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The biggest change in the next decade will likely be the administration of more tests at a computer (see, for example, Luecht, 1998; Luecht & Clauser, 2002; Wainer et al, 2000; van der Linden & Glas, 2000) Already, we are seeing hundreds of credentialing agencies in the US delivering their tests at a computer, and many more are coming on board Admissions tests such as the Graduate Record Exam (GRE), the Test of English as a Foreign Language, better known as TOEFL, and the Graduate Management Admissions Test (GMAT) are now on-line So, expanded testing at computers is a safe prediction As they shift to computer test administrations, testing agencies are moving from simply producing a xed form or parallel forms of a test to various new test designs at a computer Testing agencies have either moved to some form of linear test (parallel forms, or linear on the y tests with each examinee receiving a unique set of items subject to content and statistical speci cations) or they have moved to computer adaptive testing (CAT) Both extremes of computer-based tests, in principle, allow for exible scheduling of tests for examinees and immediate score reporting, and both are attractive features for examinees CBTs also permit, in principle, the use of new item types for assessing higher level thinking skills (Irvine & Kyllonen, 2002; Zenisky & Sireci, 2002) Our own research at the University of Massachusetts has focused on new computer-based test designs (for example, Hambleton & Xing, in press; Hambleton, Jodoin, & Zenisky, in press; Xing & Hambleton, 2004), especially multi-stage test designs (They are not new in concept because Frederic Lord carried out research on these designs in the early 1970s, but they are fairly new in terms f being used in practice) Instead of individualizing the test by optimal selection of each test item as is done in CAT, optimal selection in MST.

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In order to highlight how data should be managed, this section looks at an example of how a consumer direct retail organization can move from not having an integrated information management strategy to an environment with ef cient information management processes This example starts by escribing how customer data is currently being handled, followed by ways.

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the years If any changes are made to the data about a customer during the billing process, the data is updated only in the Customer Billing database In step 5, the customer data ows with the billing data into the third-party Financial Package This package provides the accounts receivable, accounts payable, and general ledger functionality All applications within this third-party software package use a common set of customer data Finally, in step 6, a new set of ustomer data is captured when feedback is received via phone calls, e-mails, and letters The data that is captured in step 6 is independent of the other processes and is not tied to any of the other processes This is not a recommended information management strategy, but it re ects how customer data exists in the current systems The fact that there are multiple copies of customer data is quite common Each application was designed and developed independently over a long period of time Time and budget constraints often limit the ability of application development teams to ensure enterprise integration of customer data Information management takes a broader view by looking at the data across all application systems Information management is concerned with integration and interoperability between the different systems Improvements to how data ows between different parts of the organization can be made with business process changes and some small system enhancements Figure 8-3 highlights where these improvements can be made.

The included OPEN DESIRE program for Linux solves up to 40,000 ordinary differential equations and implements exceptionally fast and convenient vector operations A smaller educational 20,000 differential-equations version for Microsoft WindowsTM can be obtained without charge from the author by sending an email to gatmkorn@aolcom The user can run, edit, and modify the example simulations keyed to the figures in this book, plus many other examples Many of our programming principles also apply to simulation programs other than DESIRE 1 introduces our subject with a few programs for small differential-equation models, including a simple guided-missile simulation The remainder of the book presents new material 2 begins with a careful discussion of models that involve sampled-data operations and sampled-data difference equations together with differential equations We model mixed analog digital systems such as simulated digital controllers and systems with limiters and switches At this point, we show that many very useful devices (eg, track-hold circuits, trigger circuits, signal generators, automatic scaling) are neatly and efficiently modeled with simple difference equations Last, but not least, we propose improved techniques for proper numerical integration of switched variables Truly powerful simulation programs need a readable notation for vector and matrix assignments, differential equations, and difference equations 3 introduces a novel vector compiler that produces very fast programs for vector and matrix operations We also demonstrate efficient use of submodels We present examples from control engineering and nuclear-reactor simulation The following chapter then shows how we use vectors to replicate complete models 4 describes practical model replication (vectorization): a single simulation run with a vector model will replace hundreds or thousands of conventional simulation runs We apply the vectorizing compiler introduced in 3 to parameter-influence studies and Monte Carlo simulation of dynamic systems with noise-perturbed parameters and random initial conditions We compare repeated-run and vectorized Monte Carlo studies of a weapon trajectory Our interactive programs produce not just time histories of system variables but also time histories of statistics such as averages, mean squares, and probability estimates We also show explicitly how to estimate probability densities 5 discusses more difficult vectorized Monte Carlo simulations involving time-varying noise, which has to be derived from periodic pseudorandom noise samples Examples include Monte Carlo simulation of a continuous random walk, another trajectory study, and two vectorized control-system simulations An inexpensive 24-GHz personal computer exercised 1000 random-input control-system models in econds We also describe a new heuristic test for the quality of pseudorandom noise.

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