Handbook of Measurement Error Models

Measurement error is a common issue in many applications, and the Handbook of Measurement Error Models provides an overview of different topics related to measurement error and statistical methods. It is a valuable resource for researchers and data analysts who want to learn more about challenges, methods, and applications in this area.

Format: Hardback
Length: 578 pages
Publication date: 18 October 2021
Publisher: Taylor & Francis Ltd

Measurement error is a pervasive issue that arises in numerous applications across various fields. It has been a longstanding concern for researchers, practitioners, and policymakers alike. While there exist numerous research monographs that summarize methods and strategies for addressing different measurement error problems, the field continues to attract significant attention. To address this need, the Handbook of Measurement Error Models offers a comprehensive overview of various topics related to measurement error. This handbook is meticulously curated to include carefully edited chapters that cover issues of measurement error and the development of statistical methods to mitigate their impacts. It strikes a balance between providing methodological insights and showcasing practical applications, making it an invaluable resource for both novice researchers seeking to embark on this field and experienced data analysts seeking to enhance their understanding of measurement error models. One of the key features of this handbook is its comprehensive coverage of past development and modern advancements in the field of measurement error. It provides a historical perspective on the evolution of measurement error models and their applications, highlighting the challenges and opportunities that have emerged over time. The handbook also emphasizes the importance of understanding the sources and mechanisms of measurement error, as well as the potential consequences of error-contaminated data on research findings and policy decisions. To address these challenges, the handbook introduces a range of off-the-shelf methods for mitigating the deleterious effects of measurement error. These methods encompass a wide spectrum of techniques, including regression analysis, instrumental variables analysis, and Bayesian inference. Each method is described in detail, with examples and applications to illustrate their practical utility. Furthermore, the handbook discusses state-of-the-art strategies for conducting in-depth research on measurement error. It provides guidance on developing and implementing research designs, collecting data, and analyzing data using advanced statistical methods. The handbook also emphasizes the importance of considering measurement error in the context of specific research questions and objectives, as well as the potential limitations and biases associated with different measurement error models. In conclusion, the Handbook of Measurement Error Models is a valuable resource for researchers, practitioners, and policymakers interested in understanding and addressing measurement error problems. It provides a comprehensive overview of various topics related to measurement error, including its sources, mechanisms, and mitigation strategies. The handbook is meticulously curated to include contributions from leading experts in the field, ensuring that it offers up-to-date and authoritative information. By leveraging the insights and methodologies presented in this handbook, researchers and practitioners can enhance their ability to produce reliable and accurate data, which in turn can inform better decision-making and policy development. Measurement error is a pervasive issue that arises in numerous applications across various fields. It has been a longstanding concern for researchers, practitioners, and policymakers alike. While there exist numerous research monographs that summarize methods and strategies for addressing different measurement error problems, the field continues to attract significant attention. To address this need, the Handbook of Measurement Error Models offers a comprehensive overview of various topics related to measurement error. This handbook is meticulously curated to include carefully edited chapters that cover issues of measurement error and the development of statistical methods to mitigate their impacts. It strikes a balance between providing methodological insights and showcasing practical applications, making it an invaluable resource for both novice researchers seeking to embark on this field and experienced data analysts seeking to enhance their understanding of measurement error models. One of the key features of this handbook is its comprehensive coverage of past development and modern advancements in the field of measurement error. It provides a historical perspective on the evolution of measurement error models and their applications, highlighting the challenges and opportunities that have emerged over time. The handbook also emphasizes the importance of understanding the sources and mechanisms of measurement error, as well as the potential consequences of error-contaminated data on research findings and policy decisions. To address these challenges, the handbook introduces a range of off-the-shelf methods for mitigating the deleterious effects of measurement error. These methods encompass a wide spectrum of techniques, including regression analysis, instrumental variables analysis, and Bayesian inference. Each method is described in detail, with examples and applications to illustrate their practical utility. Furthermore, the handbook discusses state-of-the-art strategies for conducting in-depth research on measurement error. It provides guidance.

Weight: 1252g
Dimension: 254 x 178 (mm)
ISBN-13: 9781138106406