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Books on the topic 'Web metrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Sterne, Jim. Web Metrics. New York: John Wiley & Sons, Ltd., 2002.

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2

Advanced Web metrics with Google Analytics. Hoboken, NJ: Wiley Technology Pub., 2008.

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3

Advanced Web metrics with Google Analytics. 2nd ed. Indianapolis, Ind: Wiley, 2010.

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4

Clifton, Brian. Advanced Web metrics with Google Analytics. 2nd ed. Indianapolis, Ind: Wiley Pub., 2010.

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5

Clifton, Brian. Advanced Web Metrics with Google Analytics. New York: John Wiley & Sons, Ltd., 2008.

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6

F, Almeida Virgilio A., ed. Capacity planning for Web performance: Metrics, models, and methods. Upper Saddle River, NJ: Prentice Hall, 1998.

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7

F, Almeida Virgilio A., and Menascé Daniel A, eds. Capacity planning for Web services: Metrics, models, and methods. Upper Saddle River, NJ: Prentice Hall PTR, 2002.

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8

Taylor, Z. W., and Joshua Childs. Measuring the Metaphysical School: Using Web Crawlers and Search Engine Optimization to Evaluate School Web Metrics. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529603170.

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9

(Firm), ASHRAE. Server efficiency: Metrics for computer servers and storage. Atlanta: ASHRAE, 2015.

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10

Eisenberg, Bryan, and Jim Novo. The marketer's common sense guide to e-metrics: 22 benchmarks to understand the major trends, key opportunities, and hidden hazards your web logs uncover. [S.l.]: Future Now, Inc., 2002.

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11

1968-, Kamal Eric Djira, ed. E-metrics for library and information professionals: How to use data for managing and evaluating electronic resource collections. New York: Neal-Schuman Publishers, 2006.

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12

Vasta, Davide. Web analytics: Le metriche, gli strumenti, le analisi. Milano: Apogeo, 2009.

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13

Sterne, Jim. Web Metrics. Wiley & Sons, Incorporated, John, 2003.

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14

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2010.

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15

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2012.

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16

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2010.

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17

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Limited, John, 2012.

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18

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2011.

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19

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2010.

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20

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2012.

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21

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2010.

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22

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2012.

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23

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2012.

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24

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2008.

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25

Clifton, Brian. Advanced Web Metrics with Google Analytics. Wiley & Sons, Incorporated, John, 2008.

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26

Advanced Web Metrics with Google Analytics. John Wiley & Sons, 2012.

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27

Sterne, Jim. Web Metrics: Proven Methods for Measuring Web Site Success. Tandem Library, 2002.

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28

Sterne, Jim. Web Metrics: Proven Methods for Measuring Web Site Success. Wiley, 2002.

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29

Sterne, Jim. Web Metrics: Proven Methods for Measuring Web Site Success. Wiley & Sons, Incorporated, John, 2008.

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30

Stuart, David. Web Metrics for Library and Information Professionals. Facet Publishing, 2015.

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31

Web metrics for library and information professionals. Facet Publishing, 2013.

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32

Stuart, David. Web Metrics for Library and Information Professionals. Facet Publishing, 2017.

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33

Menasce, Daniel, and Virgilio A. F. Almeida. Capacity Planning for Web Performance: Metrics, Models, and Methods. Prentice Hall PTR, 1998.

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34

Menasce, Daniel, and Virgilio A. F. Almeida. Capacity Planning for Web Performance: Metrics, Models, and Methods. Prentice Hall PTR, 1998.

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35

Menasce, Daniel A., and Virgilio A. F. Almeida. Capacity Planning for Web Services: Metrics, Models, and Methods. Prentice Hall PTR, 2001.

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36

Menasce, Daniel A., and Virgilio A. F. Almeida. Capacity Planning for Web Services: Metrics, Models, and Methods. Prentice Hall PTR, 2001.

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37

Web metrics for library and information professionals - 1. edición. Facet publishing, 2014.

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38

Social Media Metrics Secrets Secrets. John Wiley & Sons, 2011.

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39

Christin, Angele. Metrics at Work. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691175232.001.0001.

