DOCSLIB.ORG

PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition Copyright @c 2016 by G. David Garson and Statistical Associates Publishing Page 1 Single User License. Do not copy or post. PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition @c 2014, 2015, 2016 by G. David Garson and Statistical Associates Publishing. All rights reserved worldwide in all media. No permission is granted to any user to copy or post this work in any format or any media unless permission is granted in writing by G. David Garson and Statistical Associates Publishing. ISBN-10: 1626380392 ISBN-13: 978-1-62638-039-4 The author and publisher of this eBook and accompanying materials make no representation or warranties with respect to the accuracy, applicability, fitness, or completeness of the contents of this eBook or accompanying materials. The author and publisher disclaim any warranties (express or implied), merchantability, or fitness for any particular purpose. The author and publisher shall in no event be held liable to any party for any direct, indirect, punitive, special, incidental or other consequential damages arising directly or indirectly from any use of this material, which is provided “as is”, and without warranties. Further, the author and publisher do not warrant the performance, effectiveness or applicability of any sites listed or linked to in this eBook or accompanying materials. All links are for information purposes only and are not warranted for content, accuracy or any other implied or explicit purpose. This eBook and accompanying materials is © copyrighted by G. David Garson and Statistical Associates Publishing. No part of this may be copied, or changed in any format, sold, or rented in any way under any circumstances, including selling or renting for free. Contact: G. David Garson, President Statistical Publishing Associates 274 Glenn Drive Asheboro, NC 27205 USA Email: [email protected] Web: www.statisticalassociates.com Copyright @c 2016 by G. David Garson and Statistical Associates Publishing Page 2 Single User License. Do not copy or post. PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition Table of Contents Overview ......................................................................................................................................... 8 Data ................................................................................................................................................. 9 Key Concepts and Terms ............................................................................................................... 10 Background ............................................................................................................................... 10 Models ...................................................................................................................................... 13 Overview .............................................................................................................................. 13 PLS-regression vs. PLS-SEM models .................................................................................... 13 Components vs. common factors ........................................................................................ 14 Components vs. summation scales ..................................................................................... 16 PLS-DA models ..................................................................................................................... 16 Mixed methods .................................................................................................................... 16 Bootstrap estimates of significance .................................................................................... 17 Reflective vs. formative models .......................................................................................... 17 Confirmatory vs. exploratory models .................................................................................. 20 Inner (structural) model vs. outer (measurement) model .................................................. 21 Endogenous vs. exogenous latent variables ....................................................................... 21 Mediating variables ............................................................................................................. 22 Moderating variables ........................................................................................................... 23 Interaction terms ................................................................................................................. 25 Partitioning direct, indirect, and total effects .................................................................... 28 Variables ................................................................................................................................... 29 Case identifier variable ........................................................................................................ 30 Measured factors and covariates ........................................................................................ 30 Modeled factors and response variables ............................................................................ 30 Single-item measures .......................................................................................................... 31 Measurement level of variables .......................................................................................... 32 Parameter estimates ................................................................................................................ 33 Cross-validation and goodness-of-fit ....................................................................................... 33 PRESS and optimal number of dimensions ......................................................................... 34 PLS-SEM in SPSS, SAS, and Stata ................................................................................................... 35 Overview .................................................................................................................................. 35 PLS-SEM in SmartPLS .................................................................................................................... 35 Overview .................................................................................................................................. 35 Estimation options in SmartPLS ............................................................................................... 36 Running the PLS algorithm ....................................................................................................... 37 Options ................................................................................................................................ 37 Data input and standardization ........................................................................................... 40 Setting the default workspace ............................................................................................. 41 Creating a PLS project and importing data .......................................................................... 41 Validating the data settings ................................................................................................. 43 Drawing the path model ...................................................................................................... 44 Reflective vs. formative models .......................................................................................... 47 Copyright @c 2016 by G. David Garson and Statistical Associates Publishing Page 3 Single User License. Do not copy or post. PARTIAL LEAST SQUARES (PLS-SEM) 2016 Edition Displaying/hiding the measurement model ........................................................................ 48 Saving the model ................................................................................................................. 49 Model report output ........................................................................................................... 52 Checking for convergence ................................................................................................... 57 OUTPUT ............................................................................................................................... 58 Path coefficients for the inner model.................................................................................. 58 Direct, indirect, and total path coefficients ........................................................................ 59 Outer model measurement loadings and weights .............................................................. 60 Bootstrapped significance output ....................................................................................... 62 Assessing model fit: Overview ............................................................................................. 62 Measurement fit for reflective models ............................................................................... 63 Measurement fit for formative models ............................................................................... 73 Goodness of fit for structural models ................................................................................. 79 Latent variable correlations output ..................................................................................... 85 Analyzing residuals .............................................................................................................
