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1. |
Record Nr. |
UNISA996339086903316 |
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Autore |
Ould Martyn A |
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Titolo |
Business Process Management [[electronic resource] ] : A Rigorous Approach |
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Pubbl/distr/stampa |
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Swindon, : British Computer Society, 2005 |
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ISBN |
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1-78017-009-2 |
1-62870-263-X |
1-306-20696-0 |
1-906124-32-9 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (363 p.) |
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Disciplina |
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Soggetti |
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Business -- Data processing -- Management |
Management information systems |
Workflow -- Management |
Reengineering (Management) - Data processing - Management |
System analysis - Management |
Business |
Workflow |
Commerce |
Business & Economics |
Marketing & Sales |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 335-336) and index. |
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Nota di contenuto |
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Copyright; Contents; Figures; Author; Abbreviations; Preface; Introduction; 1 Basic process concepts; 2 Modelling a process; 3 Dynamism in the process; 4 Process relationships; 5 The three basic process types; 6 Preparing a process architecture; 7 Dynamism in the world; 8 Managing the modelling; 9 Discovering and defining processes; 10 Analysing for process improvement; 11 Designing a process; 12 Processes and information systems; 13 Processes and process systems; References; Index; Back Cover |
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Sommario/riassunto |
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Businesses need to adapt constantly, but are often held back by static IT systems. The 'Riva approach to Business Process Management' is a |
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way of analysing the mass of concurrent, collaborative activity that goes on in an organisation, providing a solid basis for developing flexible IT systems that support a business. |
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2. |
Record Nr. |
UNINA9910897991003321 |
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Autore |
Stemmler M (Mark) |
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Titolo |
Dependent Data in Social Sciences Research : Forms, Issues, and Methods of Analysis / / edited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[2nd ed. 2024.] |
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Descrizione fisica |
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1 online resource (785 pages) |
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Altri autori (Persone) |
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WiedermannWolfgang |
HuangFrancis L |
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Disciplina |
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Soggetti |
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Social sciences - Statistical methods |
Statistics |
Psychometrics |
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy |
Statistical Theory and Methods |
Estadística matemàtica |
Ciències socials |
Metodologia de les ciències socials |
Psicometria |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data. |
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Sommario/riassunto |
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This second edition presents a variety of up-to-date statistical issues with regard to dependent or longitudinal data such as continuous time modeling, growth curve modeling, dynamic modeling, network analysis, Bayesian network analysis, directional dependence, multilevel analysis, item response modeling (IRT), estimation of missing data of longitudinal data and other methods for the analysis of dependent data (e.g., configural frequency analysis, ecological momentary assessment, and unobserved within-group individual differences). It presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. In addition, R-scripts to recapture the presented content are provided. Researchers and graduate students in the social and behavioral sciences, education, econometrics, mathematics, biology, physics and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful. |
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