TO THERAPY; Chapter5 - The Process of Reasoning in Individual Case Formulation; ARE PRACTITIONERS ABLE TO THINK OBJECTIVELY?; WAYS OF LEARNING ABOUT SINGLE INDIVIDUALS; COMMON ERRORS IN PROCESSING INFORMATION ABOUT CLIENTS , DIFFERENT TYPES OF LOGICAL REASONINGChapter6 - Narrative and Textual Analysis in Formulation; SOCIAL CONSTRUCTIONIST NARRATIVE THERAPY; DISCOURSE: A DIFFERENT PARADIGM FOR FORMULATION?; THE INADEQUACY OF TEXTUAL ANALYSIS ALONE; CONSTRUCTING NEW NARRATIVES AND THE PROCESS OF FORMULATION; DIALOGICAL SEQUENCE ANALYSIS (STILES ET AL., 2006); A NARRATIVE APPROACH TO THE FORMULATION OF OBSESSIONS (O'CONNOR, AARDEMA, & PÉLISSIER, 2005); Chapter7 - Formulation Skills and the Therapeutic Relationship; THE THERAPIST AS HEALER OR SHAMAN; CORRELATIONAL RESEARCH ON PREDICTORS OF OUTCOME , WHAT ACCOUNTS FOR DIFFERENCES IN THERAPIST EFFECTIVENESS?SUMMARY; Chapter8 - A Functional/Systemic Framework for Case Formulation; BASIC COMPONENTS OF A BEHAVIORAL DESCRIPTION; CONVENTIONS FOR PRODUCING A FUNCTIONAL CASE FORMULATION DIAGRAM; SYSTEMIC RELATIONSHIPS; Chapter9 - Future Prospects for Individual Case Formulation; DO MENTAL HEALTH PROFESSIONALS REALLY TAKE CASE FORMULATION SERIOUSLY?; IS "GOOD-ENOUGH" THERAPY GOOD ENOUGH?; TRAINING IN CASE FORMULATION; THE DISSEMINATION OF EVIDENCE-BASED PRACTICE; THE FUTURE OF DIAGNOSTIC-LED FORMULATION; THE TREND TOWARD POSITIVE PSYCHOLOGY , Appendix -Guidelines for Assessment and Constructing an Individual Case Formulation (ICF) , English

Additional Edition: ISBN 0-12-398269-3

Language: English

UID:

Format: 1 online resource (410 pages) : , illustrations (some color), photographs, graphs, tables

