The past two decades have witnessed a dramatic increase in the demand for analyses of health and public affairs program data. Governments at the federal and state levels have stimulated, if not mandated, much of this increased de mand and spawned numerous not-for-profit and quasi-public organizations research groups, lobbying organizations, consulting firms-who actively use analytic techniques. Program analysis is a broad term that encompasses activities such as pro gram planning and prediction, program assessment, and program evaluation. Though purposes differ and techniques vary, the common denominator for all applications is the attempt to use quantitative techniques to provide compre hensive and objective analyses. Program analysis in public health and public affairs shares another common feature: In many cases the data collected for these applications are categorical in nature-that is, discrete information rep resented by categories. Whether a defendant in a criminal trial is convicted (yes, no), the number of times a person visits a community health center in a month (0,1,2,3, ... ), how a person feels about a proposed program (agree, disagree, not sure, no opinion)-these are examples of categorical data. This book describes a multivariate categorical data analysis technique the weighted-least-squares (WLS) approach developed by Grizzle, Starmer, and Koch (GSK)-applied to program analysis in health and public affairs. It is written for in-service professionals who desire an introduction to applied cate gorical data analysis and for preservice students who are studying quantitative methods.
Biostatistics, Second Edition, is a user-friendly guide on biostatistics, which focuses on the proper use and interpretation of statistical methods. This textbook does not require extensive background in mathematics, making it user-friendly for all students in the public health sciences field. Instead of highlighting derivations of formulas, the authors provide rationales for the formulas, allowing students to grasp a better understanding of the link between biology and statistics. The material on life tables and survival analysis allows students to better understand the recent literature in the health field, particularly in the study of chronic disease treatment. This updated edition contains over 40% new material with modern real-life examples, exercises, and references, including new chapters on Logistic Regression, Analysis of Survey Data, and Study Designs. The book is recommended for students in the health sciences, public health professionals, and practitioners. - Over 40% new material with modern real-life examples, exercises and references - New chapters on Logistic Regression; Analysis of Survey Data; and Study Designs - Introduces strategies for analyzing complex sample survey data - Written in a conversational style more accessible to students with real data
In this introduction to the different ways of analysing complex survey data, the authors consider new analytical approaches, review new software and introduce a model-based analysis that can be used for well-designed and relatively small-scale social surveys.
The Biostatistics course is often found in the schools of public Health, medical schools, and, occasionally, in statistics and biology departments. The population of students in these courses is a diverse one, with varying preparedness. Introduction to Biostatistics assumes the reader has at least two years of high school algebra, but no previous exposure to statistics is required. Written for individuals who might be fearful of mathematics, this book minimizes the technical difficulties and emphasizes the importance of statistics in scientific investigation. An understanding of underlying design and analysis is stressed. The limitations of the research, design and analytical techniques are discussed, allowing the reader to accurately interpret results. Real data, both processed and raw, are used extensively in examples and exercises. Statistical computing packages - MINITAB, SAS and Stata - are integrated. The use of the computer and software allows a sharper focus on the concepts, letting the computer do the necessary number-crunching. - Emphasizes underlying statistical concepts more than competing texts - Focuses on experimental design and analysis, at an elementary level - Includes an introduction to linear correlation and regression - Statistics are central: probability is downplayed - Presents life tables and survival analysis - Appendix with solutions to many exercises - Special instructor's manual with solution to all exercises
Esthetic dentistry is the ultimate challenge for the restorative dentist. A restoration must not only look good, it must also function well and endure the test of time. Esthetics in Dentistry has been an important resource in the practice of general dentistry and orthodontics for more than a quarter century. This full-colour revision of the original classic contains an incredible number of advances in techniques and materials. Dr. Ronald Goldstein presents the principles, communication techniques and treatment methods for selecting patients and creating esthetic restorations. Treatments include cosmetic contouring, bleaching, bonding, etched porcelain restorations and crown restorations.
Biostatistics for Oral Healthcare offers students, practitioners and instructors alike a comprehensive guide to mastering biostatistics and their application to oral healthcare. Drawing on situations and methods from dentistry and oral healthcare, this book provides a thorough treatment of statistical concepts in order to promote in-depth and correct comprehension, supported throughout by technical discussion and a multitude of practical examples.
Five general areas are addressed in the 36 chapters: descriptive epidemiology of diabetes in the U.S., including prevalence, incidence, sociodemographic & metabolic characteristics, risk factors for developing diabetes, & mortality. Complications of diabetes (disability, vision disorders, heart disease, infection, etc.); medical care for diabetes; economic aspects, including health insurance & health care costs; & diabetes in special populations, including Blacks, Hispanics, Asian & Pacific Islanders, Native Americans & pregnant women. Index.