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When the news moved online, journalists suddenly learned what their audiences actually liked, through algorithmic technologies that scrutinize web traffic and activity. Has this advent of audience metrics changed journalists' work practices and professional identities? This book documents the ways that journalists grapple with audience data in the form of clicks, and analyzes how new forms of clickbait journalism travel across national borders. Drawing on four years of fieldwork in web newsrooms in the United States and France, including more than one hundred interviews with journalists, the book reveals many similarities among the media groups examined—their editorial goals, technological tools, and even office furniture. Yet the book uncovers crucial and paradoxical differences in how American and French journalists understand audience analytics and how these affect the news produced in each country. American journalists routinely disregard traffic numbers and primarily rely on the opinion of their peers to define journalistic quality. Meanwhile, French journalists fixate on internet traffic and view these numbers as a sign of their resonance in the public sphere. The book offers cultural and historical explanations for these disparities, arguing that distinct journalistic traditions structure how journalists make sense of digital measurements in the two countries. Contrary to the popular belief that analytics and algorithms are globally homogenizing forces, the book shows that computational technologies can have surprisingly divergent ramifications for work and organizations worldwide.

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40

Social Media Metrics For Dummies. For Dummies, 2012.

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41

Poston, Leslie. Social Media Metrics for Dummies. Wiley & Sons, Incorporated, John, 2012.

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42

Poston, Leslie. Social Media Metrics for Dummies. Wiley & Sons, Incorporated, John, 2012.

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43

Digital Metrics Field Guide: The Definitive Reference for Brands Using the Web, Social Media, Mobile Media, or Email. Bis B.V., Uitgeverij (BIS Publishers), 2015.

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44

Decision Making Handbook: Entrepreneurship, Freelancing, Music Business, Photo Business, Finance, Marketing, TR5, Web Design, CRM Software, Sales Metrics, Skills, Social Selling and Management. Independently Published, 2022.

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45

Leadership, Management and Data Science: Business Technology, Music Business, Photo Business, Finance, Marketing, Web Design,TR5 CRM Software, Sales Metrics, Skills, Social Selling and Management. Independently Published, 2022.

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46

Robeyns, Ingrid. The Capability Approach. Edited by Serena Olsaretti. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199645121.013.5.

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This chapter analyzes the contribution of the capability approach to the literature on distributive justice. The capability approach in itself does not provide a full theory of distributive justice, but rather argues that the metric of distributive justice should be functionings and/or capabilities. The chapter critically analyzes various issues that need addressing when we adopt this metric, such as the questions of which capabilities should be selected, and how they should be aggregated in order to make interpersonal comparisons of advantage. Comparisons with other metrics of justice are also discussed, such as Rawls’s social primary goods and welfarist metrics. The chapter concludes by arguing that we should think of the capability approach to justice as a family of theories, and describes which theoretical modules are needed for a full capabilitarian theory of justice.

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47

White, Andrew C., and Eric Djiva Kamal. E-metrics for Library and Information Professionals: How to Use Data for Managing and Evaluating Electronic Resource Collections. Neal-Schuman Publishers, 2005.

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48

Fink, Alexis A., and Michael C. Sturman. HR Metrics and Talent Analytics. Edited by David G. Collings, Kamel Mellahi, and Wayne F. Cascio. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198758273.013.25.

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Taking advantage of the history of research quantifying the effects of human resource (HR) practices, leveraging current advances in information systems and opportunities presented by big data, HR metrics and talent analytics present a renewed opportunity to help drive effective HR practices. HR metrics are operational measures, addressing how efficient, effective, and impactful an organization’s HR practices are. In contrast, talent analytics focus on decision points, guiding investment decisions. The chapter provides an overview of the historic roots and current practices around HR metrics and talent analytics. Through this, we explore the role, benefits, and risks of benchmarking and utility analyses as two common approaches to setting HR metrics. We discuss how current advances in research and practice make the use of HR metrics and talent analytics a greater business necessity. We conclude the chapter with a discussion on fostering talent analytics within organizations.

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49

Golan, Amos. Foundations of Info-Metrics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.001.0001.

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This book provides a framework for info-metrics—the science of modeling, inference, and reasoning under conditions of noisy and insufficient information. Info-metrics is an inherently interdisciplinary framework that emerged from the intersection of information theory, statistical inference, and decision-making under uncertainty. It allows us to process the available information with minimal reliance on assumptions that cannot be validated. This book focuses on unifying all information processing and model building within a single constrained optimization framework. It provides a complete framework for modeling and inference, rather than a problem-specific model. The framework evolves from the simple premise that our available information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains many multidisciplinary applications that demonstrate the simplicity and generality of the framework in real-world settings: These include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, inference of strategic behavior, incorporation of prior information, option pricing, and modeling an interacting social system. This book presents simple derivations of the key results that are necessary to understand and apply the fundamental concepts to a variety of problems. Derivations are often supported by graphical illustrations. The book is designed to be accessible for graduate students, researchers, and practitioners across the disciplines, requiring only basic quantitative skills and a little persistence.

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50

Chen, Min, J. Michael Dunn, Amos Golan, and Aman Ullah, eds. Advances in Info-Metrics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.001.0001.

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Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.

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