Showing posts with label measurement. Show all posts
Showing posts with label measurement. Show all posts

Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research and Management Science)
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This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach (Statistics for Social and Behavioral Sciences) Review

Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach (Statistics for Social and Behavioral Sciences)
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Excerpts of my review from Psychometrika are above. Unfortunately half the quotes really apply to the other book I was reviewing at the time, the title by Skrondal and Rabe-Hesketh (which is also a nice book, but rather different). Anyway, this book very much deserves a five star rating. It shows how many IRT models fit together as regression models in the generalized linear (or nonlinear) mixed model framework. SAS code is provided for nearly all models in the book. The breadth of talented authors assembled by the editors is substantial. This is an excellent book for "advanced" users of IRT or people who are proficient with GLMMs and want to use them as measurement models. It is not a "beginner's book" by any stretch of the imagination, but a very good title to own if you want to start doing projects with real data. In addition to the SAS code, one of the must useful features of the book is the fact that the authors keep to only a few datasets, which means you get a very intensive look at them. Clever!

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This edited volume gives a new and integrated introduction to item response models (predominantly used in measurement applications in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. It also includes a chapter on the statistical background and one on useful software.

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Multidimensional Item Response Theory (Statistics for Social and Behavioral Sciences) Review

Multidimensional Item Response Theory (Statistics for Social and Behavioral Sciences)
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The MIRT sub-field of psychometrics has for years labored in obscurity, due in no small part to the inability of its practitioners and students to understand each other and master each other's algorithms and models. Reckase, one of the field's leaders, takes a bold step in correcting the situation. Excellently researched, clearly written, logically presented, fair and balanced, Reckase summarizes the foundations of probabilistic unidimensional models and shows how they generalize, so that persons (test examinees) and items (test questions) can be represented as points (vectors) floating around in a multidimensional space.
This is not a book for the field practitioner or the casual researcher. It does not skip over the math, and the math is hard-core. Nonetheless, it is surprisingly readable. The reader will be pleased to find himself following the gist of Reckase's explanations without difficulty, even when the mathematical details are too much.
To appreciate this work, it is important to know why MIRT is important. Unfortunately, Reckase never tells us. We understand that MIRT is motivated by the fact that items and tests are complex, that they embody multiple dimensions, that therefore a multidimensional model is necessary. This hardly touches the surface. As the fantastic drama of the Netflix contest revealed (a recently resolved contest to win $1 m. for best predicting movie ratings), we live in a world of psychological profiling and prediction, a world populated by weird and incredible mathematical models that touch on every aspect of life -- from selecting food at Safeway, to renting movies, to profiling terrorists, to guiding teacher instructional decisions, to training computers to read and understand text and recognize the spoken word. None of that is in this book. The great divide between educational psychometrics and "data mining" or "knowledge discovery" has yet to be crossed. MIRT is the subfield within educational psychometrics that will ultimately bridge that divide.
On the theory side, Reckase does not conceal his differences with the "Rasch School" of psychometrics (of which I am a member) regarding the purpose of educational measurement and modeling, though he is obviously well-versed in Rasch models and presents them well, including their MIRT flavors. He sees the purpose of a model to be "descriptive" (to describe the data closely), whereas Rasch theorists see the purpose of a model to be "prescriptive" (to prescribe the conditions under which data yield true measures, i.e., measures that are most likely to reproduce across datasets regardless of person and item samples). The models that Reckase speaks about with the confidence of personal knowledge are "descriptive" in this sense.
Due perhaps to his preference for descriptive models, I found there were certain questions that Reckase did not seem to spend time on, questions that are huge for me:
1. How well do MIRT models handle small sample sizes?
2. How do they handle missing data, whether randomly or non-randomly missing?
3. To what degree are the person and item parameters invariant across samples? Can I cherry-pick my samples and get different parameters?
These are the sorts of questions Rasch people are always asking and where the Rasch model, properly used, has much to offer.
I also found myself looking in vain for discussion of Rasch's "specific objectivity" property as relates to MIRT, often called the "invariance" property. I learned that Reckase means something else entirely by the same word. In the Rasch world, "invariance" means that item and person parameters, and the resulting response probabilities, are invariant across samples, that persons will obtain the same relative measures regardless of what items they are administered so long as the items embody the same dimension. For Reckase, "invariance" means that the origin and orientation of the coordinate system can be moved without affecting the response probabilities. It's got nothing to do with samples. So, in the end, I still don't know what, if any, invariance properties the various MIRT models discussed in the book possess, defining "invariance" in the Rasch sense as invariance across person and item samples.