In this introduction to the different ways of analysing complex survey data, the authors consider new analytical approaches, review new software and introduce a model-based analysis that can be used for well-designed and relatively small-scale social surveys.
The Biostatistics course is often found in the schools of public Health, medical schools, and, occasionally, in statistics and biology departments. The population of students in these courses is a diverse one, with varying preparedness. Introduction to Biostatistics assumes the reader has at least two years of high school algebra, but no previous exposure to statistics is required. Written for individuals who might be fearful of mathematics, this book minimizes the technical difficulties and emphasizes the importance of statistics in scientific investigation. An understanding of underlying design and analysis is stressed. The limitations of the research, design and analytical techniques are discussed, allowing the reader to accurately interpret results. Real data, both processed and raw, are used extensively in examples and exercises. Statistical computing packages - MINITAB, SAS and Stata - are integrated. The use of the computer and software allows a sharper focus on the concepts, letting the computer do the necessary number-crunching. - Emphasizes underlying statistical concepts more than competing texts - Focuses on experimental design and analysis, at an elementary level - Includes an introduction to linear correlation and regression - Statistics are central: probability is downplayed - Presents life tables and survival analysis - Appendix with solutions to many exercises - Special instructor's manual with solution to all exercises
Today, mathematics, biology, medicine, and statistics are closing the interdisciplinary gap in an unprecedented way and many of the important unanswered questions now emerge at the interface of these disciplines. Now in its Second Edition, this user-friendly guide on biostatistics focuses on the proper use and interpretation of statistical methods. This textbook does not require extensive background in mathematics, making it user-friendly for all students in the public health sciences field. Instead of highlighting derivations of formulas, the authors provide rationales for the formulas, allowing students to grasp a better understanding of the link between biology and statistics. The material on life tables and survival analysis allows students to better understand the recent literature in the health field, particularly in the study of chronic disease treatment. Biostatistics now includes a companion website to demonstrate the different applications of computer packages for performing the various analyses presented in this text. * Includes access to a companion website with further examples and a full explanation of computer packages * Over 40% new material with modern real-life examples, exercises and references * New chapters on Logistic Regression; Analysis of Survey Data; and Study Designs * Introduces strategies for analyzing complex sample survey data * Written in a conversational style more accessible to students with real data
The past two decades have witnessed a dramatic increase in the demand for analyses of health and public affairs program data. Governments at the federal and state levels have stimulated, if not mandated, much of this increased de mand and spawned numerous not-for-profit and quasi-public organizations research groups, lobbying organizations, consulting firms-who actively use analytic techniques. Program analysis is a broad term that encompasses activities such as pro gram planning and prediction, program assessment, and program evaluation. Though purposes differ and techniques vary, the common denominator for all applications is the attempt to use quantitative techniques to provide compre hensive and objective analyses. Program analysis in public health and public affairs shares another common feature: In many cases the data collected for these applications are categorical in nature-that is, discrete information rep resented by categories. Whether a defendant in a criminal trial is convicted (yes, no), the number of times a person visits a community health center in a month (0,1,2,3, ... ), how a person feels about a proposed program (agree, disagree, not sure, no opinion)-these are examples of categorical data. This book describes a multivariate categorical data analysis technique the weighted-least-squares (WLS) approach developed by Grizzle, Starmer, and Koch (GSK)-applied to program analysis in health and public affairs. It is written for in-service professionals who desire an introduction to applied cate gorical data analysis and for preservice students who are studying quantitative methods.
The past two decades have witnessed a dramatic increase in the demand for analyses of health and public affairs program data. Governments at the federal and state levels have stimulated, if not mandated, much of this increased de mand and spawned numerous not-for-profit and quasi-public organizations research groups, lobbying organizations, consulting firms-who actively use analytic techniques. Program analysis is a broad term that encompasses activities such as pro gram planning and prediction, program assessment, and program evaluation. Though purposes differ and techniques vary, the common denominator for all applications is the attempt to use quantitative techniques to provide compre hensive and objective analyses. Program analysis in public health and public affairs shares another common feature: In many cases the data collected for these applications are categorical in nature-that is, discrete information rep resented by categories. Whether a defendant in a criminal trial is convicted (yes, no), the number of times a person visits a community health center in a month (0,1,2,3, ... ), how a person feels about a proposed program (agree, disagree, not sure, no opinion)-these are examples of categorical data. This book describes a multivariate categorical data analysis technique the weighted-least-squares (WLS) approach developed by Grizzle, Starmer, and Koch (GSK)-applied to program analysis in health and public affairs. It is written for in-service professionals who desire an introduction to applied cate gorical data analysis and for preservice students who are studying quantitative methods.
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