LEADER 02863nam 2200661 a 450 001 9910455126503321 005 20200520144314.0 010 $a1-84755-048-7 035 $a(CKB)1000000000791687 035 $a(EBL)1185363 035 $a(OCoLC)226969445 035 $a(SSID)ssj0001578921 035 $a(PQKBManifestationID)16253982 035 $a(PQKBTitleCode)TC0001578921 035 $a(PQKBWorkID)14861157 035 $a(PQKB)10093706 035 $a(SSID)ssj0000379263 035 $a(PQKBManifestationID)11280049 035 $a(PQKBTitleCode)TC0000379263 035 $a(PQKBWorkID)10365839 035 $a(PQKB)11748772 035 $a(MiAaPQ)EBC1185363 035 $a(PPN)198479050 035 $a(Au-PeEL)EBL1185363 035 $a(CaPaEBR)ebr10621175 035 $a(CaONFJC)MIL872526 035 $a(EXLCZ)991000000000791687 100 $a20060105d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aChemometrics in analytical spectroscopy$b[electronic resource] /$fMike J. Adams 205 $a2nd ed. 210 $aCambridge $cRoyal Society of Chemistry$dc2004 215 $a1 online resource (236 p.) 225 0$aRSC analytical spectroscopy monographs 300 $aPrevious ed.: 1995. 311 $a0-85404-555-4 311 $a0-85404-595-3 320 $aIncludes bibliographical references and index. 327 $a3350; nothing just for sample; 3351; nothing just for sample; 3352; nothing just for sample; 3353; nothing just for sample; 3354; nothing just for sample; 3355; nothing just for sample; 3356; nothing just for sample; 3357; nothing just for sample; 4432; nothing just for sample 330 $aChemometrics in Analytical Spectroscopy 2nd Edition provides a tutorial approach to the development of chemometric techniques and their application to the interpretation of analytical spectroscopic data. From simple descriptive statistics to the more sophisticated modelling techniques of principal components analysis and partial least squares regression, this updated edition provides necessary background, enhanced by case studies.The extensive use of worked examples throughout gives Chemometrics in Analytical Spectroscopy 2nd Edition special relevance in teaching and introducing chemometrics t 410 0$aRSC Analytical Spectroscopy Series 606 $aSpectrum analysis$xStatistical methods 608 $aElectronic books. 615 0$aSpectrum analysis$xStatistical methods. 676 $a543/.50151995 700 $aAdams$b Mike J$0915744 712 02$aRoyal Society of Chemistry (Great Britain) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455126503321 996 $aChemometrics in analytical spectroscopy$92052888 997 $aUNINA LEADER 05332nam 2200421 450 001 9910647264503321 005 20230324073043.0 010 $a3-8325-5581-1 035 $a(CKB)5580000000508725 035 $a(NjHacI)995580000000508725 035 $a(EXLCZ)995580000000508725 100 $a20230324d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIota Reliability Concept of the Second Generation $eMeasures for Content Analysis Done by Humans or Artificial Intelligences /$fFlorian Berding and Julia Pargmann 210 1$aBerlin, Germany :$cLogos Verlag Berlin GmbH,$d2022. 215 $a1 online resource (ii, 143 pages) $cillustrations 225 1 $aBerufs- und Wirtschaftspa?dagogik 327 $a1 Introduction. 1 -- 2 Summarizing the Iota Concept of the First Generation. 7 -- 3 Introducing Iota Concept of the Second Generation. 11 -- 3.1 Refining the Assignment Error Matrix, the Alpha and the Beta Elements11 -- 3.2 Introducing the Chance-Correction for Alpha and Beta Elements13 -- 3.3 Refining Iota 15 -- 3.4 Reliability on the Scale Level18 -- 3.5 Assumption of Weak Superiority .20 -- 3.6 Comparing the Iota Concepts 21 -- 4 Estimation and Log-Likelihood . 25 -- 5 Simulation Study I 31 -- 5.1 Hypotheses and Design of Simulation Study I31 -- 5.2 Results of Simulation Study I .34 -- 5.2.1 Data Description and Preparation 34 -- 5.2.2 Accuracy of the Estimated Assignment Error Matrix and Categorical Sizes.35 -- 5.2.3 Accuracy of the Derived Reliability Measures .40 -- 5.3 Summary of Simulation Study I47 -- 6 Simulation Study II. 49 -- 6.1 Research Questions and Design of Simulation Study II 49 -- 6.2 Results of Simulation Study II 56 -- 6.2.1 Overview.56 -- 6.2.2 Analyses of the Deviation Between True and Estimated Sample Association/Correlation.62 -- 6.2.3 Analyses of Type I Errors.68 -- 6.2.4 Analyses of Type II Errors72 -- 6.2.5 Analysis of Correct Classification of Effect Sizes74 -- 6.3 Summary of Simulation Study II.78 -- Iota Reliability Concept of the Second Generation 7 Simulation Study III 85 -- 7.1 Design of the Study85 -- 7.2 Results of Simulation Study III .86 -- 7.2.1 Overview.86 -- 7.2.2 Potential Cut-off Values and Certainty of Reliability Effects for Deviation.89 -- 7.2.3 Potential Cut-off Values and Certainty of Reliability Effects for Type I Errors.92 -- 7.2.4 Potential Cut-off Values and Certainty of Reliability Effects for Classifying Effect Sizes 95 7.3 Summary of Simulation Study III97 -- 8 Discussion 101 -- 8.1 Conclusions101 -- 8.2 Examples for Practical Applications of iotarelr.105 -- 8.2.1 Overview.105 -- 8.2.2 Checking the Quality of Codings of New Raters 106 -- 8.2.3 Checking for Bias and Different Guidance of a Coding Scheme .110 -- 8.2.4 Improving the Quality of Codings112 -- 8.3 Limitations and Further Directions 113 -- References. 114 -- Appendix A - Confidence Intervals -- Appendix B - Illustrations of the Relationship Between Reliability and the Deviation Between the True and Estimated Association/Correlation -- Appendix C - Global Indices of Model Fit in Simulation Study II -- Appendix D - Global Indices of Model Fit in Simulation Study III. 330 $aIn educational settings, analyzing textual data via content analysis is a popular research method. The data is a valuable source of information as it offers deep insights into learning and learning outcomes. In practice, it can be used to improve classroom diagnostics and instruction. Nowadays, technology such as learning analytics can be used for the same cause. For both purposes, reliable research instruments are needed. Content analysis, often the measure of choice, is required to meet quality criteria such as objectivity, reliability and validity. However, some of the reliability measures most frequently used have lately been discussed controversially, indicating that there is room for improvement. The first generation of the Iota concept caters to the idea of improved reliability measures for content analysis done by humans or artificial intelligences. In this book, the authors introduce a refined measure: The Iota concept of the second generation. In contrast to pre-existing measures, second generation Iota can for example a. provide insights into the reliability of every single category of a scale and how a coding scheme may produce bias, b. provide rules of thumb for evaluating content analysis and c. provide possibilities for data replication and error-corrected data. This book is structured as a guide for researchers that want to learn more about the mechanics and details of the Iota concept or use it as the reliability measure of choice in their research. 410 0$aBerufs- und Wirtschaftspa?dagogik. 517 $aIota Reliability Concept of the Second Generation 606 $aArtificial intelligence 606 $aContent analysis (Communication) 615 0$aArtificial intelligence. 615 0$aContent analysis (Communication) 676 $a006.3 700 $aBerding$b Florian$01347746 702 $aPargmann$b Julia 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910647264503321 996 $aIota Reliability Concept of the Second Generation$93084484 997 $aUNINA