Systems, Models and Measures seeks to bridge the gap between the 'classical' and the newer technologies by constructing a systematic measurement framework for both. The authors use their experience as consultants in systems, software and quality engineering to take the subject from concept and theory, via strategy and procedure, to tools and applications. The book clarifies the key notions of system, model, measurement, product, process, specification and design. Practical examples demonstrate the 'architecture' of measurement schemes, extending them to object-oriented and subjective measurement. A detailed case study provides a measurement strategy for formal specifications, including Prolog, Z and VDM. The reader will be able to formulate problems in measurable terms, appraise and compare formal specifications, assess and enhance existing measurement practices, and devise measurement schemes for describing objective characteristics and expressing value judgements.

Despite the overwhelming use of tests and questionnaires, the psychometric models for constructing these instruments are often poorly understood, leading to suboptimal measurement. Measurement Models for Psychological Attributes is a comprehensive and accessible treatment of the common and the less than common measurement models for the social, behavioral, and health sciences. The monograph explains the adequate use of measurement models for test construction, points out their merits and drawbacks, and critically discusses topics that have raised and continue to raise controversy. Because introductory texts on statistics and psychometrics are sufficient to understand its content, the monograph may be used in advanced courses on applied psychometrics, and is attractive to both researchers and graduate students in psychology, education, sociology, political science, medicine and marketing, policy research, and opinion research. The monograph provides an in-depth discussion of classical test theory and factor models in Chapter 2; nonparametric and parametric item response theory in Chapter 3 and Chapter 4, respectively; latent class models and cognitive diagnosis models in Chapter 5; and discusses pairwise comparison models, proximity models, response time models, and network psychometrics in Chapter 6. The chapters start with the theory and methods of the measurement model and conclude with a real-data example illustrating the measurement model.

Systems Analysis and Modeling presents a fresh, new approach to systems analysis and modeling with a systems science flavor that stimulates systems thinking. After introducing systems modeling principles, the ensuing wide selection of examples aptly illustrate that anything which changes over time can be modeled as a system. Each example begins with a knowledge base that displays relevant information obtained from systems analysis. The diversity of examples clearly establishes a new protocol for synthesizing systems models. - Macro-to-micro, top-down approach - Multidisciplinary examples - Incorporation of human knowledge to synthesise a systems model - Clear and concise systems delimitation - Complex systems using simple mathematics - "Exact" reproduction of historical data plus model generated secondary data - Systems simulation via systems models

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Information Systems Success Measurement presents a comprehensive review of the foundations, the trends, and the future challenges of IS success measurement in order to improve research and practice in terms of the measurement and evaluation of information systems. Information Systems Success Measurement explores the foundations and trends in the definition and measurement of information systems success. Starting with an introduction that examines how the concept of "effective" or "successful" information systems has progressed as information technology and its use has changed over the past 60 years. The authors introduce the DeLone and McLean Information Systems Success Model as an organizing framework for this monograph. Section 2 identifies five eras of information systems and for each of these eras the authors consider the types of information systems used in firms, the stakeholders impacted by these systems, the relevant research about information systems evaluation, and the measurement of IS success in practice during each of these periods. Section 3 discusses the foundational research on IS success measurement. Based on the evolution of the field's understanding of IS success, important trends in IS success measurement is highlighted in Section 4. Section 5 examines the future of IS success research. Section 6 reviews empirical findings related to success factors, which influence IS success. Section 7 explores how managers can improve the methods they use to measure and track IS success. Finally, the authors offer concluding remarks in Section 8.