LEADER 03664nam 2200709 a 450 001 9910455341003321 005 20200520144314.0 010 $a1-282-37435-4 010 $a9786612374357 010 $a1-4106-0680-5 035 $a(CKB)111056486636152 035 $a(EBL)408948 035 $a(OCoLC)437087313 035 $a(SSID)ssj0000215992 035 $a(PQKBManifestationID)11221068 035 $a(PQKBTitleCode)TC0000215992 035 $a(PQKBWorkID)10193745 035 $a(PQKB)10881748 035 $a(MiAaPQ)EBC408948 035 $a(MiAaPQ)EBC5292909 035 $a(Au-PeEL)EBL408948 035 $a(CaPaEBR)ebr10274231 035 $a(CaONFJC)MIL583092 035 $a(Au-PeEL)EBL5292909 035 $a(CaONFJC)MIL237435 035 $a(OCoLC)1027192021 035 $a(EXLCZ)99111056486636152 100 $a20021219d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOrdinal measurement in the behavioral sciences$b[electronic resource] /$fNorman Cliff, John A. Keats 210 $aMahwah, N.J. $cLawrence Erlbaum Associates$d2003 215 $a1 online resource (241 p.) 300 $aDescription based upon print version of record. 311 $a0-415-65579-X 311 $a0-8058-2093-0 320 $aIncludes bibliographical references (p. 212-217) and indexes. 327 $aBook Cover; Title; Copyright; Contents; Preface; Chapter 1: The Purpose of Psychological Assessment; Chapter 2: What Makes a Variable a Scale?; Chapter 3: Types of Assessment; Chapter 4: Item Scores and Their Addition to Obtain Total Test Scores in the Case of Dichotomous Items; Chapter 5: Item Scores and Their Addition to Obtain Total Test Scores in the Case of Polytomous Items; Chapter 6: Dominance Analysis of Tests; Chapter 7: Approaches to Ordering Things and Stimuli; Chapter 8: Alternatives to Complete Paired Comparisons; Chapter 9: The Unfolding Model 327 $aChapter 10: The Application of Ordinal Test Theory to Items in Tests Used in Cross-Cultural ComparisonsAppendix A: FLOW CHART FOR A PROGRAM TO CARRY OUT A COMPLETE ITEM ANALYSIS OF ITEMS IN A TEST OR SCALE USING A SMALL PERSONAL COMPUTER; Appendix B: STATISTICAL TABLES; References; Author Index; Subject Index 330 $aThis book provides an alternative method for measuring individual differences in psychological, educational, and other behavioral sciences studies. It is based on the assumptions of ordinal statistics as explained in Norman Cliff's 1996 Ordinal Methods for Behavioral Data Analysis. It provides the necessary background on ordinal measurement to permit its use to assess psychological and psychophysical tests and scales and interpret the data obtained. The authors believe that some of the behavioral measurement models used today do not fit the data or are inherently self-contradictory. App 606 $aPsychology$xMathematical models 606 $aSocial sciences$xStatistical methods 606 $aAnalysis of variance 606 $aPsychological tests$xStatistical methods 608 $aElectronic books. 615 0$aPsychology$xMathematical models. 615 0$aSocial sciences$xStatistical methods. 615 0$aAnalysis of variance. 615 0$aPsychological tests$xStatistical methods. 676 $a150/.28/7 700 $aCliff$b Norman$f1930-$0876526 701 $aKeats$b J. A$g(John Augustus)$0876527 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455341003321 996 $aOrdinal measurement in the behavioral sciences$91957311 997 $aUNINA LEADER 05352nam 22008175 450 001 9910484125403321 005 20200701022634.0 010 $a3-662-49784-0 024 7 $a10.1007/978-3-662-49784-5 035 $a(CKB)3710000000627386 035 $a(SSID)ssj0001661212 035 $a(PQKBManifestationID)16442566 035 $a(PQKBTitleCode)TC0001661212 035 $a(PQKBWorkID)14988595 035 $a(PQKB)10080210 035 $a(DE-He213)978-3-662-49784-5 035 $a(MiAaPQ)EBC6299332 035 $a(MiAaPQ)EBC5591583 035 $a(Au-PeEL)EBL5591583 035 $a(OCoLC)945415284 035 $a(PPN)192772082 035 $a(EXLCZ)993710000000627386 100 $a20160317d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aTransactions on Large-Scale Data- and Knowledge-Centered Systems XXVI $eSpecial Issue on Data Warehousing and Knowledge Discovery /$fedited by Abdelkader Hameurlain, Josef Küng, Roland Wagner, Ladjel Bellatreche, Mukesh Mohania 205 $a1st ed. 2016. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2016. 215 $a1 online resource (XI, 109 p. 43 illus.) 225 1 $aTransactions on Large-Scale Data- and Knowledge-Centered Systems,$x1869-1994 ;$v9670 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-662-49783-2 327 $aBanded Pattern Mining Algorithms in Multi-dimensional Zero-One Data -- Frequent Item-set Border Approximation by Dualization -- Dynamic Materialization for Building Personalized Smart Cubes -- Opening up Data Analysis for Medical Health Services: Data Integration and Analysis in Cancer Registries with CARESS. 330 $aThe LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This volume, the 26th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, focuses on Data Warehousing and Knowledge Discovery from Big Data, and contains extended and revised versions of four papers selected as the best papers from the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), held in Munich, Germany, during September 1-5, 2014. The papers focus on data cube computation, the construction and analysis of a data warehouse in the context of cancer epidemiology, pattern mining algorithms, and frequent item-set border approximation. 410 0$aTransactions on Large-Scale Data- and Knowledge-Centered Systems,$x1869-1994 ;$v9670 606 $aDatabase management 606 $aData mining 606 $aArtificial intelligence 606 $aInformation storage and retrieval 606 $aAlgorithms 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aDatabase management. 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aInformation storage and retrieval. 615 0$aAlgorithms. 615 14$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aInformation Storage and Retrieval. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a005.74 702 $aHameurlain$b Abdelkader$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKüng$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWagner$b Roland$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBellatreche$b Ladjel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMohania$b Mukesh$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484125403321 996 $aTransactions on Large-Scale Data- and Knowledge-Centered Systems XXVI$92831456 997 $aUNINA