Edition: 1st edition

ISBN: 0-12-804261-3 , 0-12-804206-0

Content: Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

Note: Front Cover -- Perspectives on Data Science for Software Engineering -- Copyright -- Contents -- Contributors -- Acknowledgments -- Introduction -- Perspectives on data science for software engineering -- Why This Book? -- About This Book -- The Future -- References -- Software analytics and its application in practice -- Six Perspectives of Software Analytics -- Experiences in Putting Software Analytics into Practice -- References -- Seven principles of inductive software engineering: What we do is different -- Different and Important -- Principle #1: Humans Before Algorithms -- Principle #2: Plan for Scale -- Principle #3: Get Early Feedback -- Principle #4: Be Open Minded -- Principle #5: Be smart with your learning -- Principle #6: Live With the Data You Have -- Principle #7: Develop a Broad Skill Set That Uses a Big Toolkit -- References -- The need for data analysis patterns (in software engineering) -- The Remedy Metaphor -- Software Engineering Data -- Needs of Data Analysis Patterns -- Building Remedies for Data Analysis in Software Engineering Research -- References -- From software data to software theory: The path less traveled -- Pathways of Software Repository Research -- From Observation, to Theory, to Practice -- References -- Why theory matters -- Introduction -- How to Use Theory -- How to build theory -- Constructs -- Propositions -- Explanation -- Scope -- In Summary: Find a Theory or Build One Yourself -- Further Reading -- Success stories/applications -- Mining apps for anomalies -- The Million-Dollar Question -- App Mining -- Detecting Abnormal Behavior -- A Treasure Trove of Data -- ... But Also Obstacles -- Executive Summary -- Further Reading -- Embrace dynamic artifacts -- Can We Minimize the USB Driver Test Suite? -- Yes, Lets Observe Interactions -- Why Did Our Solution Work? -- Still Not Convinced? Heres More. , Dynamic Artifacts Are Here to Stay -- Acknowledgments -- References -- Mobile app store analytics -- Introduction -- Understanding End Users -- Conclusion -- References -- The naturalness of software* -- Introduction -- Transforming Software Practice -- Porting and Translation -- The ``Natural Linguistics´´ of Code -- Analysis and Tools -- Assistive Technologies -- Conclusion -- References -- Advances in release readiness -- Predictive Test Metrics -- Universal Release Criteria Model -- Best Estimation Technique -- Resource/Schedule/Content Model -- Using Models in Release Management -- Research to Implementation: A Difficult (but Rewarding) Journey -- How to tame your online services -- Background -- Service Analysis Studio -- Success Story -- References -- Measuring individual productivity -- No Single and Simple Best Metric for Success/Productivity -- Measure the Process, Not Just the Outcome -- Allow for Measures to Evolve -- Goodharts Law and the Effect of Measuring -- How to Measure Individual Productivity? -- References -- Stack traces reveal attack surfaces -- Another Use of Stack Traces? -- Attack Surface Approximation -- References -- Visual analytics for software engineering data -- References -- Gameplay data plays nicer when divided into cohorts -- Cohort Analysis as a Tool for Gameplay Data -- Play to Lose -- Forming Cohorts -- Case Studies of Gameplay Data -- Challenges of using cohorts -- Summary -- References -- A success story in applying data science in practice -- Overview -- Analytics Process -- Data Collection -- Exploratory Data Analysis -- Model Selection -- Performance Measures and Benefit Analysis -- Communication Process-Best Practices -- Problem Selection -- Managerial Support -- Project Management -- Trusted Relationship -- Summary -- References -- There's never enough time to do all the testing you want. , The Impact of Short Release Cycles (There's Not Enough Time) -- Testing Is More Than Functional Correctness (All the Testing You Want) -- Learn From Your Test Execution History -- Test Effectiveness -- Test Reliability/Not Every Test Failure Points to a Defect -- The Art of Testing Less -- Without Sacrificing Code Quality -- Tests Evolve Over Time -- In Summary -- References -- The perils of energy mining: measure a bunch, compare just once -- A Tale of TWO HTTPs -- Let's energise your software energy experiments -- Environment -- N-Versions -- Energy or Power -- Repeat! -- Granularity -- Idle Measurement -- Statistical Analysis -- Exceptions -- Summary -- References -- Identifying fault-prone files in large industrial software systems -- Acknowledgment -- References -- A tailored suit: The big opportunity in personalizing issue tracking -- Many Choices, Nothing Great -- The Need for