1.

Record Nr.

UNISA996339086903316

Autore

Ould Martyn A

Titolo

Business Process Management [[electronic resource] ] : A Rigorous Approach

Pubbl/distr/stampa

Swindon, : British Computer Society, 2005

ISBN

1-78017-009-2

1-62870-263-X

1-306-20696-0

1-906124-32-9

Edizione

[1st edition]

Descrizione fisica

1 online resource (363 p.)

Disciplina

658.4032

Soggetti

Business -- Data processing -- Management

Management information systems

Workflow -- Management

Reengineering (Management) - Data processing - Management

System analysis - Management

Business

Workflow

Commerce

Business & Economics

Marketing & Sales

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references (p. 335-336) and index.

Nota di contenuto

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

Sommario/riassunto

Businesses need to adapt constantly, but are often held back by static IT systems. The 'Riva approach to Business Process Management' is a



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.

2.

Record Nr.

UNINA9910897991003321

Autore

Stemmler M (Mark)

Titolo

Dependent Data in Social Sciences Research : Forms, Issues, and Methods of Analysis / / edited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024

ISBN

9783031563188

3031563182

Edizione

[2nd ed. 2024.]

Descrizione fisica

1 online resource (785 pages)

Altri autori (Persone)

WiedermannWolfgang

HuangFrancis L

Disciplina

300.721

Soggetti

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

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.



Sommario/riassunto

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.