But those are my problems, not Reckase's. This book is a significant step forward in the maturation of an extraordinarily important, but little known, field.

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First thorough treatment of multidimensional item response theoryDescription of methods is supported by numerous practical examplesDescribes procedures for multidimensional computerized adaptive testing

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Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement Review

Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement
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Measuring information security is the greatest remaining challenge for many of us. Metrics are essential for a scientific management approach, rather than relying purely on gut feel and guesswork. Standards such as ISO/IEC 27001 require the use of objective information about the status and effectiveness of information security controls in relation to the risks, in order to drive appropriate improvements in the Information Security Management System. However, it is not immediately obvious exactly what needs measuring, nor how to do it. This book lays out the foundations on which a rational measurement system can be designed to manage information security in a more objective fashion.
The author encourages readers to consider a wide variety of measurement approaches and apply them sensibly to their information security management issues. In addition to conventional information security metrics, the book draws on governance, risk management, financial management and business analysis methods, a more diverse range of approaches than is normally covered in this field. Introducing measures of organization structure and culture sets this security metrics book apart from most others.
Although the writing style is clear, this is a complex subject covered in depth. Being rather theoretical in approach, the book won't suit practitioners simply looking for a short checklist of `security things to measure'. However, those with the interest and time to study Information Security Management Metrics will be rewarded with a deeper and more rounded understanding of the issue. As such, the book is probably of most value to CISOs and ISMs tasked with implementing better security metrics, and to information security management students.

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Spectacular security failures continue to dominate the headlines despite huge increases in security budgets and ever-more draconian regulations. The 20/20 hindsight of audits is no longer an effective solution to security weaknesses, and the necessity for real-time strategic metrics has never been more critical. Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement offers a radical new approach for developing and implementing security metrics essential for supporting business activities and managing information risk. This work provides anyone with security and risk management responsibilities insight into these critical security questions: How secure is my organization?How much security is enough?What are the most cost-effective security solutions?How secure is my organization?Solid metrics are the key to cost-effective information security - you can't manage what you can't measure This volume shows readers how to develop metrics that can be used across an organization to assure its information systems are functioning, secure, and supportive of the organization's business objectives. It provides a comprehensive overview of security metrics, discusses the current state of metrics in use today, and looks at promising new developments. Later chapters explore ways to develop effective strategic and management metrics for information security governance, risk management, program implementation and management, and incident management and response. The book ensures that every facet of security required by an organization is linked to business objectives, and provides metrics to measure it. Case studies effectively demonstrate specific ways that metrics can be implemented across an enterprise to maximize business benefit. With three decades of enterprise information security experience, author Krag Brotby presents a workable approach to developing and managing cost-effective enterprise information security.

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Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming Review

Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming
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Barbara Byrne manages to make a very complicated topic seem manageable and understandable. This book is ideal for people familiar with the basics of psychology statistics, but relatively new at structural equation modeling. My only complaints are that the index is a bit sparse, so I found myself thumbing through the book frequently; and sometimes the details of how to apply the concepts directly to EQS commands were left a bit unclear. However, overall this was an excellent starter book for structural equation newbies!

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Designed to help beginners estimate and test structural equation modeling (SEM) using the EQS approach, this book demonstrates a variety of SEM//EQS applications that include both partial factor analytic and full latent variable models. Beginning with an overview of the basic concepts of SEM and the EQS program, the author works through applications starting with a single sample approach to more advanced applications, such as a multi-sample approach. The book concludes with a section on using EQS for modeling with Windows.


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