Personalization -- Developer Dashboards or ``A Tailored Suit´´ -- Room for Improvement -- References -- What counts is decisions, not numbers-Toward an analytics design sheet -- Decisions Everywhere -- The Decision-Making Process -- The Analytics Design Sheet -- Example: App Store Release Analysis -- References -- A large ecosystem study to understand the effect of programming languages on code quality -- Comparing Languages -- Study Design and Analysis -- Results -- Summary -- References -- Code reviews are not for finding defects-Even established tools need occasional evaluation -- Results -- Effects -- Conclusions -- References -- Techniques -- Interviews -- Why Interview? -- The Interview Guide -- Selecting Interviewees -- Recruitment -- Collecting Background Data -- Conducting the Interview -- Post-Interview Discussion and Notes -- Transcription -- Analysis -- Reporting -- Now Go Interview! -- References -- Look for state transitions in temporal data. , Bikeshedding in Software Engineering -- Summarizing Temporal Data -- Recommendations -- Reference -- Card-sorting: From text to themes -- Preparation Phase -- Execution Phase -- Analysis Phase -- References -- Tools! Tools! We need tools! -- Tools in Science -- The Tools We Need -- Recommendations for Tool Building -- References -- Evidence-based software engineering -- Introduction -- The Aim and Methodology of EBSE -- Contextualizing Evidence -- Strength of Evidence -- Evidence and Theory -- References -- Which machine learning method do you need? -- Learning Styles -- Do additional Data Arrive Over Time? -- Are Changes Likely to Happen Over Time? -- If You Have a Prediction Problem, What Do You Really Need to Predict? -- Do You Have a Prediction Problem Where Unlabeled Data are Abundant and Labeled Data are Expensive? -- Are Your Data Imbalanced? -- Do You Need to Use Data From Different Sources? -- Do You Have Big Data? -- Do You Have Little Data? -- In Summary ... -- References -- Structure your unstructured data first! -- Unstructured Data in Software Engineering -- Summarizing Unstructured Software Data -- As Simple as Possible... But not Simpler! -- You Need Structure! -- Conclusion -- References -- Parse that data! Practical tips for preparing your raw data for analysis -- Use Assertions Everywhere -- Print Information About Broken Records -- Use Sets or Counters to Store Occurrences of Categorical Variables -- Restart Parsing in the Middle of the Data Set -- Test on a Small Subset of Your Data -- Redirect Stdout and Stderr to Log Files -- Store Raw Data Alongside Cleaned Data -- Finally, Write a Verifier Program to Check the Integrity of Your Cleaned Data -- Natural language processing is no free lunch -- Natural Language Data in Software Projects -- Natural Language Processing -- How to Apply NLP to Software Projects -- Do Stemming First. , Check the Level of Abstraction -- Dont Expect Magic -- Dont Discard Manual Analysis of Textual Data -- Summary -- References -- Aggregating empirical evidence for more trustworthy decisions -- What's Evidence? -- What Does Data From Empirical Studies Look Like? -- The Evidence-Based Paradigm and Systematic Reviews -- How Far Can We Use the Outcomes From Systematic Review to Make Decisions? -- References -- If it is software engineering, it is (probably) a Bayesian factor -- Causing the Future With Bayesian Networks -- The Need for a Hybrid Approach in Software Analytics -- Use the Methodology, Not the Model -- References -- Becoming Goldilocks: Privacy and data sharing in ``just right´´ conditions -- The ``Data Drought´´ -- Change is Good -- Dont Share Everything -- Share Your Leaders -- Summary -- Acknowledgments -- References -- The wisdom of the crowds in predictive modeling for software engineering -- The Wisdom of the Crowds -- So... How is That Related to Predictive Modeling for Software Engineering? -- Examples of Ensembles and Factors Affecting Their Accuracy -- Crowds for transferring knowledge and dealing with changes -- Crowds for Multiple Goals -- A Crowd of Insights -- Ensembles as Versatile Tools -- References -- Combining quantitative and qualitative methods (when mining software data) -- Prologue: We Have Solid Empirical Evidence! -- Correlation is Not Causation and, Even If We Can Claim Causation... -- Collect your data: People and artifacts -- Source 1: Dig Into Software Artifacts and Data -- ...but be careful about noise and incompleteness! -- Source 2: Getting Feedback From Developers -- ...and dont be afraid if you collect very little data! -- How Much to Analyze, and How? -- Build a theory upon your data -- Conclusion: The Truth is Out There! -- Suggested Readings -- References. , A process for surviving survey design and sailing through survey deployment.