Load more

Recommended publications
  • Curriculum Vitae
    CURRICULUM VITAE Name Ankit Patras Address 111 Agricultural and Biotechnology Building, Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville TN 37209 Phone 615-963-6007, 615-963-6019/6018 Email [email protected], [email protected] EDUCATION 2005- 2009: Ph.D. Biosystems Engineering: School of Biosystems Engineering, College of Engineering & Architecture, Institute of Food and Health, University College Dublin, Ireland. 2005- 2006: Post-graduate certificate (Statistics & Computing): Department of Statistics and Actuarial Science, School of Mathematical Sciences, University College Dublin, Ireland 2003- 2004: Master of Science (Bioprocess Technology): UCD School of Biosystems Engineering, College of Engineering & Architecture, University College Dublin, Ireland 1998- 2002: Bachelor of Technology (Agricultural and Food Engineering): Allahabad Agriculture Institute, India ACADEMIC POSITIONS Assistant Professor, Food Biosciences: Department of Agricultural and Environmental Research, College of Agriculture, Human and Natural Sciences, Tennessee State University, Nashville, Tennessee 2nd Jan, 2014 - Present • Leading a team of scientist and graduate students in developing a world-class food research centre addressing current issues in human health, food safety specially virus, bacterial and mycotoxins contamination • Developing a world-class research program on improving safety of foods and pharmaceuticals • Develop cutting edge technologies (i.e. optical technologies, bioplasma, power Ultrasound,
    [Show full text]
  • (Over)Confidence Predicts Escalation of Public Commitment
    Journal of Experimental Social Psychology 69 (2017) 13–22 Contents lists available at ScienceDirect Journal of Experimental Social Psychology journal homepage: www.elsevier.com/locate/jesp Pride before the fall: (Over)confidence predicts escalation of public commitment Richard Ronay ⁎, Janneke K. Oostrom, Nale Lehmann-Willenbrock, Mark Van Vugt Vrije Universiteit Amsterdam, The Netherlands HIGHLIGHTS • We examined the effect of overconfidence on escalation of commitment. • In a private context, overconfidence is unrelated to escalation decisions. • In a public context, overconfidence is positively related to escalation decisions. • Individual differences in overconfidence are related to reputation sensitivity. • Reputational concerns drive publicly announced investment decision. article info abstract Article history: In four studies we examined the effect of overconfidence on escalation of commitment in investment tasks. Study Received 16 February 2016 1(N = 105) revealed a positive relationship between overconfidence and decisions to escalate. In contrast, Study Revised 15 October 2016 2(N = 121) showed that overconfidence was negatively related to escalation of commitment. The reversal of this Accepted 16 October 2016 effect appeared to emerge as a function of the public (Study 1) versus private (Study 2) context in which the in- Available online 20 October 2016 vestment decisions were made. In Study 3 (N = 108) and Study 4 (N = 380) we experimentally replicated this pattern of findings and found support for the explanatory role of reputational concerns. A meta-analysis of the Keywords: fi fi Overconfidence ndings from our four studies showed that overcon dence is positively related to escalation of commitment in Confidence public contexts, and that this relationship is absent when decisions are made privately.
    [Show full text]
  • University of Pardubice
    UNIVERSITY OF PARDUBICE FACULTY OF ECONOMICS AND ADMINISTRATION Institute of Economic Sciences EVALUATION OF THE ROLE OF CRUCIAL IMPACTS ON NETWORKS FOR TECHNOLOGICAL INNOVATION Ing. Anderson, Henry Junior SUPERVISOR: Assoc. Prof. Ing. Jan Stejskal, Ph.D. Dissertation thesis 2020 TABLE OF CONTENTS INTRODUCTION ..................................................................................................................... 1 1. CONCEPTUAL FRAMEWORK ................................................................................... 3 1.1. Innovation Systems: Technological Innovation Systems............................................ 3 1.1.1. Functional Processes of Technological Innovation System................................. 4 1.1.2. Emergence and Renewed Direction of Technological Innovation System ......... 7 1.2. Contemporary Indicators of Modern Technological Innovation................................. 9 1.2.1. Research Systems................................................................................................. 9 1.2.2. Financial Cradles ............................................................................................... 10 1.2.3. Human Capital ................................................................................................... 12 1.2.4. Cooperation ........................................................................................................ 13 1.3. Regional Attractiveness for Innovation and Foreign Direct Investments ................. 14 1.4. Innovation Indicators’ Contribution
    [Show full text]
  • Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
    UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi.
