Title
Missing Data: A Gentle Introduction (Methodology In The Social Sciences Series)
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While Most Books On Missing Data Focus On Applying Sophisticated Statistical Techniques To Deal With The Problem After It Has Occurred, This Volume Provides A Methodology For The Control And Prevention Of Missing Data. In Clear, Nontechnical Language, The Authors Help The Reader Understand The Different Types Of Missing Data And Their Implications For The Reliability, Validity, And Generalizability Of A Studys Conclusions. They Provide Practical Recommendations For Designing Studies That Decrease The Likelihood Of Missing Data, And For Addressing This Important Issue When Reporting Study Results. When Statistical Remedies Are Neededsuch As Deletion Procedures, Augmentation Methods, And Single Imputation And Multiple Imputation Proceduresthe Book Also Explains How To Make Sound Decisions About Their Use. Patrick E. Mcknight'S Website Offers A Periodically Updated Annotated Bibliography On Missing Data And Links To Other Web Resources That Address Missing Data.
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- Q: How many pages does the book have? A: The book has two hundred fifty-one pages. This length allows for a thorough exploration of the methodologies for addressing missing data.
- Q: What is the binding type of this book? A: The book is paperback bound. This makes it more lightweight and flexible for readers.
- Q: What are the dimensions of the book? A: The book measures five point seventy-five inches in length, zero point seventy-five inches in width, and eight point seventy-five inches in height. These dimensions make it easy to handle and store.
- Q: Who is the author of this book? A: The author is Patrick E. McKnight. He is known for his expertise in the field of social sciences, particularly regarding missing data.
- Q: What series is this book part of? A: This book is part of the Methodology in the Social Sciences Series. This series focuses on various methodological aspects in social science research.
- Q: Is this book suitable for beginners in research? A: Yes, the book is suitable for beginners. It uses clear, nontechnical language to explain complex concepts related to missing data.
- Q: Can this book help in designing studies? A: Yes, it provides practical recommendations for designing studies that reduce the likelihood of missing data. This is essential for improving the reliability of research outcomes.
- Q: How does this book address the issue of missing data? A: The book discusses both preventative measures and statistical remedies for missing data. It helps researchers understand implications for study conclusions.
- Q: Is there a website with additional resources related to this book? A: Yes, Patrick E. McKnight's website offers an annotated bibliography on missing data and other useful links. This resource is regularly updated.
- Q: Are there any statistical techniques discussed in this book? A: Yes, the book covers various statistical techniques such as deletion procedures, augmentation methods, and imputation procedures. These techniques are essential for handling missing data effectively.
- Q: What type of readers would benefit from this book? A: Researchers, graduate students, and professionals in social sciences would benefit from this book. It addresses fundamental issues regarding missing data across studies.
- Q: Does this book provide examples or case studies? A: Yes, the book includes practical examples to illustrate the methodologies discussed. This helps readers apply the concepts to real-world scenarios.
- Q: Is this book considered a textbook? A: Yes, it can be used as a textbook for courses in social science research methods. Its structured approach makes it suitable for academic settings.
- Q: What is the condition of the used copies? A: The book is a used copy in good condition. This means it is still functional and readable, making it a cost-effective choice.
- Q: What topics does this book cover in depth? A: The book covers missing data types, their implications, and statistical remedies. It emphasizes both prevention and treatment of missing data in research.