Language: English

UID:

Format: 1 online resource (370 pages)

ISBN: 0-12-816163-9 , 0-12-816164-7

Note: Front Cover -- APPLIED HUMAN FACTORS IN MEDICAL DEVICE DESIGN -- APPLIED HUMAN FACTORS IN MEDICAL DEVICE DESIGN -- Copyright -- Contents -- Contributors -- Author biographies -- Foreword by Hanniebey D.N. Wiyor -- Foreword by Molly Follette Story -- How to Use -- I - Introduction -- 1 - Introduction & -- background -- 1. Introduction -- 2. Background -- 2.1 Purpose of applying human factors in medical device design -- 2.1.1 History of human factors in medical device design -- 2.1.2 Role of a human factors engineer in medical device development -- 2.2 Promoting patient safety through applied ergonomics -- 2.2.1 Impact on the future of clinical practice and patient experience -- 3. Applicable human factors agency guidance's and standards (Ashley French, Melissa R. Lemke) -- 3.1 Determine which standards are applicable to U.S. submissions -- 3.1.1 General standards that apply to all medical devices -- 3.2 Searching for specific applicable standards -- 3.3 Human factors medical device standards for U.S. Submissions -- 3.3.1 U.S. human factors medical device guidance -- 3.3.2 Specific U.S. standards that only apply to certain devices -- 3.4 FDA/AAMI recognized international human factors medical device standards that are applicable to U.S. products -- 3.5 Other standards -- 5. Summary -- Acknowledgments -- References -- 2 - Overview of a human factors toolbox -- 1. Introduction -- 2. Contents of a human factors toolbox -- 2.1 Contextual inquiry -- 2.2 Task analysis -- 2.3 Applying human factors in design -- 2.4 Heuristic evaluation, cognitive walk throughs and expert reviews -- 2.5 Simulated use study -- 2.6 Use focused risk analysis/risk management -- 2.7 Root cause analysis -- 2.8 Known use error and post-market surveillance -- 2.9 Human factors engineering (HFE) validation/summative usability study. , 2.10 Preparing an HFE report for agency submission -- 3. Purpose of each tool -- 4. Summary -- Acknowledgments -- References -- 3 - Strategy, planning, documentation & -- traceability for human factors -- 1. Introduction -- 2. Developing a human factors strategy -- 2.1 Considering previous knowledge -- 2.2 Considering risk -- 2.3 Identifying HF activities -- 2.4 Considering budget -- 2.5 Developing the human factors report or usability engineering file along the way -- 3. Importance of documenting HF -- 3.1 Incorporating human factors in design control -- 4. Providing traceability -- 5. Summary -- 6. Further reading -- Acknowledgments -- References -- 4 - How to use this book -- 1. Introduction -- 2. Who should use this book? -- 3. How should this book be used? -- 4. Limitations -- 5. Disclaimer -- Reference -- II - Discovery & -- input methods -- 5 - Contextual inquiry methods -- 1. Introduction -- 2. What is contextual inquiry (CI)? -- 2.1 Purpose and rationale -- 4. Best practices -- 5. Importance of background information and protocol development -- 5.1 Site selection considerations -- 5.1.1 Research anticipated patient case load -- 5.1.2 Access via a "friendly healthcare provider" -- 5.1.3 RepTrax and vendor credential systems -- 5.2 International considerations -- 5.2.1 Conducting a study in the UK -- 7. Analyzing data for optimum insights -- 7.1 Data analysis -- 7.2 Developing insights -- 8. Visualization and communication -- 9. Summary -- 10. Further reading -- Acknowledgments -- References -- 6 - Task analysis -- 1. Introduction -- 2. Overall process -- 2.1 Step one: use case identification -- 2.2 Step two: task identification -- 2.3 Step three: sub-task breakdown -- 2.4 Step four: apply the perception, cognition, and manual action (PCA) model -- 2.5 Step five: potential use error identification. , 2.6 Step six: potential harm identification -- 2.7 Example task analysis with risk and task category delineation -- 3. Hierarchical task analysis -- 4. Task analysis as a design tool -- 5. Using task analysis for instructional design -- 6. Summary -- Acknowledgments -- References -- III - Human factors in design -- 7 - Applied human factors in design -- 1. Introduction -- 2. Understand your users -- 2.1 Using anthropometry and biomechanics to determine fit -- 2.1.1 Understanding percentiles -- 2.1.2 Deriving device form from anthropometry -- 2.2 Use related injury prevention -- 2.2.1 Nature of injuries -- 2.2.2 Using physiological measures to determine injury potential -- 3. Know the use environment -- 4. Device design -- 4.1 Affordances and design cues -- 4.2 Aesthetic beauty as it relates to usability -- 4.2.1 Simplicity -- 4.2.2 Diversity -- 4.2.3 Colorfulness -- 4.2.4 Craftsmanship -- 4.3 Use interaction touch points