    [Show full text]
  • Econometrics
    BIBLIOGRAPHY ISSUE 28, OCTOBER-NOVEMBER 2013 Econometrics http://library.bankofgreece.gr http://library.bankofgreece.gr Tables of contents Introduction...................................................................................................................2 I. Print collection of the Library...........................................................................................3 I.1 Monographs .................................................................................................................3 I.2 Periodicals.................................................................................................................. 33 II. Electronic collection of the Library ................................................................................ 35 II.1 Full text articles ..................................................................................................... 35 IΙI. Resources from the World Wide Web ......................................................................... 59 IV. List of topics published in previous issues of the Bibliography........................................ 61 Image cover: It has created through the website http://www.wordle.net All the issues are available at the internet: http://www.bankofgreece.gr/Pages/el/Bank/Library/news.aspx Bank of Greece / Centre for Culture, Research and Documentation / Library Unit / 21 El. Venizelos, 102 50 Athens / [email protected]/ Tel. 210 320 2446, 2522 / Bibliography: bimonthly electronic edition, Issue 28, September- October 2013 Contributors:
    [Show full text]
  • Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat
    remote sensing Article Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat Iman Tahmasbian 1,* , Natalie K. Morgan 2, Shahla Hosseini Bai 3, Mark W. Dunlop 1 and Amy F. Moss 2 1 Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia; Scopus affiliation ID: 60028929; [email protected] 2 School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia; [email protected] (N.K.M.); [email protected] (A.F.M.) 3 Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia; s.hosseini-bai@griffith.edu.au * Correspondence: [email protected] Abstract: Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in Citation: Tahmasbian, I.; Morgan, 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression N.K; Hosseini Bai, S.; Dunlop, M.W; (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their Moss, A.F Comparison of spectral reflectance, with 6 additional samples used for testing the models.
    [Show full text]
  • Organizational Capabilities for Family Firm Sustainability: the Role of Knowledge Accumulation and Family Essence
    sustainability Article Organizational Capabilities for Family Firm Sustainability: The Role of Knowledge Accumulation and Family Essence Ismael Barros-Contreras 1, Jesús Manuel Palma-Ruiz 2,* and Angel Torres-Toukoumidis 3 1 Instituto de Gestión e Industria, Universidad Austral de Chile, Valdivia 5480000, Chile; [email protected] 2 Facultad de Contaduría y Administración, Universidad Autónoma de Chihuahua, Chihuahua 31000, Mexico 3 Social Science Knowledge and Human Behavior, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador; [email protected] * Correspondence: [email protected] Abstract: While prior studies recognize the importance of organizational capabilities for family firm sustainability, current research has still failed to empirically identify the role of different types of knowledge accumulation with regard to these organizational capabilities. Based on the dynamic capabilities theory, the main goal of this paper is to address this research gap and to explore the rela- tionships between both internal and external knowledge accumulation, and ordinary organizational capabilities. This research also contributes to analyzing the complex effect of the family firm essence, influenced by both family involvement and generational involvement levels, as an antecedent of internal and external knowledge accumulation. Our analysis of 102 non-listed Spanish family firms shows that the family firm essence, which is influenced by the family involvement, strengthens only the internal knowledge accumulation but not the external one. Furthermore, our study also reveals Citation: Barros-Contreras, I.; that both internal and knowledge accumulation are positively related to ordinary capabilities. Palma-Ruiz, J.M.; Torres-Toukoumidis, A. Keywords: dynamic capabilities; internal knowledge accumulation; external knowledge accumu- Organizational Capabilities for lation; family essence; family firms; ordinary capabilities; structural equation modeling; partial Family Firm Sustainability: The Role least squares of Knowledge Accumulation and Family Essence.
    [Show full text]
  • Statistics and GIS Assistance Help with Statistics
    Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D.