and control selection -- 4.3.1 Use interaction touch points -- 4.3.2 Control selection -- 4.3.3 Layout -- 4.4 Color, materials, and finish -- 4.4.1 Color -- 4.4.2 Materials -- 4.4.3 Finish -- 4.5 Case study: applied ergonomics for hand tool design -- 4.5.1 Step 1: handle shape selection -- 4.5.2 Step 2: control selection and placement -- 4.5.3 Step 3: handle and control size -- 4.5.4 Step 4: form language and surface refinement -- 5. Software design: user experience (UX) design -- 5.1 User experience design -- 5.2 Describing the design intent and constraints -- 5.3 Communicating interactive conceptual design -- 5.4 Graphic design: detection and discrimination -- 5.4.1 Composition: grouping and organization - how does the mind group signals at a pre-attentive level? -- 5.4.2 Comprehension: meaning and working memory- can users find meaning at a cognitive level?. , 5.5 Learning and long-term memory - can users retain and recall knowledge at a metacognitive level? -- 6. Alarms (Daryle Gardner-Bonneau) -- 6.1 Designing auditory alarms -- 7. Summary -- 8. Further reading -- Acknowledgments -- References -- 8 - Combination devices -- 1. Introduction -- 2. Health care (R)evolution -- 3. Designing useable combination products -- 3.1 Support the user throughout dosing: patient centricity -- 3.1.1 The power of predicate devices: known use-related problems -- 3.1.2 Considering dose features and feedback modalities -- 3.1.3 Please, don't make me think -- 3.1.4 Demonstration devices -- 3.2 Know the environment: considering the pharmacy, the home, storage, the cat and the TV -- 3.2.1 Design clear alarms and alerts -- 3.3 Design of connected devices: those that incorporate software and smartphone applications -- 3.4 Design of packaging, labels and on-device labeling -- 4. Risk-based design approach -- 5. Developing use requirements: the evolution of user needs -- 5.1 Considerations for requirements -- 5.2 Differentiation -- 5.2.1 Differentiation of demonstration or training devices -- 6. Design of instructions for use -- 7. Special considerations for human factors testing of combination products -- 7.1 Pre-clinical versus clinical studies -- 7.2 Do not rely on training -- 7.3 Literacy versus health literacy -- 8. Summary -- 9. Further reading -- Acknowledgments -- References -- 9 - Applying design principles to instructional materials -- 1. Introduction -- 2. What are instructional materials? -- 3. Integrate instructional design with the human factors process -- 4. Include instructional designers in the cross functional team -- 5. Align instructional design with the regulatory strategy -- 6. Design the instructional materials -- 6.1 Gather industry references -- 6.2 Gather human factors inputs. , 6.3 Determine the instructional components needed -- 6.4 Design and develop instructional materials, starting with human factors inputs to draft the primary source materials -- 6.4.1 Start with low fidelity drafts -- 6.4.2 Identify sections and content required -- 6.4.3 Write effective instructions -- 6.4.4 Create effective illustrations and graphical elements -- 6.4.4.1 Elements that improve the usefulness of illustrations -- 6.4.5 Add organizational and navigational elements -- 6.4.5.1 Use headings, table of contents, index, and cues for page turning (as necessary) in printed booklets -- 6.4.5.2 Use clear identifiers, graphical treatments, and cues for page turns for large format printed sheets -- 6.4.5.3 Consistently organize electronic materials -- 6.4.6 Apply formatting to instructional materials -- 6.4.6.1 Additional formatting and layout considerations for printed materials -- 6.4.7 Develop additional instructional materials or components -- 6.4.7.1 Create effective quick reference materials -- 6.4.7.2 Create effective on-screen or on-board instructions (EPSS or GUI) -- 6.4.7.3 Create effective training videos -- 6.4.7.4 Create effective training and eLearning -- 7. Conduct formative evaluations of instructional materials -- 7.1 Include instructional materials in early formative evaluations -- 7.2 Optimize instructional materials based on human factors data -- 7.3 Optimize after late-stage formative evaluations -- 7.4 Optimize after validation -- 8. Summary -- 9. Further reading -- Acknowledgments -- References -- IV - Formative design evaluation & -- reporting -- 10 - Heuristic analysis, cognitive walkthroughs & -- expert reviews -- 1. Introduction -- 2. Background -- 3. Heuristic analysis -- 4. Cognitive walkthrough -- 5. Expert reviews -- 5.1 Syringe example -- 6. Comparability of these methods. , 7. Assessing risk and identifying design opportunities using heuristic evaluation, cognitive walk-throughs or expert reviews.

Language: English