    [Show full text]
  • Indian Statistical Institute
    CHAPTER VII INDIAN STATISTICAL INSTITUTE 7.1 The Indian Statistical Institute (ISI) came into being with the pioneering initiative and efforts of Professor P.C. Mahalanobis in early thirties (registered on 28 April, 1932). The Institute expanded its research, teaching, training and project activities and got national/international recognitions. The Institute was recognized as an “Institute of National Importance” by an Act of Parliament, known as “Indian Statistical Institute Act No.57 of 1959”. Significantly, Pandit Jawaharlal Nehru, the then Prime Minister piloted the bill in the Parliament in 1959. This Act conferred the right to hold examinations and award degrees/diplomas in Statistics. Degree courses viz. Bachelor of Statistics (B.Stat.), Master of Statistics (M.Stat.) and post graduate diplomas in SQC & OR and Computer Science were started from June 1960. The Institute was also empowered to award Ph.D./D.Sc. degree from the same year. Subsequently, Master of Technology courses in Computer Science and in Quality, Reliability & Operations Research were also started. The Institute’s scope was further enlarged to award degrees/diplomas in Statistics, Mathematics, Quantitative Economics, Computer Science and such other subjects related to Statistics by virtue of “Indian Statistical Institute (Amendment) Act, 1995. The Institute took up research activities not only in Statistics/Mathematics but also in Natural and Social Sciences, Physics and Earth Sciences, Biological Sciences, Statistical Quality Control & Operations Research and Library and Information Sciences. 7.2 Sankhya – The Indian Journal of Statistics, being published by the Institute since 1933, is considered as one of the leading Statistical Journals of the world.
    [Show full text]
  • Construction of a Neuro-Immune-Cognitive Pathway-Phenotype Underpinning the Phenome Of
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 Construction of a neuro-immune-cognitive pathway-phenotype underpinning the phenome of deficit schizophrenia. Hussein Kadhem Al-Hakeima, Abbas F. Almullab, Arafat Hussein Al-Dujailic, Michael Maes d,e,f. a Department of Chemistry, College of Science, University of Kufa, Iraq. E-mail: [email protected]. b Medical Laboratory Technology Department, College of Medical Technology, The Islamic University, Najaf, Iraq. E-mail: [email protected]. c Senior Clinical Psychiatrist, Faculty of Medicine, Kufa University. E-mail: [email protected]. d* Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; e Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria; f IMPACT Strategic Research Centre, Deakin University, PO Box 281, Geelong, VIC, 3220, Australia. Corresponding author Prof. Dr. Michael Maes, M.D., Ph.D. Department of Psychiatry Faculty of Medicine Chulalongkorn University Bangkok Thailand 1 © 2019 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 https://scholar.google.co.th/citations?user=1wzMZ7UAAAAJ&hl=th&oi=ao [email protected]. 2 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 Abstract In schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia.
    [Show full text]
  • Gröbner Basis and Structural Equation Modeling by Min Lim a Thesis
    Grobner¨ Basis and Structural Equation Modeling by Min Lim A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Statistics University of Toronto Copyright c 2010 by Min Lim Abstract Gr¨obnerBasis and Structural Equation Modeling Min Lim Doctor of Philosophy Graduate Department of Statistics University of Toronto 2010 Structural equation models are systems of simultaneous linear equations that are gener- alizations of linear regression, and have many applications in the social, behavioural and biological sciences. A serious barrier to applications is that it is easy to specify models for which the parameter vector is not identifiable from the distribution of the observable data, and it is often difficult to tell whether a model is identified or not. In this thesis, we study the most straightforward method to check for identification – solving a system of simultaneous equations. However, the calculations can easily get very complex. Gr¨obner basis is introduced to simplify the process. The main idea of checking identification is to solve a set of finitely many simultaneous equations, called identifying equations, which can be transformed into polynomials. If a unique solution is found, the model is identified. Gr¨obner basis reduces the polynomials into simpler forms making them easier to solve. Also, it allows us to investigate the model-induced constraints on the covariances, even when the model is not identified. With the explicit solution to the identifying equations, including the constraints on the covariances, we can (1) locate points in the parameter space where the model is not iden- tified, (2) find the maximum likelihood estimators, (3) study the effects of mis-specified models, (4) obtain a set of method of moments estimators, and (5) build customized parametric and distribution free tests, including inference for non-identified models.
    [Show full text]
  • Partial Least Squares Path Modeling Hengky Latan • Richard Noonan Editors
    Partial Least Squares Path Modeling Hengky Latan • Richard Noonan Editors Partial Least Squares Path Modeling Basic Concepts, Methodological Issues and Applications 123 Editors Hengky Latan Richard Noonan Department of Accounting Institute of International Education STIE Bank BPD Jateng and Petra Stockholm University Christian University Stockholm, Sweden Semarang-Surabaya, Indonesia ISBN 978-3-319-64068-6 ISBN 978-3-319-64069-3 (eBook) DOI 10.1007/978-3-319-64069-3 Library of Congress Control Number: 2017955377 Mathematics Subject Classification (2010): 62H20, 62H25, 62H12, 62F12, 62F03, 62F40, 65C05, 62H30 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.
    